A situation to explore
A seller spends less time preparing account notes and drafting follow-ups. It is tempting to increase the account target immediately. But the remaining value may lie in understanding a difficult buying decision, involving delivery colleagues or developing a different offer.
The assumption worth revisiting.
If customers arrive with better information and more ability to do work themselves, the next useful conversation may require deeper judgement. A higher activity target could consume the time that would let the team adapt to that change.
What will buyers still need our people to help them decide?
Give the capacity a purpose.
Decide which part of the buyer’s uncertainty deserves human attention before changing territories, account ratios or backfill decisions.
How this fits the capacity allocation →
Use AI to challenge your first interpretation
Help me explore this question: What will buyers still need our people to help them decide? 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.
NBER; author manuscript on arXiv · Randomised field experiment; working paper
The six-month experiment covered 7,137 knowledge workers at 66 firms. In its second half, the 80% of treated workers who used the tool spent about two fewer hours on email weekly and reduced work outside regular hours. Researchers did not detect broader changes in the quantity or mix of tasks from individual tool provision.
These are specific tool and work-pattern findings, not proof of two days released per week. Some authors were employed by Microsoft, the tool maker. Use the revised estimates rather than the earlier 3.6-hour estimate for frequent users.
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.