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 FrameworksSix 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.
Hybrid Team Emotions: Designing for Feeling in the Age of AI
84% of workers are eager to embrace AI, and 56% simultaneously worry about job security. Leaders who ignore this duality lose both trust and momentum. A framework for treating emotion as a design variable in hybrid teams.
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.
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.
Why Nordic flat hierarchies are both the best and worst thing for AI strategy
Nordic companies deploy AI 20% faster than the European average. Yet only 26% of Nordic CEOs are involved in AI strategy. The same flat hierarchy that accelerates adoption is fragmenting governance. Here is how to fix it.
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.
Team Tenets: From Leadership Principles to Practical Mechanisms
Leadership principles inspire direction, but tenets resolve the real tradeoffs your AI team faces every day. Here's how to write team tenets that accelerate decisions and align autonomous systems.
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?
Rethinking Engineering Organizations: The Block-Coinbase Contrast
Two companies, two radically different approaches to AI in engineering. Block cut 40% and called it AI transformation. Coinbase shipped 3-4x faster without losing anyone. Which path creates lasting value?
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.
Leadership Principles in the Age of AI Agents
The same principles that built one of the world's most innovative companies are now the playbook for leading AI-powered organizations. Here's how seven of Amazon's leadership principles apply when your team includes agents.
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?
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.
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.
Rethinking How We Design AI Experiences
For years, the design process for digital products has followed a familiar sequence: research users, create personas, map journeys, write problem statements, brainstorm solutions, wireframe, test, iterate.
Strive to Be Earth's Best Employer in the Age of AI
It's been a tough few weeks for some of my former AWS colleagues.
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.
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.
Hire and Develop the Best: Leading Hybrid Human-AI Teams
This month I onboarded a new team member. I thought carefully about what context they'd need to succeed - our goals, our working style, the projects in flight, where to find key documents.
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...
Deliver Results: Anthropic's Soul Documents and Value-Aligned AI
In 1942, Isaac Asimov introduced the Three Laws of Robotics - a set of rules designed to ensure robots would never harm humans. Simple, elegant, hierarchical.
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.
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?"
Earn Trust: Vocal Self-Criticism in the Age of AI
In 1997, Garry Kasparov became the first person to "lose his job" to AI when IBM's Deep Blue defeated him in chess. For years, this defeat symbolized humanity's vulnerability to machines.
Productizing Internal AI Tools: From Efficiency to Revenue
Slack started as an internal communication tool at a gaming company. AWS began as Amazon's internal infrastructure.
Claude Innovation Skills: Systematizing Innovation with AI
Claude Innovation Skills: systematizing innovation with AI
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?
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?
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.
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.
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.
Dive Deep: AI as a Force Multiplier for Understanding
For decades, business leaders faced an impossible choice when it came to understanding their markets, customers, or prospects: invest heavily in deep research or settle for surface-level insights you could afford.
Think Big: The Leadership Principle That Defined Amazon
Most organizations approach AI by asking: "How can we use AI to do what we already do, but faster or cheaper?"
Insist on the Highest Standards in the Age of AI
Insist on the Highest Standards - your role as a leader in the age of AI.
Learn and Be Curious: Why This Moment Demands It
Of all Amazon's Leadership Principles, "Learn and be Curious" is the one that resonates most with me personally. The principle states:
Are Right, A Lot: When AI Can't Tell You You're Wrong
Are right, a lot: when AI can't tell you you're wrong
Bias for Action Part 2: From Analysis Paralysis to AI Progress
Leadership Principles in the age of AI - move quickly with Bias for Action
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...
Bias for Action: Moving Quickly in the Age of AI
Leadership Principles in the age of AI - move quickly with Bias for Action
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.
Customer Obsession: Leading with Purpose in the Age of AI
Leadership Principles in the age of AI - start with Customer Obsession
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.