In our conversation, Amir talks about bringing AI in as a validation authority rather than only a builder, as well as how “quality” means when software is no longer fully deterministic. He discusses where AI is reshaping the product manager’s job, and what key PM skills AI cannot replace. Amir also shares his view on how AI increasingly sits between the customer and the brand.

Where AI is reshaping the product manager’s work

AI introduces non-deterministic behavior into products and workflows. When there’s no single correct answer anymore, how do product teams define quality — and who actually owns the definition?

We see modern products today using hybrid structured or AI-driven workflows. Some of the workflows in a website or application are deterministic, like they used to be, but others are determined by AI — different directions, different outputs, different responses. A lot of products are moving into this hybrid mode.

AI intelligence needs to be introduced into the validation of that workflow, just as it’s introduced into the product itself. Instead of executing the validation step by step — click this button, use this selector to move to the next step — the test framework should model the user’s intent. For example, “click the checkout button” to reflect the user’s intent to check out.

For testers, this is a much healthier approach, because it reflects a behavior-driven testing mindset. It validates the user flow rather than the underlying technical implementation. The test focuses on what really matters and stays independent of the code changes that have traditionally made test suites brittle and high-maintenance for testing teams.

Another example is a change in the workflow, or in the textual response to a user query. Again, the right approach is to ask AI, as the validation authority: “Does this response make sense? Does this next step in the journey resonate with the user’s intent?” A strong AI tool that has the context of the service being offered, as well as the user’s intent, can determine whether the answer is complete, appropriate, and sufficient for the user. In the same way that developers bring in AI to create a richer, less deterministic experience, I believe we should also bring AI into validation, to make sure that whatever we’re doing makes sense.

As product teams take on more tasks, where else is AI reshaping the work — and do they have the context to know when a tool is providing the full story?

For sure. I’m thrilled about what AI enables me to do in my own job, but it’s a tremendous enabler for every function in the organization, whether you’re a developer or a product manager. There’s far higher efficiency and more clarity in the alignment, as well as in the communication and planning.

For the product organization specifically, AI contributes in three areas. The first is discovery. Product managers can create compelling, high-resolution, functional mocks that users can touch and actually interact with. The underlying data holds up, and the user can see how a feature they asked for is taking shape almost in real life — as if it were already embedded in the product.

I’ve heard users say, “Well, come to think of it, now that I’m actually using it, this doesn’t make sense — that’s the wrong way to do it.” So creating a high-resolution, functional mock is a great way to qualify a direction before you build it to fill a gap.

The second area, which I’m even more excited about, extends discovery into deep elaboration. For every feature, a product manager meets weekly with three or more users to put that mock to the test. That surfaces all the things we hadn’t thought of yet — what about this situation? What about that one? Product managers come to understand the user’s workflow and needs intimately, and that’s what makes a great product manager. It’s also what drives adoption of what they’re creating. And by the time a feature reaches the developer, the mock is far further along. There’s much more democracy and much more efficiency in how features get delivered.

Third is market awareness. This has historically been a pain point for me, because so much information is available. Now, with AI, I can get a summary of what’s new in the market — partners, competition, Gartner analysts, and more — so I know what might warrant an evolution or an adjustment to my strategy.

The skills AI can’t replace

Have the skills for success evolved along with the PM’s role?

Perhaps the thing AI has exacerbated more than anything else is time management. We lack time today — all of us, no matter the role we’re in. The number one quality I look for in product management is resilience and the ability to excel at time management and efficiency. To operate well in a fast environment you need to optimize for that.

The second thing I look for is curiosity. Today’s technology and tools are not tomorrow’s. You need to stay curious and keep your eyes and ears open at all times. Don’t go stale — be open to new technologies and take on challenges. I encourage everyone to get comfortable being uncomfortable, because that’s how we learn.

Last, be a leader and an advocate — get the people around you excited about what you’re creating. That’s what motivates them.

As you mentioned, teams are using AI to generate more experiments, more features, and more releases. How do you distinguish productive acceleration from simply producing more noise?

Our world has become one big hackathon. I value innovation and creativity, so we give everyone in the organization access to AI, from developers to HR, and everyone finds their own points of efficiency. At the same time, when it comes to the product team and product strategy, every idea has to be examined against our vision, our strategy, and the core strengths of our product.

We get a lot of ideas, both internally and externally. When users come to us and say, “This is what I need,” there are AI tools that can transcribe and correlate those interviews. From there, if we can find three users who will stay with us on a weekly basis until the feature goes into development, we know we have a winner.

Who owns quality when anyone can ship

Almost anyone on a product squad can now create and deploy functionality. How does that change team dynamics and governance?

The short answer is that the jury is still out — a lot is changing. Developers are worried about swim lanes and who gets to deploy. Some companies are aggressive about letting various personas deploy code to production — product managers and others — and some are more conservative. Do we run code reviews on AI-generated code before it goes to production? Who does the testing — a human or AI? There’s a colorful continuum of opinions on this.

I recently listened to a podcast with a developer lead who had an escalated ticket in production. He said, “We’ve decided not to do code reviews — instead, we take the AI’s code, commit it to production, and deploy. Previously, when a ticket came back to me, I would have researched the root cause and fixed it myself. Now, I feed the error into AI, and it finds the problem immediately.” So you can see the promise here — AI is taking on a lot of the SDLC.

I’m extremely excited about organizations, including ours, that let product managers ship UI frontend code specifically. Users come in and talk about adoption and the frustrations they have with the UI — and usually it’s not the data or the APIs, it’s something in the UI that doesn’t make sense. Product managers can implement those changes themselves. Users feel they’re being listened to, and product managers see more adoption.

The other side effect is that developers can focus more on what really matters to the organization — the structure of the database, the application logic, the APIs. Yes, anyone can introduce bugs, but if PMs are disciplined and thoughtful, they can come to understand the user’s reality and the product better, and come to appreciate what the development team has been doing and give them room to do it. All in all, it’s a wonderful change.

What guardrails are needed when product folks are pushing things live and teams are relying on AI for more of the development piece?

There are some processes we need to keep as human-to-human. Our workflows will naturally change, and AI will take over the mundane, repetitive tasks, but I don’t believe in staff reduction. We all need to raise our standards and become more efficient. In doing so, our users will have slightly different workflows — they’ll have to evolve, and so will we as a product organization.

Staying close to your users and having those intimate conversations — where they talk about their challenges, their frustrations, the gaps, their needs and wants — is how you stay efficient. AI can’t replace that. Users are far more comfortable opening up to another human who shows genuine interest. That connection between a product manager and a user — and a product manager inspiring the organization around a vision — is valuable and it would be wrong to try to replace it with AI. AI is a wonderful, powerful aid to the product manager in this context, but it can’t replace the conversations they need to have with users and teammates.

Where the product landscape is heading

How do product leaders think about optimization when AI becomes the intermediary between the customer and the brand — chatbots and the like?

This is a very interesting and relevant topic. There’s a lot of discussion about how AI will accelerate and represent shopping workflows, and it applies across the industry — hospitality, retail, and beyond. I recently heard a compelling podcast with a VP of Product at Shopify, and his argument, which I agree with, is that brand strength will determine the sustainability of the human-brand relationship.

People care a lot about fashion and design — about what they wear and how convenient it is. In niches like skiing or road cycling, they’ll be very specific about the skis or bike they buy. At the same time, they’d be perfectly happy to let cost-benefit decide which AAA batteries to buy — for that product, maybe they don’t care as much about the brand.

In that context, we provide a solution that helps these B2C brands optimize their digital presence for both the traditional, fully human shopping workflow and the modern hybrid one. For example, a modern workflow might begin with a person accessing a bot and writing, “Find me all the Airbnbs in this area, for these dates, that look lovely and are next to a lake.” This is a kind of hybrid shopping experience where the bot returns a recommendation based on different criteria than a human would use, and then a human takes it from there to close the deal.

I can envision future workflows where you have a trusted bot that’s almost like a digital assistant. You give it your payment method, and it goes and buys a product whose brand you have no attachment to but that meets your specific criteria.

If you were going to build a product organization from scratch today, what would you do differently than four or five years ago?

I absolutely love this question. The truth is that things have accelerated, but they haven’t fundamentally changed. The skills and values I look for haven’t really shifted across my career. If I were building a product team today, I’d look for people who excel at both the discovery and delivery sides of product management. And, above all, I value curiosity — that’s my number one criteria by far.

Experience matters, but I’m far more interested in a person’s ability to adapt, to learn, and to take on challenges with grace and overcome them for the greater good. The ideal team is a group of ambitious people who genuinely care about finding and solving customer problems, improving continuously, and delivering meaningful outcomes. They challenge assumptions, support one another, take ownership, and are motivated by building great products and winning together. That’s my dream team.

What does LogRocket do?

LogRocket’s Galileo AI watches user sessions for you and surfaces the technical and usability issues holding back your web and mobile apps. Understand where your users are struggling by trying it for free at LogRocket.com.

In this episode, we’re joined by Eric Anderson, SVP of Product at Datasite, the virtual data room platform trusted with some of the highest-stakes, highest-privacy transactions in business — M&A deals, divestitures, and multi-hundred-million-dollar decisions.

In this episode, Eric shares:

• How the “The contract has changed” as the PM role has evolved. It’s not a layoff story, but product has collapsed back to subject-matter expertise, judgment, and taste

• Inside their “iteration zero” process: Where PRDs are written live in the meeting, prototypes are wired to real data, and what the new bottleneck is now that building is fast

• Plus how you build trust in your AI product when a billion dollars is on the line: citations for everything, correlation, not imperative statements, and why nobody lets the computer decide

1. “The contract has changed”

Deep into a product pilot, one of Eric’s senior principal PMs said four words that became the episode’s throughline — and, Eric says, one of the most undersold observations of the year:

“Eric, the contract has changed.”

What he meant: product work used to be process-driven, with templates, quarterly planning decks, PRDs wrangled into eight-point font. Now the idea itself becomes the center of gravity, and the process falls into line behind it. Eric described teams running an “iteration zero” meeting where a PM or designer talks through a problem out loud, and tools like Claude’s audio features draft the PRD live, in real time, while they’re still talking.

“The ability to move from idea to building a foundation to execution in minutes or hours is almost exactly why we like this job so much in the first place.”

Product takeaway: Don’t just ask “where can we bolt AI onto our existing process.” Ask what your process was compensating for in the first place. If AI removes the friction between having an idea and testing it, your rituals — standups, templates, sign-off chains — may be solving a problem that no longer exists in the same form.

2. The collapsed org chart

Eric has always talked about product careers in terms of “climbing up and down the ladder” — moving fluidly between whiteboard-level detail and boardroom-level strategy. That metaphor hasn’t changed, but the speed has — thanks to AI.

“Climbing up and down the ladder…now it’s more like riding a high-speed elevator. It’s really collapsed the org chart.”

He’s blunt about what this means for leaders who stopped doing IC work: they don’t get a pass anymore. And ICs who worried AI would replace them are finding the opposite: the busywork gets automated, and what’s left is the part of the job people actually wanted in the first place.

“If you were a leader who did no IC work, [you’re] going to have to do a lot of IC work to be successful.”

Product takeaway: Leaders need to stay close enough to the work to have real opinions on it again, and ICs need to get comfortable operating at a strategic altitude they might have deferred upward before.

3. The 2x2 that decides what AI actually gets to touch

Eric borrowed a framework from a previous LaunchPod guest, Descript CEO Laura Burkhauser: a simple 2x2 of what you love doing vs. what you don’t, crossed with what AI is actually good at yet. The goal isn’t to make people “the human harness for AI” — it’s to figure out what to hand off and what to protect.

“Look at where you can take the things that would be great to do with AI and you don’t love doing. And then look at the stuff that you love doing and needs a human to do — that’s the special stuff.”

The payoff, in Eric’s words, isn’t more free time — it’s more focus.

Product takeaway: AI adoption plans that start with “which tasks can we automate” miss half the equation. Start with what your team actually wants to be doing more of, then use AI to clear a path to it, not just to cut costs.

4. VDRs: From a room full of banker’s boxes to AI

Before it was software, a “data room” was literally a room — bankers’ boxes of documents, a rented conference room, cell phones locked away at the door. Datasite’s job has always been automating that physical process.

Now AI is compressing it further, but Eric is precise about where the line sits.

“You’re not removing the human as the source of judgment. Nobody with their finger on the trigger of a billion-dollar deal is gonna say, ‘I don’t know, Claude told me it was good, so we’re just gonna YOLO it.’”

Product takeaway: When you’re using AI for high-stakes workflows, separate the “first mile” (repetitive, low-skill-but-high-training-required work) from the judgment calls at the end. Automate the former aggressively. Never let AI quietly absorb the latter.

5. Trust isn’t a feature

Datasite’s users aren’t casually chatting with AI — they’re comparing hundreds of legal documents to decide whether a merger is safe to close. Eric walked through the two paradigms his team leans on to earn trust: citations for deep document interrogation, and disclaimers for more free-flowing chat — always inside a “walled garden” that never reaches out to the public internet.

“You’re very much in a walled garden... ‘We found that the leases in Portugal cited here tend to have this common thread you don’t see in Spain — looks like something you should probably check out.’”

Even word choice matters — Datasite frames insights as correlation, not instruction, precisely so the human stays the decision-maker.

“It’s not ‘the tool told me to’ — it’s ‘deals that successfully close tend to remedy differences like these.’”

Product takeaway: In high-stakes domains, the AI feature isn’t the differentiator — the trust architecture around it is. Citations, provenance, and careful language aren’t nice-to-haves you bolt on after the fact — they’re the actual product.

Chapters

00:00 Intro
04:43 How AI has changed the way Datasite's team works
05:32 The product org ladder has become a "high-speed elevator"
11:34 Inside iteration zero: PRDs written live in the room
14:57 Getting out of your comfort zone as AI removes scaffolding
22:44 The "four-box" framework for working with AI
26:55 Why the virtual data room is a perfect AI use case
31:31 Citations, correlation not imperative, and building trust
36:22 Conclusion

Links

• LinkedIn

• Datasite

What does LogRocket do?

LogRocket’s Galileo AI watches user sessions for you and surfaces the technical and usability issues holding back your web and mobile apps. Understand where your users are struggling by trying it for free at LogRocket.com.

In our conversation, Denise discusses what it means to lead product with people first — specifically how prioritizing team wellbeing and proactive leadership helps her manage competing priorities. She talks about how AI is transforming product work and where it can meaningfully improve both speed and outcomes. Denise shares how her unconventional career path shaped her leadership style, including the importance of hiring strong teams.

Competing priorities and leading across product teams

You’re often leading multiple functions like product management, product design, and engineering. With those three areas moving at once, how do you manage competing priorities?

Across all functions, there are a few things where I try to be extremely consistent. One of the big ones is that people come first for me as a leader. Imagine I’m in the middle of thinking about product strategy, and one of my reports writes to me and says, “Joe wants to resign.” That immediately becomes my top priority for the day.

Across all the functions I lead, the people who directly report to me get a lot of priority on my agenda. I’m supporting them and helping them solve their problems, and also helping the people in their organizations who might be anxious about someone resigning. That’s the easiest decision.

Another thing I focus on is managing proactively instead of reacting to incoming work like emails or chat messages. I try to have a very clear idea of what I want to get done each day and each week. For example, this week I have a very important presentation with the entire development organization about our product strategy for the year. I pre-book time on my calendar to make sure those things get done, whether that’s meetings where I need to collaborate with people or focused time for my own work.

There are competing priorities everywhere — both across functions and also within the same function. Even within product management, we run an organization with around 30 teams, so there are multiple product managers working in parallel.

For me, it’s about identifying where my attention is most important to give, pre-allocating time, and then reacting with whatever time I have left — answering emails, responding to chats, and dealing with everything else that comes in during the day.

When it comes to integrating AI into workflows, how are you deciding what should stay human-centric versus where AI actually improves outcomes?

In the product organization specifically, AI is greatly improving outcomes in research, especially when it comes to speed, quality, and breadth of output. I am mind-blown about how quickly and accurately we can move forward with research about new product lines using real-world examples. Even if it’s not 100 percent accurate, it gives us so much data to work with.

Product marketing used to do that research for us, and it would take a lot of time. It’d go through a queue of people, and it was an excruciatingly long process. Now, I can just use AI and say, “Give me an example of a data model for an employee in HR Core, and just give me all the data points that you think it should have.” Suddenly, I can have a detailed list with 200 fields.

AI as an accelerant for learning

Have you seen an overreliance on AI or instances of it getting in the way of people learning or building expertise?

Actually, I don’t. I think you can leverage AI for learning quite nicely, and it can sometimes be better than traditional learning tools. Think about how people traditionally learn something like research skills. Often, that comes from one-on-one coaching or mentoring, because corporate research is complex. You rely a lot on creativity and personal guidance.

Now you can replace part of that mentoring with an agent that coaches you through how to think about researching a topic. At the same time, that same system can help execute the research. In our organization, we’ve created learning agents inside our enterprise-approved tools. People have access to coaches, content curators, and other learning agents that help them develop skills and have strategic conversations about how to approach different problems.

So the learning experience is actually an area where AI can be very powerful. It can solve problems for you, but it can also teach you how to solve them — or how not to — if you ask the right questions.

You often launch many initiatives at the same time. How do you maintain an aggressive release schedule while still giving your team the space to learn from their experiences between launches?

Sometimes the speed affects me more than it affects the individual teams. Many of the people working on those projects are assigned only to that specific initiative. For them, the speed and the learning process are balanced differently.

We allocate time specifically for feedback and standardization after each product launch. The product manager will spend time after the release interviewing customers and closing the learning loop. For larger products, we often run early release or beta programs before a general release. That allows us to gather feedback and refine things before the product reaches a broader audience.

For leaders like me and a few others in the organization, things can get very busy — especially for product marketing and the go-to-market teams. But we operate at a level where we expect that kind of close follow-up, so we plan for it.

An untraditional path into product leadership

Your path into product was rather unconventional. Now that you’ve worked in many different parts of the organization, how do you feel that diverse experience shapes how you lead product teams?

My career started when I joined a software company working on customer project implementations. Later, I moved into recruiting, and we happened to sell recruiting software. Because of that, I already understood the technology from an implementation perspective. But when I actually became a recruiter myself, I finally understood the job that our customers were doing every day.

That experience turned out to be incredibly valuable when I moved into product and started defining product strategy. I had been in the users’ shoes. I knew what their day-to-day looked like and what they were trying to accomplish.

It helps in conversations with product managers because I can say, “Imagine you’re doing this job. Your day looks like this. You’re distracted by these things and still trying to achieve this outcome.” That perspective helps explain why a certain solution might not work or why the experience needs to be better.

But there was another influence on my career as well. When I was younger, I was very interested in entrepreneurship and startups. I remember going through the Y Combinator startup course and thinking about the skills you need to build a company. One of the things that really stood out to me was hiring. If you hire the wrong person in a startup with three people, there’s a good chance the company won’t survive. That made me realize how critical it is to learn how to identify the right people.

I also became obsessed with execution and efficiency, so I naturally spent a lot of time developing those skills. Overall, though, one of the biggest impacts in my leadership career is knowing how to hire, how to identify the right talent, how to structure the departments, and how to create career paths. Every single one of those elements is talent-related, and you need to have those skills as a leader.

How did that help you transition into product leadership?

I knew nothing about being a product manager when I joined as a product director, but I knew a lot about having the right people next to me. I knew the value in creating a good structure for hiring and promoting talent, and that’s what helped me through the first years — especially while simultaneously dealing with imposter syndrome and learning on the job. I was doing the right thing and building the right team, and now, we have an incredibly powerful team to show for it. I’m really proud of them all.

With my consulting experience, I learned a lot about how to communicate with the customer. Once you become an executive, you’re going to face those touch points where situations escalate, and you’re going to have to maintain your composure. That’s what I value the most about my early days.

Applying root-cause thinking and leadership at scale

Many people start in product and then learn the business side as part of their leader development. You had to learn the product side of things later on in your journey — what was that process like for you?

One skill I developed over time is learning how to dig beneath the symptoms people describe. Whether you’re talking to employees, friends, or customers, they’re people — and people talk about their symptoms and what they see. They talk about how they think they can solve a problem. A lot of the time, my job is to break through those symptoms and identify the underlying cause of the problem.

That’s one of the most important skills for a leader — understanding what’s really happening behind what people are saying. Another important skill is fighting assumptions. People make assumptions all the time without realizing it. They assume something is true and build decisions on top of that assumption. Eventually, they hit a wall. The same thing happens with symptoms. You assume what someone told you represents the full problem, and then you try to solve it.

The real challenge is learning to ask the questions that uncover the underlying issue. Sometimes you can’t get all the answers directly from customers, so you have to ask those questions internally and develop hypotheses with your team.

That sounds simple — people often just ask “why” five times — but in practice it’s very difficult. You’re most likely not even going to realize that what you’re dealing with isn’t the full picture. This is actually how my training in mathematics had a big influence on how I think about problems.

In mathematics, you can only use what has already been defined or proven. You have to work strictly with the statements that exist on the page. That mindset teaches you to separate facts from assumptions. If something hasn’t been defined or proven, you can’t treat it as true. I try to apply that approach to product work. I ask myself: Is this a confirmed fact, or is it an assumption? Is this a symptom, or do we understand the underlying cause?

Of course, business decisions are never as precise as mathematical proofs. At some point, you still have to take risks and move forward. But it helps to be explicit about what you know and what you’re assuming. You might say, “We believe this is correct with about 80 percent confidence, so we’re going to move forward.”

You’ve been at your company as it’s grown from roughly 60 people to 1,700 now. What has been the biggest transformation in that time?

I’ll actually start with what hasn’t changed, which is our culture as an organization. Our founders did a great job of crafting the company’s culture from the beginning. They wanted it to be well-defined and had a clear idea of the values they wanted to promote.

For example, we value positive people and try to avoid hiring people who complain about everything. We want people who help move projects and the company forward.

That cultural foundation makes it easier to navigate everything else as the company grows. As for what changed, the obvious things are structure and processes. When we were small, many things simply didn’t exist — career frameworks, salary structures, and other formal processes.

Over time, we added those structures. But because it happened gradually over about 15 years, it didn’t feel like a dramatic transformation. It was more of a steady evolution. We’re always reviewing how we work and asking whether decisions from the past still make sense or whether they need to change. That constant reflection prevents you from reaching a point where something suddenly feels completely broken.

What has been most important for sustaining your leadership through that growth?

Earlier in my career, there were periods where responsibilities and workload piled up. During those seasons, I would get extremely stressed, and that sometimes turned into physical symptoms. Eventually, I found tools that helped me manage that stress better. Now I’m much better at separating mentally from work when I need to.

Some of the things that helped are simple practices like breathing exercises or even ice baths. They might sound trendy, but learning how to stay calm in stressful situations made a real difference for me. There’s definitely a before and after in my career. Now I can look at a difficult day and think, “We’ll work through this.” That’s made a huge change in my professional life.

What does LogRocket do?

LogRocket’s Galileo AI watches user sessions for you and surfaces the technical and usability issues holding back your web and mobile apps. Understand where your users are struggling by trying it for free at LogRocket.com.

In our conversation, Alicia talks about building trust through user-generated content, including how reviews, ratings, and other forms of social proof help shoppers make confident purchasing decisions. She discusses her approach to creating and measuring successful UGC programs, as well as the evolving role of authenticity in ecommerce.

Designing UGC for trust and decision-making

You have deep experience in user-generated content, which can be very useful for enhancing trust or building engagement, but at scale it could overwhelm users. How do you decide what content to share, when to share it, with whom, and how does that improve decision-making?

Determining what UGC to show depends on where the consumer is in the purchasing cycle. On a search results screen, people want to know the item’s star rating and how many reviews it has. Something with five reviews is going to be viewed differently than something with 5,000 reviews. Showing that information up front can help drive someone to click to the next step.

If they’re on the cart page, for example, they’ve already made a decision. They don’t need to read reviews. The place where you show the most detail is on the product details page, where you would display the actual content of the reviews. You would have what we call pills, where you’ve sifted through the reviews and surfaced highlighted comments that come up over and over again. You’d probably want an AI summary of what people most often say, and you’d want to show both positive and negative sentiment. That’s also where you want photos.

How do you think about UGC as a dynamic product surface that actively shapes the customer journey?

User-generated content is based on social proof. Consumers are not looking at the retailer to give them a signal — they’re looking at peers and other shoppers. They want to know what others thought and what their experiences were.

It’s important to make sure there’s transparency and that shoppers believe that the reviews are from real people who actually purchased the product. You can’t have too many incentivized reviews, where someone was paid or given free product in exchange for a review. Anytime you use those carrots to draw people in, it can erode trust. It’s important to make sure there’s enough social proof and enough cues so that shoppers feel they can trust the people leaving reviews.

When someone has an average experience, they’re often not going to leave a review. The person who’s going to leave one is somebody who says, “These knives don’t cut anything,” or, “These are the best knives I’ve ever had.” You end up with either really high or really low ratings. The people who had an average experience are the customers you have to pull in and ask to post content.

If one person has a negative experience, it’s more likely that multiple people had negative experiences. If you have 100 negative reviews and three great ones, people aren’t going to believe the positive reviews. What you’re looking for is something closer to a normal curve.

Prompting for reviews at the right time

What about metrics — how do you measure whether UGC is truly transforming the user experience rather than just increasing engagement?

When I was driving UGC, the main thing I looked at was building not just a collection of reviews but what I would call a content loop. You can offer a prompt to ask for reviews, collect reviews, submit reviews, and then engage with those reviews. Then it goes back to asking for reviews again. The goal is to create a virtuous cycle.

You need to measure two sides: submission rates and engagement metrics. You also need to understand how many reviews shoppers need to read in order to make a good decision. That becomes your benchmark for the minimum number of reviews you need per item. Then, you have to know what a good submission rate looks like relative to your conversion rates. Once you’ve established that benchmark, you can measure whether reviews are helping lift conversions, increase add-to-cart rates, or reduce returns.

These are hard things to measure. You have to instrument from the beginning and find ways to isolate reviews from other parts of the shopping journey. Did the consumer look at price, shipping time, or reviews? Having a way to test and determine which component is actually driving change is difficult.

When you create that flywheel to convert passive consumers into active contributors, what are the things you try to watch for — things that might break it or cause it to slow down?

If you don’t solicit reviews at the right time, it changes the responses you get. We found it was optimal to ask for a review seven days after a product was received because that’s when most people have actually used it. But a TV might require three to six months before the purchaser can give an objective review, while a T-shirt can usually be reviewed after one wear.

We also experimented with different solicitation methods. 10 years ago, people mostly sent emails. So, we started using push notifications, advertising banners, and reminders in purchase history pages. Asking for a review while someone is already taking another action, like making another purchase or browsing the site, can be very effective. They’re more likely to give a quick thought.

Another important factor is making reviews easier to submit. Reviews used to be a large empty text box. We started offering things like star ratings and selectable keywords. We can generate a draft review, and the user can click a button that says, “I agree with this.” That way, they don’t have the mental load of seeing an empty text box and thinking, “I don’t want to fill this out.”

How does UGC come into play differently for higher and lower-consideration purchases?

Back to the knives example, say you’re buying some, and you’re not a serious home cook. You might see a four-star rating at the right price and buy immediately. But if you’re taking cooking classes and trying to become a chef, you’re going to read the reviews. You’ll want details about sharpness, food preparation, maintenance, and long-term use. When it’s a higher-risk, higher-consideration purchase, you’re more likely to lean on reviews.

The delicate balance of incentivized reviews

How do you solve the cold start problem for new product categories where the user-generated content doesn’t yet exist and you’re starting from scratch?

It’s a really delicate balance with incentivized reviews. More often than not, you’re going to have to give the product away and ask for an honest review. You have to be very precise with your language to make it clear that you’re asking for an objective review, not a positive one. The FTC watches this closely because if you’re soliciting only positive reviews, you’re influencing the outcome. You want enough reviews to provide trust signals, but not so many incentivized reviews that shoppers stop believing them.

You might launch with 25 incentivized reviews, but immediately start soliciting more reviews from regular purchasers. You can also encourage participation through recognition rather than incentives. Maybe you notify people when their review helps another shopper. Maybe you award badges when contributors reach milestones. People like to feel they’re making a difference and working toward something.

Leveraging incentivized reviews and providing trust signals to the consumer is a delicate balance. If you have two five-star reviews, no one’s going to trust that. At the same time, if you have a large number of incentivized reviews but no regular, non-incentivized reviews, shoppers won’t trust that either.

How does personalization play into UGC? Do you find that too much personalization can start to feel opaque or reduce trust?

I don’t know that personalization reduces trust. I think it’s more that a reduction in authenticity can reduce trust in things like AI. There are concerns about bot attacks and AI-generated reviews. After a while, they all start sounding the same. We had situations where reviews were submitted as slight variations of one another, and it was obvious they weren’t authentic. We had to build controls to determine where reviews came from, when they were written, and whether patterns suggested fraud. For example, one change we made was requiring users to be logged in to an account before they could submit reviews.

What creative or unique ways have you found to build a contributor community and accelerate your flywheel?

We gave top contributor badges to people who read or wrote the most reviews. But we also introduced category expertise badges, such as electronics expert or cooking expert. If someone is a cooking expert, you may place more weight on their cooking-related reviews than their apparel reviews. These designations helped contributors feel recognized and encouraged them to continue participating. We vetted them on both purchase history and review activity.

Influencers and the role of consumer confidence

What do you see for the future of user-generated content? Where is it going, in your opinion?

UGC is definitely moving away from written content on retailer websites. Social commerce, social content, and social media are becoming increasingly important. Retailers are partnering with social platforms and influencers because content is moving to TikTok, Instagram, and other channels. Retailers want to bring shoppers from that content directly into purchasing experiences.

The question becomes: who are the influencers that can provide objective commentary while also attracting an audience? And a lot of times, brands end up having some type of compensation for those influencers because of what their public reviews are worth.

When you think about ‘transformative customer experiences,’ what actually changes for the user when UGC is done well?

The biggest thing is confidence. UGC helps customers feel they’re purchasing the right thing. It can reduce return rates because consumers have more information before they buy. Beyond reviews, things like virtual fit tools and photos from real users help shoppers understand what a product will actually look like and feel like. The goal is to make customers feel closer to the experience they’d have in a physical store. UGC helps people become more comfortable making purchases they have to trust before they can actually see or touch the product.

What does LogRocket do?

LogRocket’s Galileo AI watches user sessions for you and surfaces the technical and usability issues holding back your web and mobile apps. Understand where your users are struggling by trying it for free at LogRocket.com.

In our conversation, Ken shares lessons from leading product organizations and coaching emerging leaders. He discusses the shift from individual contributor to manager, how product leaders can develop meaningful influence, and why delight still matters in product design. Ken also reflects on the growing role of AI in product development and how teams can embrace new technology without losing the human perspective that makes products truly valuable.

From individual contributor to leader

What’s the most underestimated part of transitioning from an individual contributor to a manager?

As individual contributors, we tend to see things only through the lens of our world. A lot of times we’re just trying to get our work done as efficiently as possible. As leaders, you’re often in charge of multiple individual contributors and collaborating more cross-functionally, so you start to see how each person plays an important part in a larger whole.

When you’re an individual contributor, there’s so much work to do that it’s hard to get out of the weeds and see the bigger picture. Good leaders help people see that and ask the right questions to help them consider those things. Some individual contributors do this really well, and when I see someone doing their part but also seeing how it fits into the bigger picture, that’s when I start to recognize they’re a great candidate to step into a leadership role.

What people often underestimate is just how much everyone needs to work together to make something truly magical happen. Sometimes people also underestimate their own ability to influence and guide a project. I’ve had people come to me and ask me to solve a problem for them, and I’ve tried to get better at telling them they can have that conversation with the other person and solve the problem themselves.

Encouraging people to take a stab at it and use their influence and critical thinking helps things run smoother and helps them become more of a leader themselves.

How do you handle conversations with strong individual contributors who want to become managers?

I’ve had these conversations a lot, and I usually start by asking people why they want to be a manager. Sometimes it’s because managers get more recognition, people think they’re really smart, or they get paid more money. But they may not actually have a skill set suited for management or even enjoy that kind of work.

Being a manager is not always fun. You’re dealing with administrative tasks, and a lot of problems flow to you. Sometimes people get into those roles and realize they miss the days when they could just put their head down and do the work.

That’s why it’s important for companies to create incentive structures where great individual contributors can earn good money too. That way people who are strong ICs don’t feel like they have to move into management to accomplish their career goals.

When I talk with people about it, I ask questions like: Do you like leading people? Do you have a vision you’re trying to accomplish? Do you enjoy coaching and mentoring people? If the answer is yes to those things, management can be a good path. If the answer is more like, “I just like doing my work and being done when I’m finished,” then I usually steer them away from going down that route.

Learning to influence effectively

How do you help people develop meaningful influence without doing it just for optics?

When people try to insert themselves into situations just to appear influential, it’s usually pretty transparent. Everyone feels annoyed when that happens because it slows things down and often has the opposite effect, where people start to cut that person out of conversations.

If you really want to be influential, you have to be self-aware and know whether it’s a situation you should be involved in. If you’re passionate about the topic or you have unique insight or skills for it, that’s a good time to influence.

Storytelling and data are also important. If you can tell a clear story and use data or specific examples, people tend to be persuaded much more effectively than if you simply say you want something or think something should happen.

At the end of the day, most people just want to do a good job and ship good work. If you can explain why what you’re proposing will help accomplish that goal, people are much more likely to get on board.

Using AI without losing product judgment

How are you integrating AI into your workflows while acknowledging its limitations?

AI is awesome, and it’s also super new. At this stage, what I’m encouraging my team to do is experiment with it so we become more familiar with its capabilities. But you can’t rely on it completely without using your own judgment.

If you take whatever AI gives you and submit it as your work without editing or validating it, you can get yourself into trouble pretty quickly. Humans are still needed for that judgment.

AI can be a great partner for generating ideas, and it can also be a really good editor if you ask it to critique something. But if you’re just asking it to do the work and turning it in without reviewing it, it’s not quite there yet.

I encourage my team to use it as much as possible and share what’s working well and what isn’t. That helps everyone learn how to apply it more effectively. I also remind myself that this is the worst AI will ever be, and it’s only going to get better, so we can’t ignore it.

How do you treat AI as another stakeholder in the product process?

The job of a product manager is to define the outcome you’re trying to achieve and create a path to get there. You’re defining what success looks like and how you’re going to deliver something to customers.

AI is essentially another stakeholder in that process. If you give AI a bad prompt, you get bad output. Just like working with engineers or designers, you have to clearly explain what you’re trying to accomplish — what success looks like and what outcome you’re trying to create for the customer.

The better you articulate that, the better results you’ll get. If you can tell that story clearly to AI, it can be a really great partner.

Human-centered product experiences

What happens when AI-driven features don’t actually improve the customer experience?

Because we’re human, we still understand what resonates with people. I’m an endurance athlete and spend a lot of time on Strava, which is one of my favorite products. Recently they added an AI feature that analyzes workouts and gives feedback.

When I tried it, it basically repeated what I had already written. If I titled my workout “long run” and wrote that it felt hard but I got through it, the AI would say something like, “Good job on your long run. That was hard, but you got through it.” It didn’t add any value.

I remember thinking, what product manager let this get into the product? It’s cool to experiment with AI, but you still have to ask whether it improves the customer experience. If it just repeats what the user already said, it’s not helpful.

Why is delight still important in product design?

Sometimes my sense of humor can be childish or adolescent, but I think it’s important for products to have fun moments. Especially in B2B or enterprise software, products can feel really stale. Consumer products tend to feel more alive.

Even small things can make a difference. It might be a celebration animation, something surprising in the interface, or just a design that makes people smile. Those moments interrupt people’s thinking in a good way.

A good example is the dinosaur game in Google Chrome when the internet disconnects. Another example is the old version of Solitaire where the cards bounced across the screen when you won. They could have just shown a message that said “You won,” but instead they created something memorable.

Those small moments give products life.

Is it difficult to convince leadership teams to prioritize those moments?

It can be difficult. I’ve worked with leaders who were very buttoned-up and didn’t want anything that felt playful because they thought it wasn’t on brand. Those conversations often come down to trade-offs.

Personally, I enjoy working on products that have life in them. I like companies where leadership can laugh at itself a little.

If leadership is hesitant, one approach is to test the experience with customers. If the version with something fun performs better, the conversation becomes easier. You can present the data and ask whether you should choose the version customers liked less simply because it’s more serious.

Adapting to an AI-driven future

How do you stay optimistic about the future of product roles as AI evolves?

This is something I struggle with sometimes. I’m naturally optimistic, but I haven’t seen another technology in my career that creates this level of uncertainty. AI could potentially automate a lot of what product managers do.

The way I stay optimistic is reminding myself that I’m smart, capable, and adaptable. If I’m willing to keep learning and trying new things, I trust that I’ll find my place in whatever the new reality becomes.

The people who may struggle are the ones who cling to the past and assume the technology won’t affect them. If you’re willing to learn and figure out new ways to add value, I think people will adapt.

I also think there will be a growing premium on human experiences. The more technology automates, the more people will crave human connection and human perspective.

You can often tell when something online was written entirely by AI. It’s getting better, but there’s still something that doesn’t quite feel human, and people recognize that difference.

What does LogRocket do?

LogRocket’s Galileo AI watches user sessions for you and surfaces the technical and usability issues holding back your web and mobile apps. Understand where your users are struggling by trying it for free at LogRocket.com.

Nishaat Vasi has spent eight-plus years at Zipcar – one of the original pioneers of the sharing economy – most recently as CPO. When he stepped back into the role after a stint incubating a startup spun out of Zipcar, he found a company that had done something easy to do and hard to notice: it had drifted away from its members.

Years of platform migration work had quietly pushed customer experience to the back seat. NPS was being captured the old-fashioned way through an email form after a ride, and barely 3% of members were responding. Nishaat’s mission was to change that, not just by capturing better feedback but by building it into the team’s operational processes.

The result?

Zipcar’s NPS jumped from 36 to 56, a +50% jump, driven not by a single feature, but by a sustained, cross-functional commitment to member obsession.

In this episode, Nishaat shares how he rebuilt that user-obsessed culture, what it looks like to turn unstructured feedback into operational change, and why most “AI strategy” is just theater.

1. Moving from post-ride surveys to in-context feedback (8:12)

When Nishaat returned to Zipcar, the feedback loop was broken in a predictable way: members were asked for their opinions hours after a trip ended, via email, and most ignored it entirely.

The first move was switching to in-context feedback – capturing it immediately after a trip, inside the app, while the experience was still fresh. The goal wasn’t just higher response rates. It was better signal.

“It was really about how do we get in-context feedback? Then what do we do with that in-context feedback? Outside of just getting feedback and leveraging it, it was also about how do we develop that DNA back into our product and UX teams.”

The shift paid off in ways that went beyond the data. When PMs, engineers, and operations teams could watch real session recordings and hear real user frustrations, they didn’t need to be convinced to care. The problem became visible.

Product takeaway: In-context feedback isn’t just a response rate trick. It changes what you learn and who in your organization feels accountable for fixing it.

2. Connecting NPS to P&L (16:31)

Plenty of product teams track NPS. Far fewer connect it to the business outcomes that give leadership a reason to prioritize it. Nishaat’s team did both.

After identifying their top three problem areas – car cleanliness, damage, and maintenance issues – they built out a financial model showing the cost of each. Damages don’t just hurt member experience: they accelerate asset depreciation and reduce resale value. Poor car condition drives rash behavior, which drives accidents.

“For every 100 such events, here is our hypothesis — all backed by data. We modeled this out. We look at different conditions. Here’s how many millions we’re going to lose. And this is why it’s not only damages, it actually leads to how people treat your cars, which could be accidents.”

Product takeaway: NPS is hard to act on when it lives in a product dashboard. The moment you can show leadership that moving it 10 points saves millions of dollars, it stops being a vanity metric and starts being a business case.

3. The pilot framework: Small bets, real data, then scale (21:07)

Not every problem has an obvious software solution. When Nishaat’s team decided to tackle damage detection, they took a two-stage approach: try a cheap pilot first, then build the business case for real investment.

One idea was to require photos of the car’s exterior at check-in and check-out. It was a technology solution that didn’t require a massive build, but the behavioral effect was immediate.

“Just the fact of making someone do it…changes the game, believe it or not. Whether I do anything with that data or not makes no difference. The big value is in the way people perceive the product at that point.”

When that still wasn’t enough, the team piloted off-the-shelf in-car devices with gyroscopes and impact detection – zero CapEx, no software investment, tested in two markets over three months. They expected to improve damage detection. They got a bonus: the same device also caught smoking, another major problem for Zipcar, saving the company millions of dollars.

Product takeaway: Design your experimentation model with two tiers – small pilots (sub-$100K, team-level authority, three-month timelines) and major investments (full business case required). The goal isn’t to democratize all decisions; it’s to make the cost of learning cheap enough that you don’t skip it.

4. From unstructured feedback to operational alerts using AI (32:53)

Nishaat has a clear-eyed view of AI.

His best example: a fleet manager in one market quietly removed the fuel cards kept in the visor of every Zipcar. No one filed a ticket. No one called to complain with a subject line that said “fuel card missing.” But users noticed.

Zipcar now pumps all of that unstructured signal into a single place and uses AI to identify week-over-week changes in emerging themes. When the fuel card issue spiked, the system caught it and fired a Slack alert to the right people in the right market in under 24 hours.

“It would have taken me or us at least three to four days to figure out what this problem was. This got solved, end to end, in under 24 hours. I’m like, okay, that was worth the cost of the tool itself.”

The key takeaway: The AI isn’t responding to members directly. There’s a human in the loop. But the time to detection collapsed from days to hours.

5. Why most AI strategy is “AI theater” (29:36)

Nishaat isn’t anti-AI. Far from it. But he’s skeptical of the way most organizations are approaching it – chasing the headline, not the outcome.

His framing splits AI investment into two distinct problems: getting AI into the product experience AND using AI to make internal teams more efficient. Both require the same foundation: clean, well-governed, well-documented data.

“At its core, you gotta have the data first…And so really it’s about ensuring we have the right data set up. People underestimate how much effort that takes to really create that walled garden – but that is crucial to get right.”

Product takeaway: Before you greenlight another AI initiative, ask what operating goal it’s connected to. If the answer is vague like “improve efficiency,” “enhance the member experience,” it’s probably theater. If it maps to a specific metric someone owns and is measured on, it has a chance of being real.

Chapters

00:00 Intro
01:07: Meet Nishaat Vasi, CPO at Zipcar
04:50: Zipcar and the customer experience wake-up call
05:50: Ditching the email survey for real-time feedback
08:39: Zipcar's user session watch parties
11:46: Turning NPS into OKRs
15:19: The top 3 priorities: Cleanliness, damage, and maintenance
19:13: The zero-cost pilot that now saves millions of dollars
31:03: How genAI spotted a hidden fuel card problem
35:57: Conclusion

Links

• LinkedIn

• Zipcar

What does LogRocket do?

LogRocket’s Galileo AI watches user sessions for you and surfaces the technical and usability issues holding back your web and mobile apps. Understand where your users are struggling by trying it for free at LogRocket.com.

In this conversation, Vibhu breaks down how he thinks about each surface in the ecommerce discovery journey and why getting them right requires treating autocomplete, progressive disclosure, and product presentation as distinct problems with distinct jobs to be done. He discusses the tension between helping customers express their intent and steering demand, the structural reasons why search results can explode rather than narrow as queries get more specific, and how Walmart overhauled its matching and ranking technology to fix it. He also shares a candid take on why the best monetization strategies follow great experience, not the other way around.

What autocomplete is actually for

How do you think about providing helpful guidance in autocomplete without overconstraining people’s ability to explore?

Autocomplete’s primary reason for existence is to help the user express their intent. That’s the primary goal.

We also have measurement in place and track metrics for this. One is coverage — do we offer autocomplete for a large set of keywords? The second, which is super crucial, is adoption: when we show the suggestions, do people actually like them and click on them? And the third is what we call MRR — it’s just a fancy way of saying the ranking of those suggestions. When we’re showing the suggestions, are people liking the top suggestions, or do they have to work really hard and find their way through suggestion four, five, six, seven? Having a system of evaluation and observation is very helpful.

Having said that, the nature of AI and machine learning systems has definitely evolved to an extent where secondary objectives can also be facilitated. Conversion or refinement are two examples of secondary objectives that could be layered on top of intent expression.

The objective can also change based on the moment in the journey. It’s a well-known industry fact that for the very first queries of a session — say someone wakes up, pulls out their phone, and it’s the very first query — previous session context is very helpful. As you go further in the session, what matters more and more is the activity you have performed within the session.

You could change the objective to be more focused on showing more diverse suggestions specifically when the intent is not very clear — let’s say someone has just started typing and has only typed one or two characters. In this scenario, it’s very fair game to show suggestions across different categories, different verticals, to maximize exploration. But as they start typing and the intent gets more solidified, the objective probably needs to adapt — moving more toward what people are trying to express, because the intent is now firming up.

Is there an example of an instance when an autocomplete suggestion shortened a journey prematurely?

Let’s say someone is searching for cucumbers. Should autocomplete’s role be to help them express this as quickly as possible and get out of the way? Or should it also shape the demand — “Hey, what about pickled cucumbers? What about cucumber spreads?” Different surfaces have different jobs to be done, and autocomplete is a surface where the primary job is to help people express what they want and get out of the way.

Once you take people to a listing page, it’s safer — for lack of a better word — to show more exploration. We can show different zones on the page related to directly matching intent, or alternate intent like pickled cucumbers or cucumber spreads. We try to index on letting people express what they want. The exploration piece we try to push down the journey a little bit, so that people can have an easy and quick takeoff with as little friction as possible.

Handling fuzzy and mission-based intent

How do you handle “fuzzy intent” categories — where the user isn’t searching for a product but for an outcome?

The example we love to use is drumsticks. Drumsticks is obviously the musical instrument. Drumsticks is also a green vegetable. There is also chicken and turkey drumstick. And Drumsticks is also a top-selling ice cream treat. Every person searching drumsticks has a different interpretation. It becomes challenging to put forward an experience when something vague like that comes in.

The game is, how much confidence do we have? In this scenario, it’s going to be very hard to have a high degree of confidence to lead with one of these categories. The way we handle this right now is to provide optionality at the moment. But it’s not an ideal experience. Ideally it should be a balance of optionality versus: do we have some data about the user? Can we look at some personalized signals and infer that person A is more about the chicken drumsticks, and person B is more about the ice cream Drumsticks, and then flip the optionality to a more guided experience that dynamically leads with a different ranking or ordering for person A versus something different for person B?

Back when LLMs were first introduced, we began thinking about how we could leverage this powerful technology and build meaningful experiences around it. One of the problems we always had was how to solve for customer missions versus sporadic purchases. The example here is, let’s say you’re throwing a birthday party for your kids — that’s the trigger for the shopping mission. People don’t usually go and search, “Hey, I’m throwing a birthday party with a Spider-Man theme, help me plan it.” People don’t usually search like that on shopping platforms.

We actually challenged that and said, “What if people could just enter their mission — how can I plan a Spider-Man theme party?” We built an experience around that where, just by inputting your mission, we’re able to deconstruct it and offer a solution so that you can solve for the entire mission: paper plates, balloons, T-shirts, party favors — all in one seamless interface. It’s not an ideal experience, but it’s an interesting take on how to handle some of these fuzzy intent scenarios.

Progressive disclosure and the too-many-results problem

As consumers narrow and refine, sometimes the problem isn’t a lack of options — it’s having too many. How do you think about progressive disclosure: what to show early versus later in the journey?

This is the crux of the search engine problem. The problem is not that there’s less data — the problem is there’s too much data. And the reasons are twofold, both systemic.

Compared to 20 years ago when assortment was very limited — maybe a few hundred thousand SKUs — for most retailers the assortment has now exploded. There are marketplace programs that have grown a lot, and by the nature of marketplace programs, they tend to bring a lot of tail assortment. We’re talking billions of items available in the catalog.

The secondary reason is more technical: the nature of a primitive search engine is that the more keywords you stuff into the query, the more results get generated — and that’s so counterintuitive. In an ideal world, the more specificity you add, the data should get more and more restricted. However, primitive search engines work on the concept of matching keywords in a relaxed way — either/or, if word A is matching or word B is matching or word C is matching. Because of the OR condition, the more keywords you add, the dataset keeps increasing.

To address the second problem, a couple of years ago we went on a journey we called “exact matches” — fixing the problem of specific queries. We up-leveled and completely overhauled our matching and ranking technology to adopt Google’s BERT algorithm, which is a state-of-the-art neural algorithm. We put this in our reranker — the second stage of sorting and ranking items — and it completely changed the game for us. We had massive, massive relevance gains unlike anything we’d seen for the longest time. This was a game changer.

Progressive disclosure — we also call it contextual refinement or contextual nudges — means providing a more guided and assistive experience that stays in context to where the customer is in the journey. We are investing very heavily in this space, using a lot of LLMs and query graph data to create these nudges, moving toward more progressive data sources compared to the primitive data sources we had in the past.

Do you find that users lose some control with these new progressive methods?

Yes. There is a trade-off. The more guided and assistive experiences we build, by definition, the user will lose some control. The thing is to do it tastefully and with intention.

It’s well known in the industry that most general merchandise traffic starts with a broad intent. Complex purchases especially — a coffee maker, air fryer, vacuum cleaner, bunk beds — for most people, it’s very hard to start anywhere more refined than that level of intent. It becomes very important to help someone navigate and refine because it’s going to be very hard to make a decision if they can’t think beyond “coffee maker.”

We would not want to disrupt the flow or provide a lot of navigational guidance when the user is already very specific in the journey. It needs to be contextual — showing up at the right moment, at the right point in the journey.

What you show on the product page

How do you think about highlighting product features without overloading people with too much information?

This is fundamentally rooted in the same root cause: too much data, too much to consume. Cognitive overload is a huge opportunity and problem, and endless scroll is not helping either — it’s a double-edged sword, because the list of items is just endless.

There’s no textbook answer to what the right balance is, and every retailer probably needs to run a continuous program of testing and learning. We continuously run a series of tests to keep finding the balance.

The layout you show information in — single column versus double column — can have profound implications in terms of people being able to focus versus being able to scan quickly. One of my favorite examples: a lot of the industry was following a pricing format that put the dollar price and the cent price at the same font size. But really, people are looking at the dollar amount. We built a hypothesis: if that’s how people are actually thinking, why don’t we present information in the same format — enlarge the dollar amount, reduce the cent amount? This is a standard practice in offline physical retail as well. And it did test positive.

Images are very, very crucial. Another powerful concept is badges — bestseller, or trust signals. Trust comes from social proof: if more people are doing this, then surely they must be right, and I can rely on the judgment of the crowd.

But even showing badges, it’s very easy to overdo and over-optimize to the point that it becomes detrimental. Imagine a page where every item has a badge — anything can be overdone and over-optimized, and then it stops being helpful.

Monetization and the experience cycle

Across the full discovery journey, where do you see the biggest tension between maximizing monetization and minimizing user effort?

From a customer perspective, they will probably be fine not seeing any ads. But if you’re running a business, your incentive is toward monetization, and one of the forms is advertisement.

There will be a tension between ad revenue and commerce revenue — what is known as GMV. However, there’s also going to be a right balance — a point where you can maximize both. If both ad revenue and e-commerce revenue can be maximized, that’s our ideal state. Everybody wins, the customer wins and the business wins.

Monetization and ads in themselves are not inherently bad. They actually help a lot in discovery, in providing diversity, and in keeping the ecosystem healthy. As more products and sellers onboard into the system — there are millions of items for any given intent — monetization tools like ads give an opportunity to brands that are onboarding to surface a genuinely amazing product to the customer who has a relevant intent.

One of the ways to control for this is to view the surface from the customer lens in totality. The whole page is composed of monetized capabilities and organic capabilities, and even if different teams have their own independent metrics, what can help is to think of something like whole page relevance. Is the page in totality still relevant? Yes, we’re showing ads at the top, in the middle, in the bottom — but together with the organic, can we measure that whole page relevance and trend it over time?

Is there a broader principle you hold onto when navigating that tension?

What really happens is that experience paradigm shifts occur. A new experience paradigm is created — think of the feed, or endless scroll, or the video module. The experience paradigm changes, and then monetization engines start over-optimizing it to the point that it gets over-leveraged. A few years later, the experience paradigm changes again — and it’s a never-ending cycle.

Take Google AI Mode as an example. Google for the longest time was built on its search results listing, but recently it moved fast and overhauled its entire experience toward AI mode and conversation. Now the monetization is following the ideal experience they think is the future. Similarly, Meta stories — the experience was evolved first, the question was asked: “What is the best experience for the customer?” The answer was, “Let’s try stories.” Once the experience started resonating, monetization followed, and it became one of the most successful monetized products in history.

The principle to keep in mind is to always focus on what is the best customer experience and keep evolving that — keep pushing the boundaries, thinking big, thinking bold. As it keeps evolving, the monetization will continuously keep following it. That’s how you keep a sustained winning program: you’re not getting old, you’re not getting stale, you are continuously innovating and then monetizing it as well.

What does LogRocket do?

LogRocket’s Galileo AI watches user sessions for you and surfaces the technical and usability issues holding back your web and mobile apps. Understand where your users are struggling by trying it for free at LogRocket.com.

In this conversation, Chanel talks about how starting on the customer side — before any formal design training — shaped both her approach to UX and her cross-functional leadership style. She discusses the difference between what customers say and what they actually do, how she brought a startup mindset into the organizational complexity of Dow Jones, and why she designs for architecture before aesthetics. She also reflects on the research practice she’s building at Apartment Therapy Media, and why she believes anyone at a company should be able to talk to users.

From customer support to product design

You began your career on the customer support side before moving into product design. What went into that decision, and what was the experience like?

With my first job being in customer service in tech at a startup, I was able to understand a lot of roles of the business, and I started to get curious about the design side and the UX side — but I didn’t have any formal training. So I decided to go back to school and go to New York and get my Master’s in Communication Design. It was a very research-based program, very exploratory, no design focus in particular.

It was actually challenging to get back into the digital design world. The field had changed even in a matter of two years — titles were called different things, the tooling was different. I really had to rebuild a lot of skills, and I did that through a lot of freelance projects for nonprofits. In that process, I was really able to see this trifecta of my experience: customer service is the research side and the user voice side; I still have that design part of my background of understanding aesthetics and brand guidelines; and my schooling — both undergrad and graduate — is the strategy side. It was seeing how my untraditional background could really lead to this, what product design is becoming.

A lot of people who come into product through another avenue talk about having the hard skills first and then having to learn the human side. Do you feel like your experience was the inverse — and has that been an advantage?

I think now I understand the benefit and I’m glad I’ve gone this direction. When you are on the customer side, you need to learn all sides of the business — you have to know the tech, why it works a certain way, the pricing, why are we positioning ourselves this way? It helped me understand partnerships, and less about the silos of “this is my expertise.”

And I think that’s what’s led me into my leadership style as well — a customer doesn’t see all the different departments, they see you as one company. So that’s how I like to lead cross-functionally: if a customer comes to you with a problem, it’s not a specific team’s problem, it’s cross-functional. As a leader who took a nontraditional path, I always encourage other leaders to take a chance on someone who also doesn’t have such a direct trajectory into the industry.

Thinking holistically: Startups and enterprises

You’ve worked at both startups and larger, established companies. Was the product design process similar across those environments — and did the startup experience shape how you work now?

It really does. The startup had me think of things holistically. At Eventbrite, I could talk to the engineer who built the feature, see the whole picture — you move really fast, it was easy to just go over the fence and talk to them.

But then when I went to larger organizations like Dow Jones and the Wall Street Journal, it got harder. I was there during the pandemic working on a project about shifting our print process and how our customers got their newspaper, because a lot of things were changing. In this project, there were 10 stakeholder departments — it was just so much bigger. I couldn’t have as much visibility. But my startup mindset still worked there: little by little, going to someone in each of those 10 departments, mapping out the entire customer journey, and bringing everybody together. I also went to the customer service team and got call recordings of people calling in like, “Where’s my newspaper?” — and I played those short clips for everybody in the room.

The startup mindset doesn’t go away. It’s definitely a challenge of how you build that connective tissue — it’s a little bit harder. But you can still find a way to bring a startup edge, even at a place like Dow Jones or the Wall Street Journal where people have been there a long time.

What good UX actually means

You had a very direct view into user experience because you were talking to people experiencing issues all the time. Did your definition of good UX change as your career developed?

It did. With customer service, you’re not going to hear a lot of praise — people call to complain. What that taught me is that people want to be heard. If you’re doing something good, it’s very rare that someone’s going to call and say, “Hey, I love this new feature so much. Okay, bye.” They’re not going to wait on hold to give a compliment.

Good UX isn’t about what users say — it’s what they do. They have ideas, they think they know what’s going to improve whatever issue they’re having. But good UX has taught me to also look at the numbers and get more into the data of behavior patterns, watching screens without being in the room with people, and thinking you have to read between the lines — not just the explicit feedback. You have to remove a little bit of that subjective voice, but still give people a place to be heard.

Do you think product and design teams can better leverage the insights that customer-facing teams already have?

I always say it’s the qualitative and quantitative — you really have to sink your teeth into both sides. You can look at the data to see why a feature isn’t performing, and you can go into the customer service logs and see if you’re getting calls about that certain thing, listen to some of it, do session recordings, look at clicks unmoderated and moderated. It has to be both of those working together.

How do you decide whether something is a design issue in the product versus a customer behavior issue where users need to be pushed into learning how to use the product correctly?

I really learned this when I was at the Wall Street Journal as a UX architect. You start to look at foundation and then layer in design system and aesthetic and brand voice. We were having a newsletter signup issue on the website — at first glance, people thought it was a UI problem: they’re not completing the signup, they’re not clicking the right button. But stepping away from that and mapping out the entire user flow, it was architectural: we missed a step in the process where they were registering and making an account.

By having this blueprint, by looking at the architecture, I could go to the engineers, run this by them, and they could see the layer of technicality — we weren’t even validating them. You could have a crack in your floor and put a rug over it, but that crack, you’re going to keep stepping on it. It might look nice because it’s covered up by a nice rug, but you have to take that rug up and see what the foundation is.

When design shapes strategy

Do you see scenarios where design insights actually alter the strategic direction of the product?

At my current job, one of our brands, The Kitchn, we launched a membership initiative to get more people to create an account and sign up. We needed a signup page — we looked at competitor sites, direct and indirect, and our own design aesthetic. There was a lot of variation and debate, and I decided: let’s run an unmoderated user test. Let’s get this design in front of people in different variations and just get feedback. It was unanimous: people said, “Make it easy to sign up. That’s what this page is meant to do.”

So we stripped back the fluff and the color. The strategy was to get people to sign up, and in this case, adjusting our design aesthetic helped us get to that strategy. By getting actual user feedback and watching those short videos, we could end the debate about what the page should look like. That page is now getting 50% conversion — a little bit of qualitative data there to help reinforce the strategy.

Do you encounter situations where people outside design are more focused on aesthetics, and you have to push to keep functionality at the top of the conversation?

Yes, but I feel like lately I’ve seen a shift — maybe in the industry as a whole, but also in my company. Things are going simplified. People want it to look like Apple, they want “the Netflix of media, the Uber of ...” — familiar patterns. I heard this quote in undergrad from Jim Jarmusch: “Nothing is original.” And I think my challenge now is remixing things.

We’re moving toward an industry where maybe aesthetics are feeling a little less important — a lot of things are looking like other things, especially with AI, and a lot of aesthetic is becoming similar because of that. But I think it’s bringing up a new challenge: how can you delight and surprise through function? Maybe it’s not with design, but it is with function.

Building a research culture

What were you seeing at Apartment Therapy Media that made you feel a research initiative was essential — and what was the process of building it and getting support?

When I got to AT Media, the product team was super hungry for it, super curious. There was a lot of openness to understanding it more. They were already doing surveys and using Hotjar for session recordings, but wanted to push further. The gap I noticed was actual user conversations — starting moderated and unmoderated sessions.

In at least the first quarter of starting, I did moderated sessions with users and had the CEO sit in and watch, along with people not even on the product team. It was eye-opening — a lot of the company jumped on board: “Oh yeah, this is really helpful to see.” We’re in the room looking at our website every day, and to zoom out and watch someone who doesn’t know us navigate it — “Oh my gosh, they don’t understand this part, this is confusing” — getting us to talk to people who don’t know us, and people who do know us. There was a lot of excitement, and there still is.

How has it changed the way the team works — the confidence in decision-making, the sense that the product is functioning better?

It cleared up circular conversations that I think were happening — different departments having different opinions — and it kind of ends that circular debate. Now more people across the company are even asking, “Did we do user research on this?”

My approach to research is also to democratize it. I think anybody can and should talk to our users — it doesn’t need to be formal. Recently a PM did an internal user research session watching our editors use our internal publishing tools, and it was super illuminating — the crazy workarounds they’re doing, the processes they’ve just accepted as “yeah, it’s kind of like this.” Having her do that is this customer service focus in action — the editors are like, “I feel seen now.” A lot of times you’re focused outward: “We need to launch all these features.” But we also have internal tools for our team, and it’s exciting to see how research is growing and being used in different ways.

What does LogRocket do?

LogRocket’s Galileo AI watches user sessions for you and surfaces the technical and usability issues holding back your web and mobile apps. Understand where your users are struggling by trying it for free at LogRocket.com.

Before Laura Burkhauser was the Descript CEO, she was a customer first. She loved the product so much that she knocked on founder Andrew Mason’s door and asked to work on it with him.

Before Descript, Laura built her product career at Le Tote and Rent the Runway, a path that taught her that “what got you here genuinely won’t get you there,” especially when you’re suddenly the most senior product person in the room with no one left to learn from.

In this episode, we talk about:

• Why fast-moving careers can leave you stranded at the director level — and what to do about it

• The four-stage framework Laura uses to assess how every person on her team feels about AI (from “hostile” to “rewired)

• Why automating a broken system just gives you broken results faster

• And a two-by-two for AI adoption that starts with the most human questions first: What do you hate doing? And what do you want to do more of?

1. What got you here won’t get you there (05:49)

Out of business school, Laura joined Le Tote, a startup she was already a customer of. She excelled almost immediately both as a product manager and a people manager.

Then came Rent the Runway and the director title. She was good. But being a product director is a fundamentally different job than being a great PM — and she had no one around her to show her what that looked like.

“There was no one to kind of tell me, ‘This is what a product director does. This is how you think about resource allocation. This is how you think about influencing the business strategy. This is how you start thinking about working on the business and not just working in the business.’”

She was the most senior product person at the company, and there were no peers, no models of excellence nearby, and no map for her to learn from.

The lesson she learned: when you’re the most senior person in the org chart, your growth has to come from outside the org chart.

2. The four-stage tech acceptance framework (21:39)

Most conversations about AI adoption treat it as binary: people either get it or they don’t.

The framework Laura applies at Descript tracks four distinct stages:

1. Hostile — “I don’t want to use this. I think it’s bad.”

2. Skeptical — “I’ll try it, but I think it’s going to suck. Last time I tried, it did.”

3. Converted — “I believe. I know it works. I remember to use it sometimes, and I’ve got a couple of systems down.”

4. Rewired — You think AI-first. When a new problem lands on your desk, your first instinct is to ask how AI can help you.

When Laura and her team ran their company-wide AI hackathon last year, the goal wasn’t to move everyone from “zero” to “rewired” overnight. It was to honestly assess where people were and design the right intervention for each stage.

Product takeaway: Before you design an AI adoption program, map your team against these four stages. “Hostile” people need a different intervention than “converted” people. A one-size mandates help no one.

3. “Struggle with your art, not with your tools” (15:31)

Descript has been an AI-native product since before “AI-native” was a buzzword. The original concept — to be able to edit video like you edit a document — made LLMs a natural fit the moment they arrived. When you’re editing the text, you’re editing the video.

But when the team built Underlord, their AI editing companion, they grounded it in a belief Laura says has “aged like wine”:

“The purpose of AI should be to take the struggle out of the tools that you use — and not out of you thinking about what you wanna say. Thinking about what your vision is finding your voice.”

She adds:

“AI is definitionally a derivative technology. It works by taking a statistical average of what is most likely to happen next. Without a lot of input from you — without a vision, without a director who matters — you’re gonna get derivative content.”

Product takeaway: If you’re building with AI, be specific about which part of the struggle you’re trying to remove. Removing tool friction for your users should be your goal. Removing the creative struggle removes the whole point.

4. The durability problem: What happens when your AI processes aren’t operational? (26:31)

There’s a pattern Laura has watched play out across companies: someone becomes a beacon for AI. They figure out how to use the tool(s) brilliantly and become 5x, 10x more productive. Leaders notice. And then nobody can quite figure out how to transfer what that person did.

“If that person left, none of that is durable. None of that endures.”

Laura draws a sharp contrast with how human-built systems tend to work. Bring in a great head of product, they set up product review, design review, meaningful human processes. Even if they go on leave for five months, the systems outlast their absence.

But the way most companies are building AI right now?

“If the employee who created the AI system were to leave, the AI system just leaves with them. It’s gone. Because it’s their own personal setup.”

Product takeaway: AI adoption isn’t just a people change — it’s a systems architecture problem. Build for durability from the start. If your AI capability could disappear when one employee leaves, you don’t have an AI capability. You have an AI dependency.

5. The two-by-two that actually gets people on board (29:22)

When Laura ran her product team’s AI offsite, she didn’t start with tools or capabilities or a productivity pitch. She started with the most human question she could think of:

What do you hate doing?

Specifically: looking back at the last four weeks of work, what sucked? What do you wish you could do less of? And — crucially — what do you wish you could do more of?

That’s the first axis of the two-by-two: stuff you love doing vs. stuff you hate doing.

The second axis: is AI actually good at this yet?

Because here’s the problem with pitching AI adoption as “two X productivity” to your ICs: it sounds like twice the work.

Product takeaway: The missing ingredient in most AI rollouts is the dream. Before you talk about tools, get your team to separate what they hate doing from what they love doing. Then let that map guide where AI fits — and how you talk about it.

Chapters

• 00:00 Intro

• 02:23 Laura's career journey: From fashion startups to finding her path to product

• 05:49 The "what got you here won't get you there" moment

• 08:26 Finding peers and mentors when you're the most senior person in the room

• 15:31 "Struggle with your art, not with your tools" — Descript's AI philosophy

• 21:39 The tech acceptance framework: From hostile to rewired

• 26:30 Pitfalls of AI adoption: Cost, durability, and automating broken systems
  29:22 The two-by-two framework for getting your team AI-pilled

• 32:24 Conclusion

Links

• Laura’s LinkedIn

• Descript

What does LogRocket do?

LogRocket’s Galileo AI watches user sessions for you and surfaces the technical and usability issues holding back your web and mobile apps. Understand where your users are struggling by trying it for free at LogRocket.com.

In this conversation, Ali talks about what it means to operate in a compressed product lifecycle, where the time between idea and delivery has shrunk dramatically. He shares how AI has removed traditional bottlenecks and fundamentally changed how the traditional product trio works together. Ali also discusses the growing importance of identifying the right problems with product intuition, moving from historically opinion-driven to evidence-driven decisions.

How AI is reshaping the product lifecycle

How would you describe the pace of a product lifecycle today compared to even just a few years ago?

The biggest change with the advent of AI is that it’s collapsed a lot of the steps that would go into a traditional lifecycle — the time it takes to go from an idea to a prototype or customer-facing concept, and then to a product. I can create something viable in a matter of minutes to show a customer. Back in the day, that used to take a long time.

It also used to involve a lot more organizational bureaucracy — securing resources, making a case for what we’re doing, getting the right people aligned. Much of that has fallen away. With this compressed lifecycle, the process isn’t just faster; it has fundamentally changed. Product, design, engineering, and even marketing can now work in parallel instead of waiting on each other. We saw a shift from waterfall to agile, but even agile is starting to feel less central. What matters now are the core principles: taking in feedback, learning quickly, and prioritizing the right problems.

That’s the key — figuring out what the right problems are. Doing everything for everyone doesn’t work. You need a clear point of view on where you can create the most value, while staying flexible enough to evolve as you learn.

You spent over seven years at MySpace during a pivotal era in social media. What did managing a product lifecycle look like back then, and how much of that process was shaped by the sheer time it took to build, ship, and learn?

It was a bit crazy — it felt like three jobs. I often joke that working at MySpace was like college, grad school, and a medical residency for product management.

Back then, the lifecycle had a lot more drag. Even though MySpace still operated with startup energy, we were dealing with multiple management layers and had to sort out resources just to do any meaningful prototyping. After News Corp acquired MySpace, that added even more structure, oversight, and priorities from a public company. Internal politics and bureaucracy created long gaps between idea and implementation. It took time to test something, build it, and get it in front of users. We had great tools, but the organizational side slowed things down.

Even when trying to decentralize decisions, the weight of the organization made it difficult. A lot of the process was shaped by how long it took to navigate that. Today, we can create evidence before committing. We can prototype quickly and validate ideas with customers in days instead of weeks. For example, one of our product leads identified a pain point, created a prototype, and set up a customer conversation within three days. That step alone would have taken weeks before. She didn’t need to secure resources or go through layers of approval — it simply happened, and with very little friction.

When you compress the product lifecycle this dramatically, customers feel it too. What does the faster loop actually deliver for the customer who’s waiting on a solution to a real problem?

One of a business’s greatest advantages is the ability to reduce waste. At Instil specifically, we’re not spending a lot of time, resources, or money going in directions that don’t provide value back to the business. That’s huge.

We can learn faster, build things that provide value quickly, and, as a result, realize that value as a business sooner. This allows us to be more efficient, stay focused, and ultimately grow. We call our customers partners here at Instil, and for them, the key benefit of partnering with us is our responsiveness. For example, the time between our product lead identifying a pain point, talking to customers, and seeing a potential solution can be a matter of days.

From our partners’ perspective, that feels great. They feel that we’re listening, and the distance between experiencing a problem and us shipping an improvement is shrinking.

From opinion-driven to AI-powered product development

At a company like MySpace, how much did product direction depend on leadership having the loudest voice in the room?

In the early days, leadership had a strong influence. Tom Anderson — everyone’s first friend on MySpace — was deeply embedded in the community. He quite literally lived inside the community that he founded and built, and he became the voice of the user, which worked well early on.

But that doesn’t scale, and we learned that over time. In the early days, that can be really powerful, especially for a startup. And it’s not just a MySpace thing; many companies — startups and large organizations alike — are often shaped by loud voices from founders, executives, or board members.

The challenge historically was that pushing back with evidence used to take too long and required too many resources. By the time you gathered enough data, the opportunity was gone. Today, you can create that evidence quickly. If a leader has a direction, you can test alternatives, talk to users, and present a case before decisions are locked in. That creates a more collaborative environment where you’re solving problems together instead of following opinions driven by conviction alone.

At Instil, how is AI changing the way your team moves from identifying a problem to putting something in front of a customer, and what does it demand from them from a skills perspective?

We’re leaning into tools that help us synthesize customer feedback quickly. We can spot patterns faster, explore solution directions more effectively, and get to something tangible much sooner.

In one case, we presented multiple directions to a customer and ultimately ended up choosing a third direction based on feedback. That kind of iteration used to happen much later through A/B testing or feedback channels. Now, we can get there in days without having to build out the full end-to-end product. It’s quite empowering, and it reduces waste in terms of money and effort.

This demands strong product intuition. You have more inputs, more data, and more possibilities than ever. The challenge is identifying the signal in the noise. You need to understand which problems matter and create clarity for the team. Building is easier now. Choosing what to build is harder.

You said that knowing what to build is more important than ever. How do you validate the feedback coming in?

Synthesizing the feedback and identifying the pain points — the signal from the noise — is the most critical part. On our team, we often say we never want to lead the witness when we’re gathering feedback. You have to be somewhat scientific about it — understanding what the analytics and test cases are actually telling you, while staying grounded in customer conversations.

How can you go through this process without injecting your own assumptions? How do you take the data and make informed decisions?

Getting feedback is critical, but it’s even more important to define and identify the signals that are going to help you choose what to build. In the past, you had time to process feedback slowly. Now everything moves faster, so the skill is in interpreting information quickly and using it to drive clarity and guide better decisions.

One advantage for us as a startup is that we’re a lean team. That makes things easier in some ways, because we can adapt faster. This is especially challenging for larger organizations, but it’s also becoming a forcing function — they need to operate more like startups if they want to stay ahead.

When you’re moving fast, how do you make sure that you’re still doing real discovery and building feedback loops into your roadmap?

I define real discovery as information that helps us make decisions and removes uncertainty. It should actually change decisions based on what we’re learning. That still includes conversations with customers, and increasingly through prototypes, analytics, and evaluations. Tools like LogRocket play a big part in that, especially with products like Ask Galileo. The key is to preserve learning as a discipline, and, as things change and improve, take it in, synthesize it, reduce the noise, and identify the signals.

Feedback loops are no longer structured checkpoints. They used to feel more like a segment of the process, whereas now they’re constant. Information is coming in all the time — like a fire hose. The hard part isn’t collecting all that feedback; it’s interpreting it. That has to connect to how we think about process. Even with agile development, the process can’t be as rigid anymore. Planning has to be flexible, and the roadmap, as a living, breathing document, is constantly being shaped by what we’re learning.

This is where the human element really matters — how we juggle all of this, identify the signals, and understand what actually matters for the business. It also means leadership has to be aligned, because the team needs clarity on how to absorb new information and act on it.

Evolving with the PM role

You mentioned strong product intuition. There is real fear in the product community that AI is going to do away with PMs altogether. What does AI still fundamentally not replace in the product process?

Along with product intuition, also judgment. Those are core human components that this role will always need. AI can absolutely accelerate discovery and execution, but it doesn’t decide which problems matter the most, what tradeoffs are worth making, or what kind of experience is worth creating. That still requires human judgment.

AI will take over a lot of the drafting and coordination work — PRDs, documentation, and process tasks. But it doesn’t eliminate the role; it evolves it. I’ve heard Marc Andreessen talk about how the lines between the triumvirate of product development — product, design, and engineering — are going to blur, and that resonates. PMs will do more prototyping. Designers will do more product thinking. Engineers will have even more influence on product direction. You’re still responsible for the outcome, but how you get there changes.

So, for product people, the key is being open to learning and evolving the role. The responsibilities don’t go away — they become more judgment-intensive, more cross-functional, and more outcome-oriented. The role survives, but the bar gets higher.

For PMs who are feeling more anxious than excited about this AI transformation, what’s the mindset shift you would encourage?

I can tie this directly to how I used to think when I started my career. I remember setting goals for myself, and a lot of what I judged and measured myself against was how I got things done — how I worked with different teams, set up processes, presented to different audiences, and even things like writing documents. A lot of that work is going to be absorbed or compressed by AI.

The mindset shift I’d encourage is to focus much more on the quality of your thinking, as well as the speed at which you and your team can learn. How much can you improve outcomes for the entire team, not just yourself? At the end of the day, you’re still shaping better product outcomes, and that’s a much more important measure of success. Great product leaders make their teams better.

You also need to become more hands-on and more experimental. Be fluent across disciplines — data, engineering, design, marketing, operations, and everything that’s related to the business. If you’re acting like the CEO of your product, then all of these elements come into play. You need to think about them all collectively.

And maybe most importantly, there’s so much noise around us. One of the most valuable things product leaders can do is drive clarity through that noise. Product can do that exceptionally well.

What does LogRocket do?

LogRocket’s Galileo AI watches user sessions for you and surfaces the technical and usability issues holding back your web and mobile apps. Understand where your users are struggling by trying it for free at LogRocket.com.

In our conversation, Mike discusses what it means to lead product at a media company where journalism — not software — is the core product. He shares how product and editorial teams collaborate in a fast-moving newsroom environment, and why trust and editorial integrity shape every product decision. Mike also talks about what it means to him to build products that exist in service of something bigger.

Product management in the media industry

Your current role at STAT is very different from anything you’ve done before. What do you find most rewarding about working at STAT?

I had never worked in media before. After 20-plus years in the digital industry, I didn’t know what to expect going into it. At STAT, the product is all our different channels. It’s the delivery mechanism for the content, as well as ensuring users can find what they want to consume and to keep coming back.It’s an engagement platform.

With that, the work the journalists are doing is incredible. They’ve literally been responsible for changing laws and saving people’s lives. Obviously, I’m not directly involved in any of that, but it’s incredible to work for an organization that has that kind of societal impact.

Coming from a digital agency where I was working on 40 to 50 projects a year, to now being focused on one thing lets me give a level of care to a product that doesn’t feel as transactional. At an agency, you do the job and move on to the next job. Here, you get to feel much more ownership and care over the product. It’s very different, and very enjoyable.

You mentioned your work at a digital agency, where you were building the product. At STAT, journalism is the product — and you have no say in it. What does it actually mean to be a product leader when the product that matters most isn’t yours?

It’s certainly interesting. In a digital agency, when you’re working on so many different projects, a lot of them aren’t the main product — they’re almost like lead gen or demand gen instead. In that sense, I had experience with that kind of thing. But once you move to a head of product role, there’s some expectation that you’re going to be focused on the main product. STAT was my first gig as a head of product, working on the specific product that we were selling.

From that perspective, it’s a little setting aside of the ego. It’s a different approach to how you speak about the product and what your focus is on. It’s more on the vehicle to get that product out to people — the journalism in this case. And there’s so much that goes into those products that really has to let the journalism shine.

Bridging product and editorial teams

How do you speak about the product?

It’s 100 percent on the editorial. We involve them as we’re talking about changes that will impact the delivery of their journalism. If we’re talking about changes to the website, especially article templates or how newsletters are going to be formatted, we consider them stakeholders in those projects. We certainly get their input when it comes to advertising because there are aspects about what we’re reporting on and what advertising can be that we need to consider. Especially as a health-focused publication, there are types of ads that we won’t run.

STAT is high-stakes, deeply reported journalism. How do you build for an editorial team in that newsroom environment that has to move fast and operate independently?

The editorial team has a lot of good ideas — mostly focused on how we can expand what we can do from journalism and storytelling perspectives. A lot of news is news, but a lot of it is investigative or storytelling as well. Supporting them and delivering that to the user is one aspect that they care about. How can we best tell our story? Those tend to be longer-tail projects.

But there’s also the practical side of it: “I need to accomplish a task in the CMS. I need to include this document. I need to add this widget. How do I do that?” As a small team, there’s only so much from a resource perspective you can be working on. A lot of times, those stories are time-sensitive, so it’s part of the job to drop what you’re working on and focus on those tasks so the editorial team can best tell that story.

Balancing different departments and media channels

The value proposition of STAT lives entirely in content you don’t control. What levers does product actually have to drive engagement and retention?

There are multiple channels. The website is the most obvious one, but we also have the app and newsletters, which fall under the product team as well. Each one works a little differently in trying to keep a user engaged, not just with the story they’re reading, but with the next story that’s going to grab their attention.

The journalism shines on its own. If we printed it in a newspaper or magazine, people would just keep turning the page. It’s a lot different in a digital environment where you have to encourage them to move on to the next story.

As great as it is for someone to read multiple stories in a single session, getting them to come back regularly is more important. If somebody reads 10 stories in one day, that’s not as impactful to us as them reading one story every day for 10 days. It’s really about making those channels work together to drive them back to the stories. The newsletters do a lot of heavy lifting. The app and its push notifications help build a habit around engagement that pushes people back to the website.

Especially now, we’ve seen a shift where we still get a lot of traffic from search, but we see a lot more traffic coming from something like Google Discover, which is a feed. You scroll, you click a story, you read it. A lot of times, people don’t even know what publication they’re reading. They just saw a headline and clicked it. There’s a good chance you’ll never see that user again. So how do you engage with that user in a way that informs them and gets them to potentially come back again or sign up for a newsletter?

How do you structure your roadmap when you’re balancing requests from different departments, like editorial, advertising, and the subscriber experience, all at once?

It’s a little bit political. You want to keep everybody fed. If you just focus on one team, everybody else starts feeling like they’re not going to get anything for a year. You have to make sure everybody is getting something to keep their teams moving forward, too.

But from a prioritization standpoint, we’re a business. We need to stay in business, so revenue-generating things are going to get a higher priority. That’s just reality. That doesn’t only mean ads. It includes things around gaining and retaining subscribers.

Anything that helps with revenue — whether that’s advertising, retention, or subscriber growth — is going to jump to the top of the queue. When you look at the media industry as a whole, it’s incredible how much negative change has been happening. You see layoffs constantly across publications. STAT has been healthy, but we have to ensure we stay healthy, and that involves revenue.

Leading lean teams as a product executive

You went from managing 40 engineers at an agency to leading a lean team at a 100-person company where most employees are on the editorial side. What does that shift ask of a product leader that most PMs underestimate?

First, a little bit of setting aside ego. On a small team, it means getting your hands dirty. As a head of product, you think about the high-level roadmap and making sure teams are working well together. But on a lean team, sometimes it means owning the feature yourself or stepping in when someone is out. When someone’s on vacation, I’ll say, “I’m going to own that for a week,” and that’s OK. There’s much more of an aspect of getting your hands dirty than I had when I was managing a 40-person team.

Honestly, it’s kind of fun. It’s easy to get detached from the work later in your career as you move further away from execution. There’s something rewarding about staying connected to it. At the same time, you have to balance that with higher-level responsibilities like roadmap management and budgeting. It becomes a mix of everything.

Do you have advice for product leaders who are skeptical about getting into the operational trenches like that?

Even on larger teams, it’s valuable to jump in every once in a while just to remind yourself what your direct reports are dealing with on a daily basis. As leaders, it’s easy to dictate how people should do their jobs and then create processes around them. Sometimes the most impactful thing you can do is actually immerse yourself in the work. I think it makes the processes you build more effective, and it helps you connect more deeply with your direct reports.

Ethical considerations in journalism

You have to think about ethics in a way most heads of product don’t. What are the ethical considerations when you’re working in the service of journalism, and what are the constraints?

First, it’s that you’re working in the service of journalism and trust is the number one thing. If readers don’t trust what they’re reading or how they’re consuming it, everything quickly goes out the window. It’s so easy to lose your reader base if you break their trust. That’s obviously a journalist’s guiding light, but as a product team supporting them, that has to be ours as well.

We have AI guidelines on our website that explain what we will and won’t do with AI. You see a lot of publications moving toward AI now and they’re running into issues with hallucinations around stories. You can only have so many mistakes before readers stop coming back. A lot of that drives how we do our jobs.

In terms of constraints, we’re very clear that we won’t rely on AI for content generation. We’re never going to use AI to write stories. Once we know the rules around what journalists are comfortable with, that helps us set our guidelines. Then it becomes our job to figure out where we can utilize AI tools or deliver tools that support journalists in doing their jobs more efficiently.

For example, it’s one thing to say, “Here are all of our stories.” It’s another thing to say, “We know your interests as a reader, so here are the stories we think will be most impactful to you.” Finding efficiencies that don’t break trust and still let journalism stand on its own is where our guidelines come in. That journalistic integrity aspect helps shape how we approach things.

Does it give you some freedom to have those very sharp guardrails?

It does. I mean, yes and no. I think when we’re having those conversations about, “All right, what are we comfortable doing?” and knowing exactly where the guardrails are is helpful. Whereas I feel like some organizations, or maybe even some journalists, are having thornier issues to the point of: “If we do this, will we be okay?” We never feel like we’re encroaching that far because we have those guardrails. So I do think it’s helpful. It makes us more comfortable with the decisions we make.

That’s not to say that we never run things by the editorial team like, “Hey, would we be comfortable trying something new like A, B, or C?” They might say, “We’re cool with C, but A and B, no, we’re not doing that.” I think we have a really good relationship with our editorial team. Realistically, any product team has to, and we meet with them regularly. Sometimes we’ll try to push them a little, but we’ll always defer to them. So we have a good sense as to where they’re comfortable, where they’re definitely not, and what that gray area is.

What’s the case for building a career as a ‘background player,’ a PM whose product exists in the service of something else?

No matter what you’re working on, you could tell a narrative in a way that can still make you a product hero. At the end of the day, you’re doing a job in service to a user. Whether you are building the actual product the user is using or providing the mechanism through which they consume the product, you’re still supporting both the company and the user.

That’s where the professional fulfillment comes from. I feel great about helping users consume our content because the content is really important. If we weren’t doing a good job, nobody would be getting that content. It’s incumbent on us to ensure users can access it, get value from it, and keep coming back.

What does LogRocket do?

LogRocket’s Galileo AI watches user sessions for you and surfaces the technical and usability issues holding back your web and mobile apps. Understand where your users are struggling by trying it for free at LogRocket.com.

In our conversation, Roger shares his perspective on how AI is fundamentally reshaping the product management role, including the increasing pressure on PMs to operate at higher speed and scale. Roger discusses how his time in the US Navy has influenced his approach to leadership, and also talks about his prediction that the PM role is splitting into “builders” and “peopleers.”

How AI is redefining product management

How would you describe the disruption AI is bringing to the PM role right now?

It’s nothing we have ever seen in technology. AI is a monumental shift in the way that we work, in the output that we have, the outcomes that we can achieve, and everything in between. It is both empowering and disempowering at the same time. It depends on how deeply you go down the rabbit hole. Those who embrace AI and utilize it without it being a crutch are going to succeed. Those who buck the trend and refuse are going to get out of the industry or be forced out one way or the other. I don’t think there’s an in-between — the status quo is over.

There’s a lot of focus on the technical skills and tools PMs need to stay relevant in this AI era. What skillset do you think is being overlooked?

Language. Technical skills are still being played out. You don’t need nearly as many technical skills to build as you once did. A couple of years ago, if you wanted to build a website, you’d go the WordPress route, get a template, and hope nothing broke. If it did, you’d call a friend because you couldn’t understand what was going on.

Now, all you have to do is talk to tools like Claude, Perplexity, or Codex. They’ll troubleshoot, or you can pit them against each other. There’s a lower barrier to entry into technical realms because it enables the layperson to create technology with just an interface, and it’s only getting better exponentially.

On the flip side, if you want to take something to market yourself — be an entrepreneur or PM pushing code to production — you’ll need to be more technical, but in different ways. You need a better sense of architecture, deployment methods, and how all the moving pieces work together so you can bring a product to market and be part of that release cycle. You’re not replacing a cog — you’re becoming a new one, a more efficient one. Some technical skills are waning, others are ramping up.

You mentioned language as an overlooked skill. Can you elaborate on why you feel that’s the case?

Some people rely on AI, especially large language models, as a crutch. Like anything, if you don’t use a skill, you lose it. But if you hold onto language, what you feed into AI becomes better, and what you get out of it becomes more useful and more interactive.

Language is also the key to people’s interactions, which are absolutely necessary to stay relevant in this new world. We have to be able to interact — in conversation, in writing, in everything. Language is still a key and evolving part of our society. And evolving is key — language changes, and we should embrace that.

The new expectations for product managers

PMs have always had to navigate competing pressures, like sales, engineering, and customers. How do you think about that negotiation dimension as the role of AI tools maximizes the volume, speed, and quality of outputs as a whole?

Those pressures aren’t going away. If anything, they’re becoming more demanding because of the pace of technology. AI is everywhere. You can’t swing a cat without hitting an AI banner or billboard. So the pressures are more fierce. But you can use these tools to your advantage. I recently bought a car and used AI to figure out what a good deal was. I used it to contact salespeople, pushed everything into text or email, set up a profile with my goals, and had it interact. I reviewed everything, so I was the man in the middle, but it was a much better experience.

If we apply that to PM work, it gives us an edge. A product manager is often an internal salesperson. We have to convince stakeholders. AI can help us make that case, justify decisions, and practice skills we may lack.

As those roles are getting more blurred, and PMs start to both prototype and negotiate internally, do you think people skills, as part of the PM role, are changing?

Absolutely — I believe that the PM role is forking right now. My prediction is that we’re going to have builders and peopleers. Peopleers will be comfortable networking, being on site, and interacting. Some people naturally walk into a room and leave with contacts and new friends — that’s a talent. Others can learn it, but it takes effort and overcoming discomfort. AI can help with tips and tools there.

Builders will be interpreters of feedback, analyzers of data, builders of requirements. They’ll experiment, break things, and try new tools. Organizations are still catching up, but builders who prototype and experiment will be critical. If you can’t build and prototype, there’s not going to be a big future for you in product management.

Leadership lessons from the US Navy

Switching gears from basic people skills to leadership, your path into product leadership runs through the United States Navy. What did that experience teach you about leadership skills that still show up in how you operate today?

Fear no one. My first day in boot camp felt traumatic at the time, but now I look back at it and say, “Wow, that was hilarious.” Everyone arrives around midnight, and they wake you up at four o’clock in the morning the next day. You don’t sleep a wink that first night, and first thing in the morning, they bring you in for a haircut. I remember a buddy of mine had told me, “Roger, make sure that they don’t know your name in boot camp. Just be quiet, do your thing, and you’ll get through it just fine.” Standing in that line, I’m like, “I’m not going to be known for anything.”

I was thrown into leadership. I was shy, scrawny, not someone who stood out. But I was put in charge. From that day forward, it was about trust. The people around me didn’t know me, but they trusted me. And I learned: even if you fall, your team has your back. That still applies today. I’m not afraid to throw product managers into deep waters and say, “You can do this.” And if they fall, I’ll be there. I have their back.

Some argue that people skills are innate — you either have them or you don’t. Based on your experience, do you find that to be true?

Some people are born with it, but others can certainly be taught. Like with anything, you have to be a willing participant and open to practicing. For example, I didn’t know how to be a teacher. I was a shy teenager, and even though I used to study the piano and felt like I was a musician deep down, it took a push for those skills to come out. Natural talent helps you accelerate, but others can get there too.

For PMs whose strengths are more analytical or technical, what’s your advice for building the relationship and influence side of the skills spectrum?

Be comfortable with the uncomfortable. Don’t wait to be pushed into the deep end — jump in. Fear holds people back. They think, “I can’t do that because something bad may happen.” Now, I’m not saying throw caution to the wind. We have to make data-driven decisions, but making a decision is key to progress. You need to put yourself in front of people, observe, and listen with the intent to understand.

Listening is such a key skill, and I mean true listening — listening with the intent to understand, not the intent to respond. I see this often, where people come into the conversation with an opinion on something. They come in wanting to say something, and they can’t wait for the person talking to be done so they can say the thing they want to say. So, even though they hear the person and process the words, it’s not retained. If you put those predispositions away and just listen — even if you don’t agree — you have that understanding, which then allows you to make a better decision.

Staying relevant as technology changes

Older professionals face real discrimination when any major technology wave hits. Do you see anything that bucks the ageism trend in this AI transformation age that we’re in?

Yes — empowerment. AI can act as a tutor, teacher, chief of staff, and an assistant wrapped in one. Before, someone might dismiss you or not take the time to explain. Now you can learn at your own pace. It applies to both older and younger people. My stepson struggled with lessons, but AI tutoring changed that. It’s individualized. The key is mindset. If you’re rigid, you won’t move forward. If you stay open and use these tools, it’s a game-changer.

For a PM early in their career, watching AI absorb tasks they expected to spend years mastering, what’s the most important thing they can do to position themselves well for what comes next?

It depends on the path that they come here from. You have to have curiosity and be able to go all-in on new technology. Don’t be afraid to experiment. That’s something that I’ve always been a proponent of and a practitioner of, even outside of a professional setting. I have used a combination of Codex and Claude to evaluate real estate, for example, run models on the best price per square foot, crawl different tax rolls, and more. All of these things are things that would’ve taken me an entire weekend to do, but it only took a few hours.

Overall, you have to have that curiosity, and that needs to start when you’re young and able to be flexible. Things are changing so fast that if you remain rigid, they’re going to break you. But the more flexible you are, the more you’ll be able to be successful.

What does LogRocket do?

LogRocket’s Galileo AI watches user sessions for you and surfaces the technical and usability issues holding back your web and mobile apps. Understand where your users are struggling by trying it for free at LogRocket.com.

In our conversation, Kim talks about what it takes to build people-first ecommerce teams in an increasingly data-driven and AI-enabled world. She discusses why context, human judgment, and cross-functional collaboration still matter just as much as analytics and automation, as well as how her early merchandising experiences shaped her leadership philosophy.

Balancing data with human judgement

You started your career in retail merchandising long before modern analytical tools existed — decisions were made by instinct and observation. How did that environment shape the way that you think about judgment, accountability, and decision-making today?

What’s interesting is — and I’m going to age myself here — that I remember the first time a company handed me a laptop and said, “You need to share this with your assistant.” There wasn’t any data flying around. We all used the same reports in the form of big, thick paper piles, and every Monday, we’d drag them into face-to-face meetings with management.

We were all looking at the same data. We weren’t making different dashboards or pulling from diverse sources. These were the reports, and you needed to just understand how to read them, review your numbers, and use intuition to make decisions.

Beyond that, we were out talking to people face-to-face. It was pre-Internet. That’s how I got into this field in the first place. When I was a little girl, my mother hated shopping, but I’d ask to go to the mall so we could people-watch. I don’t know why — I just thought it was exciting. I’d go into stores, talk to associates, watch customers shop, and interact with them. I was gaining something that reports wouldn’t give you — a deep understanding of customers. That’s where you realize there’s a lot of context that can be missed.

I remember when I was at Talbots, and the unfortunate events of 9/11 happened. Our numbers were down, nobody was buying, but the stores were full. Women were coming in to connect with the associates. They just wanted to talk — there was no pressure to buy. If you looked at the metrics today, you’d say, “There’s traffic — why aren’t they converting? The merchandise must be awful.” But you wouldn’t know the human side of what was really happening unless you were there — unless you had context.

What gets lost when organizations over-rotate on data and drift away from the human judgment behind it? Can you share an example of a time where the data pointed in one direction, but your instinct led you to make a different call?

It’s that context — that human piece. And you can miss opportunities you never would have thought of. When I was at The May Company, we were all given the same reports across divisions. You could compare performance and contact peers to understand differences. I was then buying special occasion dresses, and the whole company was doing well with beaded dresses — except me. It was a sore point. I bought into what the data said was working, but I couldn’t sell it.

Then I looked around, and I noticed my floor was filled with younger women — there were lots of colleges nearby. These women were going to formals and socials, and we were doing well with non-beaded dresses — cocktail dresses, little black dresses, that sort of thing. As a merchandiser, you can only bring in so many black dresses, so I added color.

The colorful dresses started selling faster. I talked to my managers and learned that people were buying them for their bridesmaids — typically eight at a time. Instead of going to a bridal store, they were buying from us. So, from that, we built a bridesmaid special-order business. That opportunity wouldn’t have happened if we had just followed the data. We had to dig deeper into what was actually happening in our market rather than blindly following the data.

Making decisions in cross-functional environments

Building a successful ecommerce business requires getting people with different priorities to move together toward a common vision. How do you create alignment across functions when everyone is optimizing for something different?

That unifying aspect is so hard. You think it’s going to be easy and that everyone’s going to align, but when you’re working on an ecommerce platform, so many things can go awry in a given day. It’s all interconnected, with systems and data feeding other systems. You might go to tech and say, “We want to add this vendor,” or “Customers are calling — is something broken?” But there’s only so much bandwidth and budget, so you have to decide which decisions or paths forward create the greatest value for the company and the customer.

Sometimes things get prioritized because they fit into work already in progress. It’s a jigsaw puzzle. Tech isn’t serving just one group — they’re a shared service. So, teams come together, talk it out, and make decisions for the greater good of the customer and of the business.

As AI becomes more embedded into how teams operate, from analysis to planning, where will human judgment and leadership still matter most?

AI does amazing things. I use it all the time — business, personal, “how do I fix this?” It gets work done in minutes that used to take days. But with that, it can’t navigate an organization. It doesn’t understand context. It can’t understand how people work together or how data is being interpreted across teams.

For example, I recently asked customer service for tracking call data. I looked at the data and started drawing conclusions, but after reviewing it, I realized it wasn’t a full year-over-year comparison. I was about to make a business decision based on incomplete data. I had to go back to the analyst and ask follow-up questions to understand what I was actually looking at. The point is that you always need context. You need to connect different inputs from different sources and experts to move the business forward. Otherwise, how do you work as a unified enterprise?

Servant leadership in practice

Aside from your professional accomplishments, you also have a Master’s degree in organizational leadership and a coaching concentration. What does servant leadership mean to you in this context?

When I first learned the term “servant leadership,” I felt validated. It was how I’ve always led. It’s always about your people, and I am very supportive of them because I truly care.

To me, servant leadership is removing roadblocks so people can operate at their best, both professionally and personally. How you approach your work bleeds into aspects of your personal life. We want employees who are happy and engaged. We spend more time with these people than we do with our own family. You want to be able to laugh together, celebrate each other’s wins, and support each other when times are tough.

For me, servant leadership is the easiest way to get people to feel empowered. I think it’s deeply important that people feel heard, are invested in their work, and feel good about what they do every day. And much of the time, that means getting out of the way. Some people won’t raise their hand or set boundaries. As a leader, it’s your responsibility to check in and make sure they’re not overwhelmed. Teams are getting smaller, work isn’t going away, and even with AI, someone has to validate the output.

So it’s about regular check-ins and stepping in when needed. If I can make a five-minute call that removes pressure for someone, I’ll do it. That’s serving your team.

You talk a lot about leaning into people’s strengths. Most leaders talk about developing people by closing gaps, but you approach it from the opposite direction. What does it look like to build a team’s strategy around what energizes people rather than what they’re missing?

I’m very big on using people’s strengths. I’m certified in the Strengths Profile methodology, which is an assessment tool that breaks down your strengths into three different categories: some things you’re naturally good at and energize you; other things you’re good at but drain your energy; and skills you haven’t tried yet but might love.

And then there are weaknesses. If writing is a weakness, like it is for me, make peace with it and find someone who’s great at it. I have a friend who’s a beautiful writer. When I need a bio for a professional publication, I ask her to take a cut at it for me. Even within friendships, that’s your team who can help the cause.

Teams work the same way. One person is great at product selection, another at analytics, another at vendor negotiation. They all work together to fill gaps.

Can you share an example from your background of a time when the culture either made or broke the conditions for a people-first leadership style to actually work?

I have a hard time in environments where autonomy is not nourished. A culture where I struggle is one where I lose a sense of decision rights and empowerment. I am fortunate to work for a leader and a company who value my independent perspective and approach to my work.

Flexibility and autonomy are really important, but people also need to own their decisions that exist within a greater framework. I had a boss who was a great mentor to me, and we’d sometimes disagree. However, we’d eventually align on a decision, and both stay invested in the agreed-upon outcome. We’d review the results together and learn from them. We were able to grow and bond with our shared outcome, and that’s what’s important to me in a work culture.

As organizations become increasingly data-driven and AI-enabled, what do you think will distinguish truly great ecommerce and product leaders from the rest?

It comes down to being nimble and being flexible. None of us knows what’s coming around the corner — whether it’s AI, a new business direction, or a situation in our personal lives. Teams need to be able to adapt. Everyone should be brought along so they can step in when needed. That happens through constant communication. We don’t work in silos. It’s about working well together so we can support each other in the best way possible.

What does LogRocket do?

LogRocket’s Galileo AI watches user sessions for you and surfaces the technical and usability issues holding back your web and mobile apps. Understand where your users are struggling by trying it for free at LogRocket.com.

In our conversation, Alex talks about the nuances of scaling a two-sided marketplace, including solving the inherent tension between supply and demand. He also discusses the human side of marketplaces and shares how Saatchi Art approaches complexities like network effects and tradeoffs between buyers and sellers.

The unique power and complexities of marketplaces

You’ve spent a significant part of your career in marketplace environments. At a high level, what makes marketplaces such a uniquely challenging and powerful business model compared to traditional ecommerce?

Marketplace businesses, if done well, drive a lot of value to customers, especially in a world with so much information overload. A good marketplace resolves friction and helps users navigate choices. Marketplaces typically thrive in fragmented industries where there is no dominant player and where there is friction or information asymmetry. For example, if I’m buying expensive artwork, how do I know I will not be scammed? How do I know whether it’s real? How can I be confident it will be delivered without damage? The transaction has risk. Marketplaces can reduce that risk and friction.

In a two-sided marketplace like Saatchi Art, you effectively have two businesses in one. You have demand and supply, and both need to be satisfied for the business to work. Another challenge is that marketplaces generally don’t own inventory. The core assets are liquidity, discovery, trust, and match quality, rather than unique inventory. The real asset is the network effect.

This leads to inherent challenges, especially the cold start problem. You have no demand without supply and no supply without demand. Typically, new marketplaces focus on demand first and “fake” supply — using contractors or internal resources — and then transition to real supply. If done well, all of this creates a moat around the business because these systems are difficult to replicate.

Measuring durable network effects

What leading indicators suggest you’re building durable loops and network effects rather than short-term liquidity spikes?

The idea that “when you have it, you will know” tends to be true. You really need to understand your business and market, but generally, traditional e-commerce metrics still apply — conversion, retention, repeat purchases, and the balance of paid versus organic traffic. There are also marketplace-specific metrics like GMV, supply liquidity, buyer-to-seller ratios, and how your top sellers are trending over time.

The strongest signal is when both sides grow together. The “holy grail” is cross-side growth — when buyers promote the marketplace, sellers promote it, and sometimes users participate on both sides. Eventbrite is a good example — in the same marketplace, you can post an event, and you can attend events. It becomes a closed-loop ecosystem where everyone can contribute to both sides. Our artists do that as well — they shop for art on our platform.

How do you make those tradeoffs without just shifting friction around the system?

Tactically, tradeoffs exist, but long-term, it shouldn’t be a zero-sum game. Both sides need to see value, otherwise the business won’t work. For example, we had issues with inaccurate inventory on our website. A listed artwork may have already sold elsewhere, like at a gallery or through friends or family. A buyer would purchase it on our site and then find out it wasn’t available, which is a terrible experience. You can imagine how much frustration the scenario causes a collector.

So, we introduced stricter listing management rules, which created more work for artists. Some pushed back, but others understood that without buyers, there is no marketplace. This is one example where we had to be more strict on the artist side to benefit collectors. On the flip side, we allow buyers to make offers, but we set a minimum threshold to protect artists from extremely low offers. That limits buyers, but ensures fairness.

A rough model we use is: if a change improves one side by X, how much does it hurt the other side by Y? You try to find the balance. If you’re forced to choose, you prioritize the side that is most constrained. Everyone will know what’s best for their own business. At Saatchi Art, the demand side is definitely more challenging.

There must be so many nuances in searching for artwork on a site. How does that break traditional thinking around discovery, matching, and conversion?

Each artwork is unique, which creates challenges for product discovery and recommendations. Traditional recommendation systems rely on popularity, but if something is popular, it’s already sold. Every new artwork also faces a cold start problem because it lacks historical popularity metrics a traditional SKU would accumulate over time. But even if a recommendation engine is able to identify a popular artwork, purchasing decisions are highly subjective, which presents an additional challenge.

We also see more browsing than searching. It’s difficult to describe an artwork in words, so users prefer to browse and discover rather than search for something specific. Constraints like size or price still matter, but beyond that, browsing is more valuable, as many of our customers say, “I’ll know it when I see it.”

Technicality and complexity in digital marketplaces

Artists aren’t typical sellers. Where do standard marketplace assumptions, such as level of technicality, fail with this audience?

Artists are very creative, but are not often as technical or business-savvy, and those skills are important for successfully selling online. More importantly, they’re not always motivated by sales. Recognition matters a lot in terms of how their work is perceived and seen. For example, all artists want to know how many views their artwork got. They want to know that someone saw what they created.

We see decisions that don’t always make business sense. An artist might cancel a sale because they want to keep the piece, or offer a discount because of an emotional connection with a buyer. We often see artists give discounts just because they want to make people happy. Their motivations are not necessarily financial — they go beyond that.

At the same time, artists who are more business-savvy and understand how to present and sell their work tend to be the most successful. We’re trying to educate them and provide them with tools to be more successful, but there’s only so much we can control in that sense.

You mentioned earlier that Saatchi Art does not hold inventory. How does that add to the unique complexities of the marketplace?

This manifests in a couple of ways. First is listing quality. All listings are user-generated, so listing quality is critical. Images, descriptions, accuracy — all of it affects buyer experience and can lead to returns and friction. On fulfillment, we rely on artists to package and ship correctly. We provide a return policy to remove financial risk for buyers, but there is still time and operational cost.

Logistics are particularly complex. We ship high-value, one-of-a-kind artwork globally from a residential address to another residential address, with packaging handled by non-professionals. You can imagine how many things can potentially go wrong here. Some countries have very strict regulations for art exports. There are many points of failure — packaging, pickup, customs, and delivery. We’ve had orders that included shipping large sculptures internationally, requiring custom crates, multiple transport methods, and special equipment for delivery. Some shipments take months.

This complexity is part of the value we provide. Without a marketplace facilitating it, many of these transactions wouldn’t happen. Our logistics and operations teams are outstanding. And this creates defensibility as well as value, because this complex operational capability is difficult to replicate.

Where AI comes into the arts

There’s a lot of talk today about AI and its impact on creative fields. Where are you cautious about using AI, especially when it comes to trust?

Accuracy and authenticity are the biggest concerns. There’s always a risk of losing the artist’s voice or producing something generic, so we have to be thoughtful about how AI is applied. AI-generated art itself created a lot of fear in the artist community when tools like Midjourney appeared. People thought artists would be replaced, but in many ways, it had the opposite effect — handmade artwork became more valuable and more appreciated.

AI-generated and human-created work can coexist — they serve different use cases and audiences. AI-generated images might work for certain contexts, but for something like a living space, people often want something authentic with a story behind it.

We do allow AI-generated artwork on the platform because the lines can be blurry, especially with photography and mixed media. It’s hard to define where AI starts and stops. Our policy is that artists must disclose what tools they used. As long as buyers know what they’re purchasing, they can decide for themselves. What’s not acceptable is misrepresentation — presenting something as handmade when it’s actually heavily AI-generated.

On the flip side, where are you seeing real value from AI in marketplaces, and particularly at Saatchi Art?

I try to operate from the principle that we should try using AI until it’s proven not to add value. It may sound a bit riskier, but I think there is a lot of potential. With that said, accuracy and authenticity are very important to us, so we are very careful and intentional about how we use it and whether it makes sense for our business and our users.

One example is description enhancement. Authenticity and artist voice are very important, so rather than giving a blank check to an AI tool, we require artists to write their description first. Then, with a strict prompt, we allow AI to enhance it while preserving the artist’s style and tone so it still sounds like them. An AI-written description is better than no description, and it also helps non-native English speakers create strong descriptions and be more competitive. They can still edit it and make it their own, but it levels the playing field.

We also use AI for styling — room visualizations to help buyers see artwork in a space. It can be inaccurate, so we take a conservative approach, focusing on higher quality outputs and making sure we don’t misrepresent scale. It gives us flexibility and cost savings compared to traditional photoshoots. The highest-impact use cases for us are personalization and search — creating user affinity scores and surfacing more relevant artwork.

We also use AI for fraud prevention, listing quality checks, and categorization. We have about 1,500 new artworks uploaded every day, all user-generated, and AI is instrumental in spotting quality issues, flagging spam, and enhancing attributes. AI unlocks capabilities that would be too expensive to do manually at scale.

What does LogRocket do?

LogRocket’s Galileo AI watches user sessions for you and surfaces the technical and usability issues holding back your web and mobile apps. Understand where your users are struggling by trying it for free at LogRocket.com.

A program manager told Sriram Iyer it would take 6 to 9 months to ship the first slice of their new product. Sriram challenged the team to do it in just five days.

At first, they laughed. Then he rolled his sleeves up, dug in with the team, and they did it. 5 days, from Monday to Friday.

Sriram has spent his career walking into companies like Salesforce, Adobe, and Freshworks, and pulling timelines apart. He calls himself the Simplifier-in-Chief, and the secret isn't the AI tooling. It's everything underneath the AI tooling that most leaders won't actually do.

In this episode, we talk about:

• Why most slow organizations aren’t suffering from a tech problem — they have a trust deficit

• How Sriram shipped a new vertical in just five days that a program manager had scoped for nine months

• Why the real constraint on AI adoption isn’t tools or budget — it’s mindset

1. Shipping in 5 days instead of 9 months

Sriram walked into a leadership meeting where 20 senior leaders were staring at a program plan with a first deliverable set to launch six to nine months out. He asked why not six weeks — then raised the stakes.

“I said, ‘I want this shipped in six days.’ And that’s when the pin dropped.”

First, three days before the sprint even started, the team identified the thinnest viable slice: not a prototype, not a POC, but “live production code” that was still “consumable” — like a slice of pizza that “still has all the toppings.”

Then came a key insight most teams skip: they didn’t need to find external customers to validate it.

“We have people in this company who are employees, but who are also potential customers, so why don’t we go ahead and talk to them?”

Those internal customer-zeros became part of the tiger team itself.

The sprint ran Monday 8 AM to Friday 5 PM — a co-located team, unlimited food, and a $1,000 bonus on the line. And woven throughout, implicitly, was the AI layer:

“The designer had Figma Make. The engineers had Cursor, and we came up with our own tool to write PRDs using Claude Code.”

By Friday, something real was shipped. It was hidden behind a flag, but in production, testable, and generating valid feedback.

2. The real AI constraint isn't tools — it's mindset

By 2026, the procurement debate is over. Most companies have already approved AI tools and written the costs into their budgets. The question being asked now isn’t which tool — it’s how we can prove the AI is working.

But Sriram’s observation is sharper than that. The bottleneck was never the tools.

“The real constraint is mindset. Imagine five hundred engineers, all of them with this tool to their disposal. Is every engineer using it the same way? The answer is clearly no.”

Some engineers are leaning in hard. Others aren’t. So the move isn’t a company-wide mandate or a training program — it’s finding the five who are already aggressive, already breaking barriers, and building the experiment around them.

“You have to find those five engineers... who have the mindset to say, ‘Yeah, let’s go ahead and break barriers, and let’s make this thing happen.’”

Then, let the results do the talking. Once the rest of the org sees what five people shipped in a week, something will shift.

“That energy, that enthusiasm is infectious. And once you show that this is doable in five days, others say, ‘Hey, I wanna be a part of that magic.’”

3. Why product managers still matter (and always will)

A lot of PMs right now are quietly asking whether their role is shrinking. If AI can write PRDs, generate specs, and prototype in minutes, what’s left?

Sriram’s answer is direct: you’re asking the wrong question.

“In the world of AI, someone’s got to answer the question, ‘Why are we doing this? What is the business rationale behind this? What is the thesis?’”

AI is exceptional at execution. It can’t tell you what’s worth executing on. And that distinction — between doing and deciding what to do — is exactly where product managers thrive.

The deeper point is about uncertainty. Every real business decision exists in a gray zone where the data doesn’t give you a clean answer. Someone has to read that room, synthesize the competing signals, and stake a position.

“There’s always gonna be a zone of gray in real life, in real business. So the product manager who can bring clarity to that room, define the why, define the thesis, and show direction is still worth amazing. No AI is going to be able to do that for you.”

The skills that made great PMs twenty years ago: clarity of thinking, customer empathy, the ability to define a thesis and defend it, aren’t becoming less valuable. They’re becoming the last defensible moat.

Chapters

00:00 Introduction
02:40 How growing up in a small business shaped Sriram's leadership style
05:14 Sriram's first principles thinking
07:11 The "thinnest slice of pizza" framework that kills scope creep
12:01 How AI tools like Cursor and Figma Make made the sprint possible
13:45 Why mindset (not tooling) is the real constraint in any org
16:32 Applying the same audacity to revenue: why not 100% growth?
20:25 Building a repeatable framework, not just a one-time stunt
22:59 Why trust and personal accountability are what make teams follow you
27:18 The product manager's role in a world where AI can do everything else
28:42 Conclusion

What does LogRocket do?

LogRocket’s Galileo AI watches user sessions for you and surfaces the technical and usability issues holding back your web and mobile apps. Understand where your users are struggling by trying it for free at LogRocket.com.

In our conversation, Hammad explores the mindset shift from project delivery to product thinking at scale. Drawing on his experience leading global product portfolios and driving digital transformation, first at NielsenIQ and now at Dallas Fort Worth International Airport, he shares how he balances global standardization with local needs, aligns technology with people and culture, and designs products for diverse user communities.

Developing products across global and regional markets

Earlier in your career, you led a global product portfolio. What was it like to balance local and regional decision-making while managing products across so many different countries?

I supported 110+ countries, and one of the objectives was to make sure that we scale these products across markets. That’s always a challenge because different markets have different dynamics. There’s always some local customization required, but at the same time, you have to keep a level of standardization across the board.

When you’re operating at a global scale, it’s about finding the sweet spot between consistency and flexibility. What we did was anchor everything on a few non-negotiable principles — things like data integrity, security, and platform architecture. Those should remain universal.

Beyond that, we gave room to the regions to adapt since one size cannot fit all. For example, something that works well in Singapore cannot be a copy-paste model in Brazil. They have different cultures and communities within them. So, we built products with a configuration layer so local teams could tailor the experience without breaking the global backbone and principles.

It’s essentially about designing a strong foundation but letting each house have its own personality. From a structure, tools, and principle standpoint, everything remains the same — but the experience can look different.

When working on a data-heavy product like you were in that role, how do you decide between improving infrastructure vs. improving data inputs?

It depends, and it varies. There are data regulations and quality metrics that come into play, and those influence the strategy. In more mature markets, you’re often in a speed and optimization mode, where you don’t need heavy infrastructure investment. In emerging markets, though, reliability and trust take priority. You’re deciding whether the market needs speed and optimization or trust-building first. That decision shapes whether you invest more in infrastructure or data quality.

From project management to product leadership

Across your career, you started out more in project management and transitioned into product leadership. What has that transition been like?

Early on, success was all about hitting deadlines and budgets. That’s very project-focused. Product leadership is different — it’s all about a lot of stakes on the table. You’re looking into how the product would be evolving, so the real success is measured more on adoption, the behavior chain, and long-term business outcomes. It’s not just about pushing the features; it’s more about sustainability and bringing more value to it.

In terms of my career transition, I was always focused on how I could step in and bring additional value. While working on programs, I realized that there is a vacuum where I could learn product development and leadership while working closely with commercial and client-facing teams. That moved naturally over time.

Even in project and program management, product teams are heavily involved, so you get exposure to how products are built. I realized project management is very structured — following timelines and delivering what’s required — but it doesn’t always question why something is being built or how it will impact users. That questioning came naturally to me, and that’s what pulled me toward product.

What parts of project management are best applied to what you do in product now? And on the flip side, what have you had to unlearn from project management that doesn’t work as well in product?

Project management is essentially structured planning, stakeholder alignment, and risk management. What doesn’t translate, though, is rigid scope. Product is about flexibility, learning, and adaptation. I had to shift from delivering predefined outputs to validating certain outcomes.

We brought in the MVP model, then, so that you’re pushing a concept, testing it out, continuing to improve it, and so on. It’s all about the change mindset, which is huge from project to product.

Effective change management and leadership

In your view, there are 2 pillars of digital transformation: tools and products, and personnel management. How do you approach both pillars simultaneously so that they’re working together and not against one another?

I learned this the hard way. When I was more on the software development side, I focused too much on features and not enough on adaptability and behavior change. But to be honest, tech and people have to evolve together. It cannot be that the people are changing, but tech is not changing. Having a shiny new tool doesn’t create value unless there’s a behavior change as well. Every major product rollout has to pair with clear roles and responsibilities as well. That’s where change leadership comes in, which is all about creating a new way of working that feels natural, not forced.

If you’re meeting resistance in the digital transformation process, how do you determine which pillar is the issue?

There’s no kind of standard governance structure, but transformation requires more than just technology — it needs cultural readiness as well. When there is a shift in willingness to make decisions and prioritize outcomes over activity, which is a product mindset, digital transformation steps in. But if you don’t see those signals, then it’s likely time to start with modernization of the tool and the process, and then take slow steps toward development.

So, when I’m assessing things for a leadership report, I’m evaluating how well leadership is rallying behind the concept of experimenting. Because when you’re stepping into product development, you sometimes do not know how this will be done.

What do you see as true digital transformation for an organization, and what do you look for as a signal that an organization is actually prepared to undergo that transformation?

Say, for example, in an aviation environment like I’m in now, you’re looking into doing a digital transformation via airport navigation improvements. At the same time, you have certain guardrails in place by specific entities, so you have to follow these guidelines and still create a good passenger experience. For this example, you want to add this tool to let people know where they are and how to navigate the airport, but you also have to place it strategically in the physical space so people can access it easily. This is where transformation comes in, and it essentially marries with the digital version of wayfinding.

On the flip side, if you’re just pushing this wayfinding tool on an app but not guiding users through how to use it, it’s going to be too complex for the average person. Essentially, with digital transformation, there are certain ways you can use innovative technologies, but at the same time, you have to optimize the entire experience around the process itself.

Designing for diverse users at scale

You have a unique challenge in that you serve a very dynamic user group made up of people who vary in digital savviness. How do you design for users with vastly different digital comfort levels?

Our passenger demographics are very different. So, when designing products, you have to look into the mix of customers and account for all demographics.

For us, that means offering multiple channels. That means apps for tech-savvy users and in-person support in critical areas for others. This product development also requires both quantitative and qualitative research. Quantitative data is all about data, but qualitative research is where the rubber meets the road — it shows usage and how people actually experience the product.

Our goal is to meet every traveler where they are, so no matter their background or familiarity with technology, they can navigate the airport confidently and comfortably.

How do you troubleshoot an issue to ensure, for example, that the way you address an in-person wayfinding challenge doesn’t interfere with the digital wayfinding solution? How do you keep those avenues complementary, not combative?

It’s an ongoing learning process. You have to go back to the MVP model, where you push certain features, test them, and improve as you go. That’s all part of learning. Through that, you can make a decision. Developing a customer experience is important for the product in that case.

Driving a product mindset inside organizations

In your role at DFW International Airport, you’ve joined a team driving the shift from a project mindset to a product mindset. How are you contributing to making this transformation impactful?

The biggest signal is redefining ownership around persistent products instead of temporary projects. With a temporary project, there’s a start and an end date, as well as clear timelines. But when you change success metrics to focus on the outcomes, the real signal is that success is about what to build and why. That’s the kind of curiosity that showcases that you’re moving in the right direction from a mindset standpoint. When teams move from asking “When will it be done?” to “What should we build and why?” that’s real progress.

In terms of user reactions, the biggest surprise to us so far has been how much small improvements mattered. Internal teams focused on big technical wins, but passengers clearly valued clarity, predictability, and reduced effort. This was a great reminder for the team that user experience is more emotional, not just functional.

This is something we’re incorporating more into our product development as well — that we have to think through from a user’s perspective completely and emotionally, rather than just viewing it as a tech upgrade on our end.

Looking back at your experience in global organizations and now owning this shift from the project to product mindset, what do you think organizations most often misunderstand about product management and digital transformation? What are people still getting wrong?

The first big misconception is that product management is only about roadmap execution. It’s not — it’s also about continuously problem framing and decision-making in uncertainty. You do not know what lies ahead as you move a step forward.

The second is that digital transformation is like a one-time program or tech upgrade. It’s actually about ongoing capabilities that you nurture over a period of time. And if you treat it like a traditional timeline-based project, they’ll miss the point that transformation is not about a one-time change — it’s about continuous improvement. Three things go together here: digital transformation, innovation, which is part of product development, and continuous improvement.

What does LogRocket do?

LogRocket’s Galileo AI watches user sessions for you and surfaces the technical and usability issues holding back your web and mobile apps. Understand where your users are struggling by trying it for free at LogRocket.com.

In this conversation, Marcos shares hard-won lessons in 0→1 product leadership. He makes the case for trusting intuition over data in early-stage work, explains why big bets generate stronger signals even when they fail, and walks through what it actually takes — in terms of team structure, PM qualities, and organizational sponsorship — to protect a new product inside a large company. Along the way, he tells the story of how a small, focused team unlocked a new category in the Brazilian fintech market.

The 0→1 mindset: Intuition, big bets, and diverse teams

When you think about what makes 0→1 product work genuinely different from growth or optimization work, what’s the most important mindset shift a product leader needs to make?

The main one is giving space to intuition over data. It’s not an easy one — data means confidence, and it’s really hard to trust your intuition. Some people can trust their intuitions easily, but not everyone. If you have a lot of experience, you need to trust in your intuition. And given that in 0→1 you don’t have enough data to optimize, you need this idea that I like to call big bets.

When I say big bets, I don’t mean building something unnecessarily big. I mean something that solves a meaningful problem and is differentiated enough. Big bets have two main benefits. One is that even if it fails and you don’t have much volume or data, because it’s a bigger change, it usually generates stronger signals. A small change in a product will not generate too much signal, but if you create a whole new big thing that’s going to solve this problem for the customer, you’re going to generate some signal even if it fails. But if it doesn’t fail, which is the best case scenario, you create more value. So, there’s only upside in thinking in terms of bigger bets when you don’t have much data.

How do you keep your teams from falling into analysis paralysis when you don’t have data to make confident product decisions?

There are two important things. One is customer empathy — really thinking about the customer beyond you as a customer, but others as a customer. And also having some sort of diversity. You have your intuition because you’re leading the team, but you also need to bring different perspectives and make sure that we are collectively trying to understand the customer.

Diversity is very simple — people are different, and you need to bring the tension. Tension is good. You bring that to offset the risks of being too biased. But at the end of the day, don’t rely too much on that. You’re never going to reach a situation in the room with so many diverse people where everyone is like, “Yes, we all agree on the problem. This is it.” Never going to happen. Then you need to make the decision based on intuition.

And then, there is a secret sauce, which I’ve seen a lot of times in bigger and more bureaucratic organizations like banks: execution. Ideation and execution have a symbiotic relationship. If the person who is ideating doesn’t trust the execution, they will try to be as perfect as possible. But if you can execute fast, you’re going to be less attached to your idea because you can learn fast and then have another idea. A lot of times, you need to trust your intuition and make sure the execution is good. Otherwise, you’ll try to be perfect and afraid to start executing.

Have you seen diversity help the decision-making process — and a lack of diversity lead to bad outcomes?

It’s very common for startups. You usually have an owner, and the owner brings people they know. They all think they think differently, but they kind of had the same experiences.

I see a lot of departmental diversity at Lonely Planet because Lonely Planet, by design, is diverse. It has people from all over the world. It’s very cross-functional and multidisciplinary because we are a publishing business that is trying to digitize. There are a lot of conversations where people bring a way of thinking that’s completely different. Whereas in the past I’ve seen, “Hey, we think we are being devil’s advocate, but actually we’re just kind of tweaking each other’s ideas a little bit.”

But there are challenges both ways. When you have a very diverse group, it’s impossible to reach a decision through a unanimous decision. The goal isn’t consensus; it’s collaboration. Every perspective that challenges your assumptions, even one you ultimately reject, stress-tests the intuition you’re going to act on. You leave the room having pressure-tested your thinking against people who genuinely see the world differently. That makes the call you’re about to make alone a sharper one. So the leader still decides – but they decide better.

Knowing when to shift from intuition to data

How do you know when it’s time to shift from intuition-led bets to data-led iteration?

Between intuition-led early stages and optimization stage, there’s a big gap. The transition between those two stages is where you continue to make big bets. Over time, you rely less and less on intuition and more and more on data as it becomes available. Your data informs the next big bet, meaning that we are not going to just change the color of a button or something on the landing page. You’re going to be asking, “Okay, what are the big problems I can solve?”

When to start the optimization stage is actually not a very easy answer. There are a lot of people in the market trying to understand how you measure product market fit. There are surveys you can use. The P&L starts to talk with you a little bit. But once you found it, that’s when you can start to optimize and drive more incremental revenue optimization decisions, like funnel optimization, increasing variety or referrals inside your app. And, usually, that’s the moment where people start to scale paid media and paid investments, which makes more sense because you want to make the most out of the money you’re investing.

Can you share an example of a time you started with a big, intuition-led bet?

When I was leading one of Brazil’s largest credit card marketplaces, we saw that Brazilians were increasingly motivated by credit card reward points. This idea of points is very normal for Americans, for Brazilians, it was just starting about 10 years ago. The big insight was that utility bills, one of the largest recurring expenses, remained a blind spot in terms of credit card usage. We saw an opportunity to unlock that category for financial institutions and help customers get more points from things they didn’t think they could get points from.

So, the goal was to start narrow and focus. Rather than building the full platform, we focused on the core problem: we want Brazilians to pay a bill with a credit card, and we want something very secure, robust. We focused 100% on the wallet functionality. It was a very simple, bare-bone mobile app. You had a wallet, you added one credit card, and then, do people want to connect their bills? Once we confirmed demand — we even secured a partnership with Visa — that started to increase the volume in our app a lot, and we decided to focus on other bottlenecks.

As more volume started to come, now data is telling us that a lot of people are having problems signing up, or they can’t find the app to download, or there was a problem with operations. Our nonexistent support operations was a bottleneck, so we decided to build a support team. All of that started to become true because we validated the demand. And then it started to become more incremental features, until it became more of an optimization stage.

Why a bill management and payment tool in a credit card marketplace? At the end of the day, we were trying to collect behavior data for the users to offer better credit cards — helping customers understand what credit card they can be approved for, given their utilities, which is basically a very good proxy for, “What is our financial condition?”

Building the right team for 0→1

There’s a common instinct in larger organizations to staff up when launching something new. How do you structure a team in a 0→1 effort?

For me, it’s very simple: keep the team small, nimble, isolated, with clear autonomy. Big orgs often think that if you hire more people, it’s faster, but it adds a lot of problems — communication, overhead, scope creep. Adding more people can end up delaying launch. As we talked about, yes, you want to use your intuition, but your goal is really to get data, the volume. If you keep postponing launch, you’re never going to get insight on what actually worked and didn’t work.

And there’s another problem that happens over time — the more people there are, the more the scope shifts. You hire smart people who want to make smart decisions, and they’re not always going to agree with you. If you have a very clear view as a leader, sometimes you need people who can make that reality in a small team.

Are there specific qualities that make a PM better suited for 0→1 work? And are there warning signs that someone who excels in a scaled environment might struggle in an early-stage context?

In addition to intuition, which you develop over years of diverse experience, and which is usually what I look for in 0→1 PMs, it’s important to be very execution-driven. Not project management execution, but a little bit of technical intuition — speaking the same language of engineering, being able to collaborate. Because a good solution is not a product top-down solution that design and engineering executes. It is a solution where you come with a POV, and then with engineering, you develop a better solution that is simpler, faster, and reaches the same goal.

I usually find those skills better in PMs who have a little bit of experience in both worlds — who understand what it means to come from an engineering background. And you want someone who has a lot of startup experience, because in a small team, you need to collaborate very closely. In a lot of bigger corps, teams are more siloed and process-driven.

On the flip side, the warning sign is someone who has only big names in their resume. They’re used to a lot of process, a lot of formality, and a lot of data. Not always, but usually. If you worked at Google, you’re used to launching something and getting enough data in two days to make some decisions. By design, you’re also used to slower execution, because of very complex technology.

Protecting innovation inside large organizations

You’ve experienced 0→1 both inside large organizations and in new ventures from scratch. What surprised you most comparing the two?

At Red Ventures, I helped open the Brazil office. Our main business model is partnerships with big organizations, including the largest bank in Latin America, where we help them build technology to reach a specific business goal. Performance-driven, not output-driven. Red Ventures also had, in Brazil, a venture builder where we used the cash-cow money to reinvest into new digital products or digital brands from scratch.

The biggest surprise was realizing that what kills innovation and speed is the system, not the people. Some people think startups are successful because they have very passionate and committed people. The reality is that there are a lot of special and committed people in big organizations everywhere, but the system — processes, beliefs, culture — creates resistance that, over time, slows down and demotivates those agents of change. I partnered with a lot of big orgs. There were pretty good people there who were so happy to see us because we were helping them overcome a resistance of the system that they couldn’t by themselves.

When you’re doing 0→1 inside a large organization, there are a lot of forces working against you — competing priorities, risk aversion, stakeholders who want to see the product grow before it’s ready. How do you protect an early-stage product from getting derailed?

It’s really important to have a sponsor — a senior leader, or anyone who is very influential — who can protect the team. A 0→1 product has different success metrics, a different review cadence, and a different tolerance for failure. If there’s no leader who understands that, it’s hard to convince everyone that the team is being successful. There’s a lot of noise in the organization — the sales team wants to use that feature, the data team wants to change the platform. And everything feels urgent because the rest of the org is making money, whereas that team is not.

The sponsor helps shield those teams from the inevitable structural incentives of a big organization.

And there is one more thing: the definition of progress. In a big org, progress means money — P&L, revenue, or deliveries — we delivered this feature, this expansion internationally. But in 0→1, progress is not that. Progress is feeling confident that the direction you’re going is right, or, even better, feeling confident that the direction you’re going is not wrong, meaning you’re going to fail a lot. If you don’t have a leader who understands that, they’ll keep incentivizing the team to produce results as soon as possible.

The biggest mistakes and what to avoid

Looking back across all your 0→1 experiences, what’s the biggest mistake you’ve seen product teams make?

Adding complexity. I’ll frame complexity in a very broad term. Adding complexity can mean adding more features that are not necessary. Every new thing you develop is more complex to develop. And it adds data noise. The more things there are for the customer to interact with, the less customers interact with each thing, so it’s really hard to know what matters.

Adding complexity can be adding more people, more ideas, more communication overhead, more scope. And adding complexity also means adding more process. Too-rigid ways of thinking usually prevent adaptation. “Oh, we need to do the two-week sprint perfectly, no change of scope.” When you are very early, you need to have a little bit of room — maybe it’s a one-week sprint, and let’s change scope if we think it makes sense. The smaller you are, the less process you need. You want to focus 100% of your time on one big thing you’re trying to solve for the customer and understand if that’s the thing that really matters. And if it doesn’t matter, let’s go to the next thing.

For product leaders operating inside companies that want to build 0→1 capability, what structural or cultural conditions have to be in place before it can actually work?

It links back to the incentive structure. You need a leader who defines what success looks like and can separate the team from the organizational noise.

The other thing is real autonomy, which can come in different levels. I’m not saying, “Hey, this team can decide whatever they want about this product.” No. It’s the job of the leader, or sponsor, to really decide the level of autonomy. Maybe it’s, “The team needs to build this thing. How they’re going to build it, and what they’re going to do is their job to explore.” Or, “We already have a design well-defined — we just need to execute.”

But make it clearly defined and give autonomy in that space. Because a team that needs to get a sign-off on every small decision will have less and less confidence. And the less decision-making they can do on the spot, the slower you’re going to go.

What does LogRocket do?

LogRocket’s Galileo AI watches user sessions for you and surfaces the technical and usability issues holding back your web and mobile apps. Understand where your users are struggling by trying it for free at LogRocket.com.

Our guest today is Julia Dalton, the SVP of Product at Capacity, an AI-powered support automation platform. Before that, we spent years at OneSpace, formerly known as Crowdsource, a crowdsourcing company where thousands of freelancers executed microtasks for major retailers. Routing rules, task chains, instruction validation, and more.

Today, that’s known as multi-agent orchestration. And Julia was doing it before it was cool.

In today’s episode, Julia shares:

• How a PRP (product request prioritization) system she designed herself in one weekend transformed Capacity’s CS feedback by replacing the chaotic “firehose” of requests with a ranked, data-backed list

• What running a human API layer taught her about prompt design, long before LLMs existed

• And the truth most teams skip — that AI agents are only as good as their instructions. AI doesn’t fix bad data; it amplifies it, and teams need to audit their data before writing a single prompt

1. The two reasons AI agents fail (22:45)

At OneSpace, Julia’s team managed thousands of freelancers doing microtasks for large retailers at scale. The lessons learned were hard and expensive: if you send out 500 product descriptions with unclear instructions, you’ve paid for 500 things you can’t use.

“You could have the best instructions on the planet, the best prompt, but if your data is wrong, you’re going to get really, really terrible results.”

The two culprits? Bad instructions and bad data.

The product takeaway: You’re not the one doing the task — you’re architecting it. That distinction changes everything about how you design agent workflows.

2. Validate your prompts before scaling (11:39)

One of the most underrated moves at OneSpace: before deploying a task to thousands of workers, they’d run a separate mini-workflow with workers whose only job was to evaluate the instructions — not execute them.

Why?

Julia says:

“What seems clear to you and what you’ve communicated is oftentimes very unclear or not as clear as you thought to the audience or to the recipients.”

Her fix? Use a separate agent (or person) whose only job is to evaluate the instructions — not execute them.

Julia does the same thing now with agents: agent-to-agent evaluation runs, logging and scoring conversations, and humans doing test passes. Recursive validation before you ever go live.

The product takeaway: What seems clear to you is often unclear to your recipient, so make sure to build a feedback mechanism for your instructions before you scale them.

3. The PRP: A weekend project that untangled the feature request firehose (22:30)

Julia’s product team was drowning in requests from CS and revenue teams. Each submitter was convinced their ask was the #1 priority.

So, she built a structured intake system herself over a single weekend.

The result?

Structured inputs, auto-classification, ARR and retention impact weighting, and a triage layer within Customer Success before anything ever reached Product. The same signals that prioritize incoming work also let the team communicate the ROI of what they shipped.

“AI only amplifies the data — so if your data is wrong, it’s going to amplify its wrongness in a major way.”

The product takeaway: Data doesn’t just help you prioritize what to build — it helps you prove the impact of what you’ve already built.

Links

• Julia’s LinkedIn

• Capacity

Chapters

00:00 Introduction
02:10 Julia's career path to Capacity
04:18 Microtasking at scale
05:59 Jula explains her workflow chains
08:36 Designing routing rules
13:15 Two failure modes
13:56 Simulating and scoring agents
17:22 Recursive prompting in practice
20:27 Data and knowledge orchestration
23:55 PRP Feedback triage system
27:02 Impact and ROI from signals
30:15 Conclusion

What does LogRocket do?

LogRocket’s Galileo AI watches user sessions for you and surfaces the technical and usability issues holding back your web and mobile apps. Understand where your users are struggling by trying it for free at LogRocket.com.

In our conversation, Minal talks about rethinking B2B UX for the Amazon-era buyer and how B2B buyers expect the same intuitive, seamless journeys they get as consumers. She explains where B2B and B2C should align — particularly in the core shopping flow — and where they should diverge. Minal also dives into the operational side of B2B commerce, sharing how AI-driven reordering, role-based guardrails, and the right performance metrics can ultimately drive long-term revenue growth.

The biggest misconceptions in B2B ecommerce

In your experience leading digital transformation for both B2B and B2C commerce, what is the biggest misconception in how product teams design the ecommerce user experience for business customers?

One of the biggest misconceptions I see, especially in B2B ecommerce, is the belief that the user experience doesn’t have to be as clean or easy to use as a B2C website. Until a couple of years ago, a lot of B2B websites were very manual. For example, you had to enter numbers instead of uploading sheets. The look and feel wasn’t very intuitive, and it didn’t allow you to move end-to-end through the discovery-to-purchase lifecycle smoothly. A huge factor behind that is that the B2B ecommerce industry had a late start compared to B2C.

But the people shopping on your B2B ecommerce site are also consumers in their day-to-day lives, and they still expect a clean experience. They still want a one-stop shop for all their needs. In fact, improving the experience in B2B can have an even bigger impact. A clean, intuitive UX reduces training time. It reduces the need to bounce between different websites to complete a purchase. And that convenience increases loyalty. So the misconception is assuming that business users will tolerate complexity just because it’s “for work.” They won’t — and they shouldn’t.

What beliefs about business buyers tend to drive digital teams toward that generic, complex spreadsheet-like experience?

I think a lot of it comes from legacy systems and legacy thinking. Most B2B organizations are still operating on older technology. They’re used to processes that are more manual than automated. Teams get accustomed to seeing things the same way all day, every day — and that familiarity turns into comfort. Over time, that comfort becomes the standard, even if the experience is clunky.

There’s also a strong belief that B2B shoppers don’t like change. That’s somewhat true because they’re doing the same tasks every day, often under time pressure, so if you change their workflow, they can get very antsy. Digital teams hesitate to modernize the experience, assuming complexity is safer than disruption. My argument, however, is that if you’re going to change it, make it easier. Don’t hide features that were visible on the page or add extra clicks. Instead, simplify it.

Another driver of that spreadsheet-like experience is not fully understanding how B2B users actually use the site. Some are power users who spend hours a day there. Others are quick in-and-out buyers — they know the exact part numbers, enter them, place the order, and leave. That spectrum matters. If you don’t research how long they’re on the site, what they’re doing, and how frequently they return, you end up defaulting to a generic, dense interface that tries to serve everyone but delights no one.

Where B2B and B2C should (and shouldn’t) align

Where should B2B and B2C experiences be nearly identical, and where should they really diverge?

I think the shopping journey itself — from discovering a product, reviewing product details, adding it to cart, checking out, and tracking when the order will arrive — can be the same, 100 percent. There really doesn’t need to be a lot of difference between B2B and B2C in that core flow.

Where they start to diverge is more on the marketing side. In B2B, you don’t necessarily need to market newer products the same way you would in B2C. Most B2B organizations operate under contract pricing for specific parts and products. Buyers often are only purchasing what’s been pre-approved. The decisions about introducing new products are typically made by the owner of the company or someone at a higher level, not by an individual buyer browsing the site. So while the end-to-end shopping experience can and should feel very similar, the marketing efforts are completely different.

What are the hidden friction points in B2B purchasing flows, and how do you mitigate them?

There are a few hidden friction points in B2B purchasing flows, and most of them come down to speed, repetition, and organizational complexity.

First, ordering itself can be unnecessarily slow. B2B buyers often know exactly what they need, such as part numbers, quantities, and SKUs. Features like quick order, where they can upload a spreadsheet instead of manually entering each item, remove a lot of friction. You can also build orders automatically using AI-driven or automated tools, or enable predictive order placements based on past behavior.

Inventory management is another big one. Predictive inventory checks can flag when a customer is running low or when a part is about to go out of stock. The system can prompt users to reorder with one click, and that kind of automation dramatically reduces effort.

Subscriptions are also powerful in B2B. In the consumer world, subscriptions mostly work for everyday essentials, such as pet supplies or water filters. In B2B, almost every product can be a subscription because purchases are repeated over and over again. Automatic replenishment can remove a lot of manual work.

Loyalty programs are another underused lever. Since B2B customers are making repeated purchases, offering loyalty points or rebates that they can earn back and apply toward future orders can strengthen retention and reduce switching.

Finally, one of the biggest friction points is role complexity. In B2B, you don’t have a single user who can buy anything. You have an approver, a quote creator, a purchaser, an owner, etc. Designing clear permissions and workflows so approvals don’t turn into bureaucracy is critical. In B2C, none of that exists — it’s one user, one cart, one checkout.

Encouraging repeat purchases in B2B ecommerce

If repeat purchasing is the backbone of B2B commerce, like you said, what are the most effective patterns for enabling that fast, low-friction reordering?

Definitely AI predictive ordering. That means creating AI bots that can go through your account, look at your ordering patterns, predict what you’re likely to need next, and suggest products so you’re not running out of stock. It helps ensure you’re ordering items at the right time instead of reacting after inventory is already low. Another big piece around reordering is warehouse inventory replenishment tied directly to the order flow. But that only works if you’re accurately tracking the exact inventory on the floor. If your inventory data isn’t right, the automation falls apart.

Business buying usually involves controls like permissions, budgets, and approvals. How do you design guardrails so they preserve governance without turning every order into bureaucracy?

It does require a bit of upfront effort from the owner of the company, but the key is building structured account management with clearly defined roles and permissions. Most mature B2B platforms, like the ones I’ve worked on at Sealed Air, HD Supply, and Lowe’s for Pros, all have account management built directly into the owner’s profile. The owner can create users and define what each person can and cannot do. Once that structure is in place, governance becomes embedded in the workflow.

A simple way to think about it is hierarchy. For example, at Interline Brands, we had just three roles: owner, who had full access to everything; quote approver, who could submit and approve quotes; and purchaser, who could add items to cart but not approve orders. If a purchaser submitted something, it automatically routed to the quote approver or owner. The permissions defined the workflow. You can imagine this at scale, like a large retailer with multiple franchise locations. Each location’s owner manages their own users and approvals within their structure. From their perspective, it’s simple: create a user, assign a role, and the system enforces the rules.

The important thing is that the guardrails are defined once and then largely “set and forget.” You want the system to handle it automatically based on predefined roles. When permissions and workflows are clearly structured upfront, you preserve governance without turning every order into a bottleneck.

Adapting to modern consumer expectations

Many organizations assume that B2B and B2C platforms must be fundamentally different products. In your experience, what actually changes when a company treats them as variations of the same core product or experience?

There are really two parts to this. Ten years ago, B2B and B2C were often treated as completely separate products. Today, more organizations are realizing that the purchase path is almost the same for both. The product catalog can live in the same PIM system. Product information can sit in the same CMS. A lot of the underlying systems can, and should, be shared.

Technically, what changes is that B2B has additional features layered on top. You might have different payment methods, contract pricing, approvals, or role-based permissions, but the core infrastructure of browse, search, product detail pages, cart, checkout, etc., doesn’t need to be reinvented. Instead of building two platforms that conflict with each other, companies are designing technology that overlaps and works in harmony.

You can see this with large retailers like Amazon, Lowe’s, and Home Depot, which serve both B2C and B2B customers. For example, Lowe’s for Pros may have a slightly different in-store experience for professional customers, but online, outside of approvals and permissions, the needs are very similar. Both a Pro customer and a consumer want to find the product quickly, buy it quickly, ensure it ships on time, and possibly arrange installation.

Culturally, the shift is even bigger. Organizations are moving from being hardcore manufacturing companies to digital companies. And in today’s landscape, it’s almost impossible to survive without investing in technology. With AI accelerating expectations, the pressure to modernize is even stronger. So when companies treat B2B and B2C as variations of the same core experience, they invest in shared platforms and customize where necessary. The core stays unified.

How much of that change has to do with how the business buyer persona has evolved now that every business buyer is also on Amazon?

For me, that’s what I call a myth buster — the idea that B2B and B2C users need to be treated completely separately. Not anymore. The same people shopping for their business are shopping for their home every day. They’re used to experiences like Amazon, which is incredibly easy to use. There’s no tutorial explaining how to navigate the site. The simplicity teaches you how to use it without formally teaching you.

Organizations that lead in digital experience have figured out how to make their platforms intuitive enough that customers don’t need instructions. They simplify the journey instead of layering on explanations. Because of that, the gap between what a B2B and B2C website needs is shrinking significantly. B2B customers now expect the same convenience.

Measuring ROI and driving digital transformation

Simplifying B2B UX can feel risky inside a legacy enterprise organization. What signals or metrics demonstrate that ease of use directly drives ROI and revenue growth?

Excellent question. The way you measure ROI from ease of use is actually very different for B2B and B2C. I’ll start with consumers because it helps frame the contrast. For consumers, lifetime value can vary widely. They’re not coming to your site with a purchase list. There’s discovery involved. You’re selling them. They’re comparing prices, warranties, and competitors. So for B2C, you often measure ROI through things like number of sessions, time spent browsing, and how effectively you convert that discovery into larger baskets. The longer they’re actively browsing and not sitting idle, the better. You’re coaching them from, “I need one item” to “I’ll buy 10.”

B2B is very different. A business user is often obligated to buy from you because of contract pricing, free shipping agreements, zone pricing, or guaranteed inventory. They typically come with a purchase ticket, so they know exactly what they need. So the ROI question becomes: how quickly can they get in, place the order, and get back to their job?

For B2B, shorter session time can actually be a positive signal. Faster ordering, high adoption of digital channels over offline customer service, and smooth reordering flows are strong indicators that UX improvements are working. Average cart size also differs, as B2B might average around seven items per order, whereas consumers may average closer to one or two. So the KPIs — session length, adoption, conversion — are interpreted very differently.

Another important distinction is acquisition cost. In B2C, you may spend significant marketing dollars to acquire and retain a customer. If their experience is poor, they leave, and that investment is gone. In B2B, customers are somewhat anchored by contracts, but that doesn’t mean UX doesn’t matter. It absolutely does — especially at renewal time. Switching vendors isn’t easy for a business, but when contracts come up for renewal, ease of doing business becomes a major factor.

So the signal that UX drives ROI in B2B isn’t, “Are they spending more time on the site?” It’s, “Are they ordering faster? Are they adopting digital instead of calling customer service? Are repeat purchases increasing? Is retention strong at renewal?” Ease of use in B2B translates into operational efficiency, loyalty, and long-term contract value — and those are very real revenue drivers.

Given that B2B represents a significantly larger share of overall commerce than B2C, why do you think so many manufacturers and legacy organizations are still slow to digitally transform their ecommerce experience?

One big reason is comfort with the status quo. Many manufacturers still believe their old Excel sheet–looking websites can continue to drive revenue because their customers are used to it. There’s a mindset of, “Why do we need a better-looking website? Why do we need to make it easier? Our customers love it.” That belief persists because B2B relationships are often contract-based, and revenue doesn’t immediately drop the way it might in B2C if the UX is poor.

But that thinking ignores what’s happening in the broader market. When competitors invest in digital transformation and create simpler, more intuitive experiences, customers start to notice. And while switching vendors isn’t easy in B2B, it absolutely happens, especially when contracts come up for renewal. That’s often when the bell rings.

Another factor is slow technology adoption. Many legacy organizations are still transitioning from manufacturing-first mindsets to digital-first mindsets. And in today’s landscape, that’s risky. It’s no longer optional to invest in technology — especially with AI accelerating expectations.

Mobile is another example. Five years ago, not having a mobile app might have been acceptable. Not anymore. Entire businesses run from phones and devices. In one consumer example, 60 percent of traffic came from mobile devices versus desktop. B2B mobile adoption may still lag in some industries, but that gap is shrinking.

Ultimately, the opportunity in B2B is enormous — but so is the risk of complacency. The organizations that move faster on digital transformation will capture loyalty and long-term growth. The ones that assume customers will tolerate outdated experiences simply because “that’s how it’s always been” may not realize the impact until it’s too late.

What does LogRocket do?

LogRocket’s Galileo AI watches user sessions for you and surfaces the technical and usability issues holding back your web and mobile apps. Understand where your users are struggling by trying it for free at LogRocket.com.

In our conversation, Gustavo talks about why creating value should be at the center of every product decision. He also shares how to evaluate initiatives, think in terms of compounding value, and lead effectively as AI changes the pace of product work.

Creating value as a product manager

You came up through the financial world and corporate development before moving into product leadership. How does that background inform the way that you think about creating value as a product manager?

Everyone in finance is always thinking about the concept of creating value. My philosophy is that creating value is the first thing that a PM should prioritize. Product people need to deeply understand how we create value, and that understanding starts with the financials.

At a basic level, a company creates value by selling products or services perceived as more valuable than the resources used to produce them. Part becomes profit, and part goes to the customer as surplus. The role of the PM is either to increase the total value the product is creating or the value that the company can extract from that value creation. If that’s the goal, the only way I can achieve it is if I understand my pricing, my costs, and how I get more customers to acquire more or retain more. You need to deeply understand each one, as well as the P&L.

A lot of PMs are excellent at craft — writing specs, running discovery, shipping features — but struggle to connect their work to how the business actually makes money. In your view, what separates a junior PM from a senior PM, and how much of that gap is about business acumen vs. product craft?

What I see a lot with junior PMs, especially the better ones, is the ability to take a specific initiative and understand all the nuances inside that feature, including the edge cases. Even junior PMs can do that well, and it’s what they focus on. Honestly, I did that when I was more junior. I remember my early days — it was Waterfall back then — creating specs with 200 pages and then blaming people for not reading them when things went wrong. The reality is that’s not helpful.

The difference with senior PMs comes from understanding value — how you trace a feature all the way from the user problem it solves to the business outcome that it drives. Then you connect the dots, which allows you to shape the scope a lot better — both the scope of what is implemented and the scope of what you communicate.

You can distill a feature to its essential scope and use that to prioritize what is really important: What’s going to create value here? What is the opportunity cost of not doing something else that could be creating a lot more value? As people get more senior in their roles, they understand that better. The good and bad news for junior PMs is that a lot of the work junior PMs used to do, agents can do. But on the other side, that frees them up to automate the boring work and speed up their career to become a senior PM as fast as possible.

How to evaluate product investments

What mental model do you use when you’re evaluating whether a new product initiative is worth pursuing?

If we had a crystal ball, the answer is net present value (NPV). What’s the value of the company, measured by discounted cash flow, if I do this initiative versus if I don’t? We don’t have a crystal ball, though, so we use proxies. To understand how much value something creates, we look at how many more users this could help us acquire, conversion lift, cost savings, or strategic positioning. Then, we connect those proxies to the company strategy and current priorities. That’s where you evaluate: is this a high-impact standalone thing, but outside of the strategy, or does it align?

At LawnStarter, we usually start any initiative with what we call an opportunity document. It’s a very simple document with two goals. One is simple math — what’s the best-case scenario for the impact this could drive? For example, someone says, “We should add a route optimization feature for our providers.” This is a common complaint from our providers in support tickets, and it could save them X hours per week, which gives us X percent more capacity and translates into Y more in revenue.

The second thing is to write down the hypotheses and the questions you need to answer to test them. For example, what is the cost of implementing a route optimization system? It’s very compute-intensive. What exactly would pros want? Do they want something automated or something with a lot of customization? How much do we need to build to give a good user experience?

And from the customer side, what happens if we start telling customers, “Instead of Tuesday, I’ll show up on Wednesday because it saves hours of driving?” Is that OK, or does it drive churn? This document might have 20 questions like that, answered by data, design, engineering, or the PM. It’s about defining the research needed before committing — asking questions, not giving answers. Then you write a very short go/no-go statement: what answers would lead us to pursue this versus not?

When you’re determining whether or not something will add value to the business, there’s a tension between evaluating initiatives in isolation vs. looking at them as a whole in the system. How do you hold both of these lenses at the same time?

First, you need to have a company strategy. Ideally, you know what the overall themes are that you’re investing in and how much you’re investing. Initiatives should fit within those, and you prioritize inside that framework. If something looks like a great opportunity but doesn’t fit, then you need to escalate and ask, “What do we need to drop if we really want to do this?”

The second part is more ambiguous. From a product leader standpoint, you need a clear vision for where the product is going. That includes understanding which areas of your product are not just one-shot away from creating value. It might take six iterations, but you understand that value compounds in those areas. There are other areas where you take one shot and create value right now.

Going back to NPV, it’s not just about what value something creates now — it’s the future value and the compounding that really matter. At LawnStarter, one area we’ve invested in over the years is how we match a customer to the ideal provider. That’s at the core of our marketplace. If we had said, “This improvement only gives us 0.1 percent,” we would never have built a great system.

Sometimes you evaluate an initiative as a standalone thing. Other times, part of the value is that it’s one step in a staircase of compound value. That’s where things get more ambiguous, and honestly, where I’ve been spending a lot of time recently, reshaping my mental model as velocity increases.

Leading with vision in an AI landscape

With AI fundamentally changing the velocity of product work, how do you prevent this idea of “We can do more” from becoming “We should do more?”

It’s funny because at the same time AI is changing everything about our jobs — my day-to-day is completely different from what it was three months ago — it’s not changing the core premises at all. It’s never been more important to have a clear, well-articulated vision that everyone on the team understands.

Before AI, you could enforce that vision by steering a team — “We’re doing this,” “Adjust this,” “This is a little off.” But now we can do so much more that if you try to operate that way, it becomes like herding cats. You won’t be able to do it. Now, the priority is, “Does everyone understand the vision?” That’s crucial because even when you’re not in the room, the direction is still clear and you don’t need to steer. It’s like internal company self-driving. You don’t need to be at the wheel, and it still goes in the right direction.

Of course, for the most critical things, I’ll still be there steering. But there are going to be many other things happening at the same time that I’m not even looking at until they’re in production. You have to trust that you’ve given enough direction. If you’re not setting a clear vision and not thinking critically about what you should do before you do it, it’s very easy for the product to become disjointed.

I’ve seen SaaS products where there are three different ways the product is trying to present some AI interface, and none of them actually solve the problem. That’s probably because it’s what PMs were able to prototype and push. That’s not a recipe for success. Things start conflicting with each other.

You mentioned that having a vision is more important than ever. Can you talk about what’s changed and what hasn’t in your role as a product leader in the last 3 months with AI?

Having the vision was always important, but a big part of what changes now is the way I’ve been talking to my team. In the near future, we’re going to get to the point that 80 percent of the tasks internally at our company are done by AI agents. It’s not enough for me to just have a vision — I need one that is actually codified so that both human PMs and AI agents understand it and act accordingly.

More recently, at LawnStarter, I rebuilt our internal knowledge system using Claude Code. We now have an entire markdown-based system in a GitHub repo that the entire company is using. I created the system so every PM could use Claude Code to create things like opportunity documents, for example. Then, when they’re doing that, the AI has links to our business context, vision, company goals, etc., so that everything can be tightened up together.

Essentially, AI gives us the first revision of, “Is this aligned?” Part of it is: can you create this alignment with code? That is very different from creating alignment by showing up in meetings and telling people, “This is not good enough. This is different from how we are treating this other situation here.” That also meant that in the last two or three months, I’ve shipped more code than in my previous 20 years. Now, we have a tool that the entire company uses, and all of its context is connected to AI agents. Yes, the job really changed, but it comes back to creating value and to our vision. It’s just with a different mechanism that is way more powerful.

Understanding value creation in the marketplace

LawnStarter is a two-sided marketplace, which means that value creation is more complex than a single-sided product, because you have to serve both the customer and the service provider simultaneously. Can you explain how this dynamic changes the way you think about what metrics actually matter?

LawnStarter is originally a marketplace where you can get someone to mow your lawn. It’s since evolved, and it’s different from traditional home services marketplaces in that we provide a lower-friction experience for the customer. You enter your address, get a price immediately, and if you accept it, we send a provider to do the job. It works like the Ubers of the world.

In terms of metrics, I once heard someone say that marketplaces are more like biology than physics, which is very true. It’s a complex system. If you compare it with a SaaS product or a DTC product, often you have one customer, which means one degree of uncertainty. You do something, and if that customer responds in a certain way, you’re doing better. If they don’t, you’re doing worse. When you have a two- or three-sided marketplace, there are all these components and interactions, so everything you do impacts many different systems and metrics at the same time.

When we’re thinking about metrics for initiatives, we need to think about what we’re trying to move, as well as what happens on the other side. For example, let’s say we’re going to make a change to our marketplace policies. We need to think about how that impacts how customers view our service, what percentage of jobs get picked up by the top pros, how that impacts retention on the customer side, and then how it impacts retention on the provider side, their routes, and profitability.

You need to go a few steps deeper in the marketplace and understand how the loop works on those metrics, and make sure you are not just thinking, “If I improve this conversion metric, everything’s going to go great.” Sometimes you improve conversion, but get worse results. In marketplaces, when you’re dealing with metrics, you need to think more about the secondary and tertiary effects on both sides.

For a PM reading this who wants to start thinking more like a business leader, not just a product practitioner, what’s the first thing they should do on Monday morning?

I think the first thing is to step back and ask yourself, “Do I fully understand how companies create value?” Especially if you don’t come from a business background, it’s worth spending time studying that. There are plenty of good books and articles, and you can honestly ask ChatGPT or Claude to explain them. Understanding the economics of how value is created, captured, and compounded is super key for a PM.

The second step is to get specific. Do you understand how your company creates value and the role of your product within that? Not just hypotheticals — do you know your average price? How does your company price services? What are the acquisition channels, how do they map to pricing, what different kinds of customers pay for things, and when they value them? And what are the costs associated with that?

It’s also important to understand the cost side of the P&L — what you spend money on that could be used as leverage or cut down. With that knowledge, ask, “Do you have a vision for where this product should be in three to five years to create dramatically more value than it does today?” Then, can you trace everything you’re doing back to that?

And finally, if your three- to five-year vision does not incorporate a world in which AI agents are everywhere, you should rethink it. They will be. It’s already starting to happen. That’s the reality we’re all in. It’s better to try to have fun with it and embrace it than fight it.

What does LogRocket do?

LogRocket’s Galileo AI watches user sessions for you and surfaces the technical and usability issues holding back your web and mobile apps. Understand where your users are struggling by trying it for free at LogRocket.com.