A situation to explore
A process produces fewer questions for the risk team. That could mean it is working better. It could also mean experienced people have left, colleagues have stopped asking, or confident AI outputs make uncertainties less visible.
The assumption worth revisiting.
If the organisation treats fewer escalations as evidence that oversight capacity can be removed, it may weaken its ability to discover the second explanation. The issue is whether challenge is becoming more effective or simply less frequent.
What would we observe if our apparent improvement were actually a loss of visibility?
Give the capacity a purpose.
Keep time for independent curiosity and contact with the people doing the work. Review the judgement required before using a lower case count to justify non-replacement.
How this fits the capacity allocation →
Use AI to challenge your first interpretation
Help me explore this question: What would we observe if our apparent improvement were actually a loss of visibility? Identify the assumptions in our current approach. Offer alternative explanations, including one that challenges our preferred answer. Separate hypotheses from evidence and suggest what we should ask the people affected.
Treat the answers as possibilities to test with people and evidence.
Go deeper into the evidence.
Reserve Bank of Australia · Central-bank survey of 105 medium and large Australian firms
Surveyed firms reported using a mix of retraining, redeployment, natural attrition and redundancy when technology displaced work. Most displaced workers were reported as redeployed with minor retraining. Firms expected further adjustments, but few reported material AI-driven productivity gains at the survey date.
Self-reported liaison sample, not representative of all employers. Technology includes more than AI; expectations are not realised job outcomes. The research does not establish a universal non-backfill policy.
Anthropic Research · Research explainer and linked randomised-study preprint
A small experiment recruited 52 mostly junior Python engineers learning an unfamiliar library. The AI group averaged 50% on an immediate comprehension quiz versus 67% without AI: a 17-percentage-point gap. The approximately two-minute completion-time difference was not statistically significant. Explanatory and conceptual uses of AI were associated with stronger understanding than wholesale delegation.
Short task and immediate quiz, not a longitudinal workplace study. Interaction-pattern comparisons are observational. The source's shorthand '17% lower' should be rendered as 17 percentage points using its reported means.
podcast · 33 min 27 sec
From AI experimentation to AI maturity
CIPD
Connects adoption with workforce readiness and returns. Useful when leaders have bought tools but cannot yet identify dependable capacity.
Listen or watch for: Which missing capability is stopping experimentation becoming a repeatable way of working?
About this resource
Practitioner discussion, including a Nationwide representative; it is not an independent evaluation of productivity gains.