One Knight in Product

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, email me at hello@oneknightconsulting.com or book a free advisory call here: https://okip.link/advice

On this episode, I speak to Simonetta Batteiger, a product leadership coach, trainer, speaker and co-organiser of ProductTank Cologne. Simonetta began her career in accounting and finance before moving into product leadership while still maintaining a strong sense of the numbers that the rest of the leadership team really cares about. We explore why product teams can be seen as cost centres rather than sources of business value, how to connect product decisions to meaningful financial outcomes, and why the ability to build more quickly with AI makes sound commercial judgement more important, not less.

Episode Highlights
We cover a lot, including:

Building more isn't the same as creating more value. Product teams can be busy delivering features without being able to explain what the business gets in return. A stronger starting point is to consider the commercial outcome an investment is expected to generate, rather than treating delivery itself as proof of success.

Product teams need to show a return, not just a cost. Headcount, infrastructure and tools appear clearly in budgets, but the value created by product work often doesn't. Understanding how an initiative might generate revenue, protect existing revenue, reduce costs or avoid future expenditure changes the conversation from funding a team to investing in an outcome.

Know the financial reality your decisions sit within. Product managers don't need to become accountants, but they should understand the basics of the P&L, balance sheet and cash flow, as well as what the organisation is trying to optimise. The size of the business, the cost of the team and the scale of a potential opportunity all matter when judging whether an investment is worthwhile.

Prioritisation requires more than choosing the biggest number. Understanding the drivers of revenue and cost helps product teams compare opportunities and develop stronger proposals. But business strategy, customer needs and longer-term opportunities still matter, e.g. an investment that enables a new market may be worth making even before its financial benefits are realised.

Roadmap disagreements often reflect competing commercial pressures. Sales leaders may be trying to meet revenue commitments while executives may be advancing a strategic opportunity. Rather than treating these requests as arbitrary interference, product leaders can uncover the underlying rationale and make trade-offs using shared business objectives.

Indirect impact is still impact. In B2B sales-led organisations, platform teams and internal products, the connection between a particular feature and revenue may be difficult to trace. That doesn't remove commercial accountability. It calls for a broader understanding of how product, engineering, sales and other teams contribute to outcomes together.

Finance should be a partner in product decisions. Learning how the business makes and spends money starts with looking at the information already available and asking finance colleagues what matters. Building a shared understanding of the numbers can improve business cases, clarify priorities and strengthen the credibility of product investment proposals.

AI can accelerate delivery while making the economics worse. Faster development isn't necessarily a better investment if increased tooling and token costs outweigh the benefits. Usage-based AI costs can also erode margins as adoption grows, so teams need a credible explanation of how AI will bring forward revenue, reduce costs or create value customers will pay for.

Technical judgement and commercial judgement now have to work together. AI's unpredictable behaviour introduces new questions about reliability, security, evaluation and operational safeguards. Understanding these risks is not separate from understanding the business. Unreliable or unsafe experiences can increase costs and damage retention.

Trust is part of the business model. Customers pay because they believe a product will deliver its promised value. When that promise breaks - through poor experiences, unsafe AI behaviour or diminished value - revenue and reputation are at risk. Building responsibly and protecting customer trust are therefore commercial priorities, not merely ethical extras.

Connect with Simonetta

LinkedIn: https://www.linkedin.com/in/simonettabatteiger

Inclusive Leaders: https://inclusiveleaders.blog

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, email me at hello@oneknightconsulting.com or book a free advisory call here: https://okip.link/advice

On this episode, I speak to the returning Rich Mironov, longtime product management consultant, product leadership coach, and founder of Mironov Consulting. Rich has spent decades working with product leaders, particularly in complex B2B organisations, and is the author of The Art of Product Management and Money Stories. We explore what the current wave of AI-enabled software development really means for product management: why faster code does not automatically create more revenue, where product judgement becomes more important rather than less, and why the role of product may shift away from delivery management towards deeper discovery, commercial thinking, and go-to-market work.

Episode Highlights

Faster software development does not mean faster revenue growth - AI may allow teams to generate code far more quickly, but customer budgets, market size, and demand do not expand at the same rate. More output only creates business value when it translates into adoption, differentiation, and revenue.

More products can mean more competition, not more opportunity - If it becomes dramatically easier for everyone to build software, markets may fill with more entrants and more features competing for the same customers. That can increase price pressure and make commercial differentiation harder rather than easier.

User attention becomes the scarce resource - Shipping dramatically more features pushes the burden of prioritisation onto customers, who have limited time and little interest in evaluating a constant stream of changes. Product teams still need to decide what matters enough to build, explain, and promote.

AI can automate obvious work, but judgement becomes more important as risk increases - Straightforward bugs and low-risk tasks may be good candidates for automation. As the size and impact of a change grows, teams still need to consider customer value, product coherence, economics, and the wider consequences of getting it wrong.

Not every customer request should become a feature - Faster implementation makes it tempting to connect feedback directly to delivery, but many requests are contradictory, poorly framed, commercially harmful, or only relevant to a small subset of users. Product management still requires deciding what NOT to build.

Today's product can quickly become tomorrow's feature - Lower development barriers make it easier for competitors and platform vendors to absorb standalone capabilities into broader products. Being able to build something is not enough; teams need a defensible reason why it should exist as a product in its own right.

Organisational incentives will shape how product managers use AI - If companies reward visible AI usage, coding, prototyping, or token consumption, product managers will naturally move towards those activities. That does not necessarily mean those activities represent the highest-value use of product management time.

Product management may become more 'barbell shaped' - As engineering needs less day-to-day coordination, product managers may spend less time in the middle of delivery and more time at the edges: understanding customers, markets, economics, and strategy before development, then supporting positioning, pricing, sales, and adoption afterwards.

AI transformation is an organisational problem, not just a tooling problem - Previous transformation waves showed that counting activity or mandating tool adoption does not necessarily improve outcomes. The larger opportunity comes from redesigning how work flows through the organisation and identifying where genuine bottlenecks and leverage points sit.

Commercial accountability needs to extend beyond Product and Engineering - If engineering can deliver more quickly, Sales and Marketing also need credible plans for turning that increased capacity into demand and revenue. Product leaders should connect roadmap expansion to explicit assumptions about markets, leads, quotas, adoption, and economic value.

Check out the Article mentioned in the interview, "AI Transformations And Agile Transformations Rhyme": 
https://age-of-product.com/ai-transformations-agile-transformations-a3-delegation-system/

Connect with Rich

LinkedIn: https://www.linkedin.com/in/richmironov/

Website: https://www.mironov.com

Money Stories Book: https://www.mironov.com/book/

Previous Episodes with Rich

Fighting Fires in B2B Product Management: https://www.oneknightinproduct.com/rich-mironov

Product Managers Need to Understand the Language of Money: https://www.oneknightinproduct.com/rich-mironov-money

Work with Me
Through One Knight Consulting, I help product companies identify growth opportunities and build the capability to pursue them. If you'd like to chat about how I can help you, e-mail me at hello@oneknightconsulting.com or book a free advisory call here: https://okip.link/advice

On this episode, I speak to Eric Ries, entrepreneur, multiple-time founder and bestselling author of The Lean Startup. Eric has spent years working with founders, leaders and organisations on how companies are built, scaled and governed, and later founded the Long-Term Stock Exchange to explore how businesses can pursue long-term profit and purpose. He's back with his new book, Incorruptible: Why Good Companies Go Bad and How Great Companies Stay Great, just about to celebrate its UK launch. In it, he examines why good companies lose their way, how incentives and governance drive that corruption, and how leaders can build organisations that stay true to their mission as they grow.

Episode Highlights
We cover a lot, including:

Good companies can be corrupted by their own success - Growth, outside capital, new ownership and leadership transitions can introduce forces that pull a company away from the purpose and behaviours that made it valuable in the first place.

It's always too early until it's too late - Leaders are often told that governance protections, mission locks or structural safeguards can wait. The problem is that by the time the threat becomes obvious, the organisation may no longer have the power to put them in place.

Your mission statement might be a lie - If the company publicly claims to serve employees, customers or a wider purpose, but its actual governance ultimately prioritises shareholder returns above everything else, there is a fundamental contradiction between what it says and how it is built.

Shareholder primacy is a choice, not a law of nature - The idea that companies exist primarily to maximise shareholder returns is relatively recent. "Mission primacy" offers an alternative: financial performance in service of a durable purpose, rather than as the purpose itself.

Some corporate "failures" are successful for the people causing them - A takeover, restructuring or strategic decision can destroy customer value and weaken the company while still enriching the executives, advisers or investors involved. If behaviour looks irrational, follow the incentives.

Trust is valuable enough to steal - Companies build trust with customers, employees and communities over years, but that accumulated goodwill can become something for new owners or decision-makers to extract. Leaders need structures that actively protect it.

You cannot command an organisation to have better character - Companies behave more like living systems than machines. Qualities such as integrity, innovation, ownership and long-term thinking have to be cultivated through incentives, structures and repeated behaviour rather than announced by leadership.

Transformation fails when leaders don't treat it seriously enough - If innovation, ethics or organisational change matters, it needs the same rigour, resources and accountability that companies already apply to finance or compliance. A speech and a workshop are not a transformation strategy.

The mission has to survive the founder - Succession exposes whether a company truly has an institutional purpose or merely reflects the personality of the person who created it. Durable companies encode their principles into structures that can survive changes in leadership.

Mission-first does not mean anti-profit - Companies built around long-term stewardship and human flourishing can create more durable economic value, not less. The alternative to extraction is not charity; it is designing the organisation to create more value than it captures.

About Incorruptible
Incorruptible: Why Good Companies Go Bad and How Great Companies Stay Great explores how companies can protect their mission as they grow, raise capital, change leadership and face pressure from investors, acquirers and other stakeholders.

Learn more about the book and related work:

Incorruptible - https://www.incorruptible.co/

How Is Incorruptible Going? - https://howisincorruptiblegoing.com/

Escape Velocity - https://www.escapevelocity.co/

Virgil - https://tryvirgil.com/

Connect with Eric Ries

Eric Ries on LinkedIn - https://www.linkedin.com/in/eries/

On this episode, I speak to Barry O'Reilly, entrepreneur, executive adviser and author of Lean Enterprise, Unlearn and his latest book, Artificial Organizations. Barry works with senior leaders around the world to improve how their organisations make decisions and perform, and has also spent recent years building and advising AI-enabled companies through Nobody Studios.

We discuss what AI changes about leadership when the goal is not simply to produce more output, but to make better decisions. Barry argues that the real opportunity is to use AI to reduce cognitive and administrative load, strengthen judgement, improve organisational context and create more space for thoughtful, high-quality work - without outsourcing the thinking itself.

Episode highlights

AI should increase thinking capacity, not just output - The most valuable gain is not producing 10 or 100 times more work, but reducing administrative load so people have more time for creative problem solving, reflection and consequential decisions.

Start with how you work, not with the tools - Rather than adopting AI because a particular tool is fashionable, identify where you create the most value, how you naturally work best and which tasks get in the way. Technology should support that operating model rather than dictate it.

Judgement is the capability leaders need to protect - AI is strong at capturing, synthesising and interrogating large amounts of information, but leaders still need to own the decision. Using AI to pressure-test thinking is fundamentally different from asking it to make the judgement for you.

Treat conversations as organisational data - Meetings, decisions and working sessions can become reusable context rather than disposable moments. Capturing and synthesising them can improve preparation, continuity, follow-through and the quality of future decisions.

Use AI as a thinking partner, not an answer machine - One of the highest-value uses is to challenge assumptions, find blind spots, generate alternative scenarios and simulate the questions a sceptical stakeholder might ask before a high-stakes conversation.

Leaders need to role-model experimentation - AI adoption is unlikely to succeed through licences, mandates or transformation programmes alone. Leaders can create safer and more useful experimentation by visibly trying new approaches, sharing what worked and what failed, and learning alongside their teams.

AI-generated output can simply move work downstream - Producing a polished 20-page document in minutes is not valuable if somebody else must spend an hour working out whether it says anything useful. Good AI-enabled work should reduce the processing burden on colleagues rather than transfer it to them.

Organisations may need an explicit 'slop policy' - Teams should establish expectations that people do the synthesis and thinking before asking others for feedback. A good recommendation should show the options considered, relevant evidence, trade-offs and a proposed decision rather than handing raw AI output to someone else.

Fresh organisational context is a competitive advantage - Generic models produce generic answers. The organisations that benefit most will be those that can maintain useful context about customers, strategy, decisions, work and relationships, and make that information available at the point where decisions are being made.

Start AI adoption with one real decision - Instead of trying to redesign an entire operating model at once, take an upcoming decision, write down how you would normally make it, then use AI to challenge that process: what assumptions are missing, what evidence matters, and how could the decision framework be made more robust?

About Artificial Organizations
Artificial Organizations explores how leaders can combine human judgement with machine intelligence to improve decision-making, organisational performance and the way work gets done.

Learn more about the book: https://artificialorganizations.com/

Learn more about Barry's work: https://barryoreilly.com/

Connect with Barry
LinkedIn: https://www.linkedin.com/in/barryoreilly/