Food for Agile Thought #507: First AI Product, POM Transformation Models, Talking Money to Stakeholders, AI Winter?
Also: Dead Teams, Fast & Scrappy Feedback, Mom Test? AI Winter?
Hello everyone!
Welcome to the 507th edition of the Food for Agile Thought newsletter, shared with 40,503 peers.
This week, Teresa Torres reflects on six lessons from building her first AI product, stressing problem focus, prototyping, architecture, evaluation, and ethical data use. Martin Eriksson argues that execution speed depends more on team organization than strategy, showing how autonomy and reduced dependencies accelerate outcomes. Also, John Cutler contrasts the transformation struggles of chaotic scale-ups with sluggish enterprises. Grant Harvey examines whether AI is a bubble or a breakthrough, hinging on efficiency gains, while Paweł Huryn and Mike Goitein highlight reverse-engineering real choices to uncover actual product strategy.
Next, Richard Mironov urges product leaders to frame trade-offs in financial terms to influence executives. Mike Fisher recommends replacing big bets with many small experiments to accelerate learning, and Sheryl Estrada reports on MIT’s claim that most AI pilots fail. Pawel Brodzinski critiques Radical Candor, emphasizing context over rigid models. Additionally, Janna Bastow challenges teams to stop waiting for structured data and embrace scrappy, ongoing feedback gathering.
Lastly, Mark Greville argues enterprise AI fails when leaders neglect human factors, calling for trust and adaptability over rigid choices. Gary Marcus and Nathan Hamiel highlight significant security risks as LLMs combine with coding agents, and Cris Beswick defends middle managers as critical for innovation and execution. Tanner Wortham warns against wasting energy on unwilling teams, and James Newhook offers practical fixes for flawed personas. Finally, Jason Cohen insists proper validation requires paying customers.
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🏆 The Tip of the Week
Teresa Torres: Building My First AI Product: 6 Lessons from My 90-Day Deep Dive
Teresa Torres shares six lessons from building her first AI product, highlighting the need to frame AI around real problems, prototype with care, refine architecture, run continuous evals, and uphold strong ethical data practices.
Source: Building My First AI Product: 6 Lessons from My 90-Day Deep Dive
Author: Teresa Torres
🎯 Product
Martin Eriksson: Strategy into Action: Organising Teams to Execute at Pace
Martin Eriksson explains that slow execution often stems from poor team organisation, not flawed strategy, and shows how separating structure from topology, minimising dependencies, and empowering teams enables real ownership and faster strategic impact.
Source: Strategy into Action: Organising Teams to Execute at Pace
Author: Martin Eriksson
John Cutler: Two POM Transformation Flavors
John Cutler contrasts two product operating model transformations: rapid scale-ups wrestling with speedboat chaos and large enterprises burdened by Titanic-like inertia, each requiring different tactics to align strategy, structure, and funding.
Source: Two POM Transformation Flavors
Author: John Cutler
Mike Fisher: Small Bets: Why Volume Beats Conviction in Product Development
Mike Fisher argues most big product bets fail, urging teams to embrace many small, low-risk experiments instead, compounding insights over time and building faster learning systems that outperform high-conviction, resource-heavy launches.
Source: Small Bets: Why Volume Beats Conviction in Product Development
Author: Mike Fisher
Rich Mironov: Finishing Our (Money) Sentences
Richard Mironov argues that product leaders weaken their case by avoiding financial framing, urging them to translate technical trade-offs into money sentences with rough revenue impact estimates to influence executive decisions effectively.
Source: Finishing Our (Money) Sentences
Author: Rich Mironov
🧠 Artificial Intelligence
(via The Neuron): Are we about to enter ‘AI winter?’
Grant Harvey explores the debate over whether AI is a bubble or a transformative engine, contrasting soaring infrastructure investment with weak enterprise ROI, and showing how unresolved efficiency problems could decide AI’s economic future.
Source: The Neuron: Are we about to enter ‘AI winter?’
Sheryl Estrada (via Fortune): MIT report: 95% of generative AI pilots at companies are failing
Sheryl Estrada highlights MIT’s claim that 95% of generative AI pilots fail to deliver impact, yet the methodology looks unbalanced, comprising convenience sampling and vague coding. Pilot failure rates are surely high, but readers should review the report themselves before treating its findings as broadly generalizable or market-moving.
Source: Fortune: MIT report: 95% of generative AI pilots at companies are failing
Author: Sheryl Estrada
Gary Marcus and Nathan Hamiel: LLMs + Coding Agents = Security Nightmare
Gary Marcus and Nathan Hamiel warn that combining LLMs with coding agents massively expands the attack surface, enabling prompt injections, watering hole attacks, and remote code execution risks that threaten developer tools and software supply chains.
Source: LLMs + Coding Agents = Security Nightmare
Authors: Gary Marcus and Nathan Hamiel
Mark Greville: Designing for humans: Why most enterprise adoptions of AI fail
Mark Greville argues most enterprise AI adoptions fail not due to technology but because leaders ignore human factors, urging focus on trust, federated innovation, concrete use cases, proper implementation, and adaptability over rigid choices.
Source: Designing for humans: Why most enterprise adoptions of AI fail
Author: Mark Greville
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➿ Agile & Leadership
Cris Beswick: The Innovation Crisis: Why Eliminating Middle Managers is Corporate Self-Sabotage -
Cris Beswick warns that eliminating middle managers undermines innovation, as they provide psychological safety, translate strategy into execution, and scale customer-focused solutions, making their removal corporate self-sabotage disguised as efficiency.
Source: The Innovation Crisis: Why Eliminating Middle Managers is Corporate Self-Sabotage -
Author: Cris Beswick
Pawel Brodzinski: Radical Candor Is an Unreliable Feedback Model
Pawel Brodzinski argues that Radical Candor is unreliable because the lines between care and aggression shift constantly, making context and relationships more important than rigid models for giving and receiving feedback effectively.
Source: Radical Candor Is an Unreliable Feedback Model
Author: Pawel Brodzinski
Tanner Wortham: Let the Dead Teams Die
Tanner Wortham argues that some teams are like dead plants: no effort revives them. Leaders waste energy on the unwilling instead of investing in teams ready to grow, eroding morale and culture.
Source: Let the Dead Teams Die
Author: Tanner Wortham
📯 The Statistical AI Parrot in Your Sprint
Your LLM tool doesn’t think. It’s a statistical AI parrot: sophisticated and trained on millions of conversations — but still a parrot. Teams that fail with AI either don’t understand this or act as if it doesn’t matter. Both mistakes are costly.
The uncomfortable truth in Agile product development isn’t that AI will replace your team (it won’t) or that it’s useless hype (it isn’t). Most teams use these tools on problems that need contextual judgment, then accept outputs without the critical thinking Agile demands.
🎓 Learn more: The Statistical AI Parrot in Your Sprint: Why AI Won’t Replace Your Agile Team (and Why Ignoring It Is a Mistake).
🛠 Concepts, Practices, Tools & Measuring
Janna Bastow (via ProdPad): Early-Stage Feedback: Fast, Scrappy Ways to Get Ahead
Janna Bastow argues that waiting for structured data is procrastination, stressing that scrappy, hands-on feedback gathering uncovers real insights and should be a lasting discipline for both startups and large organisations.
Source: ProdPad: Early-Stage Feedback: Fast, Scrappy Ways to Get Ahead
Author: Janna Bastow
Pawel Huryn and Michael Goitein: A Complete Guide To Reverse-Engineering Strategy
Paweł Huryn and Mike Goitein explain that every product already has a strategy, but most teams confuse goals or plans with it. Reverse-engineering real choices and user behavior reveals the true strategy.
Source: A Complete Guide To Reverse-Engineering Strategy
Authors: Pawel Huryn and Michael Goitein
(via IxDF): Persona Health Check: What’s Wrong with Yours — and How to Treat It
James Newhook explains how personas often fail when treated as static documents and offers practical fixes like focusing on one primary persona, grounding them in research, involving teams in their creation, and keeping them continuously updated.
(via Jason Cohen): Yes, but who said they’d actually BUY the damn thing?
Jason Cohen argues that startups often skip real validation, fooling themselves with ideas and excuses, when the only evidence that matters is finding at least ten customers ready to pay.
Source: Jason Cohen: Yes, but who said they’d actually BUY the damn thing?
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