Weeknotes 534
I did:
Workshoptastic
Spent 60% of my time at in-person workshops this week, which has me looking at the flow efficiency of all my other work with raised eyebrows, but still did this stuff:
- Helped out at two delivery playbook workshops and learned a lot from a consultant who’s been helping/challenging us.
- Went to a fantastic workshop about shaping up the vision for one of our services.
- Talked about how we might improve how we manage split tests and build a knowledgebase that helps us understand where we are more able to change user behaviour in ways that gets business results.
- Did some work with other product managers on a group-level roadmap. The really interesting stuff of talking to about a hundred stakeholders is still to come.
- Analysed some new ideas and thought about when and how is the right time to get the team involved.
- Thought about product strategy workshops using AI to create the tools and templates specifically for developing the strategy that you’re working on. Our usual tools, e.g., Lean UX Canvas are generic and fixed because they have to apply to a wide range of circumstances, but that’s no longer the case when AI can create a canvas for a specific situation.
- Chatted about where we want to go next with the user-behaviour-triggered communications capability we’re working on.
I read:
Agentic web evolution
“The interfaces mediating all of this (ChatGPT, Claude, Gemini, etc.) want to keep users inside their own surface. They’re deeply invested in building a web where agents can find the information and do the tasks a user requires without ever sending them elsewhere. To me, it seems natural that these interfaces will start rewarding sites pre-structured for agent operability.”
Where AI capability meets customer need
Started reading Jeff Gothelf’s new book ‘Product management and AI: turning capability into customer outcomes’. “Product management done well in the age of AI takes the raw capability of AI and points it at problems real customers have, in ways that change what those customers do hopefully for their benefit and then turns that into a measurable business impact. Without that discipline, AI makes teams faster at producing things nobody asked for. With it, AI becomes the most powerful accelerant we have ever had for learning what customers need and delivering it.”
AI Doesn’t Change Product Management Fundamentals
Jason Knight talks to Jeff Patton about why product management fundamentals are still so difficult to put into practice, how AI can help teams succeed or fail faster, and why understanding what customers actually do remains more important than ever.
AI week
I haven’t watched all of them yet, but there are some really interesting talks about AI particularly from universities.
Build the Agentic Enterprise
I thought:
Problems and solutions
It’s much easier it is to think in solutions than problems. We all do it and see it. Start talking about a problem and before you even realise it, before you’ve really understood the problem, you’re already thinking about how to solve it. Maybe, when we’re building products, it’s because the solution is almost always known and the same, it’s build software. But it’s much harder to think in problems because problems can be process problems, people problems, user problems, organisational problems, big problems, little problems. Problems are messy, unclear, and difficult to reach a single agreed definition. That’s why product managers have to be really comfortable with ambiguity.
Culture is how we behave
In a workshop this week, one of our senior leaders said, “culture is how we behave”, and it stuck with me. Sure, there are lots of theories about organisational culture, and they have their place, but as a product manager whose job is to change user behaviour in ways that get business results it makes me question whether a product approach is a good fit for changing culture. Previously I’d had the opinion that product thinking gets organisational change wrong because it believes that once a change is made it stays made (because that’s how software behaves), but when changes happen to people, those people can change back or change in different and uncontrollable ways, which means change doesn’t stay made. But I’m not so sure anymore. Maybe all this AI product stuff about nondeterministic tech making changes to user behaviour in unpredictable ways is getting to me.
Marginal gains, diminishing returns
A lot of product management focuses on the exciting stuff of introducing new products, but eventually, if you’re lucky, your new products become old products and then you need a different approach to managing them. Managing mature products is a marginal gains, diminishing returns game.
Marginal gains means recognising that you aren’t going to make big leaps forward on any of your goals. The only progress to be had is in lots of small changes all over the product to get the last few percent increases. Diminishing returns means understanding that those gains require more and more investment, or to put it another way, for the same investment the returns will get smaller and smaller. That point on the lifecycle where maturity crosses into decline is where you have to make some hard decisions about what to do with the product; keep investing, just do the essential maintenance work to keep it running, or retire it.