Microsoft Finance created a repeatable decision process to find AI opportunities that deliver measurable business value while managing risk, data quality, and employee adoption. Treasury’s experience—managing cash, banks, and large outsourcing relationships—illustrates the criteria and sequencing they used.
Start with processes that are well understood, repeatable, and measurable before adding AI.
Target tasks where manual effort slows work or where AI can expand coverage previously limited by capacity.
Consolidate systems and create a single source of truth, then layer intelligence into specific bottlenecks.
# What problem Microsoft Finance faced Microsoft Finance needed a practical way to decide where to apply AI across many teams and functions. The goal was to get clear efficiency gains without increasing operational risk or damaging partner trust. Treasury provided a useful example because of its scale, complexity, and control needs: it manages hundreds of billions in cash flows, works with more than 100 banking partners, and uses an outsourcing partner with roughly 1,000 people supporting business processes.
# How they evaluated opportunities The Finance organization developed evaluation criteria focused on the nature of the work rather than the technology. Criteria emphasized:
Kathy Brustad, director, Global Treasury and Financial Services, summed it up: "Always start with what your process looks like first, not what technology you want to use. We always have a metric, business impact, or business ROI in mind when we launch any AI-related project."
# Two practical value patterns Microsoft Finance found two recurring ways AI created clear value.
1) Productivity on repetitive tasks
2) Expanding organizational coverage AI can extend capacity to handle work that was previously unresourced. Treasury began designing AI agents to identify smaller unpaid invoices and follow up where personal attention had been reserved for larger accounts. This expands reach without reallocating staff, and the idea was already being evaluated though not yet in production.
# Sequence and maturity of adoption Microsoft Finance sequenced work to match organizational readiness. Early efforts focused on helping employees use AI assistants in day-to-day work. As governance and experience matured, teams moved to more advanced human-led agent workflows. Some teams are now evaluating more autonomous agents that gather information and generate recommendations while humans retain final decisions.
The organization found that adoption and behavior change were harder than building models: "AI adoption turns out to be the hardest part of this journey, versus just building the technology." The approach therefore included governance, measurement, and staged rollout to increase employee comfort and oversight.
# Practical steps other finance teams can apply
When teams treat AI as a tool applied to known processes and measure impact, they reduce risk and raise the chance of achieving clear business value.
Grupo Bimbo has moved beyond experimenting with artificial intelligence and is now embedding it into specific areas of its global operations. Working with Microsoft, the company is using Microsoft 365 Copilot, Copilot Studio, Power Platform, Azure AI Services and Power BI to automate tasks, make corporate knowledge eas
About a year ago, our teams in Microsoft Digital—the company’s IT organization—began an AI transformation the same way most organizations did: ad-hoc. Our engineers taught themselves new tools while those tools were still evolving. It worked, up to a point. Individual developers got faster. But the gains stopped there.
Microsoft’s Puneet Chandok offers a blueprint for navigating the future of work. Young engineers must master AI skills; alongside they must nurture empathy and filter. Enterprises must look at a clear path to ROI and build a team of frontier professionals who balance automation with human judgement.
For decades, productivity in finance meant closing the books faster, reducing transaction costs and improving reporting. Artificial intelligence changes that equation, because it can do more than execute routine work. It can interpret variances, detect anomalies, generate forecasts, test scenarios and recommend actions
Using AI to get concrete business results is tricky, but new research from Microsoft provides some clear pointers.
The U.S. Patent and Trademark Office has a new leader for artificial intelligence, is participating in a pilot program for a global intellectual property recordation system and is launching the latest outreach office. Here's what you need to know.
Loading more related stories...
Open the app view to save this story, compare related coverage, and continue from the same source.