A situation to explore
A proposition reaches Legal once the promise has been made internally and the commercial team is committed to its preferred design. At that point, alternatives can feel like obstacles. Earlier involvement could have changed the structure of the offer or the test.
The assumption worth revisiting.
Legal can contribute to growth by helping colleagues understand possible routes, obligations and trade-offs while the proposition is still being shaped. That requires time and a shared customer outcome, alongside the ability to challenge a promise.
Which opportunity would look different if Legal helped shape the first experiment?
Give the capacity a purpose.
Commit some released preparation time to early offer design with Revenue and Operations. Use that involvement where it changes a material customer or commercial decision.
How this fits the capacity allocation →
Use AI to challenge your first interpretation
Help me explore this question: Which opportunity would look different if Legal helped shape the first experiment? 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.