A CEO says "we need to use AI." The data team says "we've been using it for years."
Both are right. They're describing completely different things.
AI isn't one thing. In enterprise settings, it plays three distinct roles, and confusing them creates misaligned expectations from the start.
The Analyst: Traditional AI that predicts outcomes, classifies data, and supports better decisions. It's been running quietly in risk models, fraud detection, and demand forecasting for years. Proven. Often invisible. Usually well-governed.
The Creator: Generative AI that drafts, summarises, translates, and generates. The technology most people mean when they say "AI" right now. Moving fast. High value when matched to the right problem. Still needs guardrails.
The Worker: Agentic AI that plans steps, uses tools, and executes tasks across systems with limited human input. Early-stage in most enterprises. High potential. Requires the most oversight.
Each plays a different role. Each carries different risk. Each needs different governance.
The organisations making real progress are clear on which type they're working with, and what it needs from them to work safely at scale.
The ones who struggle tend to treat all three as the same thing.
Getting your leadership team to this shared understanding is usually the most valuable two hours in an AI programme.
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