Seeing how work actually happens rather than relying on processes, structures and assumptions about how it should happen.
Searching for AI use cases starts with the technology. A better approach is to find valuable work that causes problems, understand what is really happening, and use AI to help investigate before deciding what should change.
Read articleThe documented process is only part of how work gets done. Employees often compensate for gaps through checking, chasing, correcting and judgement — activity managers need to understand before deciding what to improve or automate.
Read articleAutomation works best when it follows a clear understanding of how work really happens, including the judgement, exceptions and friction hidden by the formal process.
Read articleGenerative AI can expose gaps in organisational understanding by asking what must be investigated before improvements are recommended.
AI analyses representations of organisational work, so evidence, interpretation, hypotheses and recommendations must remain distinct.
A practical way to investigate one recurring piece of work before deciding whether AI or another intervention is appropriate.
Understanding real work—including exceptions, waiting and judgement—must precede decisions about what to automate.
Automation should be assessed by its effect on organisational outcomes, not only by time saved or tasks completed.
Making judgement visible reveals where AI can support decisions, where authority can be delegated and where humans must remain accountable.
A disciplined automation sequence starts with observing work and defining outcomes before choosing technology or measuring system-wide effects.
Successful outcomes can conceal the checking, correcting and judgement people use to compensate for weaknesses in formal processes.
Invisible employee compensation can make weak processes appear healthy and must be understood before AI or automation removes it.
Small repeated checks can consume substantial capacity, but their accumulated cost should prompt investigation rather than automatic removal.
Workarounds are evidence of underlying weaknesses or legitimate needs that must be understood before their compensating work is automated.
AI can do more than perform existing work. It can help organisations examine how work actually happens before deciding what should be changed or automated.
Read articleA practical prompt for using generative AI to investigate how a piece of organisational work actually operates before deciding how it should be improved.
Read articleWhen organisational evidence is incomplete, AI should help design the next investigation instead of supplying a plausible guess.
A first conversation is free, and usually enough to tell you whether there's a leverage point worth pulling.