At our latest Product Anonymous session, Maxy Lotherington – who heads up Product and Design at InvestorHub, a FinTech scale-up – walked us through what “product sense” actually is, how to build it, and how she uses AI to sharpen it. Here are the highlights.

What is product sense?

Maxy’s simple definition: product sense is knowing something will work before you build it. It’s not magic – it’s pattern matching, backed by research (she pointed to The Intuition Toolkit for the science behind gut instinct).

At its core, product management is about figuring out what customer problem to solve, which Maxy frames as connecting the dots – pulling together customer quotes, sales notes, strategy, support tickets, and analytics into a coherent picture of the problem. If you’ve ever written a PRD with an “evidence” section, you’ve already been doing this.

Product sense is what happens when that dot-connecting becomes constant and automatic – updating in the background every time you hear something new, rather than only when you sit down to plan. It’s why strong PMs can react to an idea instantly and just know something’s off.

The other key piece: don’t just connect internal dots (customer feedback, analytics, company data). The richest, most accurate picture comes from also weaving in external dots – the wider world, other products, other industries.

Building your dot collection

Product sense is a long-term, deliberate practice, not a one-off skill you pick up. Maxy shared three ways to keep collecting dots:

1. Consume product content – newsletters and teardown resources like Growth.Design, Lenny’s podcast and the SVPG blog. Following the right people on LinkedIn helps too.
2. Use lots of products – including bad ones. Maxy’s opened Product Hunt every morning for a decade. Using more products (and noticing when something feels off) builds intuition for what works.
3. Talk and share – communities like Product Anonymous and ProductCamp are exactly this. Swapping stories about what worked and what didn’t extends your pattern library beyond your own experience.

Her bonus tip: if you’re not naturally great at remembering things you come across, write them down somewhere (she uses a tool called Mymind).

Evaluating your connected dots

Once you’ve connected dots into a hypothesis, Maxy’s framework is to stress-test it against five failure modes:

• Unanchored problems – you can’t say when or why a user would actually hit this problem.
• Fake problems – the “problem” only exists because of something you built or shipped.
• Obvious solutions – if it’s everyone’s first idea (or the customer’s own suggested fix), pressure-test it. Customers are right about their pain, rarely right about the solution.
• Shallow problems – using the “five whys,” can you get past the first layer? If not, you’re missing depth.
• Indefensible problems – if you can’t explain the problem clearly and people don’t follow, the dots you connected may not actually belong together.

The InvestorHub story: solving a problem no one asked for

Maxy shared a case study from her own company (an “end-to-end investor relations” platform). InvestorHub had built seven separate content tools – email, Twitter, LinkedIn, blog posts – each patched repeatedly based on direct feedback, with no one ever asking for them to be unified.

Drawing on external experience running a content team, Maxy recognized the pattern from a framework called COPE (Create Once, Publish Everywhere) and pushed for a single content engine that was published across all channels. No customer had asked for it, and the idea took real convincing internally – but it became InvestorHub’s most popular feature. The lesson: your richest solutions often come from dots outside your own company or industry, not just from customer requests.

Using AI to connect dots

Maxy closed with her Claude workflow. Her view: AI is excellent at connecting internal dots (company data, research, sales notes) – but that’s the easy, “average” part of the job. Where PMs add value is bringing in external context and judgment.

Her approach is to build a thorough, manually-written context library for Claude covering business context, cross-functional teams, customer segments, competitors, product/roadmap details, and team structure – updated regularly. Writing it by hand (rather than pulling from the codebase) forces her to actually understand the product deeply, and surfaces gaps in her own knowledge. With that context in place, Claude becomes a genuine thinking partner for higher-level strategic conversations, freeing her up from time spent on basic internal analysis.

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Thanks to Maxy for a brilliant, practical session – and to everyone who jumped into the workshop exercises. See you at the next Product Anonymous, and don’t forget ProductCamp is coming up on August 8th.