The difference between AI as a productivity hack and real, game-changing Enterprise AI

If six months ago was about the wonder of what AI could do, the conversation today is about the work of making it happen.

Every time I look at my phone, I have a notification about a new model that’s a million times better than the one they launched ten minutes ago.

We’ve all seen what tools like Claude Cowork can do, and we all have that one coworker who brags about how they’ve become ten times more productive (maybe it’s you…).

It’s exciting. But it’s not scaling. Reports show that, while 88% of organizations are now using AI in some way, only 10% say they’re scaling agents successfully. There is a fundamental gap between a personal productivity hack and a robust enterprise solution.

AI models are brilliant generalists. They predict based on patterns they’ve learned from the internet, which means they know what an invoice is and how a supply chain works… generally. They’re trained on generic data, so they can give you generic answers.

But when it comes to your business, you don’t need a worker who generally knows what’s going on. You need a specialist who makes smart decisions and gets work done right.

A fortune cookie gives good advice that generally sounds nice. “The seeds you plant today will bloom tomorrow.” “Believe in yourself and others will too.”

But I wouldn’t bet my business on a message from a fortune cookie. And I wouldn’t bet on AI that doesn’t have business context.

The high cost of AI’s operational blind spots

Having AI guess at the reality of your operations is not just unreliable; it’s also inefficient and costly. When you ask an LLM to figure out why an order is delayed, it will spend time and money trying to navigate complexity it doesn’t understand.

Just working out the answer might cost you more than the solution.

To be effective, your AI agents need to know your business inside and out. What’s happened in the past, what’s happening now, and what should happen next — the same things that go through your human brain when you’re making a judgement call.

Only with this context can it be deployed strategically, work alongside your people and existing operations, and drive ROI you can actually measure.

Data does not equal context

Recently, we were working with a major manufacturer that had spent a lot of money to put AI agents in the hands of their supply chain inventory management team. They brought in data from their ERP systems, from their SCM, from spreadsheets.

And still, their agents weren’t able to make heads or tails of it.

They said to the agent, “We’re seeing a spike in demand. Should we order more materials or risk potential stock-outs?”

An important decision. Cost versus risk. Supply and demand. A tale as old as time.

The agent responded, “A spike in demand can result in stock-outs. You may want to order more materials.” That’s not an answer! That’s a cop-out.

But then, when we fed those agents true business context, it became a specialist.

“Based on historical trends and seasonality, your exact stock-out risk is only 14%. So I advise against ordering more materials at this time, and instead transferring them from your warehouse in Denmark, where they have excess stock. And I recommend shipping that stock by truck, since air freight is currently delayed in that region.”

Now that’s an answer! That’s an agent you can trust to reason, decide, and act autonomously for your business.

Getting to context isn’t easy

But providing this context is hard, because it’s scattered across systems and applications, hidden in documentation, and bouncing around inside the heads of your people. (And it only becomes harder when your vendors are building walls around your data.)

And it’s not just data you need, but process data — an ontology that describes how work gets done in your business as a sequence of events. The steps, decisions, exceptions, and interdependencies that make up every process.

And it’s not just data and business knowledge, but also intelligence. A way to enable agents to reason, predict, simulate what-if scenarios, weigh pros and cons, and make recommendations.

And it’s not just data, business knowledge, and intelligence — you also need it all to be together in one place, secure yet openly accessible to your AI agents, and dynamically evolving as your business grows and changes every day.

So in short, it’s a tall order. A major challenge.

But it’s one that we think is solvable. Because we have to solve it. If we don’t, AI will never fulfill its true potential. And we’ll all be stuck cracking open fortune cookies, hoping one of them has the answer we’re looking for.

Learn more about the Celonis Context Model at Celonis:NEXT on May 19 or at an upcoming Process Intelligence Day.