About this Episode

On this episode, I speak to Jeff Patton, Chief Troublemaker at Jeff Patton Associates. Jeff is a product management consultant, coach and trainer, and the author of User Story Mapping, a book that has helped product teams around the world think differently about how they build software. We talk 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.

Episode Highlights

We cover a lot, including:

• User story mapping is still relevant, even in the age of AI. The fundamental idea is simple: tell the story of what someone does with a product, then break that experience down into smaller pieces. Too many teams mistake decomposing a feature into its component parts for understanding the user's journey, and generating a convincing-looking story map with AI doesn't mean you've done the necessary thinking.

• Product teams keep forgetting the difference between outputs, outcomes and impact. Building and releasing something is an output, what customers actually do with it is the outcome, and the resulting benefit to the business is the impact. Organisations often obsess over shipping more features without knowing whether anyone has seen, tried, used or continued using what they've already built.

• Project-based funding can undermine product thinking. Funding initiatives around fixed requirements, budgets and deadlines encourages organisations to focus on completing projects rather than achieving outcomes. Long-lived products need ongoing investment, continuous learning and the flexibility to adapt as customer needs change.

• We might be biologically predisposed to keep building things that don't work. Like with B.F. Skinner's experiments with pigeons and intermittent reinforcement, organisations may become addicted to trying new things because occasional successes reward the behaviour. Rather than understanding why something worked, they keep pressing the button, hoping the next feature will deliver the jackpot.

• Luck plays a bigger role in product success than we'd like to admit. Teams can follow all the right processes and still fail, while others stumble into successful products through unexpected customer behaviour or fortunate timing. Good product practices can't eliminate uncertainty, but paying attention to what customers actually do gives organisations a better chance of recognising opportunities and avoiding expensive mistakes.

• Building empathy takes more than claiming to be customer-centric. Product teams don't have to become their users, but they do need to experience the frustrations and constraints of using their products. Simulating real customer workflows, re-onboarding to your own product and watching people struggle with something you've built can reveal problems that requirements documents and acceptance tests never will.

• AI can help you make much bigger mistakes, much faster. Faster development makes it easier to release more features, but it doesn't make those features valuable or desirable. One company used AI to accelerate a major release to millions of customers, only to damage the experience, provoke negative reviews and threaten the business by failing to validate what users actually needed.

• AI should amplify product judgement, not replace it. AI can help generate story maps, identify gaps, evaluate designs and synthesise customer feedback, but the people using it still need the expertise to challenge its output. Effective AI usage is like Iron Man's suit: the technology provides extraordinary capabilities, but there's still a human inside who needs to know what they're doing.

• Shipping everything and switching off what fails isn't a replacement for discovery. Using customers as an unrestricted testing ground risks frustrating them and destroying their willingness to try anything new. Teams should distinguish between releasing to learn with a small, willing audience and releasing to earn with a broader customer base, using prototypes and controlled experiments to reduce risk before scaling up.

• Good product management can be used for bad purposes. Understanding customer behaviour and psychology can help teams solve meaningful problems, but those same skills can be used to exploit people's vulnerabilities and encourage addictive behaviour. Product success measured through engagement and revenue doesn't necessarily mean a product is improving people's lives, making the ethical choices behind product decisions increasingly important.

Connect with Jeff

• LinkedIn: https://www.linkedin.com/in/productdesigncoach/
• Website: https://jpattonassociates.com/

Work with Me

Through One Knight Consulting, I help product companies build better products and stronger product teams. If you're a business leader and would like to chat about how I can help you, e-mail me at [email protected] or book a free advisory call here: https://okip.link/advice

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