Without quality data, AI runs on empty

A little over a year ago, one of our clients had big ambitions for AI.

Several high-value business use cases were launched:

• document processing;
• automation;
• decision support;
• leveraging business data.

On paper, everything made sense. The needs were real. The sponsors were motivated. The teams were committed.

But very quickly, we ran into the reality that many companies are discovering today:

• data that was not ready;
• not always reliable;
• scattered information;
• incomplete master data;
• complicated access;
• governance still immature.

And suddenly, the topic was no longer really AI.

The real issue: data and operational maturity

In the end, the first use cases were delivered and considered successes.

But honestly, they were often delivered in "degraded mode":

• making do with what we had;
• working around some limitations;
• simplifying;
• scaling ambitions to the reality on the ground;
• looking for quick wins.

So yes, value was created. But were they "real wins"? Time will tell...

What the client gained

On the other hand, there is one extremely positive point: the client gained a great deal of maturity.

Today:

• the next use cases are better framed;
• data challenges are better understood;
• priorities are more realistic;
• the prospects of success are much stronger.

This is probably one of the major lessons about AI right now:

AI is not only a technology topic. It is first and foremost a matter of governance, structure, data quality and operational maturity.

And the companies that succeed over time will probably be those that accept this reality early enough.