One Knight in Product

Barry O'Reilly - How to Keep the Humanity in Artificial Organizations

1 h 6 min · 18 aug 2026
aflevering Barry O'Reilly - How to Keep the Humanity in Artificial Organizations artwork

Beschrijving

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/ [https://artificialorganizations.com/] Learn more about Barry's work: https://barryoreilly.com/ [https://barryoreilly.com/] Connect with Barry LinkedIn: https://www.linkedin.com/in/barryoreilly/ [https://www.linkedin.com/in/barryoreilly/]

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Alle afleveringen

274 afleveringen

aflevering Barry O'Reilly - How to Keep the Humanity in Artificial Organizations artwork

Barry O'Reilly - How to Keep the Humanity in Artificial Organizations

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/ [https://artificialorganizations.com/] Learn more about Barry's work: https://barryoreilly.com/ [https://barryoreilly.com/] Connect with Barry LinkedIn: https://www.linkedin.com/in/barryoreilly/ [https://www.linkedin.com/in/barryoreilly/]

18 aug 20261 h 6 min
aflevering Pavel Samsonov - AI Can Build the Solution... You Still Have to Design the Problem artwork

Pavel Samsonov - AI Can Build the Solution... You Still Have to Design the Problem

On this episode, I speak to Pavel Samsonov, Principal UX Designer at Justworks and author of The Product Picnic newsletter. Pavel has spent his career working across UX, service design and product management, including roles at Amazon and Bloomberg, helping organisations design better products by understanding the systems, processes and people behind them. We explore why great products start with better problem definition, how organisational silos undermine customer experience, why AI is making it easier to build the wrong things faster, and why genuine user understanding remains a uniquely human advantage. Episode Highlights * Design for understanding, not simplicity - Complex B2B products don't need to hide complexity; they need to present it in a way that users can understand, navigate and act upon confidently. * Customer journeys don't follow organisational charts - Teams optimise their own domains, but customers experience the whole service. The biggest opportunities often lie in fixing the gaps between teams rather than improving individual features. * Problem design matters more than solution design - Before discussing features or interfaces, ask whether you're solving the right problem, why it exists, and whether it's important enough for customers to actually care. * Actionable beats visible - Dashboards, analytics and metrics only create value when they help someone make a better decision. Data without action is little more than decoration. * Optimising your work can create someone else's workload - Shipping work isn't the same as completing work. Teams should think about who consumes their outputs and whether they're genuinely fit for purpose. * AI accelerates production, not learning - AI makes it dramatically faster to generate prototypes and features, but it doesn't shorten the time required to validate ideas, learn from customers or understand real-world usage. * High-fidelity prototypes can create false confidence - Just because AI can generate something that looks finished doesn't mean the difficult work of alignment, prioritisation, research and iteration has been done. * Synthetic users aren't a substitute for real customers - Large language models can reproduce existing knowledge but can't uncover the tacit insights, unmet needs and market opportunities that come from talking to real people. * Good product decisions require shared language - Cross-functional collaboration improves when teams focus on the decisions they're trying to make rather than debating ambiguous labels like "prototype", "MVP" or "research". * Ask better questions before building faster - AI has made building dramatically cheaper, increasing the importance of asking why something should exist in the first place. Better problem framing remains one of the highest-leverage skills in product development. Connect with Pavel * Newsletter: https://productpicnic.beehiiv.com/ [https://productpicnic.beehiiv.com/] * LinkedIn: https://www.linkedin.com/in/pavel-samsonov-44ba2833/ [https://www.linkedin.com/in/pavel-samsonov-44ba2833/]

25 jul 20261 h 9 min
aflevering Be Kaler Pilgrim - Where Does Product Go Wrong in PE-Backed Firms? artwork

Be Kaler Pilgrim - Where Does Product Go Wrong in PE-Backed Firms?

On this episode, I speak to Be Kaler Pilgrim, founder of Smithfield Search and original founder of Futureheads Recruitment. Be has spent more than three decades helping organisations build technology and product teams, and recently conducted an in-depth study of senior product leaders operating in investor-backed businesses. We explore what effective product leadership really looks like in high-growth environments, why so many organisations still misunderstand the role of product, and how AI is forcing leaders to rethink organisational design, capability and value creation. Episode highlights * Product is still too often treated as a delivery function - One of the strongest themes from the research is that organisations frequently position product management as an execution capability rather than a strategic commercial function, limiting both its influence and its ability to create value. * Leadership roles fail when organisations cannot define the problem - Businesses often hire senior product leaders without first agreeing on what challenge they actually need solving (or whether there's even a challenge to solve), creating misalignment before the role even begins. * Product leadership should be designed around organisational needs, not trends - Whether hiring a CPO, CPTO or another senior product role, organisations need to understand their specific context rather than simply adopting structures that appear fashionable. * Feature factories remain one of the biggest barriers to growth - Teams can become highly efficient at shipping work without creating measurable business impact, leading to activity without meaningful outcomes and missed opportunities to execute the investors' value creation plan. * The "Land of Lost Toys" affects more organisations than leaders realise - Many companies accumulate partially completed initiatives, abandoned priorities and unfinished experiments that reduce focus and create organisational drag. * Technical debt is ultimately a business problem - Whilst often discussed as an engineering concern, accumulated technical debt reduces confidence, slows execution and directly impacts commercial performance over time. * Commercial fluency is becoming a core product leadership capability - Product leaders increasingly need to understand business economics, value creation and financial performance, rather than focusing exclusively on product process and delivery. This enables them to have conversations that resonate with PE leaders. * AI increases the importance of judgment rather than reducing it - Whilst AI can automate many activities, strategic decision-making, prioritisation and organisational leadership remain fundamentally human responsibilities and, thankfully, humans seem to be coming back into fashion! * Product, sales and customer teams succeed or fail together - Sustainable growth requires strong alignment between teams responsible for building, selling and retaining customers, rather than treating commercial outcomes as somebody else's problem. Everyone should care about NRR. * The biggest organisational problems are often hiding in plain sight - Many of the factors that ultimately constrain growth are visible long before they appear in financial performance. PE firms need to look beyond financial metrics and get product experts in early to catch and mitigate these issues earlier. ... and much more. Check Out the CPO Report You can view the CPO Report and take the Smithfield CPO Readiness Index here: https://cpo.smithfieldsearch.com/ [https://cpo.smithfieldsearch.com/] Contact Be If you want to get in touch with Be, go here: * Smithfield Search: https://www.smithfieldsearch.com [https://www.smithfieldsearch.com/] * LinkedIn: https://www.linkedin.com/in/bekalerpilgrimexecsearch/ [https://www.linkedin.com/in/bekalerpilgrimexecsearch/]

2 jul 202657 min
aflevering Petra Wille - Strong Product Leadership in the Age of AI artwork

Petra Wille - Strong Product Leadership in the Age of AI

On this episode, I speak to the returning Petra Wille, product leadership coach, author of Strong Product People, and founder of the Product at Heart conference. Petra has spent years helping product leaders and organisations develop stronger product cultures, leadership practices, and team structures across a wide range of industries. We went deep into product leadership, especially in an age of AI where we're all being told to be builders again, and how her Product Leadership Wheel helps product leaders up their game. Episode highlights * Leadership is a distinct discipline - Strong individual contributors do not automatically become strong leaders, and leadership requires deliberate development rather than promotion by default. * Product leaders need directional clarity - One of the core responsibilities of leadership is helping teams understand where the organisation is going, why it matters, and how everyday decisions connect to broader strategy. * Coaching is an underused leadership skill - Petra argues that many leaders underestimate the importance of coaching capabilities and fail to invest enough time in helping teams grow and improve. * Culture is often invisible inside organisations - Teams frequently struggle to articulate their company culture because they are immersed in it every day, making reflection and intentional leadership even more important. * AI is changing the demands placed on leaders - Product leaders are being forced to rethink team structures, workflows, decision-making, and product experiences as AI reshapes how organisations operate. * Efficiency gains can create new problems - Faster delivery is not automatically better. Petra warns that organisations risk creating more technical debt, burnout, and shallow thinking if speed becomes the only goal. * Leadership requires optimistic narratives - In periods of uncertainty, leaders play a critical role in creating credible and motivating visions of the future for their teams and organisations. * Feedback gaps exist between leaders and teams - Many product leaders believe they are performing well, while individual contributors often see significant shortcomings, partly because organisations lack shared frameworks for discussing leadership quality. * Reflection matters more than benchmarking - Petra emphasises that leadership frameworks should help people identify growth areas and learning opportunities rather than turn development into rigid performance comparisons. * Leaders should focus on the "shipyard" - Rather than constantly jumping into delivery work, product leaders should concentrate on improving the systems, structures, and environments that enable teams to succeed. ... and much more. Contact Petra * Website: https://www.petra-wille.com [https://www.petra-wille.com/] * Product at Heart: https://www.productatheart.com [https://www.productatheart.com/] * LinkedIn: https://www.linkedin.com/in/petra-wille-b8b1329/ [https://www.linkedin.com/in/petra-wille-b8b1329/]

19 mei 20261 h 5 min
aflevering April Dunford - Obviously Awesome 2.0 : What's New With Product Positioning? (with April Dunford, Author “Obviously Awesome“ and “Sales Pitch“) artwork

April Dunford - Obviously Awesome 2.0 : What's New With Product Positioning? (with April Dunford, Author “Obviously Awesome“ and “Sales Pitch“)

On this episode, I am joined for a third (and probably final!) time by April Dunford, renowned positioning expert and author of Obviously Awesome and Sales Pitch. We explore what's changed in her updated edition of Obviously Awesome, what she's learned through delivering hundreds of positioning workshops and how she's honed her approach to reposition positioning to make it clearer for the next generation of product people. Episode highlights * Positioning is how you win - Positioning is the answer to "why pick us now?", grounding your product in real value for a clearly defined customer segment. * Positioning vs strategy - Strategy defines where you're going, while positioning reflects where you are today and must evolve as your product and market change. * AI isn't a position - Simply adding AI is no longer meaningful; if everyone has it, the focus shifts to what tangible value you deliver right now. * From unique to distinct - Capabilities don't need to be truly unique, just meaningfully different in the context of your real competitors. * Trends matter less than clarity - The industry's baseline understanding of positioning has improved, making abstract ideas like "trends" less useful than clear market definitions. * Readiness before action - Teams need to decide what they're positioning, whether they have real customers, and how product structure (single vs multi-product) affects the work. * Positioning as hypothesis - Without customers, positioning is a best guess that must be tested and refined rather than treated as fact. * Sell to the champion - Positioning should resonate with the internal champion, while objections from other stakeholders are handled separately. * Test through sales, not copy - The real validation of positioning happens in sales conversations, not by endlessly tweaking website copy. * AI is a tool, not a shortcut - AI can support positioning work, but it can't replace the thinking, collaboration and deep company context required to define real differentiation. ... and much more. Buy the new version of Obviously Awesome Look for the yellow "updated" sticker! * Amazon: https://www.amazon.co.uk/Obviously-Awesome-Product-Positioning-Customers-dp-1999023056/dp/1999023056/ [https://www.amazon.co.uk/Obviously-Awesome-Product-Positioning-Customers-dp-1999023056/dp/1999023056/] * April's website: https://www.aprildunford.com/books [https://www.aprildunford.com/books] Contact April * LinkedIn: https://www.linkedin.com/in/aprildunford/ [https://www.linkedin.com/in/aprildunford/] * Website: https://www.aprildunford.com [https://www.aprildunford.com/]

17 apr 20261 h 13 min