A situation to explore
Marketing identifies a customer hesitation and improves the message. Yet the hesitation may be reasonable: the offer is difficult to adopt, the evidence is thin or the operational promise is uncertain. More persuasive communication cannot settle those issues on its own.
The assumption worth revisiting.
Marketing can turn customer insight into a shared growth question. Product may need to change the offer, Operations the delivery and Finance the terms. That can create a stronger reason to buy than another round of content.
What would have to become true for this customer to believe our promise?
Give the capacity a purpose.
Commit some released production time to understanding a buying obstacle with the colleagues who can remove its cause.
How this fits the capacity allocation →
Use AI to challenge your first interpretation
Help me explore this question: What would have to become true for this customer to believe our promise? 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.
video podcast · 60 min 19 sec · Start at 37:58
Ethan Mollick: How to build an AI-first organization
Sana · Strange Loop
A researcher explains an experiment that challenges assumptions about individuals, expertise and teams. Start at 37:58 and ask what a team must add beyond a capable individual with AI.
Listen or watch for: If AI helps individuals cross expertise boundaries, what additional value should a human team create?
About this resource
One study’s author discussing a specific experiment, not proof that every task needs a cross-functional team. The page warns its AI transcript may contain errors.