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:
- AI doesn’t create advantage — distribution does
- AI for data — not data before AI
- 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.
