AI Implementation
vs AI Hype

There are two AI conversations happening at once. One is on stage and on LinkedIn, where everything is about to be transformed, every tool is an 'agent,' and any business not going all-in is already behind. The other is inside operations, where a manufacturing leader told MIT researchers that despite all the noise, nothing fundamental had shifted in how the work actually got done. The distance between those two conversations is the distance between hype and implementation. Living in the first one costs money. The second one is where value gets made.

How to spot the hype

Hype has a texture once you know it. It talks about capability in the abstract and never about a specific problem. It sells autonomy ('it just does it for you') and stays vague on cost, oversight, and failure modes. And it slaps the year's magic word on everything. Gartner has a name for the current version of this: 'agent washing,' where ordinary chatbots and assistants get rebranded as agents, with only a small fraction of the vendors making the claim actually delivering it. Their analysts expect a large share of today's agentic projects to be cancelled within a couple of years, not because the models can't do the work, but because they were bought on hype and applied to the wrong thing.

What implementation actually requires

Real implementation is less exciting and more effective. The widely-cited MIT study on enterprise AI found that the difference between the projects that worked and the ones that didn't wasn't model quality or regulation. It was integration, whether the tool was wired into a real workflow, learned from it, and adapted, versus being a slick demo that never touched how work got done.

That gives you a checklist that has nothing to do with the model:

1. 01A specific problem. Not 'we should use AI' but 'invoice processing takes three days and we want it to take one.'
2. 02A workflow to change. The tool has to live inside how the work actually happens, not beside it.
3. 03A way to measure it. If you can't say what 'working' looks like in a number, you can't tell success from theatre.
4. 04Someone who owns it. Cost, oversight, and the decision to pull the plug need a name attached.

The unglamorous conclusion

The businesses getting value from AI aren't the ones with the boldest slides. They're the ones that picked a dull, expensive, repetitive problem and quietly wired a tool into it until the problem got smaller. Implementation looks like work because it is work. Hype looks like magic because it isn't real.

Ignore the stage. Find your slowest, most repetitive process, and start there.