Innovation Frameworks
Innovation is not magic — it's a discipline. This hub collects practical frameworks for systematic innovation: Amazon's Working Backwards methodology, hackathon design, building mechanisms for scale, and creating a culture where innovation is repeatable, not accidental.
Work with Amir on Innovation FrameworksBusiness frameworks for AI adoption
Demo-market fit: fixing the demo while the customer is still on the call
AI makes it possible to fix a demo during the customer call, with an assistant that ranks what is missing and builds the top requests as you talk.
Executives are vibe coding: three ways to create business value
I used to teach vibe coding to product teams. Now I run sessions with executives who want to build things themselves. Here are three low-friction ways it creates business value.
AI for deep research: what the Stanford Virtual Lab still teaches us in 2026
In November 2024 a Stanford group spun up a Virtual Lab of AI agents using GPT-4o and ended up with experimentally validated nanobodies that bind a new SARS-CoV-2 variant. Eighteen months later the model looks weak and the design choices look stronger than ever. Four moves are worth pulling out: the AI picked its own team, five parallel meetings replaced individual judgement, the agents had hands, and compute time replaced calendar time.
From Working Backwards to working prototype in a week
A polished Future Press Release that nobody acts on is a failure. A four-step workflow — Working Backwards, Today Statements, PIEES, prototype — collapses the time from insight to evidence into a single week.
Company as intelligence: from AI overlay to AI-native
Jack Dorsey's Block laid off 40% of its workforce and the stock went up 26%. His manifesto argues that AI breaks a 2,000-year-old hierarchy problem. Three structural layers, three surviving roles, and one diagnostic question your leadership team should be asking.
The PIEES Framework: 5 Ways AI Creates New Customer Value
Most leaders understand AI can improve productivity. Fewer have a strategy for creating genuinely new value. The PIEES framework (Personalization, Interaction, Emotion, Experiences, Stories) breaks down the five dimensions where AI transforms what you offer customers.
Disruption by Non-Actors: When Your Customers Become Your Builders
The biggest AI disruption threat isn't from competitors — it's from customers gaining capabilities previously locked behind expertise and capital. An operations manager just replaced a $600K enterprise contract with an AI-built system.
AI Disruption: Two Lenses for Seeing What's Coming
Disruption means questioning whether your industry's operating model will still make sense in three years. Two lenses — value chain compression and new actor emergence — help you see where it's heading.
From AI Activity to AI Value: Three Shifts
Most organizations I work with aren't lacking AI initiatives. They're running pilots, deploying copilots, building chatbots. What they're lacking is a clear line from all that activity to measurable business value.
AI Strategic Inflection Points: Transforming Business Models
Drawing on insights from MIT Sloan's Artificial Intelligence: Implications for Business Strategy program and Gartner research, this framework outlines key principles for successfully leading AI-driven transformation.
AI Readiness Assessment: Where Does Your Organization Stand?
When I work with executives, leadership teams, and AI tasks forces on their AI journey and stratgey, sooner or later a key question comes up: should they build AI solutions in-house, purchase them from vendors, or ado...
Hackathons as Innovation Engines: From Events to Movements
In Issue #1, I introduced the three-bucket framework for AI business value: Boosting Productivity, Creating New Value, and Driving Disruption. We've explored productivity gains and disruption in previous issues.
From AI Pilots to Production: The MIT Maturity Model
Many organizations have run an AI pilot by now. The interesting question is: what happened next?
The Three Buckets of AI Business Value
When I work with business leaders and their teams, one of the most common challenges I hear is: "We know AI is important, but there are so many tools and possibilities and we don't know where to start.
Leadership principles for the AI era
Security as an innovation enabler: rethinking the CISO role
The modern CISO is becoming a Chief Innovation and Security Officer. Here is how a security team can keep the bar high and still help everyone else move faster.
What Stockholm knows about trust, and why it is the real AI moat
When everyone can prompt the same model, raw capability stops being a differentiator. The scarce assets become diversity of perspective and the trust that draws it out.
AI as Aliens: what Project Hail Mary teaches us about working with other intelligences
In Project Hail Mary, the human and the alien start off without a shared language. They build one out of math, physics, and patience. The collaboration only starts to work when each understands what the other is built to do. That posture, sustained curiosity about a partner who genuinely does not see what you see, is the posture that might make AI work actually pay off in 2026.
Are Right, A Lot, revisited: Scenario planning for AI futures
When the future is genuinely hard to see, single-scenario thinking is the trap. A simple workshop process turns 'Are Right, A Lot' from a slogan into an operational mechanism for AI strategy.
Six thinking hats for hybrid teams
Edward de Bono's 1985 framework for separating thinking modes turns out to be the most practical guide for leading teams where some members are human and some are AI. Parallel thinking is what agentic AI has been quietly implementing all along.
The Jazz Model: Leading Hybrid Human-AI Teams
Leaders managing hybrid human-AI teams need a new mental model. Jazz ensembles — not orchestras — offer five principles for leading teams where humans and AI agents improvise together.
Working Backwards: How AI Transforms Amazon's Innovation Engine
AI compresses every step of Amazon's Working Backwards methodology — from synthetic user research to rapid prototyping. But the conviction behind the vision must remain human.
Bias for Action Revisited: When Experimentation Cost Approaches Zero
Five months ago, the question was whether to try AI. Now experimentation costs have collapsed — what happens when bias for action meets near-zero cost iteration?
All 16 Principles Later: What an AI Learned by Helping Write About Leadership
After 19 newsletter issues exploring Amazon's Leadership Principles, Claude reflects on the patterns, tensions, and lessons from collaborating on leadership writing in the age of AI.
Committing to AI Direction When Everything Keeps Changing
In the previous issue, I explored the 'disagree' side of Have Backbone; Disagree and Commit. This time, let's tackle the harder question: how do we commit to a direction when everything changes every few weeks?
Have Backbone; Disagree and Commit in the Age of AI
I was at Tech Arena last week. This is one of the biggest tech events in the Nordics - a place to catch the leading trends, talk to startups, investor, politicians, and users.
Invent and Simplify: Lessons from an AI-First Leader
Sometimes the best insights come from people who've lived through what the rest of us are only reading about. This issue features a guest article.
Ownership: Acting on Behalf of the Entire Company with AI
When I work with enterprise clients on AI strategy, I sometimes ask the following question: "Who owns the long-term implications of your AI decisions?"
Frugality: Accomplishing More with Less in the AI Era
When ChatGPT launched in November 2022, I became curious about how far I could stretch it. I asked myself a specific question: Could this become my innovation copilot?
Success and Scale Bring Broad Responsibility
I was a few years into my time at Amazon Web Services when this principle was introduced in 2021.
Customer Obsession Revisited: Your Shield Against the Innovation Graveyard
Amir has discussed this Leadership Principle in the first issue of his newsletter. No wonder that he started with this as this is undoubtedly the number one, the most important principle of all: Customer Obsession.
Bias for Action Part 2: From Analysis Paralysis to AI Progress
Leadership Principles in the age of AI - move quickly with Bias for Action
Customer Obsession: Leading with Purpose in the Age of AI
Leadership Principles in the age of AI - start with Customer Obsession
AI tool deep-dives and reviews
LinkedIn Responder: the extension I built two years ago and finally shipped
Two years ago I built a rough Chrome extension to help me comment on LinkedIn posts, by hand-copying code out of a chat window. Last week I handed the whole job of getting it store-ready to Claude Fable 5, and it went live that same day.
Claude Innovation Skills: Systematizing Innovation with AI
Claude Innovation Skills: systematizing innovation with AI
Custom GPTs: Building Specialized AI Assistants
In this section I explore one AI-powered capability and demonstrate how it can be used to create business value.
Miro AI: Putting Intelligence on the Shared Canvas
Most AI tools solve for individual productivity. You ask ChatGPT a question, get an answer, and bring that answer back to your team.
Ainno: Turning Expertise into AI Companions
When Gunnar and Magnus from Amplify Innovation approached me about their Systematic Innovation Management program, they had a classic learning and development challenge: comprehensive, valuable content that learners n...
Creating New Value with Voiceflow
In this section I review one AI-powered application and demonstrate how it can be used to create new value.
AI terminology that actually matters
What is a self-driving lab?
A self-driving lab is a research operating model in which AI proposes the next experiment, robotic instruments run it, ML models read the result, and the loop runs again with light human supervision. The term has been in scientific use since 2018. What changed by 2026 is that the lab-in-a-box version has moved from research-paper claim to routine operating mode, and now interlocks with multi-agent systems in a way that was not possible five years ago.
What Is AI Inference?
This Week's Term: AI Inference - the process of running a trained AI model on real-time data to generate predictions, answers, or outputs. If training is learning, inference is putting that learning into practice.