You cannot outsource experience to AI

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Over years working in digital transformation, I have seen versions of the same outsourcing conversation. A business briefs an external software team, asks whether they understand, and receives a confident yes. Months later, something comes back that meets the written specification but does not work properly in the real organisation.

The development team says, reasonably, “That is what you asked for.” The business responds, also reasonably, “But that is not what we meant.” The gap is usually the operating context that was never made explicit: how the process actually flows, which exceptions matter, what the data really means and what a good outcome looks like.

Organisations eventually learned that outsourcing production did not mean outsourcing ownership. The business still had to understand the process, own the data and remain accountable for the result.

AI presents a new version of that same management challenge, only at far greater speed. It can analyse large datasets, automate manual tasks and complete in hours work that might previously have taken people weeks. That is an enormous opportunity, but it does not make the old transformation disciplines obsolete.

One of the principles I have carried through digital transformation is that sustainable change has to work across People + Process + Data + Technology. AI is not an exception to that model; it is another version of it, albeit with a more powerful technology component than most organisations have previously encountered.

The technology may be capable of doing the work, but the process still has to be understood, the data still has to be owned and trusted, and people still need the knowledge and training to use the output well. The organisation also needs to be clear about the intended outcome, because an AI system can execute the wrong interpretation remarkably efficiently.

I have seen the positive version of this in practice. In one pilot, we used AI to automate the movement of product specifications into a product lifecycle management system. We never assumed the process would become fully autonomous, so human verification, spot checks and approval remained part of the design while feedback was used to improve the model.

The result eventually reached roughly 95% accuracy and created substantial speed and scale. The remaining 5% consisted largely of edge cases that still needed human knowledge and judgement, but that did not make the 95% a failure. The value came from using AI for what it was good at while people continued to own the data, understand the exceptions and approve the outcome.

Microsoft Research surveyed 319 knowledge workers and found that, as generative AI takes on more production, human critical thinking shifts towards verification, integration and stewardship. That feels like a useful description of how work is changing.

This is not simply another argument that AI can be wrong and therefore everything needs fact-checking. Effective review depends on understanding the business context well enough to use AI properly in the first place.

An experienced finance professional may look at a P&L and sense immediately that something is off. That is not magic and it is not merely attention to detail; it comes from understanding the organisation, its normal patterns, its commercial drivers and the exceptions hidden behind the numbers. AI can produce the commentary quickly, but somebody still needs enough experience to know whether the story makes sense.

That experience cannot be downloaded with the software licence. It has to be developed, which links back to the question in the previous article: education and learning need to happen alongside adoption, not after the technology has been rolled out.

The answer is not to preserve manual work for its own sake or to make a person check every AI output forever. It is to use AI to augment human capability, improve efficiency and create scale without increasing cost at the same rate, while being deliberate about where human knowledge, approval and accountability remain essential.

Some roles will involve less manual production and more verification, interpretation, exception handling and partnership with AI. That creates a new learning opportunity rather than a simple story about lower-grade work disappearing. People will need to learn not only how to operate the technology, but how the process works, what the data means and what a good outcome looks like.

The leadership question is therefore broader than “Can AI do this task?” It is whether the organisation is introducing AI with the process ownership, data stewardship, training and human experience needed to use it effectively and remain accountable for the outcome.

For one AI use case being considered in your organisation, ask:

Are we treating this as a technology deployment, or have we designed the change across the people, process, data and technology needed to make it work?

This is the second of three articles exploring the learning gap behind AI automation.

Next: AI will change the work. Prepare your people to deliver the strategy.

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