AI isn’t a one-size-fits-all solution. This confusion is quietly costing businesses millions. Executives keep asking, “How do we use AI?” But everyone in the room is picturing something different. Some imagine predictive models. Some imagine ChatGPT. Some imagine automated workflows. And they don’t realise they are talking past each other. AI is not one thing. It’s many things, and here are three flavours to know right now. 1. Traditional AI helps you decide. 2. Generative AI helps you think and communicate. 3. Agentic AI helps you act. My view is that until companies name & understand these differences, they’ll keep approving the wrong investments, overlooking solid opportunities and wondering why impact is slow. When leaders finally see the landscape, the conversation changes from tools to outcomes. Instead of “We need AI”, it becomes: → Do we need clearer predictions? → How can we create content and insights more quickly? → Or how do we benefit from tasks automated end to end? That’s when real progress starts. If you were explaining AI to a non technical exec team, what’s the first distinction you’d make? ♻️ Repost to help teams agree on what they mean by AI. 🔔 Follow Clare Kitching for insights on unlocking value with data & AI.
I really like that distinction between analyst, creator and worker
I’ve seen a lot of comparisons and explanations - but this one with analyst-creator-worker is an easy visual way to remember.
This distinction between "decide," "think," and "act" flavors of AI is spot on. It's not just about the tool, but the outcome. My team once spent months on a sophisticated predictive model that, in hindsight, a simpler generative AI could have achieved faster and cheaper. Chasing the shiny tech, not the business problem. That's a costly mistake many leaders make.
Nicely summarised
this is so useful. people don't understand the maturity and the risks. they often think AI is magic.
Totally agree. And not every organisation is ready for all three types of AI. Where you are in your data and AI maturity decides what’s actually realistic. Some teams barely have clean data for traditional AI, let alone generative or agentic systems. Choosing the wrong “flavour” too early doesn’t just slow progress, it creates rework and wasted investment.
Woppsss... I thought of only generative AI and considered agentic the same.. But this is practical.
This is spot-on. The fastest way to stop wasting millions on AI is to replace “We need AI” with “Do we need better decisions, faster thinking, or actual doing?” Predictive → decide Generative → think & communicate Agentic → act Until leaders name these three separately, every AI conversation is just expensive noise. Brilliant framing! 🔥
Clare Kitching asking for “AI” is like walking into a DIY store and asking for ‘a tool’, and then half the time we end up trying to hammer a nail with a screwdriver. We spent decades perfecting the 'deciding' engines, yet everyone is currently distracted by the shiny 'talking' ones. Do you think boardrooms are actually ready to let the 'acting' layer take the wheel?