ER: thirty years on
ER, the U.S. medical series, is thirty years old. It’s fascinating to watch (and remember) a world without the Internet and mobile phones.
In one episode, they get an Internet-connected computer to manage patient records and end up using it to play games.
So much of the plot only works because people can’t message each other on WhatsApp or Google something they want to know, or refer a patient to another department without having to phone them.
Seven product key performance indicators
- Strategic performance – how is the product contributing to strategic business priorities, e.g., revenue?
- Customer satisfaction – how are customers rating and reviewing the product? Do they raising issues and providing feedback?
- Pipeline health – how many users are they at each stage of the product? What’s the conversion rate?
- Team health – how happy are the team? Do they have the right roles and skills? Are they communicating and creating shared understanding?
- System health – how many issues are there? How critical are they? How many customers do they affect?
- Financial status – how much does it cost to run the product? How much revenue does it generate? Is the ROI positive?
- Progress – how quickly is work progressing? How often does work ship?
The goal is to be able to draw causal connections between changes. For example, does an increase in spending, e.g., on advertising, lead to an increase in the pipeline? Or does a reduction in the speed of development progress lead to reduced customer satisfaction?
Weeknotes 513
I did:
Conjecture
Three day week so very busy having lots of conversations about all the new work we’re doing. I love this phase of product work. It’s high energy, high ambiguity, and high fidelity guessing, which is why ‘conjecture’ is my word of the week. Also did this:
- Chatted to product managers about defining outcomes and scoping work to match. It’s been a opportunity for some good product thinking about how to set scope that is deliverable by the deadline but still achieves an outcome.
- Technical proof-of-concept for bringing user behaviour data from websites into our marketing automation platform.
- Thought about ways of building consensus and support for a new product and decided using an existing product to seed usage has a good chance of success.
- Talked about three ways to use data in product work: new opportunities for product development, operational reporting and evaluation, strategic performance analysis.
I read:
Platform Product Management
Nikhil Shrivastava says, “The modern platform PM is no longer responsible only for APIs, SDKs, documentation, and developer experience. Those still matter. But in many commercial platforms, the PM is also designing business systems, operational workflows, monetization architecture, ecosystem incentives, compliance boundaries, and multi-persona product journeys.” I completely agree. Platforms are becoming the cool products, and working on just the technical stuff isn’t enough for platform product managers any more.
Vibe coding is obsolete, product management isn’t
Jeff Gothelf talks about how what Andrej Karpathy, research scientist and founding member of OpenAI, said about agentic engineering is actually just product management. I suppose some of that is true, but only about product management as it is now, not as it will be in the future. It’s a little embarrassing that engineering is changing so quickly and us product people are still hanging on to a soon-to-be out of date idea about what we do.
Seven Myths about AI and Productivity
Fascinating article by Dritjon Gruda and Brad Aeon about the productivity and economic benefits of AI. The conclusion is that its different for different organisations in different situations.
I thought:
Started vs. Finished
What does the number of pieces of work started versus the number finished say about an organisation?
| High finish rate | Low finish rate | |
| High start rate | +: Lots of work gets done. -: Lacks discernment, nothing gets stopped. | +: Good agile decision-making. -: Starting work that never gets finished is wasteful. |
| Low start rate | X | +: Maximum ROI, not much waste. -: Not getting enough done. |
Organisational taxonomies
What organisations call things, what those things mean, who gets to define them, will be increasingly important as AI becomes part of organisational operating systems. AI will need those things clearly defined to treat them consistently.
Opportunity cost is a killer
I’m doing this but could I be doing that instead, or that other thing, or doing more analysis to find things I don’t even know that I could be doing yet.
Judgement is king
“Use AI at the speed of judgement” is the new “ship only as fast as you can learn”.