Four years into the large language model era, one of the biggest barriers to AI adoption is not the capability of the technology.

It is the fact that many organisations do not actually know how work gets done. I have seen this firsthand working across both government and private sector environments.

The degree of documentation varies. Some organisations have extensive process libraries, governance frameworks and operating manuals. Others rely far more heavily on institutional knowledge and experienced individuals.

But beneath the surface, the underlying challenge is remarkably consistent.

Human behaviour.

Most companies have process documents. Few have a genuinely accurate, end-to-end view of how value flows through the organisation.

Ask someone to explain a critical business process and you will often discover something surprising: no single person knows the whole thing.

The knowledge is fragmented.

One team understands the first step. Another owns the handoff. A different person knows the exception cases. Someone else has developed the workaround that keeps the process moving when the official process breaks down.

The organisation’s operating model exists, but it exists in people’s heads.

This is not a technology problem. It is a natural consequence of how organisations evolve.

Processes grow organically. People optimise locally. Teams solve immediate problems. New systems are layered on top of old ones. Exceptions become standard practice. The person who understood why something worked a certain way moves roles or leaves, taking part of the knowledge with them.

And often there is not even one way of doing things.

Two people performing the same task may have developed completely different approaches. One relies on experience and pattern recognition. Another uses spreadsheets, shortcuts or relationships built over time. Both achieve the outcome, but the organisation has no shared understanding of the underlying workflow.

This creates a hidden challenge for AI automation.

Before you can automate a process, you need to understand the process. Not the version in the procedure manual. The real version.

The version shaped by years of accumulated decisions, informal agreements, local optimisations and individual judgement.

This is why many AI transformation programs start in the wrong place.

The question is not:

“Can AI do this task?”

The better question is:

“Do we actually understand this task well enough to describe, measure and improve it?”

If the answer is no, then you do not have an automation opportunity.

You have an operational clarity problem.

AI is not just exposing opportunities to automate. It is exposing how much organisational knowledge has never been captured, standardised or made visible.

The organisations that gain the most from AI will not simply be the ones with the best models. They will be the ones that understand themselves.

#AI #Automation #FutureOfWork #DigitalTransformation #OperatingModel