AI Doesn’t Reward Activity. It Rewards Coherence

There’s a growing urgency for businesses to become ‘AI-native’. Boards are pushing for it, budgets are flowing, and experimentation is everywhere. Yet, beneath the surface, most organisations are not transforming. They’re fragmenting.

This is being driven by a set of flawed approaches that, while well-intentioned, are quietly working against each other.

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1. The rise (and risk) of AI squads

The first is the proliferation of internal AI squads.

These teams are often positioned as the engine of transformation: cross-functional, empowered, experimental. And they do serve a purpose. They upskill teams, unlock early use cases, and build momentum.

But they don’t scale cleanly.

What starts as empowerment becomes fragmentation. Each squad builds its own tools, workflows, and agents, often solving the same problems in parallel. There’s little shared architecture, weak communication, and inconsistent standards.

The result is duplication at every level: effort, tooling, and increasingly, compute cost. More importantly, the organisation never compounds its learning. Instead of building a system, it builds pockets of activity. You get motion without momentum. 

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2. The consultancy trap: structure without shift

At the other end sits the traditional consultancy approach. Here, the issue isn’t fragmentation, it’s abstraction. Consultancies bring structure: governance models, maturity frameworks, operating blueprints. But too often, they treat AI transformation as a linear, top-down exercise. The problem is that AI isn’t linear.

It reshapes how people work, make decisions, and interact with systems. Yet many approaches underweight this behavioural shift. They define what should happen, but not how it will actually be adopted. The outcome is predictable: strategy without usage.

Tools are deployed but not embedded. Systems exist but aren’t utilised or trusted. The organisation looks transformed on paper, but behaves exactly as it did before, or worse, regresses. 

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3. The “vibe coding” symptom: speed without discipline

A newer trend is exposing the cracks in both models: speed without discipline. AI has collapsed the distance between idea and execution. What once took months can now happen in days. That sounds like progress, until you look closer.

When speed becomes the default, governance, architecture, and quality controls get bypassed. Not intentionally, but structurally. The system rewards shipping, not coherence. This is already happening inside many organisations. Teams are building faster than they can align, validate, or scale. The result is a growing layer of brittle, disconnected solutions, with vast compute expense edging less faith in the next iterations.

In effect, AI squads accelerate creation without alignment. Consultancies enforce alignment without real-world velocity and “vibe coding” sits in the middle, accelerating both problems at once.

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4. The missing layer: product thinking

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Underpinning all of this is a more fundamental gap: a lack of product understanding. AI transformation is still too often treated as a series of projects. But projects don’t compound, products do. Without a product mindset, organisations struggle to answer the most basic questions:

• What outcome are we actually driving?
• Who owns this long-term?
• How is value measured and iterated on?
• What gets scaled, and what gets killed?

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This is where many AI initiatives quietly stall. They move from pilot to pilot, never quite crossing into sustained value. Strong product thinking introduces discipline:

• Clear outcomes tied to business value
• Defined ownership and accountability
• Structured governance that enables, not blocks
• Iteration loops that prioritise what works

Without this, AI remains stuck in experimentation mode: impressive demos, limited impact.

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5. The overlooked lever: behavioural science

Even with the right structure, speed, and product thinking, one piece is still often missing: behaviour.

AI doesn’t fail because it can’t work. It fails because people don’t adopt it. Most organisations underestimate how much of transformation is psychological. Habits, biases, cognitive load, trust. These are the real barriers to adoption. This is where behavioural science becomes critical.

If AI systems aren’t designed around how people actually think and act, they create friction instead of value. They get bypassed, ignored, or used incorrectly. Adoption isn’t a training problem, it’s a design problem. The principles are well understood, but rarely applied:

• Reduce cognitive effort and decision fatigue
• Scaffold usage so people can start quickly
• Build feedback loops that create trust
• Align with instinctive behaviours, not just rational ones

In short: make AI something people want to use, not something they’re told to use. Because in an AI-native organisation, success isn’t measured by deployment - it’s measured by habitual use. So what actually works?

Becoming AI-native is not about choosing between decentralisation and control, speed and safety, or strategy and execution. It’s about integrating all of them.

That means:

• Connecting experimentation to scalable systems
• Pairing structure with real behavioural adoption
• Embedding product thinking into every initiative
• Designing for humans, not just processes

Most importantly, it means treating AI as a living capability within the business, not a transformation programme with an end date.

The uncomfortable truth

AI isn’t failing because companies lack ambition. It’s failing because their approaches are misaligned with the nature of the technology. Some takeaways to consider:

• Fragmented experimentation doesn’t compound
• Strategy without behaviour doesn’t stick
• Speed without discipline doesn’t scale
• Projects without product thinking don’t last
• Technology without human adoption doesn’t matter

The organisations that succeed will be the ones that resolve these tensions, aligning fast experimentation with disciplined execution, and structural change with behavioural reality. AI doesn’t reward activity. It rewards coherence.