A situation to explore
Suppose routine cases become quicker while exceptions, recovery and sensitive customer situations remain with the team. The average task left behind is harder. Staffing against the old volume-to-person ratio can underestimate the effort and judgement now required.
The assumption worth revisiting.
If performance targets stay fixed, capable people can appear slower just as the work becomes more complex. Removing capacity on the basis of the vanished routine work can also weaken recovery when something unusual happens.
Are we planning for the work that remains, or for the work we used to have?
Give the capacity a purpose.
Use released time to understand the remaining work mix, develop judgement and redesign coverage. Then decide which vacancies can safely be absorbed and which capabilities still need investment.
How this fits the capacity allocation →
Use AI to challenge your first interpretation
Help me explore this question: Are we planning for the work that remains, or for the work we used to have? 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.
The Quarterly Journal of Economics / Oxford University Press · Peer-reviewed workplace deployment study
Across 5,172 support agents, AI assistance increased issues resolved per hour by 15% on average. Gains were larger for less experienced and lower-skilled agents. Customer requests for supervisors declined. Lower attrition was associated with AI access, especially among newer staff; experienced high performers saw small quality declines.
One occupation, company and tool. Attrition evidence has weaker causal identification than productivity: selection into tool access could exaggerate the retention effect. This is not evidence for a general headcount reduction percentage.
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.