How Microsoft uses AI

Azure uses the Triangle System to triage incidents with AI agents. In local triage, one agent per engineering team, built on the team's historical incidents and troubleshooting guides, accepts or rejects an incoming incident on the team's behalf and can recommend the team it should move to; a global triage layer coordinates the agents to route incidents. Local triage has been in production since mid 2024 and was live for six teams in January 2025, with more than 15 onboarding; Microsoft reports triage accuracy and a time to mitigate reduction for one team as initial results.

Microsoft's Proposal Center of Excellence has run a Proposal Resource Library on the Responsive platform since 2020. Sellers and experts across the worldwide sales organization use its AI recommendations to find vetted answers for proposals, RFPs, RFIs and security, legal and compliance assessments, searching more than 18,000 question and answer pairs that the proposal team's knowledge managers and technical experts across the company keep current. The vendor reports 18,000 users and, counted over a wider pool of more than 20,000 resources, more than 200,000 uses of AI answers.

Microsoft's litigation group tested Relativity aiR for Review and aiR for Privilege against data sets its human reviewers had already coded, working with the eDiscovery provider Lighthouse, to compare AI predictions with the prior human decisions. Relativity's case study reports that aiR's predictions matched human review decisions 92% of the time and that aiR also surfaced relevant material human reviewers had missed. The team also built a model that projects annual cost savings of $1.6 million and a 45 to 60% reduction in overall review costs; these are projections, not measured savings. Microsoft's litigation group now runs aiR for Review and aiR for Privilege as a standard part of its document review process rather than a one off pilot.

Microsoft's Worldwide Payment Solutions team received many emails from the business that needed a manual answer about financing programs, internal tools and policies. A finance analyst turned an Excel FAQ library into a Q&A chatbot on Azure Bot Service, deployed in Microsoft Teams within weeks. The bot, named Leonard, uses natural language processing with active learning, and was later connected to the financing approval workflow so users can check or cancel requests. Microsoft reported time savings without figures; the write up won the Adam Smith Awards 2018 Judges' Choice.

Microsoft Treasury manages currency exposure across operations in more than 150 countries and hedges forecast exposures with FX forwards and options. Its forecasts used to come from Excel spreadsheets fed by many groups, and forecast variance fed P&L volatility. Treasury and the Cortana Intelligence team built a machine learning forecast for accounts receivable in Azure Machine Learning, and by 2019 currency teams used machine learning forecasts in balance sheet hedging. Treasury Today reports improved accuracy; EuroFinance reports a 25% lower currency impact on other income over 18 months, with no baseline given.