---
title: "How AI Broke Your Strategy"
speaker: Martin Eriksson
role: Author of The Decision Stack, Co-Founder of Mind the Product
company: Mind the Product
conference: Just Product 2026, Munich, 16 October 2026
track: Opening Keynote
slot: 09:45
duration_minutes: 29
video: https://media.product-masterclass.com/just-product-2026/video/02-martin-eriksson.mp4
slides: https://media.product-masterclass.com/just-product-2026/slides/02/Just Product 2026 - Martin Eriksson - How AI Broke Your Strategy.pdf
page: https://www.product-masterclass.com/just-product-2026/recap/talks/02-martin-eriksson.html
language: en
source: automatic transcript of the livestream, edited summary
---

# How AI Broke Your Strategy

**Martin Eriksson**, Author of The Decision Stack, Co-Founder of Mind the Product. Opening Keynote, 09:45, Just Product 2026.

> AI broke an assumption under your strategy; find which one, go up the stack until one holds, and rebuild from there.

## Summary

Martin opens with a prediction that failed. In 2016 Geoffrey Hinton said AI would replace radiologists within five to ten years. AI did get faster, cheaper and more accurate at reading images, yet the US has 16 percent more radiologists than a decade ago and they earn 44 percent more. Cheaper imaging meant preventative screening at scale, more findings, more follow up, more demand. That is the Jevons paradox, first observed with coal and steam engines in 1865. Hinton did not get the technology wrong. He assumed demand was finite. Martin's claim for the room: AI has broken at least one assumption in every company present, and most people do not know which one.

To find it you need to know where it hides. After 30 years in product, co-authoring Product Leadership and advising about 150 companies, Martin boils it down to five questions: where are we going, how do we get there, what matters now, which actions do we take, how do we choose between them. He answers them with vision, strategy, objectives, opportunities and principles, the Decision Stack, but any framework works as long as the layers connect top down through how and bottom up through why. Every layer is a decision, and every decision rests on assumptions. Most companies have a vision on a lobby poster, a strategy in a PowerPoint on a file server, OKRs in a forgotten spreadsheet, and now sprinkle AI strategy on top.

Then come the failure modes. Most companies look only at the opportunities layer and treat AI as a feature, which assumes the customer's problem is a lack of AI. Four in five people on earth have never used generative AI, and a Washington State University study found the label artificial intelligence reduces purchase intent. Chegg built CheggMate with OpenAI while the broken assumption sat higher up: students would not keep paying for answers ChatGPT gives away. The stock halved during the earnings call and revenue fell 75 percent. The second failure is AI as cost cutting. IKEA's agent resolved half of all tickets, the rest were design questions, so IKEA retrained support staff as remote interior designers and booked 1.4 billion dollars in new revenue in year one.

Intercom went furthest. Des Traynor's team saw that per seat pricing and humans staffing support were both broken, kept only the vision that businesses want to support their customers, scrapped the roadmap over a weekend and built Fin. At 99 cents per resolution, Fin passed 100 million ARR in year one, and Martin reports a 3.6 billion sale to Salesforce after a 1.3 billion valuation. His method: name your assumptions by asking what would have to be true, go up the stack until one holds, test it against tokens at zero and tokens at ten times the price, then bet at 70 percent confidence and write down the kill criteria. We are in the DOS era of AI; the best way to predict the future is to invent it.

## Key takeaways

1. Write down the assumptions under each layer of your stack using Roger Martin's question: what would have to be true?
2. Drop the AI as a feature reflex: four in five people have never used generative AI and the label lowers purchase intent.
3. Ask the Jevons question: what would customers do with your product if it were ten times cheaper, faster or more accessible?
4. When an AI agent absorbs half the workload, study what the remaining half reveals before cutting headcount, as IKEA did.
5. Check whether your business model survives AI; outcome based pricing like Intercom's 99 cents per resolution ties revenue to the product working.
6. Plan for two futures, tokens at zero and tokens at ten times today's cost, and keep only assumptions that survive both.
7. Make the bet at 70 percent confidence, write it down, and define the kill criteria that would prove you wrong.
8. Challenge upward by naming assumptions and bringing customer evidence, and ask each leader separately what the strategy is.

## Chapters (timestamps in the talk video)

- 0:00 Host introduction
- 1:12 Hinton, radiologists and the Jevons paradox
- 5:10 The Decision Stack in five questions
- 7:46 Every layer sits on assumptions
- 10:25 AI as a feature: Chegg
- 14:14 AI as cost cutting: IKEA
- 17:51 Going up the stack: Intercom
- 22:45 How to find your broken assumption
- 26:17 Make a bet, write it down

## Highlight moments

- 2:57 to 4:09: **Hinton gave radiologists five years**. The opening story in full: AI got better at imaging, radiologists grew 16 percent and earn 44 percent more.
- 6:53 to 7:46: **Every level of the stack is a decision**. Defines the Decision Stack and lands the poster, PowerPoint and spreadsheet punchline about sprinkled AI strategy.
- 12:58 to 14:14: **Chegg never asked which assumption changed**. A complete cautionary tale with the numbers: stock halved, revenue down 75 percent, students just wanted homework done.
- 15:06 to 16:35: **IKEA retrained support as interior designers**. The strongest counterexample to AI as cost cutting, ending on 1.4 billion dollars of new revenue from the same humans.
- 20:50 to 22:18: **Intercom scrapped its roadmap over a weekend**. Shows what a strategy level rethink looks like, from per seat pricing to 99 cents per resolution and the Fin outcome.
- 24:34 to 25:31: **We pass five year strategies on vibes**. The most practical challenge to product people: take assumption testing up the stack, because you cannot A/B test 2028.

## Quotes

- "Today's AI is the worst AI you are ever going to work with." (8:31)
- "Turns out Chegg's customers didn't really want Chegg. They definitely didn't want AI. They just wanted their homework done." (14:01)
- "To me, that's using once in a generation technology to shrink yourself." (14:35)
- "AI is the biggest growth lever your business has ever had, and treating it as a cost lever is a failure of imagination." (16:35)
- "We obsessively test these relatively cheap decisions, and we pass five-year strategies on vibes." (24:56)
- "You can't A/B test 2028." (25:27)

## Q&A (not in the video, summarised from the stream)

**Q: Which assumption do you hear most often that is simply wrong?**

That none of their assumptions have changed. Companies react to the speed of AI but skip going back to the drawing board to check whether their objectives, strategy or vision still hold. In his workshop the day before, that check surfaced broken assumptions quickly.

**Q: Can we use AI to explore and forecast the upper layers of the stack, or are those human only?**

AI is a strong thought partner for research, options and checking the consistency of an argument. The leap of faith at strategy level and connecting signals across customers stays human, because AI is not happy making bets. Validate its output with real people, in the spirit of Lean Startup and Steve Blank's customer development.

**Q: How do you challenge vision and strategy in a 20 year old founder led company that resists change but leans into the AI hype?**

Challenge the assumptions, not the person: say the strategy rests on enterprise customers needing X, ten interviews show they no longer do, what changes? Always bring evidence, or the highest paid person's opinion wins. His favourite trick is asking each leader individually what the strategy is and playing the different answers back to them.

**Q: Is a human in the loop a real requirement or just a limitation we accept because someone must be accountable?**

It depends on the level. Agents handling tickets or code can run without a human once evals and testing are in place. For objectives, strategy and vision a human stays in the loop, not only for accountability but because industry judgment is needed to filter the stupid ideas from the great ones.

## Mentioned

- Geoffrey Hinton, the 2016 prediction that AI would replace radiologists within five to ten years
- Jevons paradox, William Jevons on coal consumption rising after James Watt's steam engine, 1865
- Product Leadership, the book Martin co-authored to interview hundreds of product leaders
- The Decision Stack, Martin's book and framework (vision, strategy, objectives, opportunities, principles), first two chapters free via QR code at thedecisionstack.com
- Mind the Product and ProductTank, the community Martin co-founded, credited by host Sebastian for his own move into product
- Gmail launch in 2004 with 1 GB of storage, Google's bet on falling storage costs
- Washington State University study, the term artificial intelligence in product descriptions reduces purchase intent
- April Dunford, customers do not buy AI, they buy the elimination of a pain point
- Chegg and CheggMate, CEO Dan Rosensweig's May 2023 earnings call, homework help business hit by ChatGPT
- IKEA, support agent resolved 50 percent of tickets, staff retrained as remote interior designers, quote from Ulrika Biesert
- Rita McGrath, strategy is more critical in a fast moving world because organisations make many more decisions
- McKinsey report, a third of companies trying to replace at least one SaaS product
- MIT study, 66 percent of in-house software builds fail
- Anthropic, still uses Slack, Workday and Figma rather than rebuilding them
- Intercom, Des Traynor, Fin AI agent at 99 cents per resolution, company renamed Fin, reported sale to Salesforce
- IBM manual from 1979, a computer can never be held accountable so must never make a management decision
- Roger Martin, the question what would have to be true
- Annie Duke, Thinking in Bets, calling it a bet and writing down kill criteria
- Margaret Heffernan, just because we cannot predict the future does not mean we cannot prepare for it
- Xerox PARC, Alan Kay and Adele Goldberg, the graphical user interface and the line that the best way to predict the future is to invent it
- Lean Startup and Steve Blank's customer development, cited in the Q&A on validating AI output with humans

## About the speaker

Martin Eriksson has spent three decades helping organisations figure out why they are stuck and what to do about it. He has built digital products since 1994, worked at the Financial Times, Monster, Huddle, Covestor, Cazoo and EQT, and co-founded Mind the Product, the world's largest product community. His book The Decision Stack distils what it takes to align an organisation from vision to execution.

- Website: https://martineriksson.com
- The Decision Stack: https://www.thedecisionstack.com