Its internal AI rollout stalled when the company treated the technology like conventional software: distribute licenses, train employees and watch adoption. Giving a tool to more than 200,000 people did not by itself change how work was done.
The company says results improved after teams began with a specific business outcome and redesigned the surrounding workflow. One sales group mapped how account managers spent their time, then introduced separate agents for pipeline analysis, deal preparation and customer research. Microsoft reports that priority use cases tripled, revenue per account manager rose 9.4% and deal close rates were 20% higher within that group.
A cloud supply-chain team first simplified its processes and created a shared data source before deploying more than 100 specialized agents. Microsoft says selected workflows cut cycle time by as much as 75%. The agents help investigate demand changes and compare transport options, with permissions and approval thresholds limiting the actions they can take.
These are Microsoft’s own case studies rather than independent evaluations, so the results may not generalize. Their practical lesson is narrower: counting licenses or prompts is a poor substitute for measuring whether an end-to-end process actually improves.