One of the highlights of my career has always been connecting with customers and partners across industries to learn how they are using technology to drive their businesses forward. In the past 30 years, we’ve seen four major platform shifts, from client server to internet and the web to mobile and cloud to now—the next major platform shift to AI.

As today’s platform shift to AI continues to gain m omentum, Microsoft is working to understand just how organizations can drive lasting business value. We recently commissioned a study with IDC, The Business Opportunity of AI, to uncover new insights around business value and help guide organizations on their journey of AI transformation. The study found that for every $1 organizations invest in generative AI, they’re realizing an average of 3.7x return—and uncovered insights about the future potential of AI to reshape business processes and drive change across industries. 

Today, more than 85% of the Fortune 500 are using Microsoft AI solutions to shape their future. In working with organizations large and small, across every industry and geography, we’ve seen that most transformation initiatives are designed to achieve one of four business outcomes:

1. Enriching employee experiences: Using AI to streamline or automate repetitive, mundane tasks can allow your employees to dive into more complex, creative, and ultimately more valuable work.
2. Reinventing customer engagement: AI can create more personalized, tailored customer experiences, delighting your target audiences while lightening the load for employees.
3. Reshaping business processes: Virtually any business process can be reimagined with AI, from marketing to supply chain operations to finance, and AI is even allowing organizations to go beyond process optimization and discover exciting new growth opportunities.
4. Bending the curve on innovation: AI is revolutionizing innovation by speeding up creative processes and product development, reducing the time to market and allowing companies to differentiate in an often crowded field.

In this blog, we’ve collected more than 200 of our favorite real-life examples of how organizations are embracing Microsoft’s proven AI capabilities to drive impact and shape today’s platform shift to AI. We hope you find an example or two that can inspire your own transformation journey.

IDC InfoBrief: sponsored by Microsoft, 2024 Business Opportunity of AI, IDC# US52699124, November 2024 

Enriching employee experiences

Generative AI is truly transforming employee productivity and well-being. Our customers tell us that by automating repetitive, mundane tasks, employees are freed up to dive into more complex and creative work. This shift not only makes the work environment more stimulating but also boosts job satisfaction. It sparks innovation, provides actionable insights for better decision-making, and supports personalized training and development opportunities, all contributing to a better work-life balance. Customers around the world have reported significant improvements in employee productivity with these AI solutions:

1. Access Holdings Plc adopted Microsoft 365 Copilot, integrating generative AI into daily tools and, as a result, writing code now takes two hours instead of eight, chatbots launch in 10 days instead of three months, and presentations are prepared in 45 minutes instead of six hours.
2. Adobe is connecting Adobe Experience Cloud workflows and insights with Microsoft 365 Copilot to deliver generative AI-powered capabilities that enable marketers to increase collaboration, efficiency, and creativity.
3. Amadeus empowers its teams to focus their time and skills on value-added tasks with Microsoft 365 Copilot by summarizing email threads, chat, or transcripts, and summing up information from diverse sources.
4. ANZ has invested in Microsoft 365 Copilot, GitHub Copilot, and Copilot in Microsoft Edge to boost productivity and innovation across its workforce. 
5. Asahi Europe & International (AEI) has adopted Microsoft 365 Copilot, saving employees potentially 15% of time previously spent on administrative tasks.
6. AXA developed AXA Secure GPT, a platform powered by Azure OpenAI Service that empowers employees to leverage the power of generative AI while targeting the highest level of data safety and responsible use of the tool.
7. Axon Enterprise developed a new AI tool with Azure OpenAI Service called Draft One, resulting in an 82% decrease in time spent on reports, which freed up officers to engage more with their community.
8. Aztec Group enhanced productivity and client experience by trialing Microsoft 365 Copilot with 300 staff, uncovering “unlimited” use cases and plans for a wider rollout.
9. Bader Sultan & Bros. Co. W.L.L implemented Microsoft 365 Copilot to enhance employee productivity and speed up customer response times.
10. Bancolombia is using GitHub Copilot to empower its technical team, achieving a 30% increase in code generation, boosting automated application changes to an average of 18,000 per year, with a rate of 42 productive daily deployments. 
11. BaptistCare Community Services is using Microsoft 365 Copilot to save employees time as they navigate workforce shortage challenges allowing them to focus more on the people they care for.
12. Barnsley Council was recognized as “Double Council of the Year in 2023” for its implementation of Microsoft 365 Copilot, which modernized operations and reduced administrative tasks, leading to improved job satisfaction and increased creativity.
13. BlackRock purchased more than 24,000 Microsoft 365 Copilot licenses spanning all employees, functions, and locations, helping improve the Copilot experience, including co-developing new features and functions.
14. British Heart Foundation is testing Microsoft 365 Copilot and in its initial test, users estimate that Microsoft 365 Copilot could save them up to 30 minutes per day.
15. Buckinghamshire Council deployed Microsoft 365 Copilot with staff reporting productivity improvements, quality enhancements, and time savings which are enabling the different teams to do more with less. 
16. Campari Group adopted Microsoft 365 Copilot to help employees integrate it into their workflow, resulting in time savings of about two hours a week from the support of routine activities such as email management, meeting preparation, content creation, and skill acquisition.
17. Capita is using GitHub Copilot for productivity improvements as well as improvements in developer satisfaction, recruitment, and retention.
18. CDW used Microsoft 365 Copilot to improve work quality for 88% of users, enabling 77% to complete tasks faster, and increasing productivity for 85% of users.
19. Chi Mei Medical Center is lightening workloads for doctors, nurses, and pharmacists with a generative AI assistant built on Azure OpenAI Service. 
20. E.ON is focused on Germany’s energy transition, leveraging Microsoft 365 Copilot to manage the complex grid in real-time, increasing productivity and efficiency for its workforce.
21. Enerijisa Üretim has adopted Microsoft 365 Copilot to streamline meeting summaries, reformat documents, and compile reports, enabling employees to concentrate on more strategic and fulfilling activities instead of spending six hours in meetings.
22. EPAM is deploying Microsoft 365 Copilot to consolidate information and generate content and documents. 
23. Farm Credit Canada implemented Microsoft 365 Copilot which resulted in time savings on routine tasks for 78% of users, with 30% saving 30 to 60 minutes per week and 35% saving over an hour per week, allowing employees to focus on more value-added tasks.
24. Finastra used Microsoft 365 Copilot to automate tasks, enhance content creation, improve analytics, and personalize customer interactions, with employees citing a 20% to 50% time savings.
25. Four Agency Worldwide increased employee productivity using Microsoft 365 Copilot to generate ideas for creative work and support administrative-heavy processes, data analysis, and report generation, allowing staff to focus on outreach and less time doing paperwork.
26. Goodwill of Orange County developed an AI-powered app using Azure AI capabilities to help more people, including those with developmental, intellectual, and physical disabilities, work in unfilled e-commerce positions.
27. Honeywell employees are saving 92 minutes per week—that’s 74 hours a year! Disclaimer: Statistics are from an internal Honeywell survey of 5,000 employees where 611 employees responded.
28. Insight employees using Copilot are seeing four hours of productivity gained per week from data summarization and content creation.
29. Joos uses Microsoft 365 Copilot to grow its brand with worldwide collaboration by streamlining meetings, optimizing presentations, and improving communications.
30. Kantar is harnessing the power of Microsoft 365 Copilot by reducing costly, time-consuming IT processes and boosting productivity for employees.
31. KPMG Australia is using Microsoft Azure OpenAI Service, Azure AI Search, and Microsoft 365 Copilot to perform advanced text analysis of dozens of client source documents to identify full or partial compliance, or noncompliance, in a fraction of the time required for manual assessments.
32. LGT is launching Microsoft Copilot LGT to improve efficiency, showing users save an average of an hour a week even in the pilot phase. 
33. Lotte Hotels & Resorts has been creating a new work culture that allows employees to work more efficiently and focus on the nature of the work by adopting Microsoft Power Platform for automation.
34. MAIRE is leveraging Microsoft 365 Copilot to automate routine tasks, saving more than 800 working hours per month, freeing up engineers and professionals for strategic activities while supporting MAIRE’s green energy transition by reducing their carbon footprint.
35. McDonald’s China chose Microsoft Azure AI, GitHub Copilot, and Azure AI Search to transform its operations, resulting in a significant increase in AI adoption, consumption, and retention from 2,000 to 30,000 employee transactions monthly.
36. McKnight Foundation adopted Microsoft 365 Copilot for all staff, saving time, increasing productivity, and freeing space to focus on strategic priorities.
37. Morula Health is using Microsoft 365 Copilot to enhance productivity, streamline medical writing tasks, and ensure data security, ultimately improving efficiency and client satisfaction. 
38. Motor Oil Group is achieving remarkable efficiency gains by integrating Microsoft 365 Copilot into its workflows, with staff spending minutes on tasks that used to take weeks. 
39. Nagel-Group uses Azure OpenAI Service to help employees quickly access information which saves time, creates efficiency and transparency, and leads to higher-quality answers overall.
40. National Australia Bank is leveraging Microsoft 365 Copilot for daily productivity and data analysis and insights and Microsoft Security Copilot to quickly analyze millions of security event logs and allow engineers to focus on more important areas.
41. NFL Players Association integrated Azure AI Services and Azure App Service into their video review process, reducing review time by up to 73%, significantly increasing efficiency and enhancing player safety through consistent rule enforcement.
42. O2 Czech Republic boosts productivity and streamlines meetings with Microsoft 365 Copilot, revolutionizing how information is shared and making automation a part of daily work.
43. Onepoint developed a secure conversational agent based on Azure OpenAI Service, which delivers productivity gains of between 10% and 15% across all business lines.
44. Orange Group has more than 40 use cases with Azure OpenAI Service and GitHub Copilot across business functions to support employees in their day-to-day tasks, enabling them to concentrate on higher value-added activities.
45. Oxford University Hospitals NHS Foundation Trust implemented Microsoft 365 Copilot to improve staff report productivity by saving one to two hours a week, or simple formatting tasks down to a matter of seconds, enabling more resources to deliver frontline services.
46. PA Consulting transformed its sales operations with Microsoft 365 Copilot, so its people can invest more time on the activities that have the biggest impact for clients and maximize the strategic value they provide. 
47. Petrobras used Azure OpenAI Service to create ChatPetrobras, which is streamlining workflows, reducing manual tasks, and summarizing reports for its 110,000 employees.
48. Petrochemical Industries Company automates work processes to save time with Microsoft 365 Copilot from weeks to days, hours to seconds.
49. PKSHA Technology is optimizing their time on critical work by increasing efficiency in meeting preparations, data analytics, and ideation with the help of Microsoft 365 Copilot.
50. Providence has collaborated with Nuance and Microsoft to accelerate development and adoption of generative AI-powered applications, helping improve care quality and access, and reduce physician’s administrative workloads. 
51. RTI International adopted Microsoft 365 Copilot to gain productivity wherever possible, allowing staff to focus on their areas of expertise, delivering even better science-backed solutions for clients.
52. Sandvik Coromant is using Microsoft 365 Copilot for Sales to drive efficiency and accuracy, shaving at least one minute off each transaction, allowing sellers and account managers to focus their expertise on responding to customers’ needs with analysis, creativity, and adaptability.
53. Sasfin Bank built a solution on Microsoft Azure that centralized 20,000 documents to analyze contract clauses and provide real-time snapshots, moving guesswork into data-driven decision-making.
54. Scottish Water implemented Microsoft 365 Copilot reducing mundane tasks to a minimum, and thus freeing up time for employees to work on the more meaningful tasks.
55. Shriners Children’s developed an AI platform allowing clinicians to easily and securely navigate patient data in a singular location, enhancing patient care, and improving the efficiency of their healthcare services. 
56. Siemens is leveraging Azure OpenAI Service to improve efficiency, cut downtime, and address labor shortages.
57. Softchoice employees are experiencing firsthand how Microsoft 365 Copilot can transform daily workflows, realizing productivity gains of 97% reduction in time spent summarizing technical meetings and up to 70% less time spent on content creation.
58. Syensqo utilized Azure OpenAI Service to develop a custom AI chatbot in three months, which improved their internal data management, decision-making, and overall efficiency.
59. Teladoc Health uses Microsoft 365 Copilot to revolutionize its telehealth operations, automating routine tasks, boosting efficiency, and increasing productivity.
60. Telstra developed two cutting-edge generative AI tools based on Azure OpenAI Service: 90% of employees are using the One Sentence Summary tool which resulted in 20% less follow-up customer contact and 84% of customer service agents using the Ask Telstra solution.
61. Topsoe achieved 85% AI adoption among office employees in seven months, significantly enhancing productivity and business processes.
62. Torfaen County Borough Council utilized Microsoft 365 Copilot to streamline back-office processes, resulting in significant time savings and enhanced productivity for both business and children’s services teams, with further rollouts planned.
63. Trace3 leveraged Microsoft 365 Copilot to streamline and enhance processes across the business and with clients, such as reducing the time it takes human resources (HR) recruiting managers to respond to applicants within a couple of days instead of several weeks.
64. Unilever is reinventing their marketing process with Copilot, saving time on briefing tasks, automatically pulling in relevant market data, content and insights to accelerate campaign launches. 
65. Uniper SE implemented Microsoft 365 Copilot to reduce time spent on manual and repetitive tasks, and help workers focus on more pressing work, such as developing enhanced solutions to speed up the energy transition.
66. Unum Group built a custom AI application to search 1.3 terabytes of data with 95% accuracy using Azure OpenAI Service. 
67. Virgin Atlantic adopted Microsoft 365 Copilot and GitHub Copilot and is seeing real business benefits, including productivity improvements, enabling new ways of working.
68. Visier built a generative AI assistant that leverages Azure AI and Azure OpenAI Services to deliver workforce analytics and actionable insights for more than 50,000 customers.
69. Virtual Dental Care developed an AI application Smart Scan that leverages Azure to reduce paperwork for mobile dental clinics in schools by 75% and frees dentists to devote more time to patient care.
70. Zakladni Skola As Hlavkova adopted Microsoft 365 Copilot and saw a 60% improvement in handling administrative documents, decreased lesson preparation from hours to few minutes, increased inclusivity, and enhanced communication with students and parents.

Reinventing customer engagement

We’ve seen great examples of how generative AI can automate content creation, ensuring there’s fresh and engaging materials ready to go. It personalizes customer experiences by crunching the numbers and boosting conversion rates. It makes operations smoother, helping teams launch campaigns faster. Plus, it drives innovation, crafting experiences that delight customers while lightening the load for staff. Embracing generative AI is key for organizations wanting to reinvent customer engagements, stay ahead of the game, and drive both innovation and efficiency.

71. Absa has adopted Microsoft Copilot to streamline various business processes, saving several hours on administrative tasks each day.
72. Adobe leverages Azure to streamline the customer experience, harnessing the power of the connected cloud services and creating a synergy that drives AI transformation across industries.
73. Acentra Health developed Medscribe, a web application that uses Azure OpenAI Service to generate draft letters in a secure, HIPAA-compliant enclave that responds to customer appeals for healthcare services within 24 hours, reducing the time spent on each appeal letter by 50%.
74. Alaska Airlines is using Azure, Microsoft Defender, and GitHub to ensure its passengers have a seamless journey from ticket purchase to baggage pickup and started leveraging Azure OpenAI Service to unlock more business value for its customer care and contact centers.
75. Ally Financial is using Azure OpenAI Service to reduce manual tasks for its customer service associates, freeing up time for them to engage with customers. 
76. BMW Group optimizes the customer experience connecting 13 million active users to their vehicles with the MyBMW app on Azure, which supports 450 million daily requests and 3.2 terabyte (TB) data processing.
77. Boyner has tripled its e-commerce performance using Azure, seeing a rise in customer satisfaction, engagement, conversion rate, and revenue.
78. Bradesco Bank integrated Azure to its virtual assistant, BIA, resulting in reduced response time from days to hours, improving operational efficiency and client satisfaction.
79. Capgemini Mexico integrated GitHub Copilot to support scalable AI implementations which has led to improved customer experiences and increased efficiency.
80. Capitec Bank uses Azure OpenAI Service and Microsoft 365 Copilot, enabling their AI-powered chatbot to assist customer service consultants in accessing product information more efficiently, saving significant time for employees each week.
81. Cdiscount is leveraging GitHub Copilot and Azure OpenAI Service to enhance developer efficiency, optimize product sheet categorization, and improve customer satisfaction.
82. Cemex used Azure OpenAI Service to launch Technical Xpert, an AI tool used by sales agents to provide instant access to comprehensive product and customer solution information, significantly reducing search time by 80%. 
83. Chanel elevated their client experience and improved employee efficiency by leveraging Microsoft Fabric and Azure OpenAI Service for real-time translations and quality monitoring.
84. City of Burlington created two AI-powered solutions: MyFiles system using Microsoft Power Platform for building permits, and CoBy, an around-the-clock customer support assistant using Microsoft Copilot Studio.
85. City of Madrid created an AI virtual assistant with Azure OpenAI Service offering tourists accurate, real-time information and personalized responses in more than 95 languages.
86. Cognizant is making performance management more effective and meaningful with Azure Machine Learning to help clients across industries envision, build, and run innovative digital enterprises.
87. Coles Group has leveraged Azure to enhance its digital presence and improve customer engagement, rolling out new applications to its stores six times faster without disrupting workloads.
88. Commercial Bank of Dubai used Azure to upgrade its application infrastructure, improving transaction security and speed so individual customers can now open an account and start banking in about two minutes.
89. Dubai Electricity and Water Authority has significantly improved productivity and customer satisfaction by integrating multiple Microsoft AI solutions, reducing task completion time from days to hours and achieving a 98% customer happiness rate.
90. Elcome uses Microsoft 365 Copilot to improve the customer experience, reducing response times from 24 hours to eight hours.
91. elunic developed shopfloor.GPT based on Azure OpenAI Service, leading to increased productivity for customers, saving 15 minutes per request.
92. Estée Lauder Companies is leveraging Azure OpenAI Service to create closer consumer connections and increase speed to market with local relevancy.
93. First National Bank (FNB) is using Microsoft 365 Copilot for Sales to help bankers create professional, thoughtful emails in 13 native South African languages to enhance customer interactions, streamline communications, and reinforce its commitment to innovation and customer service. 
94. Flora Food Group migrated to Microsoft Fabric to offer more detailed and timely insights to its customers, enhancing service delivery and customer satisfaction.
95. Groupama deployed a virtual assistant using Azure OpenAI Service that delivers reliable, verified and verifiable information, and boasts an 80% success rate.
96. International University of Applied Sciences (IU) adopted Azure OpenAI Service to revolutionize learning with a personalized study assistant that can interact with each student just like a human would.
97. Investec is using Microsoft 365 Copilot for Sales to enhance the bank’s client relationships, estimating saving approximately 200 hours annually ultimately boosting sales productivity and delivering personalized, seamless customer experience. 
98. Linum is using Azure to train their text-to-video models faster and more efficiently without losing performance or wasting resources.
99. Lumen Technologies is redefining customer success and sales processes through the strategic use of Microsoft 365 Copilot, enhancing productivity, sales, and customer service in the global communications sector.
100. McKinsey & Company is creating an agent to reduce client onboarding process by reducing lead time by 90% and administrative work by 30%.
101. Meesho leveraged Azure OpenAI Service and GitHub Copilot to enhance customer service and software development, resulting in a 25% increase in customer satisfaction scores and 40% more traffic on customer service queries.
102. Milpark Education integrated Microsoft Copilot and Copilot Studio and in just four months, improved efficiency and accuracy of student support, decreasing the average resolution time by 50% and escalations by more than 30%.
103. NC Fusion chose a comprehensive Microsoft solution to make marketing engagement activities easier and accurately target the best audience segments.
104. Medgate, a telehealth subsidiary of Otto Group, developed a medical Copilot powered by Azure OpenAI Service that summarizes consultations, supports triage, and provides real-time translations. 
105. Pacific Gas & Electric built a chatbot using Microsoft Copilot Studio that saves $1.1 million annually on helpdesk support. 
106. Pockyt is using GitHub Copilot and anticipates a 500% increase in productivity in the medium to long term as they continue adapting AI and fine-tuning their software development life cycle.
107. South Australia Department for Education launched an AI-powered educational chatbot to help safeguard students from harmful content while introducing responsible AI to the classrooms.
108. Sync Labs is using Azure to create AI-powered solutions that have led to a remarkable 30x increase in revenue and a 100x expansion of their customer base.
109. Syndigo is using Azure to accelerate digital commerce for its customers by more than 40% and expand its customer base.
110. Telkomsel created a virtual assistant with Azure OpenAI Service, resulting in a leap in customer self-service interactions from 19% to 45%, and call volume dropped from 8,000 calls to 1,000 calls a day.
111. Torrens University chose to use Azure OpenAI to uplift its online learning experience, saving 20,000 hours and $2.4 million in time and resources.
112. Trusting Social integrated Azure services to launch AI-driven agents that are changing how banks function and transforming their customer’s banking experience.
113. University of California, Berkeley used Azure OpenAI Service to deploy a custom AI chatbot that supports student learning and help students with complex coursework.
114. University of Sydney created a self-serve AI platform powered by Azure OpenAI Service, to enable faculty to build custom chatbots for enhancing student onboarding, feedback, career simulation, and more.
115. Van Lanschot Kempen is using Microsoft 365 Copilot to reduce the time needed for daily tasks, freeing up time to invest in that crucial personal connection.
116. Virgin Money built an award-winning virtual assistant using Copilot Studio to help build customers’ confidence in their digital products and services.
117. VOCALLS automates over 50 million interactions per year, resulting in a 78% reduction in average handling time aside from a 120% increase in answered calls.
118. Vodafone Group is leveraging Microsoft’s AI solutions, including Azure AI Studio, OpenAI Service, Copilot, and AI Search, to achieve a 70% resolution rate for customer inquiries through digital channels and reduce call times by at least one minute.
119. Walmart is using Azure OpenAI Service to deliver a helpful and intuitive browsing experience for customers designed to serve up a curated list of the personalized items a shopper is looking for.
120. Weights & Biases created a platform which runs on Azure that allows developers to keep records, log successes and failures, and automate manual tasks.
121. World2Meet is providing better customer service and operations with a new virtual assistant powered by Azure.
122. Xavier College is modernizing its student information systems on Microsoft Dynamics 365 and Azure to unlock powerful insights, fostering innovation and data-driven decision making.
123. Zavarovalnica Triglav implemented Dynamics 365 and Azure OpenAI Service to streamline its operations with automated responses and smart rerouting of customer enquiries.

Reshaping business processes

Transforming operations is another way generative AI is encouraging innovation and improving efficiency across various business functions. In marketing, it can create personalized content to truly engage different audiences. For supply chain management, it can predict market trends so companies can optimize their inventory levels. Human resources departments can speed up the hiring process, while financial services can use it for fraud detection and risk assessments. With generative AI, companies are not just refining their current processes, they’re also discovering exciting new growth opportunities.

124. Accelleron used Microsoft Power Platform to support numerous business applications and simplify processes for service agents and employees, resulting in the onboard of new agents in 30 minutes, compared to two days for other solutions.
125. Accenture developed an AI-powered financial advisor that leverages RISE with SAP on Azure to enhance their infrastructure and integrate financial data.
126. Atomicwork leverages Azure OpenAI to bring together three power capabilities: a conversational assistant, a modern service management system, and a workflow automation platform.
127. Blink Ops fully embraced generative AI to build the world’s first Security Automation Copilot with more than 8,000 automated workflows to help any Security/IT task through prompts.
128. Chalhoub Group is using Microsoft Fabric to modernize its data analytics and streamline its data sources into one platform, increasing agility, enhancing analytics, and accelerating processes.
129. Cineplex is developing innovative automation solutions for finance, guest services, and other departments, saving the company more than 30,000 hours a year in manual processing time.
130. ClearBank moved its services to Azure to gain scalability and efficiency, pushing out 183% more monthly system releases, gaining both scalability and efficiency.
131. Danske Statsbaner increases productivity up to 30% with help from Microsoft AI solutions.
132. Eastman implemented Microsoft Security Copilot realizing the benefits of accelerated upskilling, step-by-step guidance for response, and faster threat remediation.
133. Fast Shop migrated to Azure creating a self-service culture of access to data, eliminating delays, reducing costs, and increasing leadership satisfaction with data while providing more agility in reporting.
134. Florida Crystals adopted a value-added solution across Microsoft products including Microsoft 365 Copilot to reduce telecom expenses and automate industrial process controls.
135. GHD is reinventing the request for proposal (RFP) process in construction and engineering with Microsoft 365 Copilot.
136. GovDash is a software as a service (SaaS) platform that leverages AI to streamline the entire business development lifecycle for government contracting companies using Azure OpenAI Service.
137. Grupo Bimbo is deploying Microsoft’s industrial AI technologies to modernize its manufacturing processes, optimizing production and reducing downtime, driving significant cost savings, and empowering global innovation.
138. Insight Canada implemented Microsoft 365 Copilot to streamline business operations, realizing a 93% productivity gains in functions including sales, finance, and human resources.
139. Intesa Sanpaolo Group enhanced its cybersecurity with AI-enabled Microsoft Sentinel and Microsoft Security Copilot, resulting in faster threat detection, increased productivity, and reduced storage costs.
140. Kaya deployed a custom implementation of Dynamics 365 and Power BI to modernize its supply chain, leading to enhanced visibility, improved planning, and streamlined inter-department operations.
141. Lionbridge Technologies, LLC is using Azure and Azure OpenAI Service to accelerate its delivery times and improve quality, reducing project turnaround times by up to 30%.
142. LTIMindtree integrated Microsoft Security Copilot, offering automated incident response, integrated threat intelligence, and advanced threat analysis.
143. Mania de Churrasco used Azure, Microsoft Power Platform and Microsoft 365 to achieve high efficiency, security, and scalability in its operations, in addition to improving its data intelligence, which indirectly participated in a 20% increase in sales year on year.
144. National Bank of Greece built an Azure-powered Document AI solution to transform its document processing, improving the bank’s accuracy to 90%. 
145. Nest Bank has revolutionized its operations by integrating Microsoft 365 Copilot and Azure OpenAI Service, resulting in doubled sales and increased daily transactions from 60,000 to 80,000 showcasing the transformative impact of generative AI in the financial sector.
146. Network Rail modernized their data analytics solution with Azure, helping engineers understand data 50% faster than before and improve efficiency, passenger experiences, and safety—all while saving costs.
147. Nsure developed an AI-powered agent that uses Copilot Studio and Power Automate to reduce manual processing time by 60% while also reducing associated costs by 50%.
148. Oncoclínicas implemented Azure to transform its entire data ecosystem with a web portal and mobile application that performs all image processing and storage.
149. Pacífico Seguros has adopted Microsoft Security Copilot to optimize its security operations and anticipate and neutralize threats more efficiently and effectively.
150. Parexel adopted Azure Databricks and Power BI, achieving an 85% reduction in data engineering tooling costs, a 30% increase in staff efficiency, and a 70% reduction in time to market for data product delivery.
151. Paysafe used Microsoft 365 Copilot to streamline meetings, information management, and document creation, addressing language barriers, eliminating time-consuming tasks, and boosting creativity along the way.
152. Planted is integrating Azure OpenAI Service to manage everyday tasks more efficiently and facilitate the search for information for innovative process development.
153. Presidio realized dramatic productivity gains saving 1,200 hours per month on average for the employees using Microsoft 365 Copilot and created 70 new business opportunities.
154. Qatar Charity used Copilot Studio to increase its call center efficiency, reducing average handle time by 30%, increased customer satisfaction by 25%, and achieved a 40% reduction in IT maintenance costs.
155. Saphyre uses Azure and AI to provide an intelligent cloud-based solution that automates and streamlines financial trading workflows around client and counterparty life cycle management, reducing manual efforts by 75%. 
156. Swiss International Air Lines migrated and modernized with Azure, achieving up to 30% cost savings, a remarkable boost in platform stability along with enhanced security visibility.
157. ZEISS Group uses Microsoft Fabric to create a secure and trusted data supply chain that can be shared effortlessly across a range of business units.
158. ZF Group builds manufacturing efficiency with more than 25,000 apps and 37,000 unique active users on Microsoft Power Platform.

Bending the curve on innovation

Generative AI is revolutionizing innovation by speeding up creative processes and product development. It’s helping companies come up with new ideas, design prototypes, and iterate quickly, cutting down the time it takes to get to market. In the automotive industry, it’s designing more efficient vehicles, while in pharmaceuticals, it’s crafting new drug molecules, slashing years off research and development (R&D) times. In education, it transforms how students learn and achieve their goals. Here are more examples of how companies are embracing generative AI to shape the future of innovation.

159. Air India has incorporated Microsoft 365 Copilot into multiple departments, unlocking a new realm of operational insights that not only provides critical data on flight punctuality and operational hurdles, but also empowers proactive, collaborative decision making.
160. Agnostic Intelligence deployed Azure OpenAI Service to eliminate time-consuming tasks, saving users up to 80% of their time, and enabling IT managers to focus on innovation and quality assurance.
161. Albert Heijn is using Azure OpenAI Service for everything from customer personalization to demand forecast and food waste projects, making it easier for its customers to change their lifestyle.
162. Amgen is using Microsoft 365 Copilot to boost productivity and has the potential to speed up drug development and support advancements in their business processes.
163. APEC leverages Azure and deep neural network algorithms to develop an app that enables healthcare providers to capture retinal images, increasing the accuracy to identify Retinopathy of Prematurity (RoP) to 90%.
164. ASOS is using Azure AI Studio to help customers discover new looks with genuine shopping insights, personalized conversations, naturalism, and even humor to enliven the shopping journey.
165. Auburn University is incorporating Microsoft Copilot to promote AI literacy, accessibility, and collaboration, with the aim to expand educational and economic opportunities for its entire academic community with AI-centric tools.
166. B3 launched an AI assistant using Azure OpenAI Service that aids 10,000 users a day to answer Brazilians’ questions about how to start investing.
167. Basecamp Research aims to build the world’s largest database of national biodiversity and apply AI and machine learning to advance bioscience.
168. Bayer is using Microsoft Copilot to contribute to feeding a growing global population and helping people lead healthier, disease-free lives.
169. Brembo leveraged Azure OpenAI Service to develop ALCHEMIX, a solution to generate innovative compounds for its brake pads, drastically reducing the development time of new compounds from days to mere minutes.
170. Canary Speech can now train new vocal models in as little as two months and handle millions of transactions per month with Azure. 
171. CapitaLand simplified internal processes increasing efficiency to more than 10,000 man-days saved per year and deployed Azure OpenAI Service to build the first AI hospitality chatbot for its lodging business.
172. Cassidy is using Azure OpenAI Service to enhance efficiency across various industries, supporting more than 10,000 companies.
173. Coca-Cola is implementing Azure OpenAI Service to develop innovative generative AI use cases across various business functions, including testing how Microsoft 365 Copilot could help improve workplace productivity.
174. Denso is developing “human-like” robots using Azure OpenAI Service as the brain to help robots and humans work together through dialogue.
175. eFishery is using Azure OpenAI for farmers to get the data and insights on fish and shrimp farming, including more precise feeding and water quality monitoring.
176. EY developed an application that automatically matches and clears incoming payments in SAP, resulting in an increase from 30% to 80% in automatically cleared payments and 95% matched payments, with estimated annual time savings of 230,000 hours globally.
177. FIDO is using Azure OpenAI Service to develop an AI tool that uses sound to pinpoint leaky pipes, saving precious drinking water. 
178. Georgia Tech is using Azure OpenAI Service to enhance the electric vehicle (EV) charging infrastructure, achieving rapid data classification and predictive modeling, highlighting the reliability of networked chargers over non-networked ones.
179. GigXR developed a solution to create the intelligence for specific AI patients using Azure OpenAI Service and other Azure services.
180. GoTo Group is significantly enhancing productivity and code quality across its engineering teams by adopting GitHub Copilot, saving over seven hours per week and achieved a 30% code acceptance rate.
181. GovTech used Azure OpenAI Service to create LaunchPad, sparking more than 400 ideas and 20 prototypes, laying the foundation for the government to harness the power of generative AI.
182. H&R Block is using Azure AI Studio and Azure OpenAI Service to build a new solution that provides real-time, reliable tax filing assistance.
183. Haut.AI provides skin care companies and retailers with customizable, AI-based skin diagnostic tools developed with the help of Microsoft AI.
184. Helfie is building a solution that caters to healthcare providers who can arm their patients with an application to more quickly and accurately access the care they need.
185. Hitachi will implement Azure Open AI Service, Microsoft 365 Copilot, and GitHub Copilot to create innovative solutions for the energy, mobility, and other industries.
186. Icertis is providing AI-based tools that will recognize contract language and then build algorithms to automatically choose the right approach based on the content of the contract.
187. ITOCHU is using Azure OpenAI Service and Azure AI Studio to evolve its data analytics dashboard into a service that provides immediate recommendations by automatically creating evidence-based product proposals.
188. IU International University of Applied Sciences (IU) is using the power of Azure OpenAI Service to develop Syntea, an AI avatar integrated into Microsoft Teams and Microsoft 365 Copilot, making learning more personalized, autonomous, and flexible.
189. Khan Academy has partnered with Microsoft to bring time-saving and lesson-enhancing AI tools to millions of educators.
190. Lufthansa Group developed an animated 3D avatar called Digital Hangar to help guide passengers from initial travel inspiration to flight booking through an exchange with an avatar in natural language.
191. Mitsubishi Heavy Industries is using Azure OpenAI Service to help accelerate digital innovation in power plants.
192. Molslinjen has created an AI analytics toolbox that has reduced fuel emissions, improved customer satisfaction, and brought in millions of additional revenue.
193. Novo Nordisk recently published initial results with predictive AI models for advanced risk detection in cardiovascular diseases, including an algorithm that can predict patients’ cardiovascular risk better than the best clinical standards. 
194. Paige.AI​ is using AI and Azure to accelerate cancer diagnoses with data from millions of images.
195. Pets at Home created an agent to help its retail fraud detection team investigate suspicious transactions.  
196. Plan Heal is using Microsoft AI to create solutions that enable patients to monitor and report health metrics so care providers can better serve them.
197. Pacific Northwest National Laboratory (PNNL) is testing a new battery material that was found in a matter of weeks, not years, as part of a collaboration with Microsoft.
198. Rijksmuseum is harnessing the power of Copilot to make art accessible at scale by joining forces with Microsoft to improve and expand the art experience for blind and low-vision community members.
199. Royal National Institute of Blind People is using Azure AI Services to develop an AI-based solution that quickly and accurately converts letters to braille, audio, and large print formats.
200. Schneider Electric provides productivity-enhancing and energy efficiency solutions and is using a whole suite of AI tools to hasten its own innovation and that of its customers.
201. SPAR ICS created an award-winning, AI-enabled demand forecasting system achieving 90% inventory prediction accuracy.
202. Suzuki Motor Corporation is adopting Azure OpenAI Service for data security, driving company-wide use with five multipurpose apps.
203. Tecnológico de Monterrey created a generative AI-powered ecosystem built on Azure OpenAI Service with the goal to personalize education based on the students’ needs, improve the learning process, boost teachers’ creativity and save time on tedious tasks.
204. TomTom is using Azure OpenAI Service, Azure Cosmos DB, and Azure Kubernetes Service to revolutionize the driver experience.
205. Unilever is partnering with Microsoft to identify new digital capabilities to drive product innovation forward, from unlocking the secrets of our skin’s microbiome to reducing the carbon footprint of a multibillion-dollar business.
206. Unity used Azure OpenAI Service to build Muse Chat, an AI assistant that can guide creators through common questions and help troubleshoot issues to make game development easier.
207. University of South Florida is using Microsoft 365 Copilot to alleviate the burden of repetitive, time-consuming tasks so faculty and staff can spend this time creatively solving problems, conducting critical research, establishing stronger relationships with peers and students, and using their expertise to forge new, innovative paths. 
208. Visma has developed new code with GitHub Copilot, Azure DevOps, and Microsoft Visual Studio, as much as 50% faster, contributing to increased customer retention, faster time to market, and increased revenue.
209. Wallenius Wilhelmsen is implementing Microsoft 365 Copilot and using Microsoft Viva to drive sustainable adoption, streamlining processes, empowering better decision making, and cultivating a culture of innovation and inclusion.
210. Wipro is committed to delivering value to customers faster and improving the outcomes across the business by investing $1 billion in AI and training 200,000 employees on generative AI principles with Microsoft Copilot.

Next steps for AI transformation

Business Opportunity of AI

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• Check out the top 5 AI trends to watch from IDC and Microsoft.
• Explore Microsoft AI solutions.
• Assess your AI readiness.
• Build a strategic plan for AI.

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The skills gap is widening, and it is our collective responsibility to bridge it. Business leaders and professionals are exploring ways to build and enhance critical skills within their teams, and Microsoft is leading the charge. 

In addition to launching ambitious skill-building initiatives such as the upcoming Microsoft AI Skills Fest, we’re also eager to spotlight innovative organization like Founderz, a groundbreaking online learning platform that has gone from a bold idea to a leader in AI skilling in just a few years. Their story is worth sharing not only because of their emerging role in ensuring the workforce is prepared for the future, but because it’s a testament to the power of innovation, perseverance, and the impact of just one company believing in a vision.  

One “yes” can change everything 

Co-founders Anna Cejudo and Pau Garcia-Mila had a simple but powerful idea: what if online business education could capture the depth, collaboration, and networking of the world’s top business schools—but in a way that was scalable, accessible, and built for the AI-powered future? 

“We felt there is still a big gap between the experience we have when we go to an on-site business school—where you meet the best professors, the best content—and the way we learn online,” explained Pau Garcia-Mila, Co-Chief Executive Officer and Co-Founder.

They spent years building the technology behind Founderz, investing in AI-powered learning models that would make online education engaging, interactive, and deeply effective. But by 2023, they were at a breaking point. Funding had run out, and they needed a breakthrough.

In a final effort, they sent three emails to companies at the forefront of AI—hoping one of them would see what they saw: a future where AI education was truly transformative. Only Microsoft responded. 

Founderz was accepted into the Microsoft for Startups Founders Hub, which provided access to industry-leading AI services, expert guidance, and essential technology to supercharge their growth. Entering the Microsoft for Startups Founders Hub at Tier 4 also unlocked USD150,000 in Microsoft Azure credits, enabling the company to scale their platform, refine their AI-powered learning model, and start delivering high-quality AI education at scale. 

Today, Founderz itself is helping change lives.

“In 2024, we had roughly 10,000 users learning AI with Founderz,” recalled Anna Cejudo, co-CEO and co-founder. “By the end of the year, we were at 50,000. And now, by the beginning of March, we reached 100,000 users training in AI. The real revolution isn’t AI—it’s education,” added Anna, highlighting the fundamental role of learning in driving change. They’ve also become a Microsoft Training Services Partner, making the decision to offer training exclusively on Microsoft AI technology.  

AI skilling done differently 

Founderz is far from being just another online course provider. Pau, Anna, and their growing team are rethinking how AI is taught by blending structured learning with real-time collaboration, personalized AI-powered support, and a hands-on approach to applying AI in real-world scenarios. 

At the heart of the Founderz learning experience are high-quality, cinematic-style lectures. Unlike traditional online courses that rely on static, slide-based presentations, Founderz films its courses in a MasterClass-style format, featuring top AI experts from Microsoft and beyond. This approach allows learners to hear directly from the people shaping the future of AI. 

Expert content in student’s native language 

But Founderz goes beyond delivering engaging content—it’s about accessibility. A core mission is to provide top-tier AI education in students’ native languages. Next-generation lip-syncing technology ensures that learners experience AI-powered content seamlessly, without language barriers.

“I can watch a Responsible AI class from Microsoft’s Mihaela Vorvoreanu in my language,” says Pau Garcia-Mila. “She speaks in first person, saying, ‘When we built this Responsible AI model at Microsoft.’ I’d love to be able to learn from the source in my mother tongue.”

Small group collaboration  

While putting “thousands of people in a virtual room” enables Founderz to pay the best professors at a lower per-student cost, the company also sought to build a platform that supported meaningful collaboration. AI-powered tools match students into small, diverse learning circles, where they tackle real-world AI challenges, share insights, and build lasting professional networks. 

Multilingual student support around the clock 

To further support its rapidly growing user base, Founderz’ team of AI-powered teaching assistants or “Fellows” provide real-time multilingual support, allowing learners to receive help in their native language while keeping operations efficient for the human support team.  

As Pau Garcia-Mila explained, “The Fellows are speaking any language in the world, but our human team sees everything in English,” ensuring seamless interaction across different languages. 

These AI-powered teaching assistants provide real-time feedback, analyze student interactions, and escalate complex questions to human instructors when needed. Whether a learner is a complete beginner or an experienced professional, Founderz ensures they receive the support they need to succeed. 

Education means opportunity

Founderz’ journey from a bold idea to an emerging AI skilling leader is proof that AI education is more than just accessing information—it’s about unlocking potential. Its pioneering efforts continue to inspire countless organizations to embrace AI education and drive meaningful transformation worldwide.

As Anna Cejudo puts it, “Education means opportunity, and if we can deliver high-quality education to as many people as possible, we’re giving them the chance to change their lives and control their futures.” 

At Microsoft, we couldn’t agree more. Microsoft’s mission has always been about creating technology that empowers others to innovate and solve real-world problems. This holds true in the age of AI. Our commitment to skilling is not just about technology adoption, it’s about people development. Over the past year, Microsoft has trained and certified over 23 million people across more than 200 countries in digital skills, with the goal of ensuring that everyone has the opportunity to succeed in a world where AI will be commonplace and a natural extension of everything we do.  

Unlock the future—Join the Microsoft AI Skills Fest 

As part of our ongoing investment in global skilling, Microsoft is bringing AI skilling to everyone with the Microsoft AI Skills Fest—a global event designed to bring together customers and partners, tech and business professionals, and AI enthusiasts to help build the skills we all need to thrive in the AI economy.  

Beginning April 8, 2025, we’re kicking off the fun with an attempt to set a GUINNESS WORLD RECORDS™ title for the most users to take an online multi-level artificial intelligence lesson in 24 hours. After that, we’re inviting everyone to continue building their skills with 50 days to explore Microsoft’s AI apps and services.  

Let’s make history together.

Microsoft AI Skills Fest

A global event designed to bring learners across the globe together to build their AI skills

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¹IDC InfoBrief: sponsored by Microsoft, 2024 Business Opportunity of AI, IDC# US52699124, November 2024.

The post Founderz: Transforming AI education to unlock opportunity appeared first on The Microsoft Cloud Blog.

In the 2025 AI Decision Brief, we share findings on how AI is rapidly evolving into what economists call a general-purpose technology. In this report, top Microsoft leaders and AI innovators say they expect generative AI to revolutionize operations, enable new and disruptive business models, and reshape the competitive landscape.

As AI grows more powerful and efficient, businesses have the opportunity to use these solutions to enhance employee productivity and well-being, improve customer engagement, and optimize business practices. In our latest webinar, Transform Your Business with Microsoft AI, I share real-world examples from leading organizations that are already driving value with AI. Plus, learn about three trusted Microsoft AI platforms designed to help you reap the benefits of this transformative technology.

At Microsoft, we’re here to help you navigate your AI transformation journey. Keep reading to find out how.

4 business benefits of AI transformation

When you apply AI to your business transformation, you’ll enjoy meaningful benefits related to employee experiences, customer engagement, business processes, and innovation. At Microsoft, we’re working with thousands of customers across industries and around the world to help them pursue these opportunities and realize the value of AI. 

Organizations are driving four major business outcomes with Microsoft AI.

1. Elevate employee experiences and boost productivity

One of the primary reasons organizations across all industries are tapping into AI is to increase productivity. According to an IDC study, 92% of surveyed companies said they’re using AI for productivity, and 43% said that productivity use cases currently provide their greatest return on investment.¹  

Plus, our customers tell us that by automating repetitive, mundane tasks, employees are freed up to focus on more complex and creative work. This shift makes the work environment more stimulating for teams and boosts their job satisfaction, which ultimately results in increased employee retention. 

2. Reinvent customer engagement

Organizations are using generative AI to help personalize customer experiences and improve customer service. These companies are tapping into AI-powered tools to:

• Provide service agents with tailored insights.
• Create customized content tailored to targeted customer segments.
• Streamline operations so customers can get information faster.
• Craft experiences that delight customers while lightening the load for employees.  

AI solutions also help organizations deliver faster customer service, leading to an increase in both customer and agent satisfaction. For service agents, sourcing the right information from a vast catalog of material can take a long time. Generative AI puts the necessary data right at their fingertips, saving them thousands of hours.

3. Streamline business processes

Many Microsoft customers are using generative AI solutions to transform operations and improve efficiency across various business functions. For example, IT teams are using AI to automate responses to routine tasks while keeping employees happy. Human resources departments are tapping into these solutions to speed up the hiring process, while financial teams use AI for fraud detection and risk assessments. 

4. Spark innovation

Organizations across industries are using generative AI to fuel innovation and speed up creative processes and product development. Using AI tools, teams can explore new ideas, design prototypes, and iterate quickly, cutting down the time it takes to get to market.  

For example, the automotive industry is using AI to design more efficient vehicles. And in healthcare, radiologists are using AI solutions to more quickly and accurately interpret detailed images.

All of these examples show the value of AI and the real outcomes that organizations are driving with these innovative capabilities. Check out more customer stories that illustrate how companies are transforming with AI.

Now let’s dive into how Microsoft AI can bring all of this to life for your organization.

Accelerate your AI business transformation with three trusted platforms

We offer three Microsoft AI platforms to help you transform your business, drive impact, and achieve more—Microsoft Copilot, Copilot devices, and the Copilot and AI stack.  

1. Microsoft Copilot

Driving AI transformation starts by bringing AI into the flow of work for every one of your employees. That’s exactly what Microsoft Copilot does. Copilot is rapidly becoming an essential solution for getting work done. 

Copilot streamlines universal tasks for every employee, from drafting emails to generating reports. This ensures that efficiency isn’t just a goal, but a standard practice. Whether it’s simplifying complex documentation for operations employees, enhancing risk management with real-time insights for financial analysts, or accelerating the onboarding process for service agents, Copilot is the intelligent assistant for the modern workforce. 

2. Copilot devices

In 2024, we introduced Copilot+ PCs—a new class of Windows PCs designed for AI transformation. These are the fastest, most intelligent Windows PCs ever built. Copilot+ PCs allow users to accomplish things you can’t on any other PC. These devices come with AI tools that enhance video calls, refine writing in documents, and supports advanced features, like real-time translations and image generation. 

Plus, this January, we introduced new Surface Copilot + PCs for Business. These devices transform the employee experience and help amplify efficiency and creativity at work.

3. Copilot and AI stack

The most advanced platform for creating AI solutions, the Copilot and AI stack empowers users to build more ambitious products by using advanced technology at each layer of the stack, including:

• Developer tools: The Copilot and AI stack begins with the world’s most-loved developer tools. Microsoft Copilot Studio is now used by more than 100,000 organizations to build AI solutions. The Visual Studio family of products, which includes both Visual Studio and Visual Studio Code, are among the most widely used development tools in the world, with more than 40 million monthly active developers. And GitHub, used by more than 100 million developers, is the home of open source. 
• AI management: To help organizations simplify and scale their AI workloads, we recently announced the availability of Azure AI Foundry. Azure AI Foundry enables organizations to design, customize, and manage the next generation of AI apps and agents at scale. 
• Data and analytics: Of course, all of this AI innovation is built on a foundation of data. If you want to use your data to gain a competitive edge, you need data solutions that are comprehensive, secure, and connected. Microsoft Azure provides a comprehensive suite of data services that are secure by default and seamlessly integrated, allowing organizations to effectively manage and use their entire data estate.

Transform with trusted AI

When it comes to AI transformation, we believe that trust is the most important element of all. At Microsoft, we’re focused on helping customers use and build AI that is secure, safe, and private.  

All of this innovation is grounded in our mission to empower every person and every organization on the planet to achieve more. And we want to help your organization accelerate your AI transformation with the Microsoft Cloud.  

To explore how Microsoft AI is driving real business outcomes for leading organizations, watch the 25-minute on-demand webinar.

Transform Your Business with Microsoft AI

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If you’re ready to take the next steps in your AI business transformation, check out Microsoft AI to get started today. 

¹IDC InfoBrief, sponsored by Microsoft, 2024 Business Opportunity of AI, IDC# US52699124, November 2024.

The post 4 real business benefits of Microsoft AI appeared first on The Microsoft Cloud Blog.

At Microsoft, our mission is to empower every person and every organization on the planet to achieve more. In this age of generative AI, we’re committed to sharing what we’ve learned to help further this mission. That’s why we wrote the 2025 AI Decision Brief: Insights from Microsoft and AI leaders on navigating the generative AI platform shift. 

This report is packed with perspectives from top Microsoft leaders and insights from AI innovators, along with stories of companies across industries that have transformed their businesses using generative AI. It’s also full of pragmatic tips to help your company with its own AI efforts. 

Here’s a more detailed look at what you’ll find in the report.

The state of generative AI today 

The world has embraced generative AI with unprecedented speed. While it took seven years for the internet to reach 100 million users, ChatGPT reached those numbers in just two months.¹ And although generative AI is relatively new to the market, adoption is rapidly expanding. In fact, current and planned usage among enterprises jumped to 75% in 2024 from 55% in 2023, according to an IDC study.²  

Put another way, AI is rapidly evolving into what economists call a general-purpose technology. But getting to the point where everyone on the planet has AI access and takes advantage of that access will require some effort, including: 

• Committing to responsible, trustworthy AI.
  For all people, organizations, and nations to embrace AI, it must be responsible, ethical, fair, and safe. As Microsoft Vice Chair and President Brad Smith says in this report, “Broad social acceptance for AI will depend on ensuring that AI creates new opportunities for workers, respects enduring values of individuals, and addresses the impact of AI on local resources such as land, energy, and water.” 
• Overcoming adoption challenges.
  Organizations face several challenges in adopting generative AI, such as skill shortages, security concerns, and regulation and compliance issues. Training employees to use AI and building data privacy, security, and compliance into your AI adoption plan are essential.
• Understanding the winning formula.
  There’s a striking difference between customers in the AI exploration stage and those who have fully embraced it. The highest-performing organizations gain almost four times the value from their AI investments than those just getting started. Plus, those high performers are implementing generative AI projects in a fraction of the time.²

Where generative AI is headed

AI capabilities are doubling at a rate four times that of historical progress.² This exponential growth tells us that the effects of AI-powered automation, scientific discovery, and innovation will also accelerate. We expect generative AI to revolutionize operations, enable new and disruptive business models, and reshape the competitive landscape in many ways, including:

• The future of work.
  As the use of generative AI in companies continues to grow, employees are starting to collaborate with AI rather than just treating it as a tool. This means learning to work with AI iteratively and conversationally. “Effective collaboration involves setting expectations, reviewing work, and providing feedback—similar to managing an employee,” explains Jared Spataro, Microsoft Chief Marketing Officer, AI at Work. 
• The organizations leading innovation.
  Startups, software development companies, research organizations, and co-innovation labs where startups and software giants collaborate on solutions will all continue to shape AI innovation.  
• Sustainable AI.
  Generative AI is helping build a more sustainable future thanks to tools that integrate renewable energy into grids, reduce food waste, and support socially and environmentally beneficial actions.

How to advance generative AI in your organization 

As we help companies move from talking about AI to translating it into lasting results, we’ve gained a unique perspective on the generative AI strategies that drive business impact. You’ll find many of them in this report, including:

• Best practices for using generative AI at scale.
  Get tips for developing a scalable AI strategy that best suits your organization, implementing your AI adoption plan, and managing your AI efforts over time. 
• Ways to accelerate your AI readiness.
  Get checklists for creating your organization’s AI business strategy, technology and data strategy, implementation strategy, cultural and mindset shift, and governance plan. 
• Customer success stories.
  See how businesses across industries—including healthcare, energy, transportation, and finance—are demonstrating what’s possible with AI now, and in the future. Plus, explore which Microsoft and AI tools they’re using to succeed.

Maximize generative AI with insights from Microsoft leaders

We couldn’t be more excited about the promise of generative AI. Whether you’ve already begun using AI at your organization or are just getting started, we’re here to help you ease the journey and maximize your results.

Get The 2025 AI Decision Brief now for Microsoft AI leadership perspectives on: 

• Empowering the future: AI access for us all—Brad Smith, Vice Chair and President.
• How AI is revolutionizing IT at Microsoft—Nathalie D’Hers, CVP Microsoft Digital (IT).
• Learnings on the business value of AI from IDC—Alysa Taylor, Chief Marketing Officer, Commercial Cloud and AI.
• The future of work is AI-powered—Jared Spataro, Chief Marketing Officer, AI at Work.
• Microsoft’s commitment to supporting customers on their AI transformation journey—Judson Althoff, Executive Vice President and Chief Commercial Officer.
• How software development companies are paving the way for AI transformation—Jason Graefe, Corporate Vice President, ISV and Digital Natives.
• How to stay ahead of emerging challenges and cyberthreats—Vasu Jakkal, Corporate Vice President, Microsoft Security Business.

2025 AI Decision Brief

Empower your organization and learn how AI is reshaping businesses through insights shared by Microsoft leaders

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¹ Benj Edwards, “ChatGPT sets record for fastest-growing user base in history, report says: Intense demand for AI chatbot breaks records and inspires new $20/mo subscription plan,” Ars Technica, February 1, 2023.

² IDC InfoBrief, sponsored by Microsoft, 2024 Business Opportunity of AI, IDC# US52699124, November 2024.

The post Maximizing AI’s potential: Insights from Microsoft leaders on how to get the most from generative AI appeared first on The Microsoft Cloud Blog.

Enter retrieval augmented generation (RAG)—an innovative approach that seamlessly integrates information retrieval with text generation. This powerful combination of retrieval and generation has the potential to revolutionize applications from customer service chatbots to intelligent research assistants.

Let’s briefly uncover the future of AI-powered language understanding and generation through the lens of retrieval augmented generation.

Key features and benefits of RAG

Here are five key features and benefits that will help you understand RAG better.

1. Current and up-to-date knowledge

RAG models rely on external knowledge bases to retrieve real-time and relevant information before generating responses. LLMs were trained at a specific time and on a specific set of data. RAG allows for responses to be grounded on current and additional data rather than solely depending on the model’s training set.

Benefit: RAG-based systems are particularly effective when the data required is constantly changing and being updated. By incorporating real-time data, RAG patterns expand the breadth of what can be accomplished with an application, including live customer support, travel planning, or claims processing.

For example, in a customer support scenario, a RAG-enabled system can quickly retrieve relevant and accurate product specifications, troubleshooting guides, or customer’s purchase history, allowing users to resolve their issues efficiently. This capability is crucial in customer-support applications—where accuracy is paramount—because it not only enhances the user experience and fosters trust but also encourages the continued use of the AI system, helping to increase customer loyalty and retention.

2. Contextual relevance

RAG excels in providing contextually rich responses by retrieving data that is specifically relevant to the user’s query. This is achieved through sophisticated retrieval algorithms that identify the most pertinent documents or data snippets from a vast, disparate data set.¹

Benefit: By leveraging contextual information, RAG enables AI systems to generate responses that are tailored to the specific needs and preferences of users. RAG also enables organizations to maintain data privacy, versus retraining a model owned by a separate entity, allowing data to remain where it lives. This is beneficial in scenarios such as legal advice or technical support.

For example, if an employee asks about their company’s policy on remote work, RAG can pull the latest internal documents that outline those policies, ensuring that the response is not only accurate but is also directly applicable to the employee’s context. This level of contextual awareness enhances the user experience, making interactions with AI systems more meaningful and effective.

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3. Reduction of hallucinations

RAG allows for controlled information flow, finely tuning the balance between retrieved facts and generated content to maintain coherence while minimizing fabrications. Many RAG implementations offer transparent source attribution—citing references for retrieved information and adding accountability—which are both crucial for responsible AI practices. This auditability not only improves user confidence but also aligns with regulatory requirements in many industries, where accountability and traceability are essential.

Benefit: RAG boosts trust levels and significantly improves the accuracy and reliability of AI-generated content, thus helping to reduce risks in high-stakes domains like legal, healthcare, and finance. This leads to increased efficiency in information retrieval and decision-making processes, as users spend less time fact-checking or correcting AI outputs.²

For example, consider a financial advisor research assistant powered by RAG technology. When asked about recent Security and Exchange Commission filings regarding a publicly traded company in the United States from EDGAR, the commission’s online database, the AI system retrieves information from the latest annual reports, proxy statements, foreign investment disclosures, and other relevant documents filed by the corporation. The RAG model then generates a comprehensive summary, citing specific documents and their publication dates. This not only provides the researcher with current, accurate information they can trust, but also offers clear references for further investigation—significantly accelerating the research process while maintaining high standards of accuracy.

4. Cost effectiveness

RAG allows organizations to use existing data and knowledge bases without extensive retraining of LLMs. This is achieved by augmenting the input to the model with relevant retrieved data rather than requiring the model to learn from scratch.

Benefit: This approach significantly reduces the costs associated with developing and maintaining AI systems. Organizations can deploy RAG-enabled applications more quickly and efficiently, as they do not need to invest heavily in training large models on proprietary data.³

For example, consider a small-but-rapidly growing e-commerce company specializing in eco-friendly garden supplies. As they grow, they face the challenge of efficiently managing and utilizing their expanding knowledge base without increasing operational costs. If a customer inquires about the best fertilizer for a specific plant, the RAG system can quickly retrieve and synthesize information from product descriptions, usage guidelines, plant zone specifications, and customer reviews to provide a tailored response.

In this way, RAG technology allows the business to leverage its existing product documentation, customer FAQs, and a scalable internal knowledge base where the RAG system expands with the business, without the cost or need for extensive AI model training or constant updates. By providing accurate and contextually sensitive responses, the RAG system reduces customer frustration and potential returns—indirectly saving costs associated with customer churn and product returns.

5. User productivity

RAG helps boost user productivity by enabling users to access precise, contextually relevant data quickly by effectively combining information retrieval with generative AI.⁴

Benefit: This streamlined approach reduces the time spent on data gathering and analysis, allowing decision-makers to focus on actionable insights and teams to automate time-consuming tasks.

For example, KPMG built ComplyAI, a compliance checker, wherein employees submit client documents and request that the application review them. The app reviews the documents and flags any legal standards or compliance requirements, then sends the analysis to the user who originally set up the task. The app handles the review and analysis, saving the requestor time and effort. Thus, the app allows the user to ramp up on the topic or issue in question much faster without requiring them to be a legal expert.

As a result, users are more likely to perceive the AI application as a helpful and integral part of their daily tasks, whether in a professional or personal context.

Get started using RAG to enhance LLMs

In summary, by leveraging the vast knowledge stored in external sources, RAG enhances the capabilities of LLMs, including improved accuracy, contextual relevance, reduced hallucinations, cost-effectiveness, and improved auditability. These features collectively contribute to the development of more reliable and efficient AI applications across various sectors. RAG-enhanced systems also help empower smaller-sized businesses to compete effectively with larger competitors while managing their growth in a cost-effective manner, without the need to hire additional staff or for substantial AI model updates and retraining.

To get started, use the following resources to start building RAG applications with Azure AI Foundry and use them with agents built using Microsoft Copilot Studio.

• Build a RAG application with Azure OpenAI Service and Azure AI Search.
• Watch our videos:
  • Retrieval Augmented Generation with Azure AI Search
  • OpenAI creates retrieval augmented generation features with Azure AI Search

• Read about retrieval augmented generation (RAG) in Azure AI Search.
• Read about enhancing responses from agents built using Copilot Studio with your index built in Azure AI Search.

Our commitment to Trustworthy AI

Organizations across industries are leveraging Azure AI and Microsoft Copilot capabilities to drive growth, increase productivity, and create value-added experiences.

We’re committed to helping organizations use and build AI that is trustworthy, meaning it is secure, private, and safe. We bring best practices and learnings from decades of researching and building AI products at scale to provide industry-leading commitments and capabilities that span our three pillars of security, privacy, and safety. Trustworthy AI is only possible when you combine our commitments, such as our Secure Future Initiative and our Responsible AI principles, with our product capabilities to unlock AI transformation with confidence. 

¹DataCamp, How to Improve RAG Performance: 5 Key Techniques with Examples, 2024.

² Lewis, P., Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks, 2020.

³ Castro, P., Announcing cost-effective RAG at scale with Azure AI Search, Microsoft, 2024.

⁴ Hikov, A. and Murphy, L., Information retrieval from textual data: Harnessing large language models, retrieval augmented generation and prompt engineering, Ingenta Connect, Spring 2024.

The post 5 key features and benefits of retrieval augmented generation (RAG) appeared first on The Microsoft Cloud Blog.

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This article outlines and defines various practices used across the RAG pipeline—full-text search, vector search, chunking, hybrid search, query rewriting, and re-ranking.

What is full-text search?

Full-text search is the process of searching the entire document or dataset, rather than just indexing and searching specific fields or metadata. This type of search is typically used to retrieve the most relevant chunks of text from the underlying dataset or knowledge base. These retrieved chunks are then used to augment the input to the language model, providing context and information to improve the quality of the generated response.

Full-text search is often combined with other search techniques, such as vector search or hybrid search, to leverage the strengths of multiple approaches.

The purpose of full-text search is to:

• Allow the retrieval of relevant data from the complete textual content of a document or dataset.
• Enable the identification of documents that may contain the answer or relevant information, even if the specific query terms are not present in the metadata or document titles.

The process of implementing a full-text search involves the following techniques:

• Indexing—the full text of documents or dataset is indexed, often using inverted index structures that store and organize information that helps improve the speed and efficiency of search queries and retrieved results.
• Querying—when a user query is received, the full text of the documents or dataset is searched to find the most relevant information.
• Ranking—the retrieved documents or chunks are ranked based on relevance to the query, using techniques like term frequency inverse document frequency (TF-IDF) or BM25.

What is vector search?

Vector search retrieves stored matching information based on conceptual similarity, or the underlying meaning of sentences, rather than exact keyword matches. In vector search, machine learning models generate numeric representations of data, including text and images. Because the content is numeric rather than plain text, matching is based on vectors that are most similar to the query vector, enabling search matching for:

• Semantic or conceptual likeness (“dog” and “canine,” conceptually similar yet linguistically distinct).
• Multilingual content (“dog” in English and “hund” in German).
• Multiple content types (“dog” in plain text and a photograph of a dog in an image file).

With the rise of generative AI applications, vector search and vector databases have seen a dramatic rise in adoption, along with the increased number of applications using dialogue interactions and question/answer formats. Embeddings are a specific type of vector representation created by natural language machine learning models trained to identify patterns and relationships between words.

There are three steps in processing vector search:

1. Encoding—use language models to transform or convert text chunks into high-dimensional vectors or embeddings.
2. Indexing—store these vectors in a specialized database optimized for vector operations.
3. Querying—convert user queries into vectors using the same encoding method to retrieve semantically similar content.

Things to consider when implementing vector search:

• Selecting the right embedding model for your specific use case, like GPT or BERT.
• Balancing index size, search speed, and accuracy.
• Keeping vector representations up to date as the source data changes.

What is chunking?

Chunking is the process of dividing large documents and text files into smaller parts to stay under the maximum token input limits for embedding models. Partitioning your content into chunks ensures that your data can be processed by the embedding models and that you don’t lose information due to truncation.

For example, the maximum length of input text for the Azure OpenAI Service text-embedding-ada-002 model is 8,191 tokens. Given that each token is around four characters of text for common OpenAI models, this maximum limit is equivalent to around 6,000 words of text. If you’re using these models to generate embeddings, it’s critical that the input text stays below the limit.

Documents are divided into smaller segments, depending on:

• Number of tokens or characters.
• Structure-aware segments, like paragraphs and sections.
• Overlapping windows of text.

When implementing chunking, it’s important to consider these factors:

• Shape and density of your documents. If you need intact text or passages, larger chunks and variable chunking that preserves sentence structure can produce better results.
• User queries. Larger chunks and overlapping strategies help preserve context and semantic richness for queries that target specific information.
• Large language models (LLMs) have performance guidelines for chunk size. You need to set a chunk size that works best for all of the models you’re using. For instance, if you use models for summarization and embeddings, choose an optimal chunk size that works for both.

What is hybrid search?

Hybrid search combines keyword search and vector search results and fuses them together using a scoring algorithm. A common model is reciprocal rank fusion (RRF). When two or more queries are executed in parallel, RRF evaluates the search scores to produce a unified result set.

For generative AI applications and scenarios, hybrid search often refers to the ability to search both full text and vector data.

The process of hybrid search involves:

1. Transforming the query into a vector format.
2. Performing vector search to find semantically similar chunks.
3. Simultaneously conducting keyword search on the same corpus.
4. Combining and ranking results from both methods.

When implementing hybrid search, consider the following:

• Balancing the influence of each search method.
• Increased computational complexity compared to single-method search.
• Tuning the system to work well across diverse types of queries and content.
• Overlapping keywords to match when using question and answering systems, like ChatGPT.

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What is query rewriting?

Query rewriting is an important technique used in RAG to enhance the quality and relevance of the information retrieved by modifying and augmenting a provided user query. Query rewriting creates variations of the same query that are shared with the retriever simultaneously, alongside the original query. This helps remediate poorly phrased questions and casts a broader net for the type of knowledge collected for a single query.

In RAG systems, rewriting helps improve recall, better capturing user intent. It’s performed during pre-retrieval, before the information retrieval step in a RAG scenario.

Query rewriting can be approached in three ways:

1. Rules-based—using predefined rules and patterns to modify the query.
2. Machine learning-based—training models to learn how to transform queries based on examples.
3. Mixed—combining rules-based and machine learning-based techniques.

What is re-ranking?

Re-ranking, or L2 ranking, uses the context or semantic meaning of a query to compute a new relevance score over pre-ranked results. Post retrieval, a retrieval system passes search results to a ranking machine-learning model that scores the documents (or textual chunks) by relevance. Then, the top results of a limited, defined number of documents (top 50, top 10, top 3) are shared with the LLM.

Learn how to start building a RAG application

RAG systems employ various techniques to enhance knowledge retrieval and improve the quality of generated responses. These techniques work to provide language models with highly relevant context to generate accurate and informative responses.

To get started, use the following resources to start building a RAG application with Azure AI Foundry and use them with agents built using Microsoft Copilot Studio.

• Build a RAG application with Azure OpenAI Service and Azure AI Search.
• Watch our videos:
  • Retrieval augmented generation with Azure AI Search
  • OpenAI creates retrieval augmented generation features with Azure AI Search

• Read about retrieval augmented generation (RAG) in Azure AI Search.
• Read about enhancing responses from agents built using Copilot Studio with your index built in Azure AI Search.

Our commitment to Trustworthy AI

Organizations across industries are leveraging Azure AI Foundry and Microsoft Copilot Studio capabilities to drive growth, increase productivity, and create value-added experiences.

We’re committed to helping organizations use and build AI that is trustworthy, meaning it is secure, private, and safe. We bring best practices and learnings from decades of researching and building AI products at scale to provide industry-leading commitments and capabilities that span our three pillars of security, privacy, and safety. Trustworthy AI is only possible when you combine our commitments, such as our Secure Future Initiative and our Responsible AI principles, with our product capabilities to unlock AI transformation with confidence. 

Azure remains steadfast in its commitment to Trustworthy AI, with security, privacy, and safety as priorities. Check out the 2024 Responsible AI Transparency Report.

The post Common retrieval augmented generation (RAG) techniques explained appeared first on The Microsoft Cloud Blog.

Although every organization’s AI transformation is unique and blueprints are scarce, we’ve learned that having the right skills across the organization is key. By implementing skill-building initiatives throughout the company, we’re reimagining how we work at Microsoft and aligning those initiatives to the functions that are critical to how we do business.

Through this process, we’re constantly uncovering valuable insights on how to lead by learning—often developing the playbooks from scratch. By applying these insights, we advance our AI transformation and benefit our workforce, customers, and partners around the world. We’re glad to share our findings with you to help your teams skill up to make the most of AI for innovation, growth, and opportunities.

Developing crucial AI skills for organizational transformation

Organizational transformation now requires AI-first skills; yet it can be challenging to plan modern and effective skill-building programs.

We understand the importance of providing our employees—both technical and non-technical—with the AI skills to grow and evolve with the business and the technology, along with the ability to apply these skills every day. Teams across Microsoft have established innovative and effective AI training programs that cater to specific roles in marketing, sales, engineering, and beyond.

Although there’s no one-size-fits-all approach to AI training, our experience has yielded some widely applicable takeaways which can be helpful to organizations that want to build AI skills. Our new e-book, 10 Best Practices to Accelerate Your Employees’ AI Skills: Lessons and experiences from Microsoft’s skilling initiatives, highlights some of the vital lessons we’ve learned that can help support you in implementing skill-building programs crucial to your AI transformation.

Sharing highlights from our AI learning experience

The e-book explores many of the lessons we’ve learned in our ongoing AI evolution. Our experiences can help inspire and inform your path forward, too, as you and your teams get skilled up and ready to power AI transformation with the Microsoft Cloud. In particular, the e-book showcases stories from AI skill-building initiatives implemented by four Microsoft teams:

• Microsoft Marketing, a diverse collective of professionals, ranging from creative roles to business strategists and technical experts.
• MCAPS Academy, the team responsible for training sellers globally within the Microsoft Customer and Partner Solutions (MCAPS) organization.
• Worldwide Learning Engineering, the team tasked with architecting and building apps and platforms that support MCAPS and some of the Microsoft skill-building offerings for customers and partners.
• The Microsoft Garage, an innovation platform that enables collaboration and experimentation through hackathons, workshops, talks, training sessions, and more.

Here’s what we learned.

1. Give space for exploration

Encourage a culture of learning by providing employees with the time and tools to explore AI.

Our Worldwide Learning Engineering team has dedicated time to delve into AI, and this fosters an environment where curiosity and innovation can thrive. Additionally, The Garage’s experiments, such as the SkillUp AI Challenge, provide employees with a sandbox for practical AI applications, encouraging both personal and professional growth.

2. Make learning fun

Create a low-pressure, engaging environment where employees can learn at their own pace.

The Garage’s SkillUp AI Challenge incorporates fun, interactive exercises that make AI relatable and enjoyable for all skill levels. Similarly, the Marketing AI practitioner hub offers gamified learning paths that enable marketers to integrate AI into their daily workflows in an entertaining way.

3. Provide clear, structured learning paths

Simplify the learning experience with structured paths tailored to different skill levels and roles.

MCAPS Academy Flight Plans offer role-specific learning paths, helping to ensure that technical and non-technical sales teams alike have clear directions for their AI learning. Moreover, the Marketing Learning team has developed a curriculum that supports marketers in becoming regular AI practitioners through well-defined learning stages.

4. Make it role specific

Adapt AI training programs to the unique needs of each role within the organization.

The Worldwide Learning Engineering team focuses on providing engineers with opportunities for deep technical engagement through dedicated learning time and advanced AI tools. At the same time, the MCAPS Academy addresses the specific needs of a different job role—sales—by blending foundational knowledge with real-world applications to enhance AI fluency.

5. Start with foundations

Begin AI training with foundational knowledge to help ensure that all employees have a solid understanding of AI basics.

The Marketing Learning team introduces marketers to AI through simple, foundational concepts before progressing to more complex applications. Likewise, the MCAPS Academy provides basic AI training to new hires before guiding them through more advanced, role-specific learning paths.

6. Have a plan to update the content regularly

Maintain the relevance of AI training programs by regularly updating content.

The Worldwide Learning Engineering team continuously refreshes its training materials to keep up with the latest advancements in AI technology. Meanwhile, The Garage schedules regular updates for its skill-building exercises to help ensure that they remain engaging and current.

7. Drive awareness and continued adoption

Promote ongoing AI learning and adoption through awareness campaigns and reinforcement.

The Marketing AI practitioner hub provides regular touchpoints to encourage consistent AI practice among marketers. Similarly, the MCAPS Academy uses newsletters and internal communications to keep the sales force informed and engaged in AI learning.

8. Set clear guidelines for responsible use

Establish and communicate guidelines for the responsible use of AI to maintain standards.

The Marketing Learning team’s curriculum emphasizes the importance of responsible AI use, providing clear guidelines and best practices. The Worldwide Learning Engineering team also integrates responsible AI principles into its training sessions, highlighting the significance of these considerations in AI development.

9. Let employees learn from each other

Facilitate peer-to-peer learning opportunities to enhance AI skills through collaboration.

The Garage hosts show-and-tell sessions where employees share their AI projects and insights. For engineers, the Worldwide Learning Engineering team organizes knowledge-sharing workshops to promote collaborative learning.

10. Leverage existing resources

Take advantage of available resources to support AI skill-building initiatives.

The MCAPS Academy makes the most of existing training platforms and materials, integrating them into its AI learning paths. And The Garage draws on external AI tools and resources to complement its interactive learning programs.

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Building a foundation for the future of AI skilling

Our experiences as customer zero for AI training have been transformative—and we’re just getting started. By empowering our teams with the right skills, we’re not only driving innovation within our organization but also setting a strong foundation for the future, supporting our employees and customers, creating business value and growth, and fostering innovation.

As organizations around the world look to build AI skills and to scale this powerful technology throughout their business, we’re glad to share these insights to support your AI transformation. Together, we can lead in the AI-powered world and unlock new levels of value for our workforce, customers, and partners—today, tomorrow, and beyond.

The post Accelerate employee AI skilling: Insights from Microsoft appeared first on The Microsoft Cloud Blog.

The AI platform shift brings immense opportunity, but the road to success isn’t always easy or clear. That’s why we’ve asked customers, partners, Microsoft experts, and other leaders to share their journeys and insights in our new podcast, Leading the Shift.

In each episode, we’ll explore how our guests are using data, AI, and cloud technologies to deliver meaningful and trustworthy innovation to their organizations, customers, and communities. You’ll hear from executives, developers, data scientists, and visionaries across industries in both the public and private sectors. We’ll discuss how the platform shift is affecting everything from data strategy, customer relationships, organizational structure, and culture, to their own lives and career journeys. And, most importantly, they’ll share what they’ve learned along the way, as well as their advice on how to get started and how to navigate bumps in the road.

The first four episodes of Leading the Shift cover a lot of ground, from how data and AI are being used for social impact, to the use of AI to create a fan remix experience for a world-famous band, to the opportunity to use data, AI, and cloud technologies to map the customer journey and truly personalize experiences, to the potential for proprietary data to transform an organization’s competitive advantage.

Throughout these conversations we aim to get at the heart of the approaches, tools, techniques, opportunities, threats, and emerging best practices that leaders are adopting as they learn by doing. We’ll see common themes emerge: the transformative power of data and AI, the importance of trust, the innovation potential of co-creation, and the value of getting hands-on during the process.

Leading the Shift

Listen and learn from our first four episodes, out now.

Listen here

Here’s what you can expect from our first four episodes:

Creating the new field of data for social impact, with Perry Hewitt, data.org

You’ve heard about data-driven products and services, and even data-driven business models. But in our premiere episode, Perry Hewitt, Chief Marketing and Product Officer at data.org, discusses how the organization is using data and AI to create an entirely new field—data for social impact.

Perry shares how data.org is working with organizations around the world to co-create solutions to some of the world’s greatest challenges, such as dispelling misconceptions about women’s health in India, upskilling migrant Venezuelan women in Chile, and creating intensive AI curricula for teachers, students and businesses in rural Mississippi.

It’s a rigorous and innovative approach built around trust, respect, and co-creation, and is as applicable to business as it is to the public sector.

Remixing the Coldplay fan experience with AI, with Robby Ingebretsen, Pixel Lab

In this episode, Robby Ingebretsen, Founder and Creative Director of Pixel Lab, an award-winning creative agency, talks about how he and his team built a fan remix experience that builds on the release of Coldplay’s new album, MOON MUSiC, and its film compendium, A Film For The Future.

The fan remix experience offers people the opportunity to join a community of Coldplay fans to create their own remix of the film using inputs that match their mood and attribute choices and contribute to the film as their piece is added to the future playback.  

It’s a wide-ranging conversation that explores the impact of digitalization and platform shifts in the music and technology industries, the impact and opportunities of AI, and how co-creation can unlock new opportunities for creativity, engagement, and innovation for customers and consumers across a range of industries. Read more about how this experience was built with Azure AI Foundry.

Data doesn’t just fuel generative AI—it goes both ways, with Shirli Zelcer, dentsu

Shirli Zelcer began her career as a statistician and is now Chief Data and Technology Officer at dentsu, an integrated growth and transformation partner to the world’s leading organizations. It’s a fascinating role, and her roots in data, combined with a deep understanding of AI, have prepared her to bring data and AI together to help dentsu and its clients unlock new sources of value.

Shirli shares a bit about her journey, including how she’s seen data and analytics evolve from a back-office practice to a C-suite priority. We explore a range of topics, including new approaches to generating audiences, the opportunity to combine structured and unstructured data to better understand customer needs and behavior, the value of synthetic data, and—underlying all of this innovation—the imperative to use intelligent data and technologies in a trustworthy, responsible, and privacy-compliant way.

Your proprietary data is your competitive advantage, with Teresa Tung, Accenture

In this episode, we talk with Teresa Tung, Global Lead of Data Capability at Accenture, about the evolving role of data in AI strategy. We explore the strategic value of proprietary data, how generative AI can help organizations realize value from unstructured data, the changing landscape of data governance, and how synthetic data can model sensitive scenarios in a safer way. We also discuss how generative AI can help organizations jumpstart data capability.

Teresa is a prolific inventor, holding over 225 patents and applications, and leads the vision and strategy that ensures that Accenture is prepared for ever-changing data advancements. It’s a real treat to hear her share what she’s seeing in the industry and what she believes will help organizations deliver value to their customers and clients.

Looking ahead with Leading the Shift

Leading the Shift will release new episodes in the last week of each month. Listen, like, and subscribe wherever you get your podcasts, including Spotify, Apple Podcasts, and YouTube.

Listen to Leading the Shift now

The post Introducing Leading the Shift, a new Microsoft Azure podcast appeared first on The Microsoft Cloud Blog.

This blog outlines the key lessons from the whitepaper, practical tips for AI red teaming, and how these efforts improve the safety and reliability of AI applications like Microsoft Copilot.

What is AI red teaming?

AI red teaming is the practice of probing AI systems for security vulnerabilities and safety risks that could cause harm to users. Unlike traditional safety benchmarking, red teaming focuses on probing end-to-end systems—not just individual models—for weaknesses. This holistic approach allows organizations to address risks that emerge from the interactions among AI models, user inputs, and external systems.

8 lessons from the front lines of AI red teaming

Drawing from our experience, we’ve identified eight main lessons that can help business leaders align AI red teaming efforts with real-world risks.

1. Understand system capabilities and applications

AI red teaming should start by understanding how an AI system could be misused or cause harm in real-world scenarios. This means focusing on the system’s capabilities and where it could be applied, as different systems have different vulnerabilities based on their design and use cases. By identifying potential risks up front, red teams can prioritize testing efforts to uncover the most relevant and impactful weaknesses.

Example: Large language models (LLMs) are prone to generating ungrounded content, often referred to as “hallucinations.” However, the impact created by this weakness varies significantly depending on the application. For example, the same LLM could be used as a creative writing assistant and to summarize patient records in a healthcare context.

2. Complex attacks aren’t always necessary

Attackers often use simple and practical methods, like hand crafting prompts and fuzzing, to exploit weaknesses in AI systems. In our experience, relatively simple attacks that target weaknesses in end-to end systems are more likely to be successful than complex algorithms that target only the underlying AI model. AI red teams should adopt a system-wide perspective to better reflect real-world threats and uncover meaningful risks.

Example: Overlaying text on an image to trick an AI model into generating content that could aid in illegal activities.

3. AI red teaming is not safety benchmarking

The risks posed by AI systems are constantly evolving, with new attack vectors and harms emerging as the technology advances. Existing safety benchmarks often fail to capture these novel risks, so red teams must define new categories of harm and consider how they can manifest in real-world applications. In doing so, AI red teams can identify risks that might otherwise be overlooked.

Example: Assessing how a state-of-the-art large language model (LLM) could be used to automate scams and persuade people to engage in risky behaviors.

4. Leverage automation for scale

Automation plays a critical role in scaling AI red teaming efforts by enabling faster and more comprehensive testing of vulnerabilities. For example, automated tools (which may, themselves, be powered by AI) can simulate sophisticated attacks and analyze AI system responses, significantly extending the reach of AI red teams. This shift from fully manual probing to red teaming supported by automation allows organizations to address a much broader range of risks.

Example: Microsoft AIRT’s Python Risk Identification Tool (PyRIT) for generative AI, an open-source framework, can automatically orchestrate attacks and evaluate AI responses, reducing manual effort and increasing efficiency.

5. The human element remains crucial

Despite the benefits of automation, human judgment remains essential for many aspects of AI red teaming including prioritizing risks, designing system-level attacks, and assessing nuanced harms. In addition, many risks require subject matter expertise, cultural understanding, and emotional intelligence to evaluate, underscoring the need for balanced collaboration between tools and people in AI red teaming.

Example: Human expertise is vital for evaluating AI-generated content in specialized domains like CBRN (chemical, biological, radiological, and nuclear), testing low-resource languages with cultural nuance, and assessing the psychological impact of human-AI interactions.

6. Responsible AI risks are pervasive but complex

Harms like bias, toxicity, and the generation of illegal content are more subjective and harder to measure than traditional security risks, requiring red teams to be on guard against both intentional misuse and accidental harm caused by benign users. By combining automated tools with human oversight, red teams can better identify and address these nuanced risks in real-world applications.

Example: A text-to-image model that reinforces stereotypical gender roles, such as depicting only women as secretaries and men as bosses, based on neutral prompts.

7. LLMs amplify existing security risks and introduce new ones

Most AI red teams are familiar with attacks that target vulnerabilities introduced by AI models, such as prompt injections and jailbreaks. However, it is equally important to consider existing security risks and how these can manifest in AI systems including outdated dependencies, improper error handling, lack of input sanitization, and many other well-known vulnerabilities.

Example: Attackers exploiting a server-side request forgery (SSRF) vulnerability introduced by an outdated FFmpeg version in a video-processing generative AI application.

8. The work of securing AI systems will never be complete

AI safety is not just a technical problem; it requires robust testing, ongoing updates, and strong regulations to deter attacks and strengthen defenses. While no system can be entirely risk-free, combining technical advancements with policy and regulatory measures can significantly reduce vulnerabilities and increase the cost of attacks.

Example: Iterative “break-fix” cycles, which perform multiple rounds of red teaming and mitigation to ensure that defenses evolve alongside emerging threats.

The road ahead: Challenges and opportunities of AI red teaming

AI red teaming is still a nascent field with significant room for growth. Some pressing questions remain:

• How can red teaming practices evolve to probe for dangerous capabilities in AI models like persuasion, deception, and self-replication?
• How do we adapt red teaming practices to different cultural and linguistic contexts as AI systems are deployed globally?
• What standards can be established to make red teaming findings more transparent and actionable?

Addressing these challenges will require collaboration across disciplines, organizations, and cultural boundaries. Open-source tools like PyRIT are a step in the right direction, enabling wider access to AI red teaming techniques and fostering a community-driven approach to AI safety.

Next steps: Building a safer AI future with AI red teaming

AI red teaming is essential for helping ensure safer, more secure, and responsible generative AI systems. As adoption grows, organizations must embrace proactive risk assessments grounded in real-world threats. By applying key lessons—like balancing automation with human oversight, addressing responsible AI harms, and prioritizing ethical considerations—red teaming helps build systems that are not only resilient but also aligned with societal values.

AI safety is an ongoing journey, but with collaboration and innovation, we can meet the challenges ahead. Dive deeper into these insights and strategies by reading the full whitepaper: Lessons From Red Teaming 100 Generative AI Products.

The post Enhancing AI safety: Insights and lessons from red teaming appeared first on The Microsoft Cloud Blog.

The Business Case for Responsible AI: Read the new whitepaper from Microsoft and IDC

At Microsoft, we are dedicated to enabling every person and organization to use and build AI that is trustworthy, which means AI that is private, safe, and secure. You can learn more about our commitments and capabilities in our announcement about trustworthy AI. Our approach to safe AI, or responsible AI, is grounded in our core values, risk management and compliance practices, advanced tools and technologies, and the dedication of individuals committed to deploying and using generative AI responsibly.

We believe that a responsible AI approach fosters innovation by ensuring that AI technologies are developed and deployed in a manner that is fair, transparent, and accountable. IDC’s Worldwide Responsible AI Survey found that 91% of organizations are currently using AI technology and expect more than a 24% improvement in customer experience, business resilience, sustainability, and operational efficiency due to AI in 2024. In addition, organizations that use responsible AI solutions reported benefits such as improved data privacy, enhanced customer experience, confident business decisions, and strengthened brand reputation and trust. These solutions are built with tools and methodologies to identify, assess, and mitigate potential risks throughout their development and deployment.

AI is a critical enabler of business transformation, offering unprecedented opportunities for innovation and growth. However, the responsible development and use of AI is essential to mitigate risks and build trust with customers and stakeholders. By adopting a responsible AI approach, organizations can align AI deployment with their values and societal expectations, resulting in sustainable value for both the organization and its customers.

Key findings from the IDC survey

The IDC Worldwide Responsible AI Survey highlights the importance of operationalizing responsible AI practices:

• More than 30% of respondents noted that the lack of governance and risk management solutions is the top barrier to adopting and scaling AI.
• More than 75% of respondents who use responsible AI solutions reported improvements in data privacy, customer experience, confident business decisions, brand reputation, and trust.
• Organizations are increasingly investing in AI and machine learning governance tools and professional services for responsible AI, with 35% of AI organization spend in 2024 allocated to AI and machine learning governance tools and 32% to professional services.

In response to these findings, IDC suggests that a responsible AI organization is built on four foundational elements: core values and governance, risk management and compliance, technologies, and workforce.

1. Core values and governance: A responsible AI organization defines and articulates its AI mission and principles, supported by corporate leadership. Establishing a clear governance structure across the organization builds confidence and trust in AI technologies.
2. Risk management and compliance: Strengthening compliance with stated principles and current laws and regulations is essential. Organizations must develop policies to mitigate risk and operationalize those policies through a risk management framework with regular reporting and monitoring.
3. Technologies: Utilizing tools and techniques to support principles such as fairness, explainability, robustness, accountability, and privacy is crucial. These principles must be built into AI systems and platforms.
4. Workforce: Empowering leadership to elevate responsible AI as a critical business imperative and providing all employees with training on responsible AI principles is paramount. Training the broader workforce ensures responsible AI adoption across the organization.

Read the whitepaper: The Business Case for Responsible AI

Advice and recommendations for business and technology leaders

To ensure the responsible use of AI technologies, organizations should consider taking a systematic approach to AI governance. Based on the research, here are some recommendations for business and technology leaders. It is worth noting that Microsoft has adopted these practices and is committed to working with customers on their responsible AI journey:

1. Establish AI principles: Commit to developing technology responsibly and establish specific application areas that will not be pursued. Avoid creating or reinforcing unfair bias and build and test for safety. Learn how Microsoft builds and governs AI responsibly.
2. Implement AI governance: Establish an AI governance committee with diverse and inclusive representation. Define policies for governing internal and external AI use, promote transparency and explainability, and conduct regular AI audits. Read the Microsoft Transparency Report.
3. Prioritize privacy and security: Reinforce privacy and data protection measures in AI operations to safeguard against unauthorized data access and ensure user trust. Learn more about Microsoft’s work to implement generative AI across the organization securely and responsibly.
4. Invest in AI training: Allocate resources for regular training and workshops on responsible AI practices for the entire workforce, including executive leadership. Visit Microsoft Learn and find courses on generative AI for business leaders, developers, and machine learning professionals.
5. Stay abreast of global AI regulations: Keep up-to-date with global AI regulations, such as the EU AI Act, and ensure compliance with emerging requirements. Stay up-to-date with requirements at Microsoft Trust Center.

As organizations continue to integrate AI into business processes, it is important to remember that responsible AI is a strategic advantage. By embedding responsible AI practices into the core of their operations, organizations can drive innovation, enhance customer trust, and support long-term sustainability. Organizations that prioritize responsible AI may be better positioned to navigate the complexities of the AI landscape and capitalize on the opportunities it presents to reinvent the customer experience or bend the curve on innovation.

At Microsoft, we are committed to supporting our customers on their responsible AI journey. We offer a range of tools, resources, and best practices to help organizations implement responsible AI principles effectively. In addition, we are leveraging our partner ecosystem to provide customers with market and technical insights designed to enable deployment of responsible AI solutions on the Microsoft platform. By working together, we can create a future where AI is used responsibly benefiting both businesses and society as a whole.

As organizations navigate the complexities of AI adoption, it is important to make responsible AI an integrated practice across the organization. By doing so, organizations can harness the full potential of AI while using it in a manner that is fair and beneficial for all.

Discover solutions

• Read the whitepaper: The Business Case for Responsible AI.
• Watch the webinar: The Business Case for Responsible AI.
• Learn more about Microsoft’s commitment to responsible AI.

¹IDC’s 2024 AI opportunity study: Top five AI trends to watch, Alysa Taylor. November 14, 2024.

IDC White Paper: sponsored by Microsoft, 2024 The Business Case for Responsible AI, IDC #US52727124, December 2024. The study was commissioned and sponsored by Microsoft. This document is provided solely for information and should not be construed as legal advice.

The post Explore the business case for responsible AI in new IDC whitepaper appeared first on The Microsoft Cloud Blog.