AI Business Value
Not all AI value is created equal. The three-bucket framework distinguishes between boosting productivity (doing what you do faster), creating new value (AI-enhanced products and services), and driving disruption (fundamentally changing business models). Understanding which bucket you're operating in shapes strategy, investment, and expectations.
Work with Amir on AI Business ValuePIEES 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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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?
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.
"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.
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.
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.
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.
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.
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.
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.
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.
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.
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...
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.
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.
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.
Perplexity for Enterprise Research
In this section I review one AI-powered application and demonstrate how it can be used to create value.
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.
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 ...
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.
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...
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.