A situation to explore
A customer struggles to reach an outcome that spans your product, their own work and the service around it. A roadmap organised by features can improve the part you own while leaving the real obstacle untouched.
The assumption worth revisiting.
A growth opportunity may require a service change, different onboarding, a partner or a commercial decision. Product needs colleagues who can alter those parts of the experience, and a shared reason to do so.
Where does our responsibility stop before the customer has obtained the value?
Give the capacity a purpose.
Use released capacity to explore the whole customer job with Revenue, Customer and Operations. Test the smallest change that addresses the actual obstacle.
How this fits the capacity allocation →
Use AI to challenge your first interpretation
Help me explore this question: Where does our responsibility stop before the customer has obtained the value? 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.