A situation to explore
When routine requests are resolved earlier, the cases reaching people can become more emotional, ambiguous or commercially important. An unchanged handling-time target can reward closing these cases quickly when the customer needs someone to understand them.
The assumption worth revisiting.
A staffing decision based only on fewer contacts misses the value of judgement in the contacts that remain. It can also remove the people who notice patterns and help other functions prevent the next complaint.
What should a person now do that the routine service cannot?
Give the capacity a purpose.
Allocate time to complex resolution, learning and feedback into Product and Operations. Absorb vacancies only after understanding the remaining service promise.
How this fits the capacity allocation →
Use AI to challenge your first interpretation
Help me explore this question: What should a person now do that the routine service cannot? 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
Beyond the bot: Building empathetic customer experiences with agentic AI
McKinsey · McKinsey Talks Operations
Encourages customer leaders to decide where human attention adds value as routine interactions change, rather than treating capacity as a headcount calculation alone.
Listen or watch for: Which moments should receive more human attention if routine contact becomes easier?
About this resource
Consultancy and vendor discussion with forecasts and selected examples; automation percentages are not transferable targets.