Category: The Convergence of AI and Execution

Introduction

Over the past year, AI adoption has exploded. In the Nordics, nearly every company now reports that it has implemented AI in some form. On paper, that should translate into a wave of productivity, growth, and competitive advantage. Only it doesn’t.

A recent BCG study (The Nordic AI Inflection Point: Value Creation or Value Bubble?) shows that while 99% of Nordic companies have adopted AI, only around 4% report significant returns on their investments. At the same time, executives expect AI to deliver 25–30% improvements in both revenue and cost.

This gap between adoption and value is not subtle and it’s not limited to the Nordics. A global enterprise study shows the same pattern (Enterprise AI adoption in 2026: Why 79% face challenges despite high investment):

• Near-universal AI adoption
• Heavy usage across employees and executives
• Only a minority seeing real business impact

More strikingly, over half of executives report that AI adoption is creating internal tension rather than clarity — exposing gaps in strategy, ownership, and execution.

AI is not just failing quietly. It is actively stressing organizations that are not designed to absorb it. Which raises an uncomfortable question: Are we creating value — or a value bubble?

This Is Not a New Problem

In a previous post, I argued that AI doesn’t create advantage but distribution does based on facts that:

• AI is becoming commoditized
• Models are widely accessible
• Tools are rapidly diffusing

So advantage cannot come from AI itself. It must come from how AI is embedded, scaled, and operationalized. The BCG findings are a direct confirmation of this. So AI is everywhere, but execution is not.

The Wrong Debate: Data Before AI

At the same time, many organizations seems to be stuck in a different discussion: “We need better data before we can scale AI.”

I’ve argued the opposite in “AI for data — not data before AI” and that waiting for perfect data is one of the most reliable ways to delay value indefinitely.

Data improves when it is used:

• In real workflows
• Under real decisions
• With real feedback loops

So we end up with two truths:

• AI alone does not create advantage
• Data alone does not unlock AI

And yet, most organizations behave as if one of them will.

The Two Traps Killing AI Value

What we see in practice is a predictable pattern.

1. The Tool Trap

Companies deploy AI as tools:

• Copilots
• Assistants
• Automation add-ons

These deliver local gains but they don’t change outcomes, they don’t scale and they don’t compound.

2. The Foundation Trap

Others go the opposite direction:

• Multi-year data programs
• Master data management initiatives
• Platform modernization

AI becomes a future promise and not a present capability.

The False Choice

This leads to a false dichotomy:

• AI first
• Or data first

The reality is neither.

• You don’t get better data before AI
• You don’t get value from AI without execution

Both positions assume a linear path and AI value is not linear.

What Actually Works: AI in the Loop

The companies that are capturing real value are doing something different.

They are not thinking in steps like: Data → Platform → AI → Value

They are building feedback systems: AI → Usage → Better Data → Better Workflows → Scale → Value

But this only becomes real when you look at how it is designed.

A repeatable pattern looks like this:

• Start with a concrete workflow (e.g. demand planning, pricing, campaign execution)
• Apply AI to improve one critical decision point
• Use the output to expose data gaps and inconsistencies
• Fix only the data that matters for that workflow
• Expand AI across adjacent steps
• Gradually connect the process end-to-end

For example:

• Deploy AI in demand forecasting
• Uncover inconsistencies in product hierarchies and sales signals
• Fix those selectively
• Extend into inventory and replenishment

Over time, the workflow becomes:

• More accurate
• More automated
• More integrated

This is not just iteration.

It is system design.

Good AI systems are not built top-down. They are grown through use — and then engineered for scale.

From Tools to Workflows

The BCG report highlights a critical distinction:

• Most companies invest in tools
• Leaders invest in workflows

That difference matters.

Because:

• AI applied to tasks creates efficiency
• AI embedded in workflows creates advantage

Why Most Companies Stall

When AI fails to scale, it’s rarely about the models.

It’s about the system.

• Tool Trap → Fragmentation
• Foundation Trap → Delay

Both lead to the same result:

• Pilots everywhere
• Duplication of effort
• No compounding value

The deeper causes are structural:

• Fragmented data
• Decentralized ownership
• Unclear decision rights
• Limited execution capacity
• AI treated as IT

The system is not designed to absorb and scale AI.

So AI remains additive and not transformative.

AI Doesn’t Fail. Systems Do.

AI is not underdelivering, but organizations are.

Or more precisely: AI doesn’t fail, it exposes systems that were already failing.

What we are seeing is not an AI gap, it’s a system gap:

• Between ambition and execution
• Between tools and transformation
• Between experiments and scale

The Trilogy

Across three posts, the pattern becomes clear:

1. AI doesn’t create advantage — distribution does
2. AI for data — not data before AI
3. AI is everywhere. Value is not

Together: AI value is not created by technology or data alone, it’s created by systems that connect them.

The Next Phase of AI

The next phase will not be defined by better models. It will be defined by better systems.

Today’s tools are built as standalone assistants:

• Copilots
• Chat interfaces
• Isolated automation

They optimize individuals and not systems.

The tools themselves reinforce the Tool Trap. Which means: Organizations are not just using AI incorrectly and they are buying products that make correct usage harder.

What This Means in Practice

If you want to capture AI value:

• Stop measuring progress by tools
• Stop waiting for perfect data
• Stop layering AI on top

Instead:

• Start with workflows
• Build feedback loops
• Design for reuse
• Treat AI as part of the operating model

This is not a maturity curve, it’s a design choice.

Conclusion

You don’t win with AI because you have access to it. You don’t win because your data is perfect. You win when your organization can turn AI into systems that scale.

Advantage comes from system design:

• Not tools
• Not data in isolation
• Not default ways of working

Because in the end: AI doesn’t create advantage, distribution does – and distribution is built through systems — whether you design them intentionally or not.

The difference is simple: Some companies design them, but most don’t.

Introduction

Over the past year, I’ve written three posts that—at the time—felt consistent.

First, I described four categories of AI solutions, arguing that complexity determines where AI works and then, I introduced the trade-off between speed and precision, where fast systems are imprecise and precise systems are slow.

Both were true at the time.

Lastly I introduced the Wiggum Loop which argues that institutional memory is useless.

The original model

The underlying assumption in the two first posts was simple. AI is most effective when problems are well-bounded, precision requirements are low, and iteration costs are small. It struggles when precision is critical, domain knowledge is deep, and errors are expensive. In other words, AI accelerates simple work, while humans remain essential for complex work.

The crack in the model

The Wiggum Loop challenges that assumption. If solutions can be reached through repeated iteration rather than upfront understanding, then precision is no longer a prerequisite—it becomes something you converge on. This changes the equation. Complexity no longer blocks AI in the same way; it simply increases the number of iterations required.

From capability to convergence

The original model was about capability—what AI can do well. The emerging model is about convergence—how quickly a system can explore the solution space and arrive at something that works. Once iteration is cheap and automated, the constraint shifts. It is no longer about whether we can solve a problem, but whether we can recognize when it has been solved.

Reinterpreting the three posts

Seen together, the three posts describe a transition. 

• Four Categories of AI Solutions describes where AI works today. 
• Speed vs. Precision explains why it works unevenly. 
• The Wiggum Loop shows what changes when iteration becomes dominant.

The model does not disappear—it shifts.

The new boundary

The real boundary is no longer complexity or precision. It is whether a problem can be expressed in a way that supports iteration. That requires a clearly defined outcome, explicit constraints, and a way to evaluate results. If those exist, iteration can often replace deep understanding; if they do not, it cannot.

This does not remove expertise—it relocates it. The hard part is no longer solving the problem directly, but defining what success looks like, encoding the right constraints, and deciding how results are evaluated.

What this means for organizations

This is not just a technical shift—it changes how organizations create value. Historically, value came from expertise, experience, and accumulated knowledge. Increasingly, it comes from defining problems clearly, encoding constraints explicitly, and running and governing iterative systems. The center of gravity moves.

The uncomfortable alignment

Taken together, the three posts lead to a slightly uncomfortable conclusion. Much of what we treat as essential organizational knowledge is actually context-bound constraint—decisions made under conditions that no longer apply.

If iteration can rediscover solutions faster than we can recall them, then memory becomes less valuable than exploration. That has consequences. Expertise shifts from knowing answers to defining problems and constraints. Institutional memory becomes less of an authority and more of a hypothesis archive—useful, but not decisive. Roles built around recall and experience start to erode, while roles focused on framing, validation, and governance become more central.

This does not remove humans, but it changes what humans are for—from remembering why things failed to defining what success looks like.

Where this leaves us

The original model still holds, but it is no longer the full picture. AI is not just a tool for solving known problems faster—it is becoming a system for exploring unknown solutions through iteration.

There is a subtle tension here. This trilogy itself depends on cumulative understanding, where each post builds on the last—a small act of institutional memory arguing against institutional memory. Exploration does not replace memory entirely; it changes what kind of memory matters. Constraint-memory becomes less valuable, while model-building and interpretation become more important.

Final thought

We started by asking where AI works. We then asked how precise it needs to be. The emerging question is different: how fast can we iterate—and how well can we recognize success?

That is the thread connecting all three posts, and it is where the model begins to break.