One Knight in Product copertina

One Knight in Product

One Knight in Product

Di: One Knight in Product
Ascolta gratuitamente

Offerte di stagione | 0,99 €/mese per i primi 3 mesi

A seguire 9,99 €/mese – si applicano condizioni. Puoi disdire mensilmente.
I’m your host, Jason Knight, and One Knight in Product is your chance to go deep into the wonderful world of product management, product marketing, startups, leadership, diversity & inclusion and much more! My goal with One Knight in Product has always been to bring real chat to the over-idealised world of product management and mix thought leader interviews with day-to-day practitioners from around the world. I want to ask hard, but fair, questions and bring some personality and good, old-fashioned dry British humour to building products. Subscribe to and share the best product podcast! No others come close 😎Copyright 2020-2025 All rights reserved. Economia
Episodi
  • Jeff Patton: AI Doesn't Change Product Management Fundamentals

    Jeff Patton: AI Doesn't Change Product Management Fundamentals
    Oct 21 2026
    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/...
    Mostra di più Mostra meno
    1 ora e 7 min
  • Simonetta Batteiger: Product Managers Need to Understand the Financials... Not Just the Users

    Simonetta Batteiger: Product Managers Need to Understand the Financials... Not Just the Users
    Oct 10 2026
    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/simonettabatteigerInclusive 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
    Mostra di più Mostra meno
    1 ora e 6 min
  • Rich Mironov: AI Should Change Product Management... Just Not The Way You Think

    Rich Mironov: AI Should Change Product Management... Just Not The Way You Think
    Sep 18 2026
    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.comMoney Stories Book: https://www.mironov.com/book/ Previous Episodes with Rich Fighting Fires in B2B Product Management: https://www.oneknightinproduct.com/rich-mironovProduct 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
    Mostra di più Mostra meno
    1 ora e 2 min
adbl_web_anon_alc_button_suppression_t1
Ancora nessuna recensione