AI Strategy
AI strategy is the process of aligning artificial intelligence initiatives with business goals. It involves identifying high-value use cases, building adoption roadmaps, securing executive buy-in, and measuring results. A strong AI strategy focuses on business value — not technology for its own sake.
Work with Amir on AI StrategyBusiness frameworks for AI adoption
Build, ask, then finish: letting business users build their own apps
In a build, ask, then finish order, a subject matter expert builds a first working app before asking IT. How IT can make that safe for more people to build.
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.
Running AI work wide, not just long
Most of the conversation about capable models is about jobs that run longer. There is a second direction nobody talks about: the same job run many times over, at once. It works, and it breaks in a place you would not expect.
Speak the language of your audience, or watch your AI pilots stall
AI pilots rarely fail because the model was not good enough. They fail on the human side, when the experts who own the problem get lost in a fog of jargon. Translation is the lever.
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.
The narrative is the mechanism: closing the 94-versus-6 AI value gap
Three independent studies last year landed on the same number. About 5-6% of organizations capture meaningful value from AI. The other 94% have AI in production and not much to show for it. RAND's review of a thousand projects says 63% of the gap is human, not technical. Before it is anything else, the AI value gap is a narrative gap.
HR's dual mandate in the age of AI
Most AI-in-HR coverage flattens the work into one story: automate recruiting, save HR people time. That undersells the opportunity by half. There are two distinct mandates HR has to lead, on different rhythms, with different team postures. Miss either one and the transformation fails.
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.
PIEES data flywheels: closing the loop between collection and value
96% of organizations investing in AI report productivity gains. Only 5% capture value at scale. The gap is a loop problem. Turning the PIEES framework into five compounding data flywheels is how you close it.
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.
Value Chain Compression: How AI Is Breaking Pricing Models and Unbundling Industries
Two trillion dollars in software market cap evaporated in 30 days. Insurance brokers lost 12% in a single day. The hourly billing model is dying. Here's the framework for understanding which value chain links are next.
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.
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?
"Which AI Should I Use?" Is the Wrong Question
Before selecting AI tools, define the problem being solved. Not all hard problems are the same kind of hard - and matching problem types to model strengths is the emerging leadership skill.
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.
Intent Engineering: The Missing Layer in Enterprise AI
We've taught AI what to know. We haven't taught it what to want. That gap is why most companies still see no tangible value from AI - and the fix starts with something Peter Drucker told us decades ago.
Building AI Agents That Work: 10 Design Principles for Business Leaders
Ten practical principles for leaders building or evaluating AI agent systems - whether for customer experience, internal operations, or any business function.
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.
Agent Teams: The Next Frontier of AI Business Value
I ran two sessions this week with AI champions at a customer I'm working with. The topic: what agents actually mean, and what the role of humans becomes when working with them.
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.
The Unstructured Data Goldmine: Your Hidden AI Advantage
At the MLOps Sweden meetup last week, hosted at AI Sweden, I had two conversations that surfaced something worth spending time on.
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...
AI and the Future of Work: Beyond Technical Transformation
When we think about how AI will transform the workforce, we often focus on technology and tools.
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.
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?
Mapping AI Value Across Modalities
As we've said several times before, the most common mistake I see is starting with the technology. Business leaders want to know which AI tool to buy, which model to use, which vendor to choose. But that's backwards.
Building the Business Case for AI Investments
When I review AI initiatives with business leaders, I see a concerning pattern. Teams are excited, budgets are allocated, tools are being evaluated. But when we ask "How exactly will this create value for your business?
Driving Disruption: The Third AI Value Bucket
In Issue #1, I introduced a three-bucket framework for thinking about AI business value:
The Product Era of AI Has Arrived
In the last 60 days, we've witnessed a remarkable shift. OpenAI's Sora 2 hit 1 million downloads in five days with a complete video creation platform including social features and identity insertion.
When AI Doesn't Know What You Know: Building Knowledge Moats
When I work with organizations on AI initiatives, I often hear: "We've implemented ChatGPT" or "We're using Claude" or "We're rolling out Copilot.
The Five Levels of AI Autonomy
Everyone's racing to build "AI agents," but most companies are thinking about this wrong.
Boosting Productivity: The First AI Value Bucket
For decades, getting insights from your data meant one of two things: either you knew SQL and could query databases directly, or you submitted requests to your data team and waited for reports.
The AI Customer Journey Framework
In Issue #1, I introduced the three-bucket framework for AI value creation: Boosting Productivity, Creating New Value, and Driving Disruption.
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
Ownership beyond your own team: who has a say in your AI rollout
Amazon's Ownership principle asks leaders to act beyond their own team. Ideas from Audrey Tang on involving the people your AI rollout affects.
Adaptive leadership: leading your team through AI transformation
What should a manager do when their team goes through an AI transformation? Adaptive leadership starts by asking whether the challenge is technical or adaptive.
Ownership: build one AI frontier team before you scale
I think now is the time to build one team that experiments with AI. Three or four people, real freedom to fail, an outcome they own, and AI agents as part of how the team works. Start with one, and let it teach you how to scale.
Think bigger: what long-horizon AI models ask of leaders
The newest models stay on a single job for hours, sometimes days. The capability is obvious once you use them. Working out what to hand over turns out to be the harder problem, and it is a leadership problem more than a technical one.
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.
Create AND communicate: bold direction in the age of AI
Amazon's Think Big principle reads 'leaders create and communicate a bold direction that inspires results.' The verb pair is the 2026 test. AI can amplify your reach across forty markets by lunch, or it can write the direction for you in a safer, blander voice. Pick wrong and the conviction drains out before the message arrives.
Is your HR function ready to lead the AI transformation?
Strive to be Earth's Best Employer asks whether your people are ready for what's next. In 2026 the prior question is: is your HR function ready to make them ready? Four preconditions decide whether HR earns a seat at the AI strategy table or watches it get written without them.
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.
Synthesizing minds: recognizing exceptional talent in the AI age
AI now handles six of Gardner's eight intelligences, leaving interpersonal and intrapersonal as the distinctly human domains. The T-shaped professional is giving way to a sideways E, and 'recognize exceptional talent' needs a new definition.
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 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.
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?
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?
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.
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.
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.
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.
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.
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.
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
Bias for Action: Moving Quickly in the Age of AI
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
SciSpace: AI-powered literature research for the rest of us
The Stanford Virtual Lab sits at the ambitious end of AI in science. Most working researchers and most executives trying to understand their R&D function will meet AI through something much more modest first. SciSpace is a good example: 280M papers, chat with your library, ~550 prebuilt task agents, inline citations as you write. The leverage is in pointing it at your own corpus first.
ElevenLabs: The AI Voice Platform That Makes Emotion Audible
ElevenLabs delivers AI voice synthesis with emotional nuance that separates it from every alternative. With 3,000+ voices, 75ms latency, and expressive mode across 171+ languages, it is the infrastructure for voice-first AI experiences.
Shape of AI: A Pattern Library for AI UX Design
In Section 2 I argued that the old design process isn't producing great AI experiences - and that teams need new principles for building products people actually love.
Google AI Studio and Gemini 2.5 Pro: Enterprise AI Powerhouse
When Mika writes about paradigm shifts - about recognizing when optimization stops being enough - he's describing something happening right now in one of the most consequential business tools ever built: Excel.
Perplexity for Enterprise Research
In this section I review one AI-powered application and demonstrate how it can be used to create value.
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.
Teaching AI Your Workflows with Claude Skills
In this section I review one AI-powered application and demonstrate how it can be used to create new value.
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 AI theater?
AI theater is the performance of AI adoption without the substance behind it: the grand keynote, the flashy demo, the pilot that never reaches production, the usage leaderboard that signals progress while producing little real value.
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 Spring?
AI Spring is the post-transformer renaissance in artificial intelligence, the seasonal opposite of historical AI Winters. The 2026 data point: Swedish AI startups raised nearly €1B in 2025, triple the prior year, with the highest unicorn density outside Silicon Valley. Spring happens in places where leaders choose to light something up.
What is jagged intelligence?
Frontier AI models pass the bar exam and miscount letters in 'barrier' on the next tab. Andrej Karpathy's term 'jagged intelligence' explains why 95% of enterprise AI projects produce no measurable P&L impact, and why average benchmarks hide the problem.
What is AI memory?
AI memory is the emerging set of techniques that let AI systems remember who you are, what you've told them, and how you work, across conversations, sessions, and tools. Most organizations are only using two of its five layers.
What Is an AI Persona?
An AI persona is the deliberately designed personality and emotional character of an AI system. It is not cosmetic; it causally drives behavior and adoption.
What Is Shadow AI?
Shadow AI is the unsanctioned use of AI tools by employees without IT approval or security review — the 2026 evolution of shadow IT that's faster, harder to detect, and significantly more dangerous.
What Is an Agent Harness?
If the AI model is the brain, the harness is the body — the infrastructure layer that connects thinking to doing, and the make-or-break factor for agents in production.
What Is Model Routing?
Model routing is the practice of directing different tasks to different AI models based on problem nature, required capability, cost, speed, and quality requirements.
What Is MCP (Model Context Protocol)?
This Week's Term: Model Context Protocol (MCP) - an open standard that enables AI assistants to connect with external data sources and tools through a universal interface, allowing applications to provide context to ...
What Are AI Agents? The Five Levels
AI Agents are autonomous AI systems that can reason about problems, decide which tools to use, take actions across multiple steps, and iterate on their work without human intervention at each decision point, moving be...
What Is Human-in-the-Loop (HITL)?
This Week's Term: Human-in-the-Loop (HITL) - a design pattern where AI systems operate with ongoing human oversight, intervention, or validation at critical decision points rather than running fully autonomously.