Your Team Is Using AI.The Business Isn’t.
AI is everywhere.
Your sales team is using ChatGPT. Marketing is experimenting with prompts. Someone in RevOps has built a spreadsheet with an LLM bolted on the side. On the surface, it looks like progress. But beneath that activity is a more uncomfortable truth:
Despite $30–40 billion in enterprise investment into generative AI, MIT research shows that 95% of organisations are seeing zero measurable return (MIT NANDA).
That’s not a technology failure. It’s a commercial one.
The real problem with AI adoption is that most businesses haven’t failed to adopt AI. They’ve failed to operationalise it.
AI is being used:
Individually, not organisationally
Tactically, not strategically
In tools, not in systems
The result is scattered usage that creates outputs but never compounds into advantage. Everyone is busy. Very few are better off. Ad hoc AI creates activity, not advantage
When AI lives in personal tools and isolated workflows, a few things always happen:
Knowledge fragments
Insight sits in individual ChatGPT sessions and disappears when the tab closes.Outputs lack consistency
Two people ask the same question and get wildly different answers.Nothing compounds
Decisions don’t feed future decisions. There’s no learning loop.Commercial impact is invisible
AI activity isn’t connected to pricing, GTM execution, or revenue outcomes.
In short, AI becomes noise.
Why “everyone has ChatGPT” changes the game
For a brief moment, access to AI felt like an advantage. That moment has passed. Models are rapidly commoditising. Capabilities are converging. Access is no longer scarce.
Which means competitive advantage has shifted. The advantage is no longer using AI. It’s how AI is designed into how your business operates, especially in commercial operations, where AI actually creates value
In most businesses, real value is created and captured in a small number of places:
Go-to-market decisions
Pricing and packaging
Sales execution
Revenue operations
Customer expansion and retention
If AI is not embedded into these workflows, it will never show up in results. This is why so many AI initiatives stall at the pilot stage. They optimise tasks, not systems.
The difference between tools and operational intelligence
There is a fundamental difference between:
AI as a tool
AI as an operational intelligence layer
Tools assist individuals while operational intelligence changes how decisions are made, how work flows, and how value compounds across the organisation.
That requires:
Shared data context
Defined workflows
Governance and visibility
Feedback loops between action and outcome
Without this, AI remains peripheral.
Why this is a commercial design problem
Most failed AI projects didn’t fail because:
The models were bad
The tools were weak
The teams lacked enthusiasm
They failed because AI was never designed into:
Commercial decision-making
Execution rhythms
Accountability structures
AI was added on, not built in. This is the same reason most GTM strategies fail. They live in decks, not in operations. The small percentage of companies seeing real ROI from AI are doing something different.
They are:
Designing AI into core commercial workflows
Treating AI as a system, not an experiment
Measuring value creation and value capture explicitly
Letting insight compound over time
They are not “doing more AI”. They are building smarter commercial operations.
From ad hoc usage to advantage
The shift is not dramatic. It’s deliberate.
From:
Personal usage → Organisational systems
Prompts → Processes
Outputs → Outcomes
Experimentation → Advantage
That shift is where AI starts to matter. AI will not transform your business by default. Left unmanaged, it becomes:
A productivity sideshow
A governance risk
A missed opportunity
The winners in this next phase will not be those who adopt AI fastest, but those who design it into how value is created and captured.
That’s the real work ahead.
