It is a task that is not taken lightly by the Jets’ coaches, front office and scouting department, who will be featured in the team’s draft room as they make their final decisions.

But that gathering is just the tip of the iceberg when it comes to player evaluation, and the Jets are careful to explore every avenue available when assessing the thousands of players who are draft-eligible each season. Utilizing the latest technology to help them make more informed decisions is now a critical part of the team’s overall strategy.

“The draft is one of the primary ways in which we’re able to acquire talent, and it’s an extremely important event for us,” says Dan Zbojovsky, senior director, football operations for the Jets. “It’s a year-long process and oftentimes multi-year process for us to evaluate these players coming out of college and then get the opportunity to select them.”

A ‘Titan’ off the field

In the traditional draft scenario, teams receive dispatches from scouts around the country who file reports on players that range from height and weight measurements to 40-yard dash times to vertical leaps. Colleges will conduct Pro Days, in which scouts are invited to see a team’s top performers run drills and catch passes. And independent scouting services provide roundups of prospects major and minor, with their own evaluation systems.

In short, there’s a lot of information on a lot of potential draftees, and that doesn’t even include each NFL team’s own preferences based on organizational philosophy, coaching schemes and roster needs. While some teams prefer to keep things more analog, the Jets have been at the forefront of embracing technology to help them prepare not only for the draft but also the fast-paced nature of an NFL season.

The team’s proprietary Titan app (winkingly named after the team’s original moniker, The Titans of New York) is the team’s “mothership” for football operations – a custom-built web application that contains essential tools for draft preparation, scouting and personnel strategy.

“Titan is really the hub behind everything we do on the football side,” says Paul Marsh, senior director of application development. “It’s a legacy application of 15 years now through many, many different iterations, but it’s always remained Titan. It is where all of our scouting and football data is housed. It is the view into that data and it enables the powers that be to help make their decisions and come up with their plans to help make the team win.”

Titan is built on Microsoft technology, including Microsoft Azure, GitHub Copilot and GitHub Actions. Marsh’s team relies on GitHub Copilot to speed up coding, prototyping and iteration, helping them gain greater efficiency when time is tight leading up to the draft. GitHub Actions are used to automate, build and deploy pipelines, enabling frequent updates and continuous integration across Titan’s modules.

Another key element of Titan is the team’s draft/trade calculator, a points-based tool the Jets use to evaluate draft-day trade scenarios. In real time, New York’s football brain trust can plug in picks, compare values and determine whether a proposed trade would result in a net gain or loss for the team.

“This is a UI that was designed really by our [general manager] and his close advisors to work the way that they want to work,” says Marsh, who has been with the Jets for 24 seasons. “And it simply allows them to kind of dig into the information and game plan on what they’re going to do going forward into the draft.”

The Jets’ process has paid off with several important contributors being acquired via the draft, including wide receiver and 2022 Offensive Rookie of the Year Garrett Wilson, 2025 No. 7 selection Armand Membou, defensive end Will McDonald IV, running backs Breece Hall and Braelon Allen, and tight end Mason Taylor.

Old school, meet new school

For Zbojovsky, who is entering his 19th season with the franchise, the draft successes reflect the balance the team uses when combining the old-school scouting mentality and the technology and analytics of the new school of player evaluation.

“[Titan] is an extremely important internal website for us, and we’ve made a lot of really cool advancements over the years on it,” he says.

“I think everything has its piece of the puzzle. On certain players, some parts might be a bigger piece of that puzzle. We like to, of course, rely on our film work as the foundation of our reports and our scouting evaluations, and then we can utilize all these other cool tools or data points to help inform those evaluations and really help us, whether it be our stacking of our players or comparing players to each other. And really it adds a little bit of an objective piece into what can be a largely subjective evaluation off film.”

Zbojovsky and Marsh work closely with each other to ensure that any late-bloomers, fast-risers or strategic adjustments are reflected quickly in Titan so that everyone is on the same page.

“We always joke that if the GM wanted to come down to our office and say, ‘I need a button here, here and here, and I need it to do these things,’ we’re working on that right out of the gate as soon as he leaves that office,” Marsh says.

“We’re able to turn around very, very quickly because we’re able to push those changes right into our Microsoft stack and get them in front of him before he hits the end of the hall. It’s the trust that we can get things done very quickly because these guys have deadlines that don’t move. We can’t push back the draft. We can’t push back free agency.”

The fastest agent at the Combine

The Jets recently wrapped their time at the NFL Combine in Indianapolis, where all 32 teams convene to scout draft prospects as they go through a whirlwind of testing and drills. Numbers and measurements are flying fast and furious, so the Jets, along with the league’s other squads, use the NFL Combine App to help surface the official Combine data to coaches and scouts.

A custom Copilot AI agent is built into the NFL Combine App to allow coaches and scouts to surface fast insights and prospect comparisons with natural language questions that allow teams to get information on, for example, the average, highest and lowest linebacker results for each drill since 2015.

“The Copilot feature not only allows us to ask questions and filter through the information that’s present at the time, but also compare that back to previous years,” Zbojovsky says.

“So you start to really be able to stack how this player not only performed against this cohort here, but also against players that are currently in the NFL. And that helps you start to really understand where that player’s performance metrics on the field might fit within the players that he’s going to be joining in the league.”

Let the countdown begin

In a league where every decision matters and every potential advantage could swing the final score, both Marsh and Zbojovsky are thankful that the Jets continue to see technology as an integral part of scouting and preparation.

“We really are in a great spot for what we need to do. It keeps us nimble,” Marsh says. “Talking to other organizations and other technology companies, they are impressed with how quickly we’re able to iterate and move to get those solutions. We’re not bogged down. We’re given a lot of flexibility and the trust to do what we need to do.”

When the Jets turn in their pick, it will be so much more than writing a name on a card to hand to the commissioner. It will be the final product of research, data, scouting and technology all coming together to welcome the next potential superstar to the NFL.

“A lot of work goes in from a lot of people throughout the organization,” Zbojovsky says.

“We incorporate a lot of different data points and different types of evaluations, whether it be analytics or our scouts. We put a lot of work behind that to make sure we get our board right in advance and then we see how things fall on draft day. We look forward to success in April.”

Top photo courtesy of the Jets.

Learn how the NFL is using AI on and off the field to enhance operations and read how technology could help the Minnesota Vikings build next year’s winning edge. 

Elliott Smith writes about AI and innovation at Microsoft, from how the Premier League is transforming its online presence to why AI may play a major role in saving the Amazon rainforest. Previously, Smith worked as a sports reporter in Washington, D.C., Washington state and Texas, covering high schools to the pros. You can contact him on LinkedIn.

The post The New York Jets are happy to have a ‘Titan’ in their corner at the NFL Draft appeared first on The Microsoft Cloud Blog.

Yet despite broad cloud adoption, migration and modernization remain complex. Legacy architectures, fragmented environments, and persistent skills gaps continue to slow progress, pushing organizations to find ways to migrate faster while minimizing operational risk. 

The IDC study highlights agentic AI as a critical unlock. These intelligent systems automate assessments, orchestrate migration and modernization efforts, and optimize operations across hybrid environments—helping organizations shift from periodic, manual initiatives to continuous, adaptive modernization. This momentum is driving unprecedented growth, with IDC forecasting the public cloud services market will reach USD1.9 trillion by 2029. 

While migration frameworks may be horizontal, their real-world impact is industry-specific. Healthcare, financial services, and manufacturing each face unique constraints shaped by regulation, operational risk, and mission-critical systems. 

In this blog, we explore the key migration and modernization challenges across these three industries—healthcare, manufacturing, and financial services—through real customer stories that highlight the tangible impact cloud adoption is delivering today.

Healthcare: Modernizing securely while powering next-generation clinical experiences

Healthcare faces the toughest modernization headwinds: strict regulations (HIPAA/HITECH, HITRUST), fragmented clinical data across electronic health records (EHRs) and imaging systems, aging on-premises infrastructure resulting in high Capex, and heightened exposure to ransomware.¹ Clinical environments also demand extremely low latency and high reliability.

The IDC study notes that these constraints slow modernization—but accelerate the need for it, as organizations push to scale telehealth, imaging workloads, genomics pipelines, and AI-powered clinical workflows.¹ 

What healthcare organizations need, according to the IDC study: 

• Secure, compliant integration across EHRs, picture archiving and communication systems (PACS), genomics systems, and Internet of Things (IoT) medical devices.¹
• Elastic compute for high-throughput imaging and genomics. 
• Stronger disaster recovery and recovery time performance.¹
• Ambient documentation and AI-supported diagnostics.
• Secure clinician collaboration and modern patient digital front doors.

Customer spotlight: Franciscan Health

Facing aging infrastructure and disaster recovery risks, Franciscan adopted a pragmatic workload placement strategy—moving its Epic EHR to Microsoft Azure.

The results included: 

• $45 million in savings over five years after migrating Epic to Azure.
• 90% faster disaster recovery compared to the prior environment.
• Around a 30-minute failover, reduced from hours.
• $10–$12 million per day in potential downtime risk avoided.

Learn more about Franciscan Health’s journey to migrate its Epic EHR to Azure.

Healthcare’s modernization mandate is clear: reduce operational risk, meet regulatory demands, and harness cloud AI to improve patient outcomes. 

Financial services: Enabling real-time intelligence and automated compliance

Financial institutions operate in one of the most regulated environments, including the payment card industry data security standard (PCI DSS), the Sarbanes-Oxley Act (SOX), the Gramm-Leach-Bliley Act (GLBA), Basel capital frameworks, and know your customer (KYC) and anti-money laundering (AML) requirements, and rely heavily on legacy mainframes that are difficult to modernize. Today, regulatory pressure is intensifying further as new frameworks such as the EU’s Digital Operational Resilience Act (DORA) and the EU AI Act raise the bar for operational resilience, third-party risk management, model transparency, and ongoing compliance monitoring. Under DORA, financial services firms must demonstrate continuous information and communication technology (ICT) risk management, advanced incident reporting, and resilience testing across critical systems and cloud service providers. Meanwhile, the EU AI Act introduces governance requirements for high-risk AI systems, including explainability, data lineage, human oversight, and auditability—with direct implications for fraud models, credit scoring, and customer decisioning platforms.

IDC interviews highlight accelerating demand for real-time risk analytics, fraud detection, digital onboarding, and infrastructure elasticity to support peak activity—capabilities that are increasingly mandated, not optional.¹

Key challenges the IDC study identifies: 

• Strict data residency, model risk governance, explainability, and eDiscovery requirements.¹
• Heightened expectations for operational resilience, cyber defense, and third-party risk oversight.
• Legacy systems and common business-oriented language (COBOL)-based batch processes resistant to change.
• Rapidly evolving regulatory mandates requiring continuous compliance rather than point-in-time audits.

Cloud—especially especially platform as a service (PaaS) and managed services—helps financial institutions shift from static, batch-driven compliance to continuous controls and real-time observability. By reducing batch windows from hours to minutes, modern cloud platforms enable real-time insights, automated evidence collection, resilient architectures, and policy-driven compliance workflows aligned with DORA and AI governance requirements.¹ Learn more about how Microsoft can help financial institutions navigate these requirements. 

Customer spotlight: Crediclub

To accelerate product innovation and meet expectations from Mexico’s national banking and securities commission (CNBV), Mexican fintech Crediclub modernized its databases to a serverless platform as a service (PaaS) architecture and adopted microservices.¹

The impact:

• Uptime improved from around 80% to 99.5%.
• 90% reduction in network latency through Multiprotocol Label Switching (MPLS) and dark fiber.
• Rapid deployment of new financial products via Kubernetes and DevSecOps.

For financial institutions, modernization is no longer just about efficiency—it is foundational to resilience, trustworthy AI, and regulatory compliance at scale. 

Manufacturing: Unifying IT and OT for predictive, data-driven industrial operations

Manufacturers operate in one of the most complex operating environments—defined by legacy and proprietary operational technology (OT) protocols, historically air-gapped manufacturing execution systems (MES) and supervisory control and data acquisition (SCADA) systems, and globally distributed supply chains. Stringent low-latency requirements for safety-critical systems, intermittent connectivity at the edge, and the need to protect intellectual property further compound the challenge. The ability to modernize and unify these environments—without compromising safety, reliability, or performance—represents a critical inflection point for industrial transformation.

Unique modernization challenges according to the IDC study:

• Ultra-low latency requirements for safety-critical operations.
• Massive telemetry ingestion and time-series analytics at scale.
• Operational complexity across global, distributed supply chains.
• Secure protection of intellectual property across edge and cloud environments.

Opportunities unlocked by cloud:

• Predictive maintenance with IoT ingestion.¹ 
• Reduced unplanned downtime and improved overall equipment effectiveness (OEE).
• Digital twins for plants, lines, and products.
• Computer vision for real-time quality and safety. 
• High-performance computing (HPC) simulations for engineering and design. 
• Standardized, global data models.

Customer spotlight: ASTEC Industries

ASTEC unified fragmented systems across its rock to road value chain—from aggregate processing through asphalt production and paving—by adopting Azure, modernizing to timeseries databases, and building a universal connectivity platform using Azure IoT Hub, Azure Events Hub, and Power BI.¹

The results:

• Realtime operational visibility across fleets.
• Predictive maintenance for reducing downtime.
• New digital services supported by connected equipment.

Manufacturing’s modernization imperative: unify OT and IT, scale real-time intelligence, and enable global efficiency. 

Microsoft’s approach: Continuous, intelligent, collaborative modernization 

Microsoft’s strategy is grounded in a simple principle: modernization should be continuous, intelligent, and collaborative. The IDC study emphasizes that successful enterprises adopt a balanced, multipath migration strategy, blending rehost, replatform, refactor, and software as a service (SaaS) substitution based on workload criticality.¹

Microsoft enables this approach through a comprehensive set of tools and offerings, including Azure Copilot and GitHub Copilot. Agentic automation enables:

• Discovery and dependency mapping.
• Security assessment and 6R recommendations.
• Application refactoring, code remediation, and modernization. 

Azure Migrate provides unified discovery, assessment, migration execution, and modernization services. Azure Accelerate complements this with a coordinated framework that includes:

• Guided deployments through Cloud Accelerate Factory.¹ 
• Funding and Azure credits for planning, pilot, and rollout. 
• Expert partners and tailored skilling programs.

The IDC study concludes that organizations using Microsoft Azure for migration and modernization achieve lower operational costs, improved resiliency, faster modernization timelines, and stronger security postures—especially in regulated industries.¹

Looking ahead: Agentic modernization as the foundation for AI-ready enterprises

Across all industries, IDC’s findings are consistent: agentic AI is emerging as the new force multiplier for modernization, enabling organizations to keep pace with rising complexity, regulatory demands, and competitive pressure. 

Healthcare, financial services, and manufacturing each face unique constraints—but cloud modernization remains the foundation for innovation, operational excellence, and enterprise AI. 

Microsoft’s approach gives organizations the unified automation, intelligence, and tooling they need to modernize securely and at scale. 

• Explore how Azure Copilot accelerates cloud migration.
• Discover GitHub Copilot for app modernization.
• See how Azure Accelerate supports transformation.

¹ IDC White Paper, Cloud Migration and Modernization Strategies for Healthcare, Financial Services, and Manufacturing, February 2026.

The post Modernizing regulated industries with cloud and agentic AI appeared first on The Microsoft Cloud Blog.

That’s why the question isn’t whether to invest in AI—it’s where it will make the biggest difference.

The best place to start? Identifying the AI business use cases that align to the specific needs and priorities of your organization and your industry—because these use cases are what turn potential into measurable outcomes.

At Microsoft, we’ve seen that the most successful AI strategies are grounded in industry context. From financial services and retail to manufacturing and healthcare, organizations are applying AI in practical, targeted ways to solve complex challenges and unlock new opportunities.

Let’s take a closer look at the AI use cases in business that are driving transformation today—through proven industry examples.

AI Use Cases for Business Leaders—get the e-book
Financial services firms transform operations and experiences with AI
Banking, insurance, and capital markets firms are under growing pressure to modernize. They’re expected to deliver more personalized service, manage costs, and stay ahead of evolving regulatory demands—while also providing seamless, secure, and relevant experiences across every interaction.

To meet these expectations, financial institutions are turning to AI business applications to address specific challenges across customer service, compliance, and operations. Key use cases include:

Delivering more personalized customer service through AI-powered agents that can resolve issues in real time and scale human support.
Enabling more relevant and timely engagement by equipping relationship managers with AI tools that provide real-time insights into customer behavior, market signals, and product performance.
Enhancing compliance and fraud detection with AI models that support transaction monitoring and automated regulatory reporting.
Reducing operational costs and improving efficiency by using AI to automate document-heavy tasks like loan processing, claims management, and compliance reviews.
These use cases are already driving results. Aditya Birla Capital, a diversified financial services group in India, adopted AI across its banking, insurance, and asset management businesses. The company increased lead generation through more personalized experiences, boosted contact center productivity by 20%, maintained strong compliance while accelerating digital transformation, and reduced operating costs by over 40% through automation and greater efficiency.

Learn more about AI Use Cases for Financial Services
Retailers drive shopper conversions with AI-powered innovations
Retailers are navigating rising customer expectations, supply chain disruptions, labor shortages, and fierce competition across digital and physical channels. To stay ahead, they must deliver more personalized experiences, operate with greater agility, and equip employees to deliver faster, smarter service.

AI is helping retailers address these challenges with several high-impact use cases across the value chain:

Delivering personalized shopping experiences with AI agents that recommend products in real-time based on customer preferences, behavior, and trend data—boosting conversions, reducing returns, and increasing loyalty.
Empowering store and service employees with AI business solutions that provide instant answers to store procedures and policies, inventory, product details, and customer insights—increasing productivity and enhancing customer service.
Improving supply chain visibility by unifying customer, product, and operational data in AI-powered platforms that enhance forecasting, inventory planning, and targeted marketing.
Strengthening security and resilience through AI-powered threat detection and adaptive protection that defends against credential theft, unauthorized access, and malicious actors.
These AI use cases are delivering measurable results. ASOS, a go-to destination for young fashion lovers, is a standout example of personalization. The retailer uses an AI-powered conversational interface to curate product selections based on shopper preferences and highlight the latest trends—all while maintaining brand voice. This results in increased engagement, higher conversions, and improved customer satisfaction.

Companies like Carvana, an online used car retailer, and Albert Heijn, a leading grocery store chain in the Netherlands, are also seeing strong results with AI-powered shopping assistants that deliver fast, intuitive, and highly personalized experiences at scale.

Discover how to transform your retail organization with AI-powered tools
Manufacturers transform the value chain with AI
Manufacturers are under intense pressure to remain competitive in the face of global supply chain disruptions, rising costs, evolving customer expectations, and the need to meet sustainability goals. To stay ahead, they must improve equipment reliability, increase production efficiency, and accelerate innovation across the entire value chain.

AI is helping manufacturers address these demands with targeted use cases that drive both operational and strategic impact across the value chain:

Reducing unplanned downtime through AI-powered predictive maintenance that monitors equipment health and alerts teams to potential failures before they occur.
Improving product quality and yield by using AI-powered visual inspection and real-time defect detection to catch issues earlier and reduce waste.
Accelerating product development with generative design and AI-assisted coding that shortens engineering cycles and reduces time to market.
Enabling faster decision-making on the factory floor by giving teams access to real-time performance metrics through natural language interfaces and AI agents.
These AI use cases are already helping leading manufacturers drive results. On the factory floor, Rolls-Royce, a global manufacturer of power systems for aviation and industrial markets, is using AI to monitor engine health and prevent around 400 unplanned maintenance events annually—saving millions and improving overall reliability. The company also applies AI to improve defect detection, increasing machine usage by 30%, and reducing fault resolution time from days to near real time.

Schaeffler, a global automotive and industrial supplier, uses AI agents and real-time data access to enhance reporting, decision-making, and troubleshooting—improving uptime, productivity, and yield across its operations.

Design to delivery: Leading generative AI use cases to modernize manufacturing processes
Healthcare organizations improve care and research with AI
AI is reshaping the entire healthcare ecosystem—including how providers deliver care, how payors manage populations, and how life sciences organizations accelerate innovation. Healthcare leaders are working to improve outcomes, reduce provider burden, expand access, and drive research breakthroughs, all while managing rising costs and maintaining compliance.

To meet these demands, organizations are turning to AI to support critical use cases across care delivery and innovation:

Streamlining clinical workflows with AI assistants that surface critical information in real time and automate tasks—giving providers more time to focus on patient care.
Enhancing patient engagement with AI tools that help individuals access health information, schedule appointments, and stay connected with providers.
Supporting clinical decision-making through AI models that improve diagnostics, disease detection, and treatment planning, while enabling more efficient and equitable care models using multimodal AI insights from unified healthcare data.
Accelerating drug discovery and development by enabling researchers to collaborate more effectively, uncover insights from large volumes of data, and reduce clinical trial timelines.
These use cases are already driving measurable impact. At Beth Israel Lahey Health (BILH), the medical center’s AI-powered app gives care teams real-time access to thousands of critical care documents—improving efficiency, policy compliance, and the overall quality of care.

Syneos Health, a global biopharmaceutical solutions provider, is applying AI to improve predictive modeling and accelerate clinical trial site activation time by 10%, helping bring lifesaving therapies to patients faster.

Learn more about AI Use Cases in Healthcare
Let’s put the right AI use cases into action for your industry
Across financial services, retail, manufacturing, and healthcare, organizations are applying AI through proven use cases that reflect the specific needs and challenges of their industry—and are already seeing measurable impact.

At Microsoft, we’re building on insights from thousands of customer engagements to help you identify where AI can make the biggest impact for your organization and your industry.

See more examples of how businesses use AI to drive impact and growth. Explore Microsoft AI Use Cases for Business Leaders: Realize Value with AI.

AI Use Cases for Business Leaders
Explore how businesses use AI to drive impact and growth.

Get the e-book

The post AI for business impact starts here: Proven AI use cases by industry appeared first on The Microsoft Cloud Blog.

Build AI agents to drive business processes with Model Context Protocol servers
To provide some context, generative AI is fundamentally reshaping the way organizations work, introducing a new way of interacting with technology—using natural language to simplify and accelerate tasks. This innovation is driving unprecedented productivity gains, streamlining complex processes that once required manual effort and specialized tools. As this technology matures, we’re entering the next phase: the autonomous enterprise, where organizations and people use technology, particularly AI and automation, to operate and adapt in an age of rapid transformation and innovation. Where there once was “an app for that,” there will now be “an agent for that”.

This transformation isn’t just about automation—it’s about people. By putting intelligent agents in the hands of every employee, organizations are empowering individuals to focus on higher-value work, make decisions faster, and drive innovation. Sales teams can deepen customer relationships without being bogged down by administrative tasks. Finance professionals can move from manual reconciliation to strategic forecasting. Marketers can go from idea to execution, and product managers can orchestrate complex workflows with clarity and speed.

The Autonomous Enterprise is the future of business. Business applications will work with agents built by Microsoft and our partners. In this new era, organizations aren’t just streamlining operations, they’re amplifying human potential and accelerating their journey to the autonomous enterprise.

This is why we’re so excited about the Dynamics 365 ERP and CRM MCP servers. These servers help eliminate data and application silos, allowing agents to work seamlessly across processes and enable new autonomous scenarios for improved business functionality and productivity.

Dynamics 365: Agent-ready business applications
Agentic AI is an AI system that can take actions generated by the system, with very limited or even no direct human intervention. Autonomous actions built into agents operating across various business processes, industries, and segments, can make businesses more efficient and responsive. Designed not just to support tasks, but to operate autonomously, AI agents can intelligently orchestrate workflows and make context-aware selections. But how do you create a context-aware agent when data, information, and processes are ever-changing?

MCP standardizes how applications provide context to language models, enabling seamless integration with different data sources and tools. This open standard connects AI assistants and agents to various systems where data resides, such as content repositories, business tools, and development environments. An MCP-compliant agent uses rich contextual information to act efficiently, unlike a non-MCP-compliant agent, which lacks necessary context.

Using the MCP server, makers can easily connect agents to existing knowledge sources and APIs, enabling them to interface directly with Dynamics 365 applications. Actions and knowledge synchronize automatically, facilitating real-time updates and the evolution of functionality. This model significantly simplifies agent development and minimizes ongoing maintenance efforts.

Diagram illustrating how different agents and clients connect to an MCP-compliant server to access data and actions from Dynamics 365 and other business applications.
Central to this innovation is Microsoft Copilot Studio, which provides a standardized protocol for agents to seamlessly interact with Dynamics 365 applications, helping to ensure consistency, reliability, and scalability. Security and governance are also prioritized from the start as Dynamics 365 MCP servers require authentication and enforce authorization. Agents that access Dynamics 365 through the MCP server must authenticate as a valid Dynamics 365 user, helping to ensure the benefits of Entra ID identity protection. This also prevents escalation of privileges, meaning the agent will only be able to perform the MCP actions that they are authorized to do. The MCP servers are also made available to Microsoft Copilot Studio using connector infrastructure. This means they can employ enterprise security and governance controls such as Data Loss Prevention controls and multiple authentication methods. 

For partners and customers, MCP standardization dramatically reduces complexity, accelerates development, and increases time to value.

MCP-compliant agentic AI
At Microsoft, we bring a deep understanding of critical business processes for small and medium business (SMB) as well as large enterprise organizations through our market-leading Dynamics 365 ERP and CRM business solutions—combined with our industry-specific expertise delivered through our Microsoft Cloud for Industry solutions. This combination of experience and expertise uniquely positions us to deliver on the needs of customers across size, business process, industry, or region.

Our newly introduced set of MCP servers enable multiple scenarios across business processes. Below are a few examples of what’s possible with Dynamics 365, Microsoft Cloud for Industry, and our broad ecosystem of partners.

Sales and service
Custom agents and AI assistants can now be connected to Microsoft Dynamics 365 Sales, Microsoft Dynamics 365 Customer Service, and Microsoft Dynamics 365 Business Central applications through MCP servers. Agents can retrieve and update CRM data, create quotes, and complete orders. They can also complete orders, get order/case summaries, and email drafts. These MCP servers open endless possibilities in automating tedious jobs in sales and service functions, irrespective of company size or industry.

For example, telesales representatives can use intelligent assistants, such as Claude, connected to Dynamics 365 MCP servers to prioritize leads, qualify them, generate quotes, and send personalized emails—without needing to switch contexts or rely on complex integrations. And when customers encounter an order issue, service representatives can resolve it quickly by using Dynamics 365 Customer Service data to retrieve/update case information and create replacement orders in real time.

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Supply chain and finance
The AI procurement agent illustrated below efficiently validates purchase requisitions against company policies, existing inventory, and delivery records to identify a suitable supplier that meets the criteria for cost, speed, sustainability, and reliability. It further consolidates multiple items from the same supplier into one purchase order and sends it for purchase. The agent can significantly enhance efficiency in procurement processes, where timely and budget-conscious supply delivery is critical.

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Business Central
For small and medium size businesses, for example, looking to optimize sourcing information and vendor compliance, the custom agent demonstrated here can quickly identify shipments containing materials that require compliance checks. The agent provides guidance on recycling requirements and updated sourcing standards, reads supplier contracts, and suggests next steps like confirming vendor certifications and updating shipment checklists. A solution like this could streamline the compliance process, which can help customers gain a competitive advantage.

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Partners using the MCP server
Our partners play a crucial role in driving innovation and delivering value to customers. We’re dedicated to making Dynamics 365 MCP servers accessible, enabling our customers and partners to develop diverse agent scenarios across industries and business processes, regardless of their business application vendor. Today we’re also announcing the creation of an “AI business applications” coalition with XX and XX as founding members. These organizations along with Microsoft are committed to delivering MCP standard compliant solutions.

With MCP server becoming the standard of the future for agents, partners can use it to more quickly and efficiently orchestrate headless business services in ERP and external systems. It turns simple intent into action, automating procurement for faster, efficient, and resilient supply chain operations. Our ecosystem of partners has started using MCP server for Dynamics 365 to create a host of industry-specific agents.

Avanade, an early adopter of Microsoft 365 Copilot for Sales and a leading Microsoft partner, is excited to use MCP servers for Dynamics 365 to enrich their AI-powered request for proposal (RFP) Insights agent. This agent helps sellers summarize, evaluate, and respond to RFPs using historical Dynamics 365 data, further streamlining proposal generation. While initially for internal use, Avanade is exploring deployment for clients in engineering, construction, and professional services.
Emission AI agent by Fellowmind will use AI and MCP servers for Dynamics 365 to automatically classify and organize purchase transactions to prepare it for Greenhouse gas (GHG) emission accounting purposes by categorizing spend-types (e.g. office supplies, raw materials, travel expenses) through data extraction, classification, algorithms, taxonomy mapping, and real-time feedback and learning. The agent provides support to procurement and environmental, social, and governance (ESG) professionals, helping them streamline their processes and achieve more accurate results.
HSO’s PayFlow Agent improves invoice payment efficiency in accounts payable. Streamlining timely payments and reducing inquiries that require manual intervention leads to faster resolutions and enhanced supplier relationships. Using MCP server for Dynamics ERP MCP, PayFlow processes seller payment inquiries, identifies invoice statuses, matches them against buyer receipts, and retrieves tracking information to notify responsible parties to either remit payment promptly or set an expectation of when payment can be received. 
JourneyTeam is enriching its Strategic Account Manager agent that accesses MCP servers for Dynamics 365 to optimize lead engagement. The agent summarizes historical services and projects, compares lead summaries and interests, compiles recommendations, then, after manual reviews, will initiate next steps by utilizing MCP servers, Microsoft Azure AI Search, and Document Intelligence.
MCA Connect is building a smart sourcing agent that accesses MCP servers for Dynamics 365 to automate requisition processing, supplier assignment, and workflow submission. The MCP servers give the agent access to actions like getting open requisitions, approving vendors, and assigning suppliers based on supplier performance metrics without the need to create new APIs and integrate with Dynamics 365.
Publicis Sapient Hummingbird is building an agent to improve lead management using MCP servers for Dynamics 365 to access data that will streamline the process of managing business-to-business leads. This agent automates lead qualification, scoring, and personalized engagement, accelerating hot leads to quotes faster and nurturing warm leads through a series of targeted emails. This innovative approach enhances efficiency, improves customer experience, and drives higher conversion rates and revenue growth.
RSM is building intelligent, secure, and context-aware agents that accelerate workflows, improve decisions, and expand capabilities by embedding them directly into real-world business processes. These agents, developed using Microsoft Copilot Studio, will access MCP servers for Dynamics 365 to support humanitarian logistics by coordinating critical supply chains, helping to ensure timely delivery of life-saving equipment, and automating procurement tasks.
TTEC is building a post-service upselling agent that accesses MCP servers for Dynamics 365 to prospect for warranty plans after a purchase, turning each sale into an upsell opportunity. The agent will help drive personalized sales and service conversations at scale by using the knowledge, tools, and actions from the MCP server.
As we look ahead, the convergence of intelligent agents, standardized platforms, and deep domain expertise will define the next frontier of business transformation. The ability to harness autonomous capabilities will define tomorrow’s market leaders. Businesses that act now will gain a decisive competitive edge and chart a course toward sustained success. The autonomous enterprise is no longer a vision of the future—it’s here, built with Microsoft and its partner ecosystem.

Join us at Microsoft Build 2025 to explore how MCP servers are transforming Dynamics 365 and the broader Microsoft Cloud–MCP server focused sessions at Microsoft Build 2025.

Let’s shape what’s next, together.

The post The autonomous enterprise: How generative AI is reshaping business applications appeared first on The Microsoft Cloud Blog.

At Microsoft, prioritizing sustainable practices with suppliers has also helped us uncover new opportunities for innovation and cost optimization.  

Our new guide, Reduce Risk, Create Resilience: Advancing Supply Chain Sustainability, outlines how data intelligence and collaboration can transform supply chains to be more agile, sustainable, and resilient. We offer lessons from our own experience as we strive to meet our ambitions to be carbon negative, water positive, and zero waste by 2030, all while protecting ecosystems. And we provide actionable insights and practical steps to help customers address reporting pressures, mitigate risks, and seize opportunities for innovation, ultimately driving resilience and long-term value.

Reduce Risk, Create Resilience

Start advancing supply chain sustainability

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Aligning data and systems for the road ahead 

Organizations are facing growing regulations and customer expectations to prioritize sustainable practices. To make meaningful change, organizations and their suppliers must share data and work together to address challenges across the ecosystem.  

Advanced analytics and AI are transforming supply chain sustainability at Microsoft. By leveraging these technologies, we accelerate day-to-day work, gain real-time insights, and drive co-innovation to meet sustainability reporting requirements as well as business continuity needs. 

For instance, our procurement team used Microsoft Cloud for Sustainability to centralize supplier emissions data and streamline processes. By integrating AI-powered automation, such as automated review of supplier assurance letters, we reduced survey processing time by 92%. 

Eckes-Granini, a European producer of name-brand fruit juices, is committed to ensuring that all its raw ingredients are sustainably sourced by 2030. The company used Microsoft Intelligent Data Platform including Microsoft Azure and Microsoft Power BI to connect to essential data sources and create precise visualizations of suppliers’ progress. This enables Eckes-Granini to track risks and follow up with suppliers accordingly. Now almost 70% of Eckes-Granini’s juice ingredients meet sustainability standards, and the company is better prepared to respond to Germany’s Supply Chain Due Diligence Act.

Choosing carbon-free electricity solutions 

Carbon-free electricity (CFE), such as wind, solar, and hydroelectric power, can be a powerful lever for decarbonization through improved fuel efficiency while helping to reduce exposure to varying fuel prices. These solutions can play an integral role in industry decarbonization goals, such as the International Maritime Organization’s target of reducing a 50% of their absolute CO₂ emissions from 2008 levels, by 2050.^{1 2} Organizations can help scale these efforts and the benefits of CFE in supply chains by launching supplier enablement programs.  

To help meet our own carbon reduction targets, Microsoft now requires suppliers to transition to 100% carbon-free electricity for the goods and services delivered to Microsoft by 2030. We’re making this easier for suppliers through our Supplier REach Portal, co-created with 3Degrees, to streamline access and procurement of CFE; and Zettawatts’ Supplier CFE Program, to provide assistance for reaching our CFE requirement, from understanding CFE procurement to discussing goals, developing plans and budgets, and reviewing agreements. 

Turkish energy company Enerjisa Üretim established a round-the-clock remote operation center that receives more than 50,000 signals per second from its large network of hydropower, wind, and solar plants. It processes the data using an Azure-based solution including Azure IoT Hub, Azure Digital Twins, and Azure Machine Learning. The solution delivers real-time monitoring and data analytics on power plant performance—all in one centralized location. It also uses Microsoft Azure OpenAI Service to forecast future outcomes, predicting average daily production for turbines for up to two months.

Creating new value from resource optimization 

The World Economic Forum estimates that adopting circular business models could unlock up to USD4.5 trillion in value by 2030.³ By adopting circularity, companies can not only help meet regulatory requirements but also drive new innovation, enhance their brand reputation, and differentiate their business. 

With a focus on the long-term value of resources, companies can uncover ways to reduce environmental impact while also increasing value for the business. Supply chains are central to this opportunity. 

To help meet our goal of becoming a zero waste company by 2030, Microsoft set a target of reusing or recycling 90% of our cloud hardware by 2025. We not only reached that target a year early—we exceeded it. In 2024, we reached a 90.9% reuse and recycling rate of our cloud servers and components.

Within our global datacenters, Microsoft Circular Centers are foundational to this work, enabling us to process and route decommissioned servers and hardware components to their next useful lives, such as internal reuse, other electronic supply chains, or academies that train datacenter technicians. 

Reinventing supply chain logistics  

Supply chain logistics challenges are bigger than any one company. To meet growing regulatory and market pressures, organizations need to leverage data and AI technology across their ecosystems and industries. Sharing data at a more granular level, they can identify opportunities to improve infrastructure and boost sustainability across whole logistics networks.  

Applying this principle, Microsoft has moved our cloud supply chain to renewable diesel in our road freight operations in Europe and California while keeping existing equipment in use. 

We’re also advancing aviation decarbonization by integrating sustainable aviation fuel (SAF) into shipments of cloud hardware. Through multi-year agreements, we’re working to reduce air freight emissions and help scale the adoption of SAF across the industry.³ 

Results so far include: 

• 50% lower emissions for road freight operations in Europe and California, using renewable diesel.
• 17,000 metric tons of carbon dioxide equivalents (mtCO2e) saved through SAF, compared to conventional transportation fuels.
• 73% lower relative carbon intensity of our cloud logistics supply chain since 2022 through lower-carbon transportation and logistics consolidation. 

Microsoft sustainability solutions

Accelerate your progress with transformative data and AI capabilities from Microsoft

Discover more

Get started mapping your sustainable supply chains 

Supply chain sustainability is unique for every organization. Take the first steps by exploring the ins and outs of using data technology and collaboration to drive environmental reporting compliance, adaptability, customer and stakeholder satisfaction, and innovation.  

• Dive in with the guide: Reduce Risk, Create Resilience: Advancing Supply Chain Sustainability. 
• Explore Microsoft Cloud for Sustainability.
• Read about AI transformations for sustainability.

¹ The Intergovernmental Panel on Climate Change (IPCC) defines decarbonization as: “The process by which countries, individuals or other entities aim to achieve zero fossil carbon existence. Typically refers to a reduction of the carbon emissions associated with electricity, industry and transport.” (IPCC)

² Decarbonized supply chains are resilient supply chains, McKinsey, 2022.

³ Circular Transformation of Industries: Unlocking Economic Value, World Economic Forum, 2025.

The post Reduce risk and improve resilience: Insights from Microsoft on advancing supply chain sustainability appeared first on The Microsoft Cloud Blog.

Now, the focus is starting to shift. Productivity gains are just the beginning of a broader transformation—one that goes beyond efficiency to reshape industry processes, decision-making, and customer experiences.

ROI of AI

How can industry leaders increase ROI?

Watch the video series

True AI transformation requires an industry perspective

In our conversations with customers, many are asking about the next wave of AI. They’re ready to expand beyond general AI applications and invest in solutions tailored to their specific challenges. While organizations recognize AI’s value and potential, it must address industry-specific needs to deliver the biggest results. 

Take healthcare, for example, where engaging with patients requires sensitivity, data privacy, and personalized care. That’s very different from retail, where personalization is all about tailored shopping recommendations and seamless experiences.

The same contrast exists in manufacturing and transportation. Manufacturers focus on optimizing production lines for efficiency and quality, while fleet operations prioritize logistics, route optimization, and fuel costs.

These differences show why AI adoption is moving beyond general-purpose tools to industry-specific solutions that drive even greater impact. In our recent video series on the Return on Investment (ROI) of AI, we explore this trend, real-world use cases, and how AI is transforming industries like financial services and retail. These insights reflect what I’m seeing in conversations with customers about the changing AI landscape.

Where to invest in AI for maximum impact

Today’s leaders are looking beyond AI for productivity and asking a bigger question: Where should we invest next to drive business growth? The key is to align AI investments with mission-critical priorities.

So, is the AI for industry buzz real, and is it worth the investment? The answer is yes—here’s why:

• Industry-specific AI solutions tackle complex challenges—such as regulatory compliance in financial services, seamless omnichannel shopping experiences in retail, and asset troubleshooting in manufacturing.  At Microsoft, we’ve worked with thousands of customers to identify industry use cases where AI delivers meaningful business results. Building on these insights, we offer customizable AI agents designed to accelerate time to value for our customers.
• Customizable AI agents in Microsoft Copilot Studio help businesses tailor AI to their needs. Agent Builder, a feature within Copilot Studio, simplifies customization with industry-specific knowledge and low-code tools. In addition, our customers have access to a wide range of adapted AI models to accurately and effectively address their unique needs.
• AI models, developed in collaboration with partners, and built for specific industries make adoption easier across every sector and region. These fine-tuned models are trained on industry data to support business-critical use cases.

To see industry-specific AI in action, let’s explore key use cases in financial services, retail, manufacturing, and healthcare.

Driving growth in financial services with AI

Financial services organizations are leading the way in AI adoption, and it’s paying off. They’re realizing a 4.2 times average ROI on generative AI initiatives¹—the highest across industries. Discover how PicPay uses Microsoft AI to answer product and service questions quickly and securely.

Key use cases for AI in finance industry include:

• Banking: AI enhances customer interactions, improves fraud detection, and streamlines meeting preparation.
• Insurance: AI speeds claims processing and resolution, identifies upsell opportunities, and improves customer engagement.
• Capital markets: AI personalizes client presentations, generates predictive insights, and accelerates research.

Watch the video to explore AI business transformation in financial services.

Retailers solve complex challenges with AI

Retailers are realizing a 3.6 times ROI on generative AI initiatives,² and some are tackling customer acquisition, profitability, supply chain reliability, and data complexity. Learn how ASOS, a British online fashion retailer, uses Azure AI Foundry to surprise and delight young fashion lovers with engaging, inspirational experiences.

Key use cases for AI in retail industry include:

• Personalized Shopping Agent engages in natural language conversations, delivering tailored recommendations and assisting with specific requests.
• Store Operations Agent integrates product search, inventory, orders, omnichannel pricing, and incident management into existing applications.
• AI-powered insights help retailers create targeted marketing campaigns that boost engagement and increase sales. See how Microsoft Cloud for Retail connects customers, employees, and data.

Watch the video to see why the retail industry is embracing AI.

How AI powers smarter manufacturing

Manufacturers are achieving a 3.4 times ROI on generative AI initiatives.³ They’re also using AI to speed time to market, streamline application lifecycle management, and simplify manufacturing processes. See how Schneider Electric addresses the company’s most pressing issues by innovating with Azure OpenAI Service.

Key use cases for AI in manufacturing industry include:

• AI-powered generative design accelerates product development by automating design processes, refining models in real time, and freeing teams to focus on manufacturability and compliance.
• AI-assisted coding helps developers write, debug, and optimize code faster, enhancing industrial software development and connected product functionality.
• AI-powered factory insights provide real-time data for root cause analysis, production loss reduction, and asset maintenance, boosting efficiency and safety.

Learn more about Microsoft technology in the manufacturing industry.

AI helps empower the healthcare workforce and enhance patient care

In healthcare, AI is transforming medical data management, personalizing clinician and patient experiences, and helping to improve patient outcomes—delivering a 3.3 times ROI on generative AI initiatives.⁴ See how AI innovation empowers healthcare teams to refocus on the clinician-patient connection at Northwestern Medicine, Overlake Medical Center & Clinics, and Atrium Health.

Key AI use cases for healthcare include:

• AI assistants help streamline clinical documentation, surface information, and automate tasks to improve efficiency, satisfaction, and patient care.
• Advanced healthcare AI models are designed to enhance disease detection, diagnostics, and treatment planning.
• Multimodal AI generates insights from unified healthcare data to identify care gaps faster for early intervention, develop more tailored care plans, improve the accuracy of diagnoses, and allocate hospital resources more effectively.

We’re here to help you drive AI success

Organizations that invest in industry-focused AI applications and stay current with AI industry trends are realizing the greatest ROI with AI. We’re here to help you take action now and position your business for innovation, efficiency, and competitive advantage.

Watch the ROI of AI video series to learn more about AI ROI.

^{1, 2, 3, 4} IDC InfoBrief: sponsored by Microsoft, 2024 Business Opportunity of AI, IDC #US52699124, November 2024.

The post Beyond productivity: How industry-specific AI fuels growth appeared first on The Microsoft Cloud Blog.

In a recent playbook, Accelerating sustainability with AI: Innovations for a better future, we outlined our five plays to advance sustainability, providing insight into our work at Microsoft and how business leaders around the world are creating a new path forward.

The reason to choose AI for this work? It has three unique abilities that can help organizations overcome key bottlenecks. AI can: (1) measure, predict, and optimize complex systems, (2) accelerate the development of sustainability solutions, and (3), empower the sustainability workforce. These capabilities make AI a critical enabler of progress.

Accelerate sustainability with AI

Explore actions and innovations

Get the playbook

How can business leaders harness AI to accelerate resilience, efficiency, and sustainability in their organization?

I recently met with Lindsay Myers, Vice President, Commercial Cross Solutions at Microsoft, who leads our Commercial Sustainability business, to talk more about this guidance and how business leaders can harness AI to accelerate resilience, efficiency, and sustainability in their organizations.

Toby: Hi Lindsay, before we dive into the playbook, can you share your thoughts on how organizations are adopting AI to address these interconnected goals of resilience, efficiency, and sustainability?

Lindsay: It’s important to highlight how interconnected these goals are in many organizations today. We often see initiatives started by sustainability teams result in significant cost savings for organizations. This might be efficiency gains for existing operations, or entirely new approaches like digital twins that enable rapid iteration before initial prototypes are built. When companies choose an approach like digital twins, it can reduce the materials needed for physical models—saving time and costs—while improving resilience through agility.

Explore customer and partner examples of AI innovation

Toby: Can you give me some examples of customers and partners who are doing this work today?

Lindsay: AI is making a real difference in helping organizations prepare for climate risks, innovate for maximum efficiency, and solve complex challenges. For example, in Germany, where urban flooding is a major concern, cities are searching for innovative ways to mitigate the impacts of heavy rainfall and its impact on communities and infrastructure. Esri, a global leader in geographic information system (GIS) software is helping cities unlock the power of digital twins driven by geospatial data and AI. This solution helped the City of Stuttgart cut its reality mapping time from five months to 24 hours, enabling local government and public safety staff to understand potential impacts and make decisions faster.

Stadtwerke München (SWM), the municipal utilities company serving Munich, has made it its mission to drive every aspect of the city’s energy, heating, and mobility transition forward. To accomplish this, it needed maximum-efficiency processes, such as predictive infrastructure maintenance and optimized operations planning. It has turned to Microsoft Azure and Azure IoT to efficiently provide power to its public transport fleet of 100% electrified vehicles.

Unlock new possibilities with data and AI

Toby: Those are inspiring examples; they give a real sense of AI’s potential. The playbook outlines 5 plays, or ways that organizations can unlock this potential. Could you describe some of these?

Lindsay: Let’s talk first about the first two plays and how they work together.

Investing in AI solutions to measure, predict, and optimize complex systems can drive both innovation and efficiency, helping companies focus on the most strategic priorities for business resilience.

For example, Mitiga Solutions, a global leader in climate risk intelligence and a Microsoft Climate Innovation Fund investment leverages AI, high-performance computing, and advanced climate models to predict the impact of physical climate hazards on any asset, anywhere in the world, from now until the end of the century. This helps infrastructure, commercial real estate, insurers, and companies across industries comply with climate disclosure regulations while proactively strengthening their resilience.

 With AI-powered solutions, businesses can swiftly tackle complex challenges across their own supply chains and for their customers. This not only positions companies as leaders in sustainability but can also unlock new market opportunities and enhance their competitive advantage.

It’s crucial to build a strong digital and data infrastructure to maximize AI’s potential—your AI is only as good as the data it relies on. That’s why having high-quality, representative data and the right processing infrastructure is essential. It enables teams to make informed decisions and provides accurate input for AI applications.

For many of our customers and partners, these two plays are closely linked. The foundational work involves bringing all the necessary data together in one place, like in Microsoft Fabric. What’s amazing about Fabric is it lets you reason over both internal and external data, which is incredibly helpful for things like regulatory reporting.

Once your data is set up properly, your team can use solutions such as Microsoft Copilot to ask questions of their data, generate reports, and learn from industry best practices. Copilot streamlines these tasks, reducing manual work and enabling practitioners to focus their time on new strategic initiatives.

Minimize resource use in AI design and operations

Toby: When I talk to organizations looking to adopt AI, customers and partners often want to learn more about what Microsoft is doing to reduce the environmental impact of AI. Could we talk a bit about that?

Lindsay: Absolutely. Let’s talk about play 3 and how that relates to our work at Microsoft.

AI has its own energy and water demands, so it’s crucial to minimize resource use and move toward powering AI systems with carbon-free energy. In addition, since AI infrastructure is often concentrated in specific regions, it is essential to support the local communities where datacenters are located. At Microsoft, we’re innovating across three critical areas to continue to advance the sustainability of cloud and AI services:

1. Optimizing datacenter energy, water, and waste efficiency while protecting ecosystems.
2. Advancing low-carbon materials and creating global markets to promote industry-wide sustainability.
3. Enhancing the energy efficiency of AI and cloud services.

Many of our customers and partners want to know not only what we’re doing, but also what they can do to manage resource use. Our Well-Architected Framework sustainability guidance provides a great starting point, as well as small language models that perform specific tasks using fewer resources than larger models.

Build workforce capacity to use AI for sustainability

Toby: The pace of innovation in this domain is incredible. Is there anything more you’d like to add in terms of how your team helps leaders move their ideas from concept to implementation?

Lindsay: The way forward on this journey is through people working together, and this is an area where we can help customers and partners make progress. Let’s talk about the final play first:

For companies to be able to put AI’s three game-changing capabilities to work, they must have skills to use AI effectively. Microsoft has training programs focused on building AI fluency, supporting nonprofits, businesses, and governments in advancing workforce AI technical skills and promoting safe and responsible AI development.

Microsoft’s AI learning hub can empower customers on their AI transformation journey, and customers can also use Copilot to connect with their data in Microsoft Cloud for Sustainability and sustainability data solutions in Microsoft Fabric. With these tools, employees can quickly gain insights, understand gaps, and identify what’s needed to move initiatives forward.

Toby: Thank you, Lindsay!

Transform business using generative AI

For business leaders wanting to put these plays in action and guide their organizations through effective AI adoption, we’ve published 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.

The post Harnessing AI for resilience, efficiency, and sustainability appeared first on The Microsoft Cloud Blog.

To address reporting complexity, organizations need reliable methods of centralizing, analyzing, and reporting on disparate ESG data that’s spread-out across their value chains, and for driving insights to take action. Microsoft Cloud for Sustainability is built to meet this need, equipping organizations with powerful ESG data readiness and reporting capabilities and AI-powered actionable insights. With solutions built on Microsoft Cloud for Sustainability, companies can more easily respond to emerging regulations, identify ways to improve progress, and find new business models and value.  

Microsoft Cloud for Sustainability

Data and AI capabilities to help you transform for the future

Explore our solutions

In this blog, we’ll zero in on the latest Microsoft Cloud for Sustainability reporting and AI capabilities that can help you confidently tackle your 2025 reporting goals. 

Drive efficient reporting processes—from assessment to approval  

ESG reporting involves wrangling not just disparate data but varying reporting standards that examine new sustainability dimensions and specific areas like Scope 3 (indirect) supply chain emissions, and water and waste management. Collaboration and AI assistance can be key to streamlining. 

External reporting in Microsoft Sustainability Manager (formerly Project ESG Reporting), now generally available, equips organizations with templates, frameworks, and AI-powered insights to reduce reporting complexity, enhance efficiency, and provide greater transparency to processes. Using these tools through Microsoft Power Platform or in Microsoft Sustainability Manager, organizations can seamlessly connect their ESG data, organize and review it, and collaboratively create voluntary and regulatory disclosure reports across a range of standards and frameworks including: 

• Corporate Sustainability Reporting Directive (CSRD)
• Australian Sustainability Reporting Standards (ASRS)
• Business Responsibility and Sustainability Reporting (BRSR) 1 and 2
• Global Reporting Initiative (GRI)
• International Financial Reporting Standards (IFRS) 1 and 2
• Sustainability Accounting Standards Board (SASB) 

Other features include approval and audit workflows to help ensure compliance and data accuracy; Microsoft Sustainability Manager profile and data integration; workflow UI to support task management and content approval; disclosure generation in Microsoft Excel; and extensibility for custom template creation.

Aiming to foster collaboration and enhance data quality and transparency, sustainability services partner Fellowmind is working alongside Microsoft to support customers using the preview capabilities of external reporting in Microsoft Sustainability Manager.  

“External reporting in Microsoft Sustainability Manager is a true game changer to our customers as they strive to comply with the EU’s Corporate Sustainability Reporting Directive (CSRD). Customers can leverage their existing Microsoft platform technology and data for efficient and compliant sustainability reporting.” 

Louise Ol-Ers, Fellowmind Group Sustainability Manager, Fellowmind 

Learn more about external reporting in Sustainability Manager.

Find facts and finish reports faster with Microsoft Copilot 

Synthesizing large amounts of data in comprehensive reports involves large-scale data processing and reporting in multiple formats simultaneously. By enabling Microsoft Copilot in Microsoft Sustainability Manager (preview), you can simplify drafting from different source documents, greatly speeding up disclosure reporting.  

Copilot in Microsoft Sustainability Manager uses a large language model to help you write qualitative and quantitative responses to fulfill ESG disclosure requirements. You can upload documentation and draft responses with references for various requirements, such as CSRD, GRI, and IFRS. 

Relieved of having to individually review large amounts of data to create a cohesive response for every requirement, with Copilot in Microsoft Sustainability Manager, you can fast-track your way to the report review phases. This feature is enabled by admins in external reporting in Microsoft Sustainability Manager.  

Create emissions, water, waste, and CSRD preparatory reports  

With Copilot in Microsoft Sustainability Manager, you can also use natural language to generate emissions, water, waste, and CSRD preparatory reports. Simply enter a trigger phrase, such as “Create a CSRD report” and describe the report you want to create, based on detailed parameters, for example: “Create a CSRD report in English for the year 2023 called ‘2023 CSRD preparation report’ for ‘Contoso Corp’ organizational unit.” 

Learn more about creating emissions, water, waste, and CSRD reports with Copilot in Microsoft Sustainability Manager.  

Do more with Copilot in Microsoft Sustainability Manager 

Of course, Copilot in Microsoft Sustainability Manager can help with much more than report preparation. With a growing set of skills, Copilot in Microsoft Sustainability Manager can help you: 

• Analyze documents when you upload ESG-related documents and ask questions about their contents.
• Create calculation models by simply describing the calculation model you’d like to create.
• Query your organizational data to quickly extract the information you need, such as a particular assessment report value or an organizational unit’s revenue.  

Learn more about Copilot in Microsoft Sustainability Manager.  

Explore sustainability solutions with Microsoft  

Insight to impact: AI use cases to advance sustainability

Explore five actionable ways that organizations use AI.

Read the e-book

• Bookmark Microsoft Industry Blogs: Sustainability for the latest updates, including new capabilities in Microsoft Cloud for Sustainability.
• Discover solutions with Microsoft Sustainability Manager.
• Explore the Microsoft Cloud for Sustainability Community, where you can find answers to questions and connect with peers and experts.
• See what’s new in Microsoft Cloud for Sustainability. 

¹ ESG News Survey, “Global ESG Regulation Increases by 155% Over the Past Decade,” 2023.

The post Expedite reporting with enhanced tools and AI in Microsoft Cloud for Sustainability appeared first on The Microsoft Cloud Blog.

Beyond the illnesses they cause, food safety incidents have significant negative effects on economies, farmers, the environment in the form of food waste, and governments. Returning to the example of the United States for a moment, the federal government each year budgets over $7 billion of its tax revenue to foodborne illness response programs.³ This is a reactive system, and to reduce the human, financial, and environmental effects of food safety incidents, we need to become more proactive.  

The good news is that we have the tools at our fingertips to create much more predictable food systems. Removing the farming sector’s dependencies on paper record-keeping is a simple first step, as it increases the visibility and reliability of reports. With this groundwork, farmers can start digitizing the food system and using generative AI to analyze large datasets, identify trends, and present insights in easily digestible language and visualizations through tools like Copilot in Excel and Copilot in Power BI.  

Farmers and food suppliers can detect important issues easily with generative AI solutions, like a disruption in the cold chain between the farm and the grocer, which can lead to spoilage. Generative AI can also be used to check for compliance issues and security breaches. It can suggest process improvements, track demand, and trigger alerts that automate real-time responses—all with the goal of responding to food safety incidents before they transform into public health incidents. 

Paving the way for the advancement of AI 

Microsoft Copilot and industry-specific AI agents built by partners with specific expertise in the food production industry represent a potential leap forward in preventative food safety, but they aren’t the only benefit digitalization represents. Other solutions, themselves part of the roadmap toward generative AI adoption, are already enabling meaningful change for food producers. Recent advancements in both Internet of Things (IoT) sensors and the AI technology behind them have enabled technology to mimic the human senses of sight, hearing, and smell to improve traditional food sorting, grading, and inspection processes. Azure Data Manager for Agriculture helps collect data on farms, aiding in the identification of conditions likely to introduce bacteria to crops. 

For example, a food processing company can digitize its quality control process with the help of Microsoft Power Apps, Power BI, and Dataverse. Together, these technologies help the company better capture real-time data, generate more insightful reports and improve overall operational efficiency.

As companies build out capabilities like these, they gain the type of financial benefits and actionable insights and can simultaneously establish a deeper pool of information for future generative AI solutions to draw from. Microsoft Fabric also plays a crucial role in building an AI-ready data estate. By integrating data sources like IoT sensors, temperature monitors, and historical data, Fabric helps companies establish more comprehensive data platforms. With the advanced predictive analytics these platforms can generate, food suppliers can reduce product recalls, prevent the spread of counterfeit goods, minimize food waste, and increase consumer trust.  

Bringing better farming data into the mix 

By consolidating its data, increasing the number of advanced sensors it employs, and tracking broader types of data, the food production industry is making way for even greater advancement. Copilot and customized agents can rapidly analyze every stage of the food supply chain, from farm to table. Today’s visual recognition technology often identifies contaminants in food products faster and in smaller concentrations than its human counterparts. Generative AI models can use this data to aid in the detection of foreign objects and pathogens in either raw ingredients or finished food products. Analysis of historical and real-time data from temperature sensors in food production and warehousing facilities can help alert producers to conditions that contribute to excess food spoilage. When an agent recognizes farming or food processing irregularities, it can generate predictions based on historical data, check for compliance issues, and suggest operational improvements. By bringing together farm-specific data like local weather conditions, soil makeup, and pest populations, agents could help predict and mitigate seasonal risks to crops.

Looking ahead 

The future of food safety will rely on the continued integration of technology and data into the world’s food production and distribution processes. Customized agents powered by AI can perform tasks and provide decision support to improve food safety. These agents can be built to analyze vast amounts of data from spreadsheets, handwritten documents, voice memos, and videos, uncovering previously undetected errors and missing information.  

Companies in the farming sector can leverage Microsoft Copilot Studio to develop their own intelligent agents that assist with their most critical and risk-prone agricultural processes. Using the low-code interface of Copilot Studio, businesses can quickly create and deploy custom applications without extensive coding knowledge, enabling them to automate tasks such as crop monitoring, pest detection, and resource management. Companies can also choose to collaborate with Microsoft partners with industry-specific expertise, ensuring their solutions are tailored to their specific needs and comply with industry regulations. This partnership approach not only accelerates innovation but also ensures the deployment of robust and effective AI-powered solutions. 

By maximizing the potential of generative AI in food safety, we can predict and prevent many of the sector’s most prevalent issues, improve food quality, and prevent many food safety incidents. There are tremendous opportunities ahead, and collaboration between food producers, the regulatory bodies that oversee them, and technology companies are key to the success of these initiatives. By working together, we can create a safer and more sustainable food system for everyone. 

¹ Food Logistics, Food Recalls in 2024 are Surging. What’s the Crisis Response?, September 2024.

² World Health Organization, Foodborne Diseases Estimates.

³ U.S. Food & Drug, FDA Seeks $7.2 Billion to Protect and Advance Public Health by Enhancing Food Safety and Advancing Medical Product Availability, March 2023.

The post The growing need for AI in food safety appeared first on The Microsoft Cloud Blog.

Beginning in August 2024, Microsoft launched a new datacenter design that optimizes AI workloads and consumes zero water for cooling. By adopting chip-level cooling solutions, we can deliver precise temperature control without water evaporation. While water is still used for administrative purposes like restrooms and kitchens, this design will avoid the need for more than 125 million liters of water per year per datacenter.*

Zero-water evaporation and the quest for ultra-low Water Usage Effectiveness 

These new liquid cooling technologies recycle water through a closed loop. Once the system is filled during construction, it will continually circulate water between the servers and chillers to dissipate heat without requiring a fresh water supply. 

We measure water efficiency through Water Usage Effectiveness (WUE), which divides total annual water consumption for humidification and cooling by the total energy consumption for IT equipment. We are continually investing in improving the design and operation of our datacenters to minimize water use. In our last fiscal year, our datacenters operated with an average WUE of 0.30 L/kWh. This represents a 39% improvement compared to 2021, when we reported a global average of 0.49 L/kWh.  This WUE reduction is due to our ongoing efforts to actively reduce water wastage, expand our operating temperature range, and audit our data center operations. We also expanded our use of alternative water sources, such as reclaimed and recycled water, in Texas, Washington, California, and Singapore. 

We have been working since the early 2000s to reduce water use and improved our WUE by 80% since our first generation of datacenters. As water challenges grow more extreme, we know we have more work to do. The shift to the next generation datacenters is expected to help reduce our WUE to near zero for each datacenter employing zero-water evaporation. As our fleet expands over time, this shift will help reduce Microsoft’s fleetwide WUE even further.

Mitigating energy impacts 

Traditionally, water has been evaporated on-site to reduce the power demand of the cooling systems. Replacement of evaporative systems with mechanical cooling will increase our power usage effectiveness (PUE). However, our latest chip-level cooling solutions will allow us to utilize warmer temperatures for cooling than previous generations of IT hardware, which enables us to mitigate the power use with high efficiency economizing chillers with elevated water temperatures. 

The result is a nominal increase in our annual energy usage compared to our evaporative datacenter designs across the global fleet. Additional innovations to provide more targeted cooling are in development and are expected to continue to reduce power consumption. 

Pilot projects and implementation 

Although our current fleet will still use a mix of air-cooled and water-cooled systems, new projects in Phoenix, Arizona, and Mt. Pleasant, Wisconsin, will pilot zero-water evaporated designs in 2026. Starting August 2024, all new Microsoft datacenter designs began using this next-generation cooling technology, as we work to make zero-water evaporation the primary cooling method across our owned portfolio. These new sites will begin coming online in late 2027. 

Advancing sustainability: Sustainable by design 

Learn more about how Microsoft is advancing the sustainability of cloud and AI through our blog series:  

• Sustainable by design: Advancing the sustainability of AI
• Sustainable by design: Transforming datacenter water efficiency
• Sustainable by design: Innovating for energy efficiency in AI, part 1 

*Based on our FY 2024 global average withdrawal WUE of 0.30 L/kWh.

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