Designing better organisational systems by removing friction, reducing waste and improving the flow of information and decisions.
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 articleOnce you understand how work actually happens, do not simply allocate the existing tasks between people and technology. Redesign from the outcome, decide what work is genuinely necessary, and only then determine where people, conventional automation and AI belong.
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 articleAI can remove visible friction while making unnecessary or poorly designed work less likely to be questioned.
Automation 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 articleAs organisations begin delegating work to artificial agents, managing the technology is no longer enough. Someone still needs to manage the work, the authority given to the agent and the consequences of its actions.
Read articleUnderstanding 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.
Prioritising valuable organisational problems keeps the intervention space wider than a portfolio framed around AI use cases.
Starting with the work allows AI to emerge as an appropriate conclusion of diagnosis rather than a predetermined solution.
Using AI tools effectively is different from knowing what organisational work is worth improving and whether change made it better.
A first conversation is free, and usually enough to tell you whether there's a leverage point worth pulling.