Myth #13: More AI Means More Productivity

The logic sounds reasonable. If one AI project saves time or reduces cost, ten AI projects should create ten times the value. So companies build long lists of use cases, launch pilots across every function and measure progress partly by how much AI activity they have underway.

But more AI projects do not automatically mean more productivity.

The problem is usually not the technology or even the money. It is that all those projects need the same people.

The same data team is asked to support five use cases. The same IT team has to build the integrations. The same business experts are pulled into workshops, testing and validation. The same operations teams are expected to run the business while learning new processes and testing new tools. The same leaders sit in steering committees for projects that all somehow became priorities.

Put enough AI initiatives into the organization at the same time and they start competing with each other.

ForecastingProcurementTransportation

↓ All depend on ↓

The same data, IT and business teamsOperations still has a business to run.

You can see it very quickly in practice. A data engineer's week gets divided across three projects:

Operations has three different pilots asking for testing at the same time. A business expert is invited to six workshops because she is one of the few people who actually understands how the process works.

“Everyone is busy. Very little gets finished.”

This is one reason companies can end up with an impressive list of AI pilots and surprisingly little impact on the P&L. Each project may have a perfectly reasonable business case on its own. The problem appears when you put all of them together and assume the organization can implement them simultaneously.

There is another issue. AI projects don't simply introduce another piece of software. If they work, they change how work gets done.

A planner may stop manually reviewing hundreds of exceptions and focus only on the twenty that need judgment. A dispatcher may no longer spend the morning collecting status updates. A procurement team may receive recommendations instead of building the analysis themselves. Approval steps may disappear. Responsibilities may move.

People need time to learn the new process and, more importantly, to stop using the old one.

If you change four parts of someone's job at the same time through four separate AI projects, don't be surprised when adoption is poor. Most people still have a day job.

This is where companies often make the problem worse. A pilot stalls, so another use case is launched because it looks easier. Then another vendor comes with an interesting demo. Another function wants its own AI initiative. Soon the company has twenty pilots, dozens of workshops and a very impressive AI roadmap, but very few solutions being used every day.

Activity starts being confused with progress.

The better approach is much less exciting: do fewer things and finish them.

Choose the use cases where there is a clear economic benefit and a business team willing to implement the change. Give those projects the data, IT and business resources they need. Put them into production. Make sure people actually use them. Measure whether the expected result happened.

Then move to the next ones.

There is another advantage to doing this in sequence. The first project should make the second easier.

1. The integration you built should be reused.
2. The data you cleaned should remain available.
3. The process definitions you agreed should become the standard.
4. The security and approval work should not start from zero every time.

That is how AI starts to scale: by building capabilities that the next use case can reuse, instead of running fifty independent pilots.

There is no shortage of things companies could do with AI. In most large organizations, you could probably identify hundreds of use cases in a few weeks. That is not the difficult part.

The difficult part is deciding which ones you are actually going to implement.

Look at every AI initiative currently running and identify which people and teams each one depends on.

Prioritize them, finish the ones that matter, and use what you build to make the next ones easier.

The lesson

More AI can create more productivity. More AI activity does not. Ten pilots competing for the same people, data and IT resources can deliver less than three projects that are properly implemented and actually used.

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