Leader Spotlight: Using AI as a force multiplier for product judgment, with Adi Thacker
Adi Thacker is Senior Vice President of Product Management at Poshmark, where he leads end-to-end shopping experiences spanning search, feed, recommendations, advertising, and emerging AI-enabled commerce. He began his career working in product management at Silicon Valley companies including OnSite Systems and Infinera. Adi then joined product management at Intuit before transitioning to consulting at Accenture. In his most recent endeavors, he has built and scaled marketplaces and monetization businesses at Facebook, TikTok, and Poshmark, and also founded WriteWell, an e-learning marketplace.
In our conversation, Adi explains why AI is a force multiplier for high-judgment product managers rather than a replacement for them, and why outcomes, rather than output, still decide whether a team is winning. He shares what separates high-craft product organizations from everyone else, and talks about how marketplaces successfully scale, including how to layer advertising onto a marketplace without cannibalizing the core business.
How AI is changing the PM craft
There’s a lot of talk that AI has reduced the cost of execution and normalized the PM function, and that the more traditional execution skills are less valued now. Do you agree? And are there PM capabilities that are more valuable now?
Product management is a vector, not a scalar. Direction matters more than speed. AI has definitely accelerated many parts of the product development cycle, from discovery to prototyping to building, testing, and launch. Teams are now able to do more with smaller pods — with fewer engineers and analysts. And judgment and direction are even more important and elevated in the craft of product management than they were before. Great product management has always been about driving outcomes for the customer, which are closely tied to the business, versus shipping more features.
From that perspective, AI is a force multiplier for the high-taste, high-judgment product manager who knows how to prioritize the right problems for the target customer. It gives them speed, and it also gives them greater conviction because many AI tools help de-risk and accelerate solutions. But it has also diminished the value of the orchestrator product manager (the facilitator archetype) and the feature-shipping product manager (the output or feature-delivery archetype). That’s a shift in the skills that will be increasingly valued as AI usage continues to grow.
With AI making the build process easier, you can ship nearly any feature you can think of. So, how do you ensure you’re creating customer value rather than just shipping features?
This problem — the tendency to be output instead of outcome-oriented — existed before AI, and it will likely be exacerbated post-AI as it becomes ever easier to ship features. The key is to go back to the fundamental principles of staying outcome-oriented for your customer and the business.
Let’s say you’re a feature PM or you own an experience on a marketplace or a social networking app. First, you need clear measures of customer value metrics and qualitative feedback to ensure you are improving customer experience. For example, if you’re tweaking the algorithm on an app like a rideshare, you’ll want to ensure you’re matching the driver and rider more efficiently. If you change the user experience in your product, you’ll want to ensure you’re improving customer satisfaction and engagement.
The key is not just launching something for the sake of launching it, but making sure the measure of success (qualitative feedback or a quantitative metric) that you’ve carefully chosen has improved the product experience — this could be personalizing your feed for shopping, improving search relevance, or introducing an AI-enabled experience The measures of success need to improve whether you ship one feature using AI or a series of 10. It’s about advancing the outcome you’ve chosen as a beacon of success, versus the volume of features that were shipped because the new tool in the shed allowed you to do this faster. AI can accelerate the work, but the PM remains accountable for whether the work creates real value.
In complex ecosystems like marketplaces and social networks, lots of teams are working on different parts of the same system — buyers and sellers, consumers and creators. AI may help us ship faster, but we still have to make sure those features create real value for the whole ecosystem. A win in one area isn’t really a win if it comes at the expense of another.
In complex products, improving one metric isn’t enough — the change should make the overall system better. For example, sending more push notifications may drive more conversions, but it’s not a real win if each notification becomes less effective or hurts another part of the experience.
As AI helps teams ship faster, strong measurement becomes even more important. We need to know that a feature improves the broader business, doesn’t hurt partner-team metrics, and creates a true net gain. Metrics tell us what happened, but not always why. That’s why we also need to talk to customers and use research to understand the full picture.
Finally, from a culture standpoint, we want teams to embrace AI as a tool but incentivize outcomes instead of usage. Recognition, promotion, and performance management systems should be attuned to outcomes, because then you create the right incentives for using AI in organizations.
Is it difficult for early-stage PMs to gain the experience to be more strategic, when their role is more operational by nature?
Sure. Early on, product managers tend to be more execution focused, but execution still means driving impact for customers and the business. Even though early-career product managers seldom pick ambiguous problem spaces, they should be cognizant of whether their work is succeeding or failing, and as a consequence, learning whether their solutions are solving customer problems via the measures we discussed earlier.
Being strategic means developing judgment to prioritize the right customer segments and their most pressing problems for building products and features that move the needle for the business. More than ever before, AI-enabled customer research tools like VoicePanel help us better understand customer needs. Such tools provide insights at scale, quickly and cost-effectively, to better inform PMs — early or seasoned to sharpen their understanding of customer problems and the opportunity at hand.
Product management taste develops over time. The key to good taste and judgment is having multiple reps at shipping products from discovery to impact. Another complementary method is to observe patterns and information architecture in the most popular apps — the small details like how they get to know you when you first sign up, or how they reach out when and how often they want you to come back. The best apps do this almost invisibly well so that you are entirely focused on the content versus trying to access the content. The best PMs (early or seasoned) should connect the dots and bring them to the context of your customer and your product, to see what’s missing or what can be elevated.
You’ve built teams at startups and at much larger companies like Facebook, TikTok, and Poshmark. Are there differences in how you build high-performing product orgs depending on the size and stage of the company?
Oh, significant differences. The stark difference is in product craft. For me, product craft is about the motions of how product gets built — the people building the product, the tools and methods they use, and the fundamental philosophies and beliefs embodied while building products. There are certain behaviors typical of high-product-craft organizations, typically found in larger companies — the behemoths like the Facebooks and the Amazons of the world.
First is customer obsession. Most of these teams deeply understand people’s problems and their conscious and unconscious needs. They get to the root of what motivates people to use products. Typically, it’s not a feature, but a people problem. For example, people use TikTok for entertainment, not because they’re looking for short-form videos. Having a well-developed user research function that can understand customer motivations in a very non-leading, non-biased way is super important. Empathy and understanding are number one — especially understanding the deep psychological motivations that customers themselves aren’t able to articulate!
Two is having a product culture that rewards bold initiatives and celebrates failure. You can’t do anything big unless the culture normalizes failing fast and learning quickly. This encourages product managers to make bigger, broader bets that can really move the needle for the business.
Three is a very high sense of accountability and ownership. There are always mandates that come from the top, but typically these organizations hire and/or coach product managers into having a high sense of accountability and ownership around their product area. This translates to deeply understanding the customer problems in the space, prioritizing them, building solutions, and measuring impact in terms of: did my feature improve the day in the life of the customer?
Four is the measurement piece — being really thoughtful around metrics or measures of value in general, both qualitative and quantitative. Ask: How do I know that the customer is better off after shipping this product or feature? If you are in the lead-generating business — say you’re an auto dealer or Tesla — all you can collect is a lead, so the core measure of value for the digital/online team is likely lead volume (weekly or monthly).
But teams can be poorly incentivized if their work is goaled on leads. A good and responsible product team sets goals for weekly (or monthly) leads generated, but also guards themselves against low-quality leads by monitoring the conversion rate (this becomes the “guardrail metric”). The message is to choose your incentives wisely because the organization responds accordingly!
Say your leads generated grow 30 percent quarter over quarter, but your conversion rate (cars sold divided by leads) drops by 40 percent, then the team basically just gamed your metric incentive. Being careful with your selection of metrics and making sure you’re not gaming the system is critical.
The tooling for experimentation, customer insight gathering, and team rhythms around planning and alignment are highly evolved at high-product-craft companies. At smaller companies, startups, or emerging small-cap companies, these are typically developed by bringing in someone who has spent significant time at a high-craft product organization. That’s the biggest difference, and it’s usually a journey of transformation that needs to take place.
A significant portion of your career has been spent in marketplaces. Is there an aspect of marketplace product management that you feel may be misunderstood?
Typically, marketplaces are bootstrapped by aggregating supply — whether it’s cars on eBay, collectibles, or commerce on Amazon — you build supply and then generate demand. Once you have a sufficient selection of clothes and shoes, or labor or rides, and have figured out your demand-gen engine, you work on improving the matching liquidity and efficiency of connecting demand and supply. Some marketplaces are nationwide or global, like eBay, Etsy, and Poshmark. Some are highly local, like Thumbtack, Facebook Marketplace, ride-sharing, or dating sites.
What is often misunderstood is that the effect of demand, supply, and matching on overall marketplace growth and health varies significantly based on the different segments or dynamics within each marketplace. For example, take Facebook Marketplace. It’s a classified marketplace, which could be demand- or supply-constrained. It’s a global marketplace, so if a product management team’s mandate is to grow in a specific country, they’ll first need to understand whether that market is demand-constrained or supply-constrained, because usually it is one or the other.
Good marketplace product managers work with analytics partners to quickly identify the highest-leverage opportunity within the marketplace. Facebook Marketplace created artificial suppression tests on both the supply and demand sides. If there’s a hypothesis that a specific country is more supply-constrained (there are more shoppers than sellers and listings), the marketplace would artificially suppress certain listings from showing up in searches or browse for a finite amount of time. This enables the team to detect whether overall transactions decrease more acutely than the suppressed listings on the supply side.
If transactions drop more acutely, you know the market is supply-constrained. Similarly, if the hypothesis is that your marketplace is demand-constrained — that the bigger opportunity is bringing in more shoppers — and transactions fall more acutely, you know the market is demand-constrained.
Understanding which side the market is constrained on, then going one level down to understand exactly which category — automobiles, rentals, electronics, or clothing — gives product managers the right unlock and where they need to invest, whether to increase the shopper funnel or increase listings. It’s a de-averaging on either side, and it will provide the highest ROI investment opportunity to grow the ecosystem.
Marketplace health and the advertising layer
How does having an ads layer on top change the way you think about the health of the marketplace, the liquidity, and long-term value creation?
Most marketplaces, like Amazon, Walmart, eBay, Etsy, and even Poshmark, have two main revenue lines: transactional revenue, which is a commission on the commerce they facilitate, and advertising revenue. Advertising is a monetization lever, but it’s also a marketing tool for sellers. Many businesses on marketplaces are willing to spend money to increase distribution and sell-through of their inventory. So it’s a marketing service — but there’s a tension between introducing advertising and preserving the shopping experience so it doesn’t feel irrelevant.
Marketplace health matters because having sellers turn over inventory more rapidly via faster sell-through retains your best sellers and keeps them listing more. On the shopping side, it’s all about showing the right ad to the right shopper at the right moment, based on their sensitivity to advertising and the experience they’re in. If a shopper is brand new to the marketplace or has a really specific query, they’re very high intent, so you probably do not want to show them an ad. But if a shopper is just browsing through what’s popular, their intent is lower at that moment; there is an opportunity to insert relevant advertising responsibly.
It is very easy to become greedy and irresponsible when you run an advertising business, because the revenue realization is almost immediate. Most marketplace advertising models are click-based, so every click generates revenue. The responsible product organization wants to ensure that, as you diligently insert advertising, it’s accretive rather than cannibalizing the transactional business. That comes with introducing ads in the right volume (or ad load) at the right stage in the shopping journey for the right user.
We typically ensure this by leveraging a concept called a no-ads holdout — a population of shoppers that sees no ads for 6–12 months, so we can understand the long-term longitudinal effects of advertising. Even though we see an increase in revenue in the short term, you want the long-term health of the marketplace to be unchanged; we don’t want to see a drop in visitors or shoppers in the long term. If the holdout is significantly healthier than the population that sees ads, we know we’re showing too many ads and need to dial down in certain areas. The best teams do this very responsibly.
How do you decide when it’s the right time to introduce advertising in a new marketplace? Is it based on the size of your shopper population or their behavior?
It’s both, actually. At Facebook, they always said monetization is step 99 in the product development process. There are two types of customers on marketplaces: demand and supply, and buyer and seller. You want product-market fit on both sides, so you have healthy cohorts that retain over the long term — buyers who keep coming back, say 30, 60, 90 days out, and the same for your sellers. The key when you’re launching a marketplace is to first make sure you have product-market fit and have maintained healthy retention for both demand and supply audiences.
You also figure out channel-market fit — your economics of acquiring users via owned or paid channels are sustainable, so you know your long-term value-to-customer-acquisition-cost ratios and your payback periods. That’s the first necessary condition. The second is scale — significant scale and size, where you feel good about volume and growth rate.
Amazon introduced sponsored advertising for sellers 10 years after launching, and eBay introduced promoted listings 20 years after launching. These large-scale marketplaces think of advertising as a service that benefits sellers — a business expansion lever, a monetization engine — but never at the expense of the core business. Unless you have a healthy ecosystem of demand and supply being orchestrated at scale, you can’t introduce advertising. It’s at least a decade after inception — that’s what we’ve seen historically, and that’s a responsible way to do it.
As product discovery evolves, what do you anticipate when it increasingly starts from sources like ChatGPT, Perplexity, or AI agents rather than in the marketplace itself?
There are a couple of implications. It’s very hard for consumer businesses to battle intent and customer journeys from emerging media such as ChatGPT and others. If consumer behavior has started shifting toward a new source for discovering, say, clothing for their upcoming trip to Venice, you can’t really change that.
The implication is ensuring you show up well in those experiences. If you’re a marketplace — whether labor or commerce or travel — how does information on your site, app, or feeds need to evolve so you’re more discoverable in these moments, for users looking for a recipe or an outfit for their next vacation or party? SEO is what companies did for search, so how do we evolve for agentic to make platforms more discoverable?
Google announced a new protocol that encourages commerce vendors to send more attributes for greater discoverability. So the question becomes: if consumers have embraced this new modality for discovery, how do we make sure we show up well? Also, if consumers get accustomed to conversational interactions just as they did with using the search bar after Google introduced Search in the early 2000s, then how should these modalities be introduced within apps and websites, and how do they coexist with traditional modalities like search and browse?
How do we do it in a way that accommodates emerging behaviors, or at least cohorts of users that prefer this emerging modality, without throwing out the baby with the bathwater? How do we introduce this gently in a way that accommodates the early adopters, but also the more traditional users of apps? Google is testing that and trying to figure it out.
And eventually, as newer behaviors become more the default, we’ll need to ensure our experiences also include this modality as an option, or eventually the default.
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