A situation to explore
A customer explains what they expected, what made it difficult and what they tried to do instead. Service can resolve the case and close it. The same evidence may also reveal a recurring unmet need that customers would value someone solving well.
The assumption worth revisiting.
Not every complaint points to growth. The question is whether other customers share the need, whether they value a solution and whether the business can deliver it. That investigation crosses Customer, Product, Operations and Finance.
What was this customer trying to achieve that our current offer does not help them do?
Give the capacity a purpose.
Reserve time to carry selected customer evidence into opportunity discovery. Keep the link to the original experience while testing the wider commercial hypothesis.
How this fits the capacity allocation →
Use AI to challenge your first interpretation
Help me explore this question: What was this customer trying to achieve that our current offer does not help them do? 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.
Organization Science / INFORMS · Peer-reviewed field experiment
In a P&G innovation experiment involving 791 professionals, AI-assisted individuals matched unaided teams on proposal quality. AI teams were 9.2 percentage points more likely than unaided individuals to produce a top-decile solution.
One company and early innovation tasks, not realised growth. The AI-team versus unaided-team top-decile difference was not statistically significant. The final analysis excluded non-randomised participants; 776 participants supplied complete post-task data.
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.
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.