A situation to explore
Ten colleagues each save a few minutes. A report then adds those minutes together and converts them into a fraction of a salary. The arithmetic can be correct while the implied saving is unavailable: the work still needs coverage, and the freed time may be scattered across people and days.
The assumption worth revisiting.
This matters when a business case assumes both a lower cost base and extra work from the same hours. Capacity may instead avoid future hiring, absorb demand or improve decisions. Those are legitimate sources of value, but they have different routes into the accounts.
What has to change operationally before this time saving becomes the value in our business case?
Give the capacity a purpose.
Create a shared view of usable capacity and its chosen destination before promising a payroll reduction or funding a second initiative with the same time.
How this fits the capacity allocation →
Use AI to challenge your first interpretation
Help me explore this question: What has to change operationally before this time saving becomes the value in our business case? 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.
ACM CHI 2025; Microsoft Research · Peer-reviewed survey
319 knowledge workers supplied 936 examples of AI use. Greater confidence in AI was associated with less reported critical-thinking effort; confidence in one's own ability was associated with more. Thinking shifted toward checking information, integrating output and overseeing the task.
Self-reported correlations. This does not prove AI causes declining intelligence or long-term skill loss.
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.
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.