Across banking, capital markets, and insurance, some of the fastest-moving institutions are not simply “adopting AI.” They are becoming Frontier Firms, AI-powered organizations built around human-agent collaboration. In a sector where the cost of error is high, the financial services sector is emerging as an early proving ground for the Frontier Firm model.

The Microsoft 2025 Work Trend Index highlights a widening AI divide. While many organizations remain stuck in pilot mode, Frontier Firms are scaling agentic AI across their operations.

Our work with financial services leaders worldwide shows a clear pattern. The winners in the next generation of innovation will be those that combine human judgment with AI and agents, without compromising security, compliance, or customer trust. Critically, these advantages are best enabled through migration to a modern cloud foundation that can scale AI responsibly and reliably.

The crossroad: Modernize or let legacy debt grow?

Legacy systems have powered financial services for decades. Yet the very qualities that once made them indispensable—custom integrations, tightly coupled architectures, and deeply embedded processes—now create friction and fragility. Increasingly, they can be expensive to maintain, slow to change, and difficult to secure end-to-end. Worse, they can inherently constrain data access across the business, which limits advanced analytics and AI from delivering full value in key areas like customer engagement, fraud prevention, credit decisions, underwriting, and financial crime.

In many institutions, this accumulated technical debt is, in effect, an understated balance-sheet liability. It can increase operational overhead, complicate resilience planning, and broaden the cyber-attack surface. At the same time, regulators are demanding that firms prove stronger controls while, competitively, digital-native challengers are showing what’s possible when technology is designed for continuous change.

Modernization can help answer many of these challenges by helping position firms to gain competitive advantages that go well beyond cost efficiency. As workloads become increasingly cloud-native (in other words, designed to be built, updated, and scaled continuously in the cloud rather than tied to legacy infrastructure), organizations can launch new services faster, respond with agility, and use AI as part of everyday operations.

Waiting to migrate can increase risk and cost

A variety of factors are converging to increase the urgency of modernizing.

• Regulatory pressure is growing. Requirements for operational resilience, third-party risk oversight, data governance, and AI accountability are becoming more explicit and more enforceable. In Europe, the Digital Operational Resilience Act (DORA) raises the bar on stress testing, incident reporting, and information and communication technology (ICT) governance. In parallel, the European Union AI Act introduces demanding expectations for high-risk AI, including transparency, explainability, and bias mitigation. Globally, frameworks shaped by Basel guidance and securities regulators continue to push for stronger risk management, auditability, and controls across financial operations.
• Customer expectations are becoming non-negotiable. “Digital-first” now means more than building a polished mobile app. It means enabling instant transactions, proactive service, and personalized guidance—delivered consistently across channels. Doing all this at scale means that data must move securely and quickly, products should evolve continuously, and controls must be embedded rather than bolted on.
• The threat landscape is getting scarier. Threat actors are using automation and AI to increase both scale and sophistication. In a legacy environment, security improvements often arrive as point solutions, unevenly applied, and hard to validate. Cloud architectures, implemented with the right governance, help enable consistent identity controls, continuous monitoring, and policy-based protection that can be audited and improved over time.

Migration as a lever for innovation

Migration is too often framed as a technology initiative. For business and risk leaders, the more useful long-term view is as to regard it as a control and value strategy, a way to embed governance into the operating fabric of the firm.

This is why many transformation leaders manage cloud adoption as a sequence rather than a singular initiative, with a pathway from rehosting (“lift-and-shift”) through optimization and ultimately to AI acceleration. In this framing, modernization is not the finish line; it is the first step of compounding advantage.

Cloud migration, when managed well, can support a compliance‑by‑design approach, by which policy, identity, and data protections are consistently enforced. It can strengthen operational resilience through architectures that are built for redundancy, automated recovery, and continuous validation. And it can create an innovation pathway by making agentic AI practical to deploy and manage.

The AI-first divide: Cloud as operating model

As we see with Frontier Firms in financial services, innovation leaders tend to treat cloud architecture as more than an infrastructure choice. They use it as an operating model to standardize controls, build reusable platforms, and design processes that are increasingly AI-operated but human-led. The payoff can show up in faster deployment cycles, a lower cost per transaction, and predictive insights that make customer experiences more personal and operations more resilient.

Reaching that maturity typically requires progress across four transformation engines:

• Infrastructure modernization
• Legacy systems migration
• Systems modernization (including new business systems)
• Data modernization with AI integration

Financial services firms face stricter scrutiny than most industries, so the differentiator is not speed alone, it’s the ability to sustain speed while continuously demonstrating security, compliance, and control effectiveness.

We see this in practice across the industry. For example, UBS, following its acquisition of Credit Suisse, migrated a mission‑critical records platform from mainframe to a cloud‑native service on Microsoft Azure, reducing total cost of ownership by nearly 60% and improving their ability to meet regulatory demands. After LSEG migrated its high-volume, mission-critical Autex Trade Route (ATR) trading network from on-premises to Azure, the gains in scalability and resilience helped them absorb a sudden 400% surge in trading volumes with zero incidents. And the National Bank of Greece modernized document processing to improve accuracy and enable faster, more digital customer journeys. The common thread is not a single tool or model, it’s a cloud foundation that supports governed data, resilient operations, and repeatable innovation.

Turning migration into long-term value

For many firms, the hardest part of migration is not the technology; it’s making the journey auditable, repeatable, and aligned to risk appetite. That’s why a structured approach matters.

The Microsoft Cloud Adoption Framework, tailored for financial services, is designed to help institutions align cloud modernization to business outcomes while addressing the governance realities of the industry: data sovereignty expectations, operational resilience, and security-by-design. Importantly, cloud migration need not undermine data sovereignty; done right, migration strengthens locality, control, and compliance through governed architectures.

In practice, migration means helping businesses to build a compliant foundation, innovate responsibly, and maintain continuous control visibility as they scale. Microsoft supports this with financial-services-ready architectures, built-in governance and security capabilities, and a broad set of certifications and controls. Just as importantly, we work closely with customers and regulators globally to help ensure that cloud adoption can be evidenced properly in terms of risk reduction, resilience, and measurable operating improvement.

Trustworthy AI starts with the cloud foundation

Boards and regulators are right to focus on AI governance. Generative AI, agentic systems, and intelligent automation can improve productivity and customer outcomes, but only when they operate on governed data, with strong identity controls, clear lineage, and auditable policies. Those prerequisites are difficult to achieve in fragmented legacy environments.

Cloud migration creates the conditions for AI to be adopted responsibly, with modern data platforms and pipelines, elastic compute for experimentation and scale, consistent policy enforcement, and continuous monitoring.

To help institutions navigate migration with confidence, Microsoft combines a financial-services-tailored methodology with practical tooling and built-in governance. The Cloud Adoption Framework for financial services provides a proven, risk-aligned approach to planning and executing secure migrations. Azure Migrate and the Azure cloud migration and modernization programs help accelerate discovery, modernization, and execution with guidance and incentives. And capabilities like Microsoft Purview and Microsoft Defender for Cloud help establish compliance guardrails and security posture management from day one.

Lead the next generation with cloud

Migration is not the end state of digital transformation. It is the foundation for Frontier transformation, one which can enable firms to innovate faster, demonstrate stronger controls, and adapt quickly to new demands and opportunities.

The financial services firms that lead in the next generation of financial services will not be those that move the fastest in a single quarter. They will be the ones who modernize with technology that is durable, designed for operational resilience and evidence-based governance, and that makes innovation repeatable. Cloud migration is the inflection point where these powerful advantages become possible.

Learn more

• To explore a five-phase, risk-aligned approach to cloud migration that helps build the foundation for AI-first financial services, read the e-book: Becoming an AI-First Firm in Financial Services.
• Register for the Migrate and Modernize Summit to learn from industry leaders.
• Explore the Microsoft Cloud Adoption Framework for Financial Services.

The post Why cloud migration is key to realizing AI value in financial services appeared first on Microsoft AI Blogs.

Manufacturing is moving beyond local optimization toward a closed loop of end-to-end intelligent orchestration. Looking back at CES 2026, we can see that the industry narrative is quiet but fundamentally shifting. 

Across what we’re seeing with customers globally, three shifts stand out. First, the system shift. The operational foundation is evolving from digital to intelligent: more resilient, more real-time, and critically, more governable. Second, the data shift. The digital thread is no longer a static archive. It is becoming a living system—continuously updated and directly powering decisions as conditions change. Third, the work shift. We’re moving from copilots that assist individuals to agents that can collaborate and take on tasks—so the workflows themselves become more self-driving.

Together, these forces are raising the bar. Companies now need an end-to-end intelligent chain that turns AI from isolated point solutions into an organizational capability—reusable, scalable, and auditable. Drawing on Microsoft’s long-term work with manufacturers worldwide, and on how technology is evolving, I’d like to offer a practical framework for building that intelligent chain—so leaders can convert insight into action, and pilots into capabilities that scale.

AI use-case map for manufacturing: End-to-end intelligence from design to service

Scene One: Digital Engineering: Turning R&D into a profit engine

The role of the digital thread is evolving. Traditionally, it served primarily as a system of record—aggregating and archiving data. With AI and a unified data platform, it is becoming a real-time decision backbone spanning design, manufacturing, and service. Knowledge generated at one stage can now be applied immediately to improve outcomes in another. Generative and agentic AI are accelerating the core engineering loop—design, simulation, manufacturability analysis, and engineering change management—shortening iteration cycles and pushing risk discovery earlier in the process. Engineering data is no longer an R&D-only asset; it increasingly informs factory scheduling, quality strategies, maintenance policies, and service feedback loops.

This shift is already visible in practice. HARTING, a leader in industrial connectors, has deployed an AI assistant powered by Azure OpenAI and Microsoft Cloud for Manufacturing, making connector design faster, simpler, and more intuitive than ever before. Customers can describe their requirements in natural language, and the AI translates these inputs into technical specifications, guiding them to the right product within a minute. Customers can also visualize their configurations in 3D, enhancing confidence in their decisions. Siemens DI provides comprehensive cutting-edge software, hardware, and product lifecycle management solutions for industries including automotive and aerospace.

Using Microsoft Azure AI, Siemens DI developed a Microsoft Teams application for its industry-leading product lifecycle management (PLM) solution, Teamcenter. This solution analyzes unstructured voice content in multiple languages, automatically generates summary reports, and delivers information precisely to the relevant design, engineering, or manufacturing experts within Teamcenter. Through this intelligent collaboration mechanism, field issues are resolved faster, and knowledge transfer efficiency is significantly enhanced.

Scene Two: Intelligent Factory: AI is rewriting scheduling, quality, and maintenance

Production, maintenance, quality, and inventory remain the four core modules of factory operations—and that does not change in a smart‑factory context. What is changing is how these modules run. AI is systematically reshaping their operating logic: inventory management is moving from static rules to dynamic optimization based on real-time signals; quality management is shifting toward earlier, more precise judgments through computer vision, time‑series forecasting, and anomaly detection; and maintenance is evolving from after‑the‑fact repairs to predictive maintenance—progressing further toward adaptive process control.

As OT and IT capabilities mature, factories are gaining the ability to reason and respond directly at the point of value creation—on the shop floor, in real time. Frontline teams, empowered by multimodal Microsoft Copilot, can push the boundaries of what they can diagnose, decide, and execute. Over time, this human‑machine collaboration forms operational “agents” that can be deployed into production lines and day‑to‑day routines—turning intelligence into repeatable execution.

Global candy maker Mars operates manufacturing facilities across 124 locations worldwide. To safeguard its global equipment network, Mars partnered with Microsoft to deploy the Microsoft Defender for IoT solution. This enables visual management and threat detection for industrial equipment within stringent air-gapped production environments. Simultaneously, the solution transmits critical security data to a centralized system, enhancing data visibility while ensuring production continuity. International technology group Körber has transformed its market-leading PAS-X MES product into a cloud-based software as a service (SaaS) solution to address the stringent and multifaceted production management demands of the pharmaceutical sector. Using the robust stability of Microsoft Azure, Microsoft for Manufacturing, and Microsoft Azure Kubernetes Service, this solution enables customers to achieve greater flexibility and scalability. Simultaneously, by integrating data from IT and OT systems such as enterprise resource planning (ERP), supply chain management (SCM), and manufacturing execution system (MES), it delivers near real-time, actionable insights from diverse systems to employees. This significantly enhances equipment uptime, employee productivity, product quality, and overall output.

Scene Three: Resilient supply chain: From insight to execution with agentic AI

Early AI in supply chains mostly provided forecasts and dashboards. Valuable as they were, humans still needed to translate insights into action. The next step is agentic AI that executes—coordinating with suppliers, triggering replenishment or re-planning, optimizing inventory, and managing exceptions in logistics. When this happens, the traditional plan–execute–feedback loop transforms into a continuous intelligent system. The result is more than improved service levels—it enhances structural resilience and sustainability, as the system senses disruptions earlier, acts faster, and learns continuously.

A China-based electronics manufacturer, Xiaomi has built a unified after-sales supply chain management platform based on Microsoft Dynamics 365 and Microsoft Power Platform, using Azure for system integration and multilingual support. Utilizing Dynamics 365 Customer Service, Xiaomi has created a work platform that integrates financial processes, data integration, and security authentication across multiple communication channels. This platform also visualizes current inventory and proactively monitors and manages inventory levels in real time, enabling collaborative management between frontline services and backend supply chains. As a global leader in the smart terminal and home electronics industry, TCL is reshaping the industry landscape with its “Hardware + AI + Ecosystem” strategy, building a full-scenario ecosystem spanning multiple devices. Beyond driving innovative applications of Azure cloud and AI technologies in manufacturing, supply chains, and user experiences, TCL has pioneered the integration of Azure OpenAI, multimodal interaction, the intelligent Microsoft Copilot® assistant, and the Artificial Intelligence Generated Content (AIGC) ecosystem into smart TVs, smartphones, tablets, air conditioners, and other home appliances. This enables seamless cross-device connectivity and immersive experiences.

Scene Four: Connected customer: The product doesn’t end at delivery

In an AI-native model, product delivery is no longer the finish line. Customer experience continues through Over-the-Air (OTA) updates, AI-guided diagnostics, predictive service, and personal recommendations. AI enables a true closed loop—from customer feedback to engineering, factory, service, and back—turning experience into a growth driver rather than a cost center. None of this can scale without trust. As AI moves from recommendation to execution, governance becomes essential. Organizations need model governance, data and access control, OT and endpoint security, and explainability with rollback capabilities. This layer underpins all use cases, ensuring AI operates safely and reliably.

Epiroc, a Swedish mining and infrastructure equipment manufacturer, uses Microsoft Azure Machine Learning to build predictive maintenance and equipment performance models, transforming machine data into actionable customer insights. By identifying potential failures in advance and optimizing maintenance planning, Epiroc delivers a more proactive and transparent service experience, deepening customer relationships while opening new service-driven growth opportunities. Lenovo partnered with Microsoft to deploy the Microsoft Dynamics 365 Sales platform, thereby transforming its global customer relationship management (CRM) system.

By consolidating fragmented customer data and standardizing sales processes onto a unified digital platform, Lenovo achieved end-to-end visibility from lead management to opportunity tracking. The transformation improved collaboration efficiency, strengthened data-driven decision-making, and reinforced a more customer-centric operating model. In the “Hyper-Competition in High Dimensions” of the smart electric vehicle industry, NIO significantly boosts R&D efficiency by generating 610,000 lines of code daily through its intelligent GitHub Copilot® copilot, achieving an acceptance rate of up to 33%. The in-vehicle assistant NOMI, built on Azure OpenAI, enhances driving safety and user experience through precise contextual interaction. Simultaneously, Microsoft security solutions safeguard NIO’s complex IT environment and hybrid AI platform, automating daily threat detection and enabling cross-device security coordination.

Scene Five: Trust, safety, and OT security: The non-negotiable foundation

None of these AI use cases can scale without trust. Once AI moves from a recommendation system to an execution system, governance becomes essential. Manufacturing organizations need four core trust capabilities: model governance (ModelOps and Responsible AI), data and access control (Zero Trust architecture and industrial data protection,) OT and endpoint security, and explainability with controllability and rollback, so decisions can be understood, constrained, and safely reversed when needed. This is not a separate chapter; it forms the operating layer beneath all use cases, ensuring AI operates safely and reliably across the organization.

Ford, a longstanding automotive manufacturer synonymous with innovation, has deployed Microsoft solutions—including Microsoft Defender, Microsoft Sentinel, and Microsoft Purview—across its global operations. This initiative enhances visibility, automates responses, and strengthens data governance within its hybrid environment as companies worldwide face escalating cybersecurity threats. AI models learn from every interaction to improve detection capabilities and reduce false positives. With a unified security platform, Ford can focus on business strategy while reducing complexity and boosting operational efficiency. Smart pet device leader PETKIT is currently upgrading its systems on the Azure platform to achieve standardized device connectivity, telemetry data aggregation, and global compliance and security for users worldwide. Microsoft’s products and services not only enhance the company’s technological depth but also provide a cloud-plus-AI platform for global market replication.

2026: The inflection point when AI shifts from “more” to “different”

Once an end-to-end intelligent chain is in place, AI’s role inevitably shifts from offering advice to executing processes—and manufacturing moves from isolated efficiency gains toward full system redesign. In this sense, 2026 will be the year this transformation is proven on a scale. It will be a demanding moment for industry, but also a rare opportunity for leaders to make a true step change. This shift is becoming visible across several dimensions.

In 2026, AI in manufacturing will no longer exist as a collection of pilots. Instead, it will function as an enterprise nervous system—continuously sensing, learning, and coordinating decisions across functions. Organizations will move from experimenting with AI to running with AI, shifting from exploratory adoption to responsible, repeatable execution at scale.

Second, the ability to scale AI will become a key competitive differentiator. AI should not be confined to isolated applications but integrated into cross-departmental and cross-business collaboration to unlock its full potential. In other words, the gap between enterprises no longer lies in whether they deploy AI, but in their ability to achieve scalable implementation across the entire end-to-end value chain. Research from MIT and McKinsey suggests that leading enterprises can achieve up to four times the impact in half the time by building unified data and governance foundations.¹

Third, technical readiness will help define 2026. Edge inference, OT and IT integration, industrial networking, and model governance have matured to the point where AI can operate directly where value is created—on the plant floor, in real time, and within the flow of work. AI is moving beyond general content generation toward deep operational integration, spanning equipment, processes, quality, and logistics, and becoming an integral part of closed-loop industrial control.

Beyond technology, people, governance, and culture will emerge as true differentiators. In 2026, the primary constraint for many manufacturers will be organizational readiness—the ability to share data responsibly, collaborate across silos, and build AI literacy and operating rhythms that sustain change. Research on scaling AI highlights the “10–20–70 rule”: roughly 10% of success comes from algorithms, 20% from technology and data foundations, and 70% from people and processes.¹ Scaling AI effectively therefore requires building skills, accountability, and safety-and-governance capabilities in parallel with the technology itself.

Finally, the maturation of industry standards and ecosystems will accelerate broader AI adoption. Manufacturers face converging pressures—from geopolitics and cost to compliance and supply chain resilience. According to public records, 81% of manufacturers cite fear of falling behind as a primary driver of adoption.² The implication is clear: the question is no longer “Do we need AI?” but “Can we afford not to evolve?” As industrial data semantics, standardized APIs, reference architectures, and increasingly packaged solutions mature, time-to-value will shorten and complexity will fall—making AI feasible for a much broader set of manufacturers.

From insight to action: A 2026 checklist for manufacturing leaders

At this point, the question is no longer abstract: can your organization turn AI capabilities into sustainable, day-to-day operations—rather than pilots and demos? In conversations with manufacturers around the world, this question consistently separates leaders from laggards:

• Strategic clarity: Have you defined the core business problems AI must solve, beyond simply “adopting AI”?
• Data foundation: Can your data platform support real deployment, not just proof-of-concept results?
• Operational readiness: Are your factories and supply chains prepared for AI-powered routines in daily execution?
• Workforce capability: Does your workforce have the baseline skills to work effectively with AI systems?
• Ecosystem usage: Do your partners and platforms support continuous upgrades and rapid scaling?
• Governance and security: Is governance strong enough for AI to move from recommendation to execution?
• Resilience impact: Is AI measurably strengthening operational resilience?

We can already see the direction of travel toward the future. But trends alone do not create leaders. Execution does. The real differentiator will be who can turn AI from concept into action, from tool into capability, and ultimately from capability into resilience.

Advancing intelligent manufacturing with Microsoft

Manufacturing is entering a new phase—powered by actionable data, increasingly autonomous systems, and a more empowered workforce. Companies that unify their data, drive autonomy across planning and execution, and integrate the value chain through digital threads and digital twins will be best positioned to convert operational excellence and innovation into sustained growth.

Against this backdrop, Microsoft continues to work closely with manufacturers to expand what is possible across design, production, supply chain, and service. By combining cloud, data, and AI platforms that are advanced yet practical to deploy, we aim to help organizations build end-to-end intelligent operations—accelerating innovation while maintaining security, responsibility, and scale.

¹ KPMG, Intelligent manufacturing A blueprint for creating value through AI-driven transformation.

² businesswire, Ninety-Five Percent of Manufacturers Are Investing in AI to Navigate Uncertainty and Accelerate Smart Manufacturing, June 2023.

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Deepfakes threaten trust in news, elections, brands and everyday interactions, leading us to question what’s real. Determining what’s authentic or manipulated is the subject of Microsoft’s “Media Integrity and Authentication: Status, Directions, and Futures” report, published today. The study evaluates today’s authentication methods to better understand their limitations, explore potential ways to strengthen them and help people make informed decisions about the online content they consume.

The authors conclude that no single solution can prevent digital deception on its own. Methods such as provenance, watermarking and digital fingerprinting can offer useful information like who created the content, what tools were used and whether it has been altered.

People can be deceived by media if they lack information like its origin and history, or if its information is low-quality or misleading. The goal of the report is to provide a roadmap to deliver more high-assurance provenance information the public can rely on, according to Jessica Young, director of science and technology policy in the Office of the Chief Scientific Officer at Microsoft.

Helping people recognize higher-quality content indicators is increasingly important as deepfakes become more disruptive and provenance legislation in various countries, including the U.S., introduce even more ways to help people authenticate content later this year.

Media provenance has been evolving for years, with Microsoft pioneering the technology in 2019 and cofounding the Coalition for Content Provenance and Authenticity (C2PA) in 2021 to standardize media authenticity.

Young, co-chair of the study, explains more about what it all means:

What prompted the study?

“The motivation was two-fold,” Young says. “The first is the recognition of the moment we’re in right now. We know generative AI capabilities are becoming increasingly powerful. It’s becoming more challenging to distinguish between authentic content — like content that was captured by a camera versus sophisticated deepfakes — and as a result, there’s a huge uptick right now in interests and requirements to use those technologies that exist to disclose and verify if content was generated or manipulated by AI.

“The moment has been building, and we have a desire to help ensure that these technologies ultimately drive more benefit than harm, based on how they’re used and understood.”

Young adds that the paper is meant to inform the greater media integrity and authentication ecosystem, including creators, technologists, policymakers and others to understand what is and isn’t possible currently and how we can build on it for the future.

What did the study accomplish, and what did you learn?

The report outlines a path to increase confidence in the authenticity of media. The authors propose a direction they refer to as “high-confidence authentication” to mitigate the weaknesses of various media integrity methods.

Linking C2PA provenance to an imperceptible watermark can bring relatively high confidence about media’s provenance, she says.

She notes the report has a lot of caveats too, such as how provenance from traditional offline devices like cameras, which often lack critical security features, can be less trustworthy because it’s easier to alter.

It isn’t possible to prevent every attack or stop certain platforms from stripping provenance signals, so the challenge, Young says, “is figuring out how to surface the most reliable indicators with strong security built in — and, when necessary, reinforce them with additional methods that allow recovery or support manual digital-forensics work.”

How is this study different from others?

Young says their study investigated two “underexplored” lines of thought for the three methods of verification. They define the first as sociotechnical attacks, where provenance information or the media itself could be manipulated to make authentic content appear synthetic or fake content seem real during the validation process.

“Imagine you see an authentic image of a global sporting event with 80% of the crowd cheering for the home team,” she says. “The away team engages in an online argument claiming, ‘Hey, no, that’s all a fake crowd.’ Someone could make one small, insignificant edit to a person in the corner of the picture and current methods would deem it AI generated — even if the crowd size was real. These methods that are supposed to support authenticity are now reinforcing a fake narrative, instead of the real one.

“So, knowing how different validators work, even through really subtle modifications, you could manipulate the results the public would see to try to deceive them about content,” she says. The second key topic builds on the C2PA’s work to make content credentials more durable, while also addressing reliability. This is where the research is especially novel, Young says. “We looked at how provenance information can be added and maintained across different environments — from high-security systems to less secure, offline devices — and what that means for reliability.”

Why is verifying digital media so difficult?

Authenticating media is complex because there’s not a one-size-fits-all solution, Young says.

“You have different formats that have different limitations or trade-offs for the signals they can contain,” she explains. “Whether it’s images, audio, video — not to mention text, which has a whole different array of challenges — and how strong the solutions can be applied there.”

Young says there are different requirements and opinions about what level of transparency is appropriate as well. In some cases, users might not want any of their personal information included in the digital provenance of a piece of media, while in others, creators or artists might want attribution and to opt-in for having their information included.

“So, you have different requirements or even considerations about what goes into that provenance information,” she says. “And then, similar to the field of security, no solution is foolproof. So, all the methods are complementary, but each has inherent limitations.”

Where do we go from here?

Young says that as AI-made or edited content becomes more commonplace, the use of secure provenance of authentic content is becoming increasingly important. Publishers, public figures, governments and businesses have good reason to certify the authenticity of the content they share. If a news outlet shoots photos of an event, for example, tying secure provenance information to those images can help show their audience the content is reliable.

“Government bodies also have an interest in the public knowing that their formal documents or media are reliable information about public interest matters,” Young says.

She adds that as AI modifications to media become “increasingly common” for legitimate purposes, secure provenance can provide important context to help prevent an average reader or viewer from simply dismissing that content as fake or deceptive.

“For the industry and for regulators, we note how important continued user research in this area is to drive towards more consistent and helpful display of this information to the public — to make sure it’s actually meaningful and useful in practice,” Young says.

“We have a limited set of technologies that can assist us, and we don’t want them to backfire from being misunderstood or improperly used.”

Learn more on the Microsoft Research Blog.

The post A new study explores how AI shapes what you can trust online appeared first on Microsoft AI Blogs.

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 Microsoft AI Blogs.

Yet social services leaders are under growing pressure to deliver both human outcomes and financial stewardship at scale. Backlogs, fragmented records, and manual evidence reviews strain frontline staff, while delayed verification and siloed data expose programs to error and misuse. The challenge is no longer choosing between inclusion and integrity. Modern eligibility systems must deliver both. 

Why does this matter now? 

The financial implications are significant. Around the world, governments are confronting the cost of improper payments, fraud, and administrative inefficiencies: 

• In the United States, the Government Accountability Office reports that 16 federal agencies estimated about $162 billion in improper payments in FY2024, with roughly 84% due to overpayments.¹ 
• In the United Kingdom, public sector analyses estimate £33 Billion to £59 billion annually in fraud and error.² 
• In Australia, the Australian National Audit Office reports that in 2021–2022, Services Australia delivered $124.7 billion in welfare payments, with an estimated 6.71% in overpayments.³ 
• In India, a government press note summarizing a quantitative assessment highlights ₹3.48 lakh crore in cumulative savings attributed to leakage reduction enabled by the country’s Direct Benefit Transfer program.⁴ 

At the same time, large-scale digital identity and cash transfer reforms around the world demonstrate  what’s possible when delivery systems modernize. These transformations show that improving both inclusion and fiscal stewardship is not only possible—it’s already underway. Modernizing eligibility is no longer just an IT upgrade. It is a service delivery transformation, a fiscal stewardship strategy, and a trust- building effort between governments and the people they serve.

Microsoft’s point of view 

Microsoft’s point of view is simple: modern eligibility is not about replacing human judgment with automation. It is about augmenting frontline staff with secure, interoperable, AI-enabled tools that fit into the systems governments already rely on. 

That’s why our approach emphasizes identity as infrastructure, evidence as data, and AI with humans in the loop—so agencies can modernize incrementally, maintain accountability, and adapt as policies evolve. 

What changes when eligibility is designed around real lives? 

When eligibility systems are designed around programs rather than people, friction is inevitable. Households move across life events faster than policies or systems can adapt, forcing staff to reconcile fragmented records, incomplete documentation, and outdated rules. 

Leading agencies are addressing this by treating eligibility not as a one-time decision, but as a continuous, connected process—grounded in strong identity, structured evidence, and shared data across programs. 

What modern eligibility looks like

Modernization is not a monolithic system replacement. It is a set of incremental, coordinated capabilities that governments can adopt without wholesale replacement.

Below are the core capabilities that define modern eligibility today. 

Identity as eligibility infrastructure 

Eligibility starts with a foundational question: Who is applying, and is it really them? 

Identity theft doesn’t just divert public funds—it can lock legitimate residents out of help. Treating identity as a side project is increasingly a risk. 

In South Australia, the Department of Human Services uses Microsoft Entra ID to strengthen identity protection through role-based access controls, multifactor authentication, and print and screen access safeguards. These steps help protect sensitive records and support secure self-service—without adding friction for legitimate users. 

Turning documents into usable data 

Documents are often the hidden tax on benefit delivery. Much of the delay in eligibility processing comes not from policy rules but from handling paperwork—reading scans, re-entering information, or chasing missing pages. 

The Czech Republic’s Ministry of Labor and Social Affairs addressed this by using Azure AI Document Intelligence to extract data from paper forms and accelerate payment of childcare allowances. The Jenda portal also gives families visibility into application status and connects them to upskilling opportunities—illustrating how digitizing evidence can improve both speed and experience. 

Connecting fragmented records to see the full picture 

A resident may interact with multiple programs, often across separate systems. Fragmented data can lead to duplication, inconsistent decisions, or missed support. 

Singapore’s Central Provident Fund Board modernized its data management approach with Azure Databricks to serve more than four million people with a more holistic view—a strong example of how connected data improves outcomes while reinforcing integrity. 

Aligning eligibility with life events

Eligibility is not static. Circumstances change: employment shifts, caregiving arrangements evolve, households expand or contract. 

Modern systems use AI—responsibly and with humans in the loop—to: 

• Collect and structure evidence 
• Surface relevant context 
• Reduce administrative effort 
• Route complex cases to specialists 

The Washington, DC Child and Family Services Agency (CFSA) built an AI-enabled platform that saves 45 minutes per intake and expects even greater time savings for investigations, while enabling new features to be deployed faster and at lower cost. 

All AI capabilities described here align with Microsoft’s Responsible AI principles and maintain human accountability throughout the process. 

Detecting anomalies earlier to protect funds

Fraud and error often exploit timing: delayed verification, siloed data, or missing crosschecks. 

European public sector fraud authorities are increasingly looking to augment AI‑powered analytics platforms with broader datasets, such as sanctioned entities and dormant companies, to strengthen early detection capabilities and help investigators surface potential risks sooner.

A practical path forward for social services and government leaders

Many eligibility modernization efforts stall because they focus on a single dimension—speed, cost reduction, or compliance—at the expense of the others. Microsoft’s approach is designed to advance service delivery, integrity, and trust together, using platforms that governments already operate and govern. That balance is what allows modernization to endure beyond a single program or funding cycle. 

Whether a program is just beginning modernization or aiming to scale next-generation capabilities, leaders can start with achievable, high-value steps: 

• Start where friction is highest: Identify the program with the heaviest documentation burden or the largest backlog. Early wins build momentum and trust. 
• Treat identity as foundational: A strong identity layer protects against impersonation and enables secure self-service for residents and staff. 
• Digitize the evidence pipeline: Use document intelligence to convert evidence into structured data so staff can focus on exceptions—not re-keying information. 
• Connect data to reduce duplication and missed support: A holistic view—especially at the household level—helps ensure decisions reflect real circumstances and prevents duplicative benefits. 
• Embed continuous integrity: Use signals, analytics, and network insights to focus oversight where risk is highest without creating barriers for eligible residents. 
• Measure what matters: Track speed, accuracy, integrity, and resident experience together. Modernization that improves only one dimension rarely endures. 

This is where Microsoft differentiates—enabling agencies to modernize eligibility without sacrificing accountability, trust, or program continuity.

A more trusted, human-centered future for benefits 

For social services leaders, the next step isn’t a wholesale system replacement. It’s identifying where eligibility friction is highest—and where stronger identity, smarter evidence handling, or connected data could immediately improve outcomes for residents and staff. 

Learn how agencies are applying these capabilities today and explore where modernization can start in your own programs.

Are you attending HIMSS Global Health Conference and Exhibition in March this year? Make sure to check out the Microsoft sessions and expo booth.

¹US Government Accountability Office

²Global Government Finance

³Australian National Audit Office

⁴Government of India Press Information Bureau

The post Right benefit, right person, right time: How AI is reshaping administration of benefits programs worldwide appeared first on Microsoft AI Blogs.

Today, a new architectural approach is finally emerging, and it is agentic. The rapid advance of agentic AI represents an evolution from reactive interactions to goal-oriented experiences across all aspects of banking. Unlike traditional keyword-based bots, agentic assistants can understand intent, maintain memory, take initiative, and orchestrate tasks across systems. They can support multi‑step workflows, operate within defined policies, and assist customers in a single, intelligent pane of access.

For banking, these advanced capabilities have finally aligned to ever-higher levels of customer expectations to make agentic AI not only viable but increasingly leveraged by leading banks.

Why banking needs a new model

Most customer-facing automation in banking is now rule-based. Traditional chatbots merely answer questions. They don’t finish tasks, much less resolve important needs. They rely on keyword matching, offer minimal personalization, and they operate as single channel interfaces that usually escalate issues instead of resolving them. Too often, this leads to low containment, long cycle times, and customer frustration.

Agentic AI assistants change the equation. They can integrate deeply into core systems, understand identity and consent policies, and provide end-to-end workflow orchestration that delivers more positive outcomes.

AI models now support multistep reasoning, secure APIs allow policy-aware actions, and cloud environments enable industry-grade identity, consent, and auditability.

The time is now for agentic AI

The rapid adoption of broad-scale agentic AI solutions in banking is the product of the convergence of some powerful trends:

• AI-native experiences have reset customer expectations: Consumers increasingly expect proactive, personalized, and frictionless digital interactions.
• Industry competition is intensifying: Highly innovative banks and financial institutions are scaling customer-facing AI capabilities and raising the bar for the entire market.
• Secure orchestration is now achievable: Banks have built robust foundations for consent, governance, compliance, and identity, all of which are essential for safe agentic actions.
• Models can now execute multi‑step tasks: Banking no longer needs to settle for static flows and limited interactions; assistants can complete complex journeys from disputes to onboarding.

As these factors accelerate, agentic banking is fast gaining momentum. In fact, it is already operational today for many financial institutions.

A three-step blueprint for agentic solutions

Microsoft’s blueprint to help banks develop game-changing innovations includes a structured, deliberate path for adopting agentic AI across internal and customer-facing scenarios. Rather than layering AI onto outdated workflows, institutions must redesign experiences with outcomes in mind. This can be done through the development of three steps of AI innovation:

Step 1: Internal employee assistants

In this step, banks strengthen the maturity of AI innovations internally, by improving employee productivity and supporting back office workflows such as Anti-Money Laundering (AML) routing, document gathering, and payment operations. This phase establishes the organizational readiness needed for external experiences.

Step 2: External customer assistants (owned channels)

In this step, banks introduce customer-facing assistants within their digital properties, such as websites and mobile apps. These solutions initially target a narrow set of journeys to help validate measurable outcomes and build confidence, setting the stage for scale, including deeper transactional use cases.

Step 3: External customer assistants on third-party platforms

Once confident, banks can deliver rich, new AI-enabled experiences beyond their own digital properties, helping to stay foremost in the customer relationship. Even as the front door shifts to non banking platforms, banks can retain primary engagement by anchoring identity and execution within governed, policy driven solutions that can incorporate agentic AI assistants from multiple platforms (ChatGPT, Gemini, Microsoft Copilot, and so on).

Real-world impact in agentic banking is well underway

Across the customer journey, agentic experiences are transforming outcomes. Here are just four areas where we work with customers to deliver measurable benefits.

Disputes and fraud resolution

Disputes and fraud incidents are among the most stressful and urgent customer interactions in banking. These moments demand precision, empathy, and speed —which traditional chatbots usually can’t deliver. Agentic assistants change this experience by understanding transaction context in real time, anticipating customer needs, explaining next steps with clarity, and orchestrating complex actions across compliance, fraud, and operations systems. They help manage escalation intelligently while keeping customers informed with conversational transparency.

Commerzbank’s introduction of an AI-powered assistant called “Ava” demonstrates the impact of this shift. Built with Microsoft Foundry Agent Service, Ava reportedly now resolves about 75% of customer conversations autonomously. The result is a dramatic reduction in response times, more consistent fraud handling, and meaningful relief for human agents who can focus on high complexity cases requiring expertise and judgment.

Product discovery and onboarding

Even when banks offer strong products, customers often struggle to understand differences, evaluate eligibility, or navigate onboarding processes. Static comparison charts and rigid forms create barriers that trigger abandonment. Agentic assistants address this gap by offering contextual, conversational discovery. They can analyze eligibility, financial behaviors, and long-term goals to guide customers toward the most relevant products, compressing the time from interest to completion.

For instance, ABN AMRO’s migration to Microsoft Copilot Studio showcases these benefits at scale. Their customer facing assistant “Anna” now supports millions of customer interactions annually, automating more than half of them. Customers receive tailored recommendations and seamless onboarding, while the bank benefits from reduced abandonment and increased conversion rates across key products.

Payments and money movement

Customers today simply expect that payments should be fast, intuitive, and free of error. Instead, many people frequently encounter multiscreen forms, confusing validation steps, and interfaces that are prone to mistakes. Agentic AI helps eliminate much of this friction. Customers can simply say what they want to do—for example, “send rent,” “transfer to my savings,” “pay my credit card”—and the assistant determines the optimal method, confirms details, and applies safeguards automatically.

A good example of this is Bradesco’s deployment of generative AI into its virtual assistant “BIA.” After integrating Microsoft Azure OpenAI and Data Lake services, BIA reportedly achieved an 82% first level resolution rate and an 89% retention rate in the first week. Response times fell from days to hours, and usage surged. Payments became conversational, secure, and reliable, helping build long term customer confidence while improving operational efficiency.

Financial guidance and servicing

Financial decisions are deeply personal and often complex. Customers want clarity, reassurance, and the sense that their institution understands their broader financial picture. Agentic assistants support this by combining institutional expertise with personalized context. They can remember life events, adapt to changing goals, and help explore scenarios, understand options, and stay informed about their financial commitments.

Virgin Money embodies this evolution through its award-winning assistant, “Redi.” Built with Microsoft Copilot Studio and Dynamics 365 Customer Service, Redi reportedly now supports millions of customers and delivers what they need more than 90% of the time. The guidance feels informed and tailored, strengthening trust and deepening long-term relationships. Employees report smoother workflows, while customers experience consistency and clarity across channels.

Advancing digital transformation with agentic AI

For banks, technology is finally catching up with customer expectations. The shift is transforming digital experiences from reactive support into proactive engagement.

Agentic AI solutions are defining the next generation of customer experiences, and banks that move now can better position themselves to gain durable competitive advantages by modernizing operations from the inside out and engaging customers in ways that were not previously possible.

Microsoft provides an unmatched set of platforms and services that combine data intelligence, orchestration, and observability to help build, deploy, govern, and scale agentic assistants. Our investments in Security for AI, Zero Trust, and AI governance, help banks keep agentic experiences safe and trusted across the AI lifecycle. This means that with the right blueprint banks can navigate this moment with confidence, clarity, and control.

Explore how agentic AI can modernize banking experiences

• Get a practical transformation framework and see how Frontier Firms lead with agentic AI in our Becoming a Frontier Firm: AI in Financial Services e-book.
• Watch Leading at the Frontier: AI in Financial Services to see how leaders in financial services are successfully using AI to transform complexity into opportunity.
• Visit our blog for stories of how Microsoft for Financial Services helps firms accelerate business value.

The post The agentic moment in banking: A blueprint for better customer experiences appeared first on Microsoft AI Blogs.

Microsoft Global Skilling helps people and organizations build the skills they need to thrive in an AI‑powered world. Within Global Skilling, the Learning Lab is the innovation engine—a team focused on designing, testing, and evolving modern learning experiences to continuously improve how skills are developed, validated, and applied in the flow of work. 

AI is reshaping how organizations work. Teams aren’t just adopting new tools—they’re also figuring out how those tools fit into existing workflows, roles, and expectations, all while trying to keep pace with business demands in a rapidly changing landscape. It’s a heavy lift. As the leader of the Learning Lab team, I’m navigating these same pressures, along with my team members, as we balance day-to-day delivery with the need to evolve our processes in real time. That’s why we’re embedding AI assistants and agentic workflows into internal processes—using them not only to work differently but also to learn differently. Through experimentation, we’re uncovering new ways to streamline operations and improve the learner experience for our global audience.  

This blog highlights three of our team’s most impactful AI initiatives that could also benefit your organization. Inspired by these projects, we developed A Practical Guide for Bringing AI into Your Business Processes, featuring real-world examples and actionable ideas for integrating AI and human expertise across your organization. 

A Practical Guide for Bringing AI into Your Business Processes

Download the guide

3 impactful AI initiatives leading the way

1. Reducing time-intensive coordination to optimize research 

The challenge of coordinating teams for research  

Before any learning materials can be built, our team conducts extensive research to understand new technologies, identify required skills, and validate what learners need. This early-stage analysis requires input from multiple stakeholders and a deep review of internal documentation, product roadmaps, and existing training materials.  

How AI is helping accelerate our research tasks and optimize cross-team input 

One of the biggest bottlenecks for our research workflows has been the time it takes to synthesize information and align teams around what a course should achieve. To improve this, we began experimenting with Researcher in Microsoft 365 Copilot and persona-based agents to support our research and planning stages. Our new process looks like this: 

• Researcher synthesizes internal documentation, product roadmaps, and existing training materials to surface emerging themes and identify knowledge gaps. With the ability to process thousands of pages in minutes, it flags potential course objectives the team might have missed.
• In parallel, persona-based agents simulate the perspectives of stakeholders from varying teams to help validate ideas before bringing them to the key decision-makers.
• Throughout this process, our team members guide these AI tools through every step—providing the business context, analyzing AI outputs to identify gaps or inconsistencies, refining direction, and ensuring consideration of broader business objectives.  

In our experience with AI handling synthesis and early-stage validation, we’ve reduced the time required for core research processes from two weeks to just one day. This significant time savings extends to every course developed with this method, enabling us to redirect focus toward shaping stronger strategies, aligning content with business impact, and accelerating decision-making across teams.

Applying this approach in your organization 

AI-supported research and planning can help you make sense of complex information faster and build alignment earlier in your decision cycles. By using AI to synthesize documents, surface patterns, and validate assumptions, you can reduce the effort required to get teams on the same page. Your team members can then focus on refining strategy, confirming business priorities, and shaping higher-impact decisions. This combination improves speed and clarity throughout cross-functional work.  

Explore A Practical Guide for Bringing AI into Your Business Processes to learn more about how you can apply this in processes like: 

• Drafting onboarding plans that human resources (HR) leaders can tailor to company culture.
• Developing quarterly sales plays informed by shifting buyer behavior and competitor activity.
• Creating campaign briefs rooted in audience insights, market trends, and performance data.
• Developing forecasting assumptions by synthesizing inputs from sales, operations, and historical data. 

2. Transitioning from manual maintenance to continuous quality improvements 

The challenge of shorter content lifecycles  

We maintain thousands of courses and lab environments as part of our skilling initiatives for Microsoft technologies. With the fast pace of product evolution, it can be challenging to keep learning content accurate and functional.  

How GitHub Copilot became the maintenance partner for the team 

We recognized that the demands for maintaining learning content were increasing beyond our capacity to manage effectively. So we integrated GitHub Copilot into the content maintenance workflow like this: 

• GitHub Copilot tools analyze content repositories—flagging inconsistencies, identifying outdated examples, and recommending updates based on current documentation.
• Throughout this process, our team reviews and refines the AI-generated recommendations. When GitHub Copilot flags an issue, we evaluate how those changes might apply to other training courses. We also ensure that all revisions align with learning objectives and verify that security and accessibility standards are met.
• Then GitHub Copilot helps implement some of the suggested updates, like generating new code samples or suggesting environmental configurations that align with the latest product releases. 

As a result, our team has reduced the time we spend on routine content maintenance by up to 25%. And with these time savings, team members can shift from reactive updates to proactive innovation—evaluating emerging skills, shaping next-generation modules, and exploring how agents, simulations, and personalized learning could improve outcomes. 

Applying this approach in your organization 

AI-assisted maintenance can help you keep large, fast-changing content ecosystems accurate and up to date without overwhelming your teams. By using AI to surface inconsistencies, flag outdated material, and recommend updates, you can dramatically reduce time spent on routine fixes. Your experts can then focus on reviewing changes for accuracy, regulatory needs, and strategic intent. This balance enables you to maintain quality at scale while freeing your teams to invest in higher-value innovation.  

Explore A Practical Guide for Bringing AI into Your Business Processes to learn more about how you can apply this in processes like: 

• Maintaining and updating sales enablement content as product and service offerings evolve.
• Keeping product messaging frameworks and campaign assets consistent and up to date.
• Updating help center articles and support workflows after feature releases.
• Updating contract templates and clause libraries to align with new regulatory guidance.

3. Delivering inclusive learning at scale through diverse content formats 

The challenge of content relevance and engagement  

Our learners span every continent, speak dozens of languages, and have their own preferred learning methods. Creating multimodal, accessible, and inclusive learning experiences while managing constant content updates was stretching the team thin.  

How AI helps scale and translate content for global learners  

To support different learning styles and languages, we’re piloting how to create immersive, inclusive learning through two experiments with AI: 

1. We’re using AI tools to turn a single source of training content, like a session transcript or recording, into multiple formats, such as videos, podcasts, and recap summaries. This multimodal output lets us update learning materials at the pace required by our global audience and helps ensure that we’re reaching learners in their preferred formats.
2. We’re piloting an AI-powered tool that not only translates content but also generates avatars that deliver multilingual voiceovers with more natural lip-sync, eliminating one of the most distracting elements of dubbed content. 

Early results show that we can now recover up to 15 hours per course we develop—time our team can spend on more nuanced work that AI can’t do, like adapting cultural references, verifying that tone and pacing match learning objectives, and maintaining brand voice. 

Applying this approach in your organization 

AI-powered localization can help you deliver content that feels native to every audience you service, no matter the language or market. By pairing AI’s speed in translation, voiceover, and prompt generation with your team’s expertise in cultural nuance and brand standards, you can scale global engagement without diluting quality. This combination lets you reach more learners, customers, and employees while keeping your message consistent and relevant across regions.  

Explore A Practical Guide for Bringing AI into Your Business Processes to learn more about how you can apply this in processes like: 

• Localizing campaign assets for regional markets across languages and cultural norms.
• Tailoring pitch decks and demos for industry-specific or region-specific buyers.
• Creating multilingual chatbot responses and support scripts for global customers.
• Adapting standard operating procedure and process documentation for different facilities or regional regulations. 

Building skills and strengthening our AI strategy

As AI becomes an extension to the Learning Lab, we’ve discovered that it’s much more than just implementing new tools—it’s also a journey of building technical and human skills across the team. Our experiments require every team member to stretch into new capabilities, from process optimization and innovation to strengthening collaboration and creative problem-solving. As a result, we’ve been able to spend less time on repetitive tasks and to dedicate more energy to the kind of creative, relationship-driven work that leads to exceptional learning experiences. 

Looking to build skills for you and your teams? Explore AI Skills Navigator, the agentic learning space that brings together AI-powered skilling experiences and credentials that help individuals build career skills and organizations worldwide accelerate their business.

The post How to bring human expertise and AI together: 3 impactful initiatives appeared first on Microsoft AI Blogs.

With advances in AI—especially intelligent agents and the automation patterns emerging from agentic design—insurers worldwide are recasting their most critical operations and offerings. From marketing and customer engagement through underwriting and claims processing, the industry is rapidly evolving, with AI as a central driver.

At Microsoft, we identify organizations that embed AI agents deeply across their operations as Frontier Firms. These are innovation leaders who are blending human judgement with AI agents and who, according to a November 2024 IDC study commissioned by Microsoft, report returns roughly three times higher than slow adopters.¹

Insurers and other financial services companies make up the highest concentration of Frontier Firms, which is not surprising given the competitive nature of the sector and the outsized impact of agentic AI.

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How AI is transforming the end-to-end insurance value chain 

Insurers can potentially realize transformative benefits with AI without needing to replace their core platforms, but rather by augmenting and accelerating them through targeted, extensible, AI-powered capabilities. Through advances such as intelligent agents and the automation patterns emerging from agentic design, insurers are consolidating fragmented workflows into connected, intelligent, adaptive systems.

Consider the impact on claims processing. In 2024, more than 30 million personal auto claims were reported in the US alone.² Each one typically required adjusters one to three days just to gather, read, and interpret documents. The slow, manual nature of traditional claims processing is one of the most labor intensive and high impact functions in insurance. It is also where agentic AI delivers some of the fastest return on investment (ROI). For example, AI can help automate document understanding and summarization for faster and more accurate processing. In policy and coverage validation, it can help reduce back-and-forth queries between adjusters and underwriters and speed the approval of well-qualified claims. In contextual triage and routing, it can help improve the productivity of employees across claims processing by enhancing early fraud detection and reducing delays caused by manual sorting or misrouting. With millions of claims processed annually and cycle times measured in days or weeks, even modest improvements can potentially create significant financial and customer experience gains.

Agentic AI is reshaping much more than claims. Across the value chain, a unified agentic ecosystem can deliver measurable outcomes.

In underwriting, agents can automate information gathering processing to help sales agents submit more complete requests for quotes to underwriters. Agents can help interpret submissions, orchestrate scenarios and catastrophe modeling, and assist in generating proposals aligned to client mandates.

In marketing and distribution, agents can redefine the customer experience by increasing personalization at scale with speed and boosting sales opportunities. Agents can flag top renewals and generate personalized outreach, help prioritize leads, optimize campaigns and prepare tailored client briefs and pitch materials in seconds.

In customer onboarding and service, service become more anticipatory and less reactive. Agents can help validate information across documents automatically and detect missing forms or inconsistencies early. Virtual assistants can answer inquiries around-the-clock with contextual accuracy and trigger proactive outreach if a customer shows signs of churn or claim frustration.

In risk and compliance, teams move from firefighting to orchestrating safe, scalable operations. Under the direction of qualified processionals, agents can help monitor exposures continuously across economic, climate, and portfolio data, read regulatory updates and support assessment workflows, and help detect fraud by surfacing potential issues to the appropriate teams and workflows.

How agentic AI is benefiting insurers worldwide

Already, we’re seeing the impact of agentic AI building on the benefits of generative AI to deliver transformative new benefits for insurers.

For example, Generali France is transforming insurance operations with intelligent agents that empower front‑line workers and experts across the business to achieve a people-centric vision for product and service delivery. The firm has built more than 50 agents with Microsoft Copilot Studio and Azure OpenAI to address a broad range of specialized used cases. These agents do more than generate content, they act across complex information flows, from extracting information from unstructured data and running hyper-personalized marketing campaigns, to assisting with content creation and standardizing responses to requests for proposals (RFPs). These powerful solutions allow experts to focus on judgment and customer care, measurably helping Generali achieve top‑ranked customer satisfaction.

Elsewhere, a major global insurer strengthened its crisis response in near real-time by using AI to rapidly compare property locations with public wildfire evacuation data. Instead of hours of manual analysis, teams quickly generated clear, actionable risk insights, improving situational awareness and enabling faster, more confident communication with stakeholders.

Another insurance and financial services company took a proactive approach to risk mitigation, using AI to scan records for a brittle material linked to structural failures in older buildings, helping to identify and assess risks before losses could occur.

These real-world scenarios are only the tip of the iceberg, giving an early view of the broader transformation that is quickly redefining the competitive landscape. In upcoming blogs, we will share deeper examples and customer‑aligned scenarios across the end-to-end insurance value chain.

The journey to becoming a frontier insurer starts now

To unlock the value of agentic AI, Microsoft offers an end‑to‑end cloud and AI platform that insurers can incorporate powerful agents into their technology ecosystems. Microsoft Foundry provides the developer platform for building, testing, deploying, and orchestrating AI agents and applications, and Microsoft Agent 365 offers a control plane to help govern, secure, monitor, and manage agents across an enterprise, regardless of where they were built. This means that insurers can design, customize, deploy, and integrate intelligent agents across the value chain, with enterprise‑grade governance and a comprehensive suite of AI models and services.

Microsoft further strengthens this foundation with industry‑specific data models, process frameworks, and prebuilt connectors that simplify integration with core insurance systems, analytics environments, and workflow applications. This helps ensure faster time‑to‑value and accelerates modernization of claims, underwriting, servicing, and risk operations.

And critically, insurers also benefit from a deep, global partner ecosystem of trusted technology and solution providers who are well versed in delivering mission-critical solutions on the Microsoft Cloud, combined with our deep, long‑standing expertise in the insurance sector. Together, this ecosystem empowers insurers to innovate confidently, scale securely, and realize measurable impact with agentic AI.

The journey to agentic AI involves identifying high-impact workflows early, creating a unified data platform, addressing governance from the start, and empowering teams with smart change management. By embracing a frontier firm mindset—human led, agent operated—insurance leaders can unlock new value and innovate in the new competitive landscape. To continue your AI journey, contact your Microsoft representative or technology partner.

Next steps on your journey to agentic AI

• To explore solutions and resources for insurers, visit Microsoft for Insurance.
• To learn how frontier firms in financial services are using AI to improve efficiency, innovation, and customer satisfaction, get the e-book.

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

² Verisk, ClaimSearch Trends Report, 2024 Year-end Analysis

The post From bottlenecks to breakthroughs: How agentic AI is reshaping insurance appeared first on Microsoft AI Blogs.

This year, Microsoft had a clear focus at DTECH 2026: help utilities move from pilots to production by unifying IT and OT data, applying AI where it measurably improves reliability, affordability, and productivity and do so with the security and governance necessary for critical infrastructure.

Microsoft-led sessions explored moving beyond experimentation to focus on how utilities are turning unified data and AI into repeatable, operational outcomes. The takeaways from DTECH this year point to the next chapter of grid modernization—one defined by execution at scale, not pilots. 

What utility leaders reinforced: Keeping pace with change

Utility leaders consistently pointed to the increasing speed of change across the grid. Planning and operations are being pushed to respond faster as load growth becomes larger, more concentrated, and more volatile. Electrification is reshaping peak demand profiles, while capital programs are under pressure to deliver measurable value earlier—even as timelines continue to compress. 

At the distribution level, operational complexity is increasing. Distributed energy resources, electric vehicles, flexible demand, and new market programs are turning distribution systems into highly dynamic environments that demand better visibility, orchestration, and cybersecurity. Utilities are managing bidirectional power flows, evolving protection schemes, and the reality that smaller, distributed assets can have outsized system-level impacts—raising the bar for visibility, orchestration, and cybersecurity.

As a result, resilience is no longer episodic; it is a daily operating requirement. Fragmented data and manual coordination continue to limit situational awareness and slow response during major events.

Industry leaders were realistic about these constraints. Equipment lead times, workforce availability, and regulatory requirements mean that near-term reliability gains often come from improving how existing assets and systems are planned and operated. As a result, progress is increasingly measured by how effectively insights are translated into operational decisions, supported by secure and scalable platforms.

Trusted data as the foundation for AI in operations

Utilities generate vast amounts of data across assets, outages, telemetry, imagery, work management systems, and customer platforms. In many organizations, this data remains distributed across systems with inconsistent definitions, varying latency, and uneven governance.

These conditions slow analysis, create conflicting views of performance, and limit the ability to move from insight to action. Without a consistent and trusted data foundation, AI initiatives struggle to scale beyond isolated use cases. 

Microsoft is focused on helping utilities establish governed data foundations that support analytics and AI across planning, operations, field work, and customer engagement. By enabling scale across use cases—rather than building one‑off pipelines—utilities can align around shared definitions, apply consistent security controls, and collaborate without duplicative effort. 

This matters because the highest value use cases are inherently cross domain. Outage performance, capacity planning, and major event readiness all depend on data that spans systems and organizations. A unified data foundation allows AI to support these decisions with clarity, traceability, and operational relevance. 

From siloed AI solutions to agentic operations

Another notable theme at DTECH 2026 was the growing interest in agent-enabled workflows. Utilities are looking beyond standalone AI tools toward systems that can support multi-step workflows across planning, operations, and field execution, while maintaining appropriate oversight by subject matter experts across the workforce.

The focus is squarely on practical outcomes. Earlier risk identification, clearer paths from signal to action, and stronger coordination across teams are driving interest in these approaches, as utilities seek to move faster.

Human oversight remains foundational. Operators and engineers expect AI systems that surface options, explain their rationale, and reference trusted data—while operating within clearly defined governance boundaries. In regulated, safety‑critical environments, this human‑in‑the‑loop model must align with role‑based access, operational constraints, and established safeguards.

Partner innovation making modernization deployable

Grid modernization depends on strong ecosystem collaboration. No single entity can deliver it alone. What matters is interoperability—how solutions work together across planning, operations, outage restoration, field productivity, and major event response.

That focus was clear in the announcements from Microsoft and our partners at DTECH 2026:

• Dragos—Microsoft and Dragos announced an expanded partnership focused on helping organizations modernize and secure their cyber-physical operations. By combining Dragos’ OT threat intelligence and detection capabilities with Microsoft’s cloud, AI, and security platforms, utilities can strengthen the safety, reliability, and resilience of the critical systems that power businesses and communities. 
• GE Vernova on Azure—GridOS Data Fabric and DDLR are now on Microsoft Azure, combining GE Vernova’s operational expertise with Microsoft’s cloud, AI, and analytics.
• Hitachi—Hitachi Energy’s Ellipse EAM is being combined with Microsoft Dynamics 365, Microsoft Fabric, Copilot, and Microsoft Foundry to create a unified solution that manages data, analytics, and business operations, supports asset operations, and provides visibility of equipment across entire networks for more reliable services, safer operations, and fewer emergency repairs.  
• Itron—The new Itron Intelligent Edge Operating System (IEOS) Connector for Microsoft 365 Copilot uses trusted grid-edge data to redefine grid edge intelligence by applying AI at scale to optimize operations, enhance predictive insights, and enrich customer experiences.
• Schneider Electric—Microsoft’s AI, cloud, and data capabilities are integrated in the One Digital Grid Platform, enabling operations to move from prediction to execution in minutes.

These developments reflect continued progress toward reference architectures and reusable patterns that reduce bespoke integration and support broader adoption across utility environments.

Security and resilience built into modernization

Security remains a core consideration as IT and OT environments converge and connectivity at the edge increases. Utility leaders emphasized the importance of approaches that function across hybrid architectures and reflect operational realities.

Identity, access management, monitoring, and governance must be consistently applied across cloud, edge, and on‑premises systems. Resilience improves when operators have timely visibility, clear decision paths, and automation that supports established operating practices.

What comes next

DTECH 2026 highlighted a clear direction for grid modernization; utilities are prioritizing:

• Trusted data foundations spanning IT and OT.
• AI and agent-enabled capabilities embedded in operational workflows.
• Secure architectures designed to support reliability, governance, and resilience.

Microsoft will continue to work alongside utilities and industry partners to advance these priorities and support grid operations that can adapt to increasing complexity while delivering reliable outcomes for customers and communities. 

Turn insight into action

• Explore the possibilities of AI transformation with Microsoft
• Read the latest International Energy Agency’s World Energy Outlook
• Learn how to achieve more in the energy and resources industry with trusted data and AI solutions from Microsoft
• Explore the Reinventing the Utility Playbook: AI Agents for Grid Growth, Storm Response, and the Workforce 2.0 DTECH 2026 session
• Learn more about the AI-Powered Utilities: The Next Wave of Innovation DTECH 2026 session

The post DTECH 2026: How Microsoft and our partners are accelerating AI innovation for utilities appeared first on Microsoft AI Blogs.

Read the latest Cyber Pulse report

Agent building isn’t limited to technical roles; today, employees in various positions create and use agents in daily work. More than 80% of Fortune 500 companies today use AI active agents built with low-code/no-code tools.² AI is ubiquitous in many operations, and generative AI-powered agents are embedded in workflows across sales, finance, security, customer service, and product innovation. 

With agent use expanding and transformation opportunities multiplying, now is the time to get foundational controls in place. AI agents should be held to the same standards as employees or service accounts. That means applying long‑standing Zero Trust security principles consistently:

• Least privilege access: Give every user, AI agent, or system only what they need—no more.
• Explicit verification: Always confirm who or what is requesting access using identity, device health, location, risk level.
• Assume compromise can occur: Design systems expecting that cyberattackers will get inside.

These principles are not new, and many security teams have implemented Zero Trust principles in their organization. What’s new is their application to non‑human users operating at scale and speed. Organizations that embed these controls within their deployment of AI agents from the beginning will be able to move faster, building trust in AI.

The rise of human-led AI agents

The growth of AI agents expands across many regions around the world from the Americas to Europe, Middle East, and Africa (EMEA), and Asia.

According to Cyber Pulse, leading industries such as software and technology (16%), manufacturing (13%), financial institutions (11%), and retail (9%) are using agents to support increasingly complex tasks—drafting proposals, analyzing financial data, triaging security alerts, automating repetitive processes, and surfacing insights at machine speed.³ These agents can operate in assistive modes, responding to user prompts, or autonomously, executing tasks with minimal human intervention.

And unlike traditional software, agents are dynamic. They act. They decide. They access data. And increasingly, they interact with other agents.

That changes the risk profile fundamentally.

The blind spot: Agent growth without observability, governance, and security

Despite the rapid adoption of AI agents, many organizations struggle to answer some basic questions:

• How many agents are running across the enterprise?
• Who owns them?
• What data do they touch?
• Which agents are sanctioned—and which are not?

This is not a hypothetical concern. Shadow IT has existed for decades, but shadow AI introduces new dimensions of risk. Agents can inherit permissions, access sensitive information, and generate outputs at scale—sometimes outside the visibility of IT and security teams. Bad actors might exploit agents’ access and privileges, turning them into unintended double agents. Like human employees, an agent with too much access—or the wrong instructions—can become a vulnerability. When leaders lack observability in their AI ecosystem, risk accumulates silently.

According to the Cyber Pulse report, already 29% of employees have turned to unsanctioned AI agents for work tasks.⁴ This disparity is noteworthy, as it indicates that numerous organizations are deploying AI capabilities and agents prior to establishing appropriate controls for access management, data protection, compliance, and accountability. In regulated sectors such as financial services, healthcare, and the public sector, this gap can have particularly significant consequences.

Why observability comes first

You can’t protect what you can’t see, and you can’t manage what you don’t understand. Observability is having a control plane across all layers of the organization (IT, security, developers, and AI teams) to understand:  

• What agents exist 
• Who owns them 
• What systems and data they touch 
• How they behave 

In the Cyber Pulse report, we outline five core capabilities that organizations need to establish for true observability and governance of AI agents:

• Registry: A centralized registry acts as a single source of truth for all agents across the organization—sanctioned, third‑party, and emerging shadow agents. This inventory helps prevent agent sprawl, enables accountability, and supports discovery while allowing unsanctioned agents to be restricted or quarantined when necessary.
• Access control: Each agent is governed using the same identity‑ and policy‑driven access controls applied to human users and applications. Least‑privilege permissions, enforced consistently, help ensure agents can access only the data, systems, and workflows required to fulfill their purpose—no more, no less.
• Visualization: Real‑time dashboards and telemetry provide insight into how agents interact with people, data, and systems. Leaders can see where agents are operating, understanding dependencies, and monitoring behavior and impact—supporting faster detection of misuse, drift, or emerging risk.
• Interoperability: Agents operate across Microsoft platforms, open‑source frameworks, and third‑party ecosystems under a consistent governance model. This interoperability allows agents to collaborate with people and other agents across workflows while remaining managed within the same enterprise controls.
• Security: Built‑in protections safeguard agents from internal misuse and external cyberthreats. Security signals, policy enforcement, and integrated tooling help organizations detect compromised or misaligned agents early and respond quickly—before issues escalate into business, regulatory, or reputational harm.

Governance and security are not the same—and both matter

One important clarification emerging from Cyber Pulse is this: governance and security are related, but not interchangeable.

• Governance defines ownership, accountability, policy, and oversight.
• Security enforces controls, protects access, and detects cyberthreats.

Both are required. And neither can succeed in isolation.

AI governance cannot live solely within IT, and AI security cannot be delegated only to chief information security officers (CISOs). This is a cross functional responsibility, spanning legal, compliance, human resources, data science, business leadership, and the board.

When AI risk is treated as a core enterprise risk—alongside financial, operational, and regulatory risk—organizations are better positioned to move quickly and safely.

Strong security and governance do more than reduce risk—they enable transparency. And transparency is fast becoming a competitive advantage.

From risk management to competitive advantage

This is an exciting time for leading Frontier Firms. Many organizations are already using this moment to modernize governance, reduce overshared data, and establish security controls that allow safe use. They are proving that security and innovation are not opposing forces; they are reinforcing ones. Security is a catalyst for innovation.

According to the Cyber Pulse report, the leaders who act now will mitigate risk, unlock faster innovation, protect customer trust, and build resilience into the very fabric of their AI-powered enterprises. The future belongs to organizations that innovate at machine speed and observe, govern and secure with the same precision. If we get this right, and I know we will, AI becomes more than a breakthrough in technology—it becomes a breakthrough in human ambition.

Get the full Cyber Pulse report

To learn more about Microsoft Security solutions, visit our website. Bookmark the Security blog to keep up with our expert coverage on security matters. Also, follow us on LinkedIn (Microsoft Security) and X (@MSFTSecurity) for the latest news and updates on cybersecurity.

¹Microsoft Data Security Index 2026: Unifying Data Protection and AI Innovation, Microsoft Security, 2026.

²Based on Microsoft first‑party telemetry measuring agents built with Microsoft Copilot Studio or Microsoft Agent Builder that were in use during the last 28 days of November 2025.

³Industry and Regional Agent Metrics were created using Microsoft first‑party telemetry measuring agents built with Microsoft Copilot Studio or Microsoft Agent Builder that were in use during the last 28 days of November 2025.

⁴July 2025 multi-national survey of more than 1,700 data security professionals commissioned by Microsoft from Hypothesis Group.

Methodology:

Industry and Regional Agent Metrics were created using Microsoft first‑party telemetry measuring agents built with Microsoft Copilot Studio or Microsoft Agent Builder that were in use during the past 28 days of November 2025. 

2026 Data Security Index: 

A 25-minute multinational online survey was conducted from July 16 to August 11, 2025, among 1,725 data security leaders. 

Questions centered around the data security landscape, data security incidents, securing employee use of generative AI, and the use of generative AI in data security programs to highlight comparisons to 2024. 

One-hour in-depth interviews were conducted with 10 data security leaders in the United States and United Kingdom to garner stories about how they are approaching data security in their organizations. 

Definitions: 

Active Agents are 1) deployed to production and 2) have some “real activity” associated with them in the past 28 days.  

“Real activity” is defined as 1+ engagement with a user (assistive agents) OR 1+ autonomous runs (autonomous agents).  

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