AI adoption is rising. That doesn’t mean organisations know whether it is working.
Rising AI adoption tells us little about whether work is improving. The useful questions concern people, workflows, capacity and evidence.
Read insightAI use is spreading through everyday work. These articles look at what changes for people and managers, where checking or rework grows, and what evidence helps a business decide whether the work improved.
Read about AI use in everyday tasks, differences between teams, checking and rework, and what managers need to see before they can judge its value.
Rising AI adoption tells us little about whether work is improving. The useful questions concern people, workflows, capacity and evidence.
Read insightAI time savings only matter when they create genuine capacity and that capacity is used well. Here is a more defensible way to measure AI value at work.
Read insightAccess, activity and training completion show whether AI is being used. They don’t show whether it’s improving judgement, consistency or the quality of work.
Read insightThe cost of workplace AI often appears as rework, duplicated effort and additional review before it appears clearly in budgets or performance reporting.
Read insightUnmanaged experimentation becomes expensive when tool spend, duplicated testing and unclear ownership spread faster than evidence of value.
Read insightWhen every team develops its own AI habits, organisations inherit inconsistent workflows, uneven standards and avoidable management overhead.
Read insightManagers are often expected to support AI-enabled work without enough visibility into where it’s used, how outputs are checked or where judgement is weakening.
Read insightShadow AI isn’t only a security concern. It’s what happens when work changes faster than leadership can see, guide and support it.
Read insightWAIA brings guidance, examples from real work and evidence strength together so managers can decide what needs attention.