Most of the AI products getting hype right now are built for people like us: the tech industry. But your customers don’t care how the AI works — they just want their problem solved in a way that’s easy for them.
Brian McMullin, SVP and Head of Product at Network Solutions, has spent 15 years building for small businesses at companies including HubSpot, ezCater, Wayfair, and SamCart. In that time, he’s yet to meet a business owner who wakes up thinking, “I can’t wait to use AI today.” For many of them, a prompt box might as well be a terminal window.
So his team doesn’t ship the box. They ship the finished work artifact, and all the user has to do is claim it.
In this episode, Brian shares:
Why doing the work for small businesses first, instead of handing them an empty prompt box, is what gets them to try AI
His warning about baking inference into the core of your product before token pricing settles — and the two models he’s testing to find out what small businesses will actually tolerate
And what SamCart taught him about churn: customers left because basic things were broken, not because features were missing
1. Nobody wakes up wanting to use AI
Brian’s customers get maybe a couple of hours a week for anything that isn’t serving their own customers (marketing, website updates, invoicing, etc.). Nobody in that position is going to spend it learning what a good prompt looks like. So his team skips the box, and even the menu of agents to pick from:
“Rather than showing them the prompt box, or even, ‘Hey, we have these agents, come use a marketing agent’ — we need to take one or two steps further and say, ‘We’ve used this agent to map out your next two weeks of social media posts. Go schedule them, and if you want us to keep doing this every two weeks, turn it on right here.’ Come and claim it.”
The customer’s only job is to approve something that already exists.
Product takeaway: Make the action you want to be the easiest one available. For non-technical users, that means doing the work speculatively and asking for confirmation, not instruction.
2. Proactive AI only works if it’s specific to that customer’s business
There’s an obvious failure mode here, and Brian names it first: if you do the work for someone and make it generic, then you’ve just built a spam machine.
“It can fall flat if it doesn’t feel personal… The tricky part there is what you need to drive that level of personalization is ultimately context.”
His argument is that this is where established software companies have a real edge. Everyone has the same models, but not everyone has years of history about how a particular business actually operates.
Product takeaway: Take inventory of what you already know about each customer that a competitor would have to learn from scratch.
3. Be careful about building inference into the parts of your product people already pay for
Serving one more customer used to cost almost nothing. Inference broke that — and telling a small business that the same outcome now costs more is a hard message no matter how you frame it. This leads to a caution you don’t often hear from someone shipping AI features:
“It’s actually almost a little bit of a warning of how deeply to embed AI into your core product and value proposition. Until we get to more stability in token pricing, the more you bake it into the core of what you do, the more you’re going to have to figure out how to have a predictable cost structure for your customers.”
Domain registration works fine without AI, so Network Solutions has some insulation. Products where AI sits inside the workflow people already bought have none.
Product takeaway: “AI-native” is a positioning decision with a variable cost attached. Work out which parts of your product would still be worth the subscription with the inference switched off.
4. Nobody has landed on the right way to charge for AI yet
Brian’s team surveyed customers on pricing and found no consensus at all, so they’re running both common models: tiered usage, which risks the problem of paying for credits you don’t use, and a flat monthly price per agent, which puts a paywall in front of something customers haven’t tried yet. Worth listening to his breakdown of the tradeoffs on each. But the sequencing argument underneath matters more than the choice:
“The biggest risk you have is that nobody actually uses the product in the first place. There is a temptation to build complex monetization models and put in tons of limits and over-engineer that aspect before you’ve truly found product-market fit.”
Product takeaway: Strict limits imposed early mostly buy you a smaller sample of the usage data you need to price correctly. Let people use it, then decide what to charge.
5. What SamCart taught Brian about churn
At SamCart, Brian’s team was building good monetization tools for creators, and marketing was excellent at selling them. Then they read the churn reasons:
“None of it was about these new features not driving them revenue. It was about, ‘I can’t get this basic thing to work. I need to add my team and I can’t do that. I can’t connect my payment processor in the way that I want to, and I’m not able to take payments.’”
He also found that customers often buy for optionality and never touch the feature they bought for, which makes the core experience, not the headline feature, the thing that keeps them.
Product takeaway: Retention is a lagging indicator, so argue for core experience work on frequency and breadth of usage instead. In the episode, Brian walks through how he makes that case to a CFO asking for ROI on a login page.
Chapters
00:00 Introduction
02:11 Brian's product journey from developer to SMB product leader
04:05 How small businesses went from distrusting AI to have AI FOMO
07:33 SMBs don't wake up excited to use AI, they want to close the deal
10:05 Stop showing the agent, ship the finished work
13:11 Inference costs break the old SaaS margin math
16:23 Testing free credits and tiered usage with small businesses
18:10 What usage-based pricing does to ARR and predictability
23:51 Churn is a pile of micro-annoyances, not one event
26:29 What SamCart's churn data revealed about broken basics
30:18 Conclusion
Links
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.