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.

• Accuracy: 90%, initial results, as of January 2025

  "The initial results are promising, with agents achieving 90% accuracy and one team saw a reduction in their TTM of 38%, significantly reducing the impact to customers."

  Claimed by: organization
• Time to repair reduction: 38%, one team, initial results

  "The initial results are promising, with agents achieving 90% accuracy and one team saw a reduction in their TTM of 38%, significantly reducing the impact to customers."

  Claimed by: organization

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.

• Users served: 18,000, authenticated users of the library

  "18K authenticated users leverage Responsive AI to quickly find proposal content and answers for security questionnaires, legal assessments, and highly technical bids"

  Claimed by: vendor
• Interactions handled: at least 200,000, uses of AI powered answers in proposals and assessments, cumulative

  "The Field used AI-powered answers — drawn from over 20,000 resources — more than 200,000 times in sales proposals, RFPs, RFIs, and security, legal, and compliance assessments."

  Claimed by: vendor
• Time saved per task: 20 minutes, per search for proposal content

  "The Field saves 20 minutes per search for proposal content, totaling more than $17M worth of time spent on customer relationships and building pipeline instead of searching for content."

  Claimed by: vendor
• Hours saved: 93,000 hours, cumulative seller hours, period not stated

  "Sellers gained 93K additional hours to spend on customer relationships and building pipeline, instead of searching for answers and proposal content."

  Claimed by: vendor

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, that the team built a model projecting annual cost savings of $1.6 million and a 45 to 60% reduction in overall review costs, and that aiR also surfaced relevant and privileged material human reviewers had missed. 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.

• Accuracy: 92%, aiR predictions compared with prior human review decisions on previously reviewed data sets

  "aiR's predictions matched human review decisions 92% of the time."

  Claimed by: vendor
• Cost reduction: at least 45%, projected annual reduction in overall review costs, aiR versus traditional human review

  "a 45-60 percent reduction in overall review costs"

  Claimed by: vendor
• Cost savings: USD 1.6 million, projected, per year

  "projected annual cost savings of $1.6 million"

  Claimed by: vendor