FEATURE
An AI system may flag that transaction volumes at a particular location have fallen sharply. It cannot necessarily know all the variables: whether local roadworks have reduced foot traffic, whether a machine has been moved, whether customer behaviour has changed or whether another business has opened nearby.
Similarly, a system might identify increased demand for a particular product. Someone working closer to the business may know that it is connected to a promotion, seasonal change or a shift in customer preferences.
AI can provide the signal, but people provide the context and make the decision.
That distinction matters as organisations look for opportunities to automate more business processes. The objective should not be to remove human judgement wherever possible; the goal is to remove unnecessary work around that judgement.
There is an important distinction between identifying a pattern and understanding it.
If technology can reduce repetitive reporting, information gathering and manual analysis, employees can spend more time on decisions that require commercial knowledge, customer understanding and experience.
Measure outcomes, not AI adoption
One of the risks of the current AI cycle is that organisations begin measuring progress by how many tools they have introduced.
‘ Using AI’ shouldn’ t be a business objective in itself. A more useful test is whether the technology makes a particular process meaningfully better. Before adopting an AI capability, leaders should ask:
• Which specific problem will it solve?
• Will it remove work from an existing process, or simply add another platform to manage?
• Can employees actually act on the information it produces?
• Is the underlying information reliable?
• Where should human judgement and accountability remain?
• How will we know whether technology has improved an outcome?
The quality of the underlying information is particularly important. AI is only as useful as
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INTELLIGENT CIO APAC www. intelligentcio. com