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 Industry Blogs.

That is where AI comes in. To break the infrastructure bottleneck and shift the industry from ambition to delivery, Microsoft is announcing an AI for nuclear collaboration with NVIDIA, to provide end-to-end tools that streamline permitting, accelerate design, and optimize operations across the industry.

This set of technologies brings disciplined engineering to the entire lifecycle of a nuclear plant—spanning site permitting, design, construction, and continuous operations. By enabling these capabilities within a connected, AI-powered foundation, we are empowering energy developers to make highly complex work repeatable, traceable, secure, and predictable—slashing development timelines and eliminating rework without sacrificing safety.

The digital foundation for nuclear at scale

The only thing that may be more complex than building a nuclear plant is designing and permitting one. Permitting alone can take years, cost hundreds of millions of dollars, and involve an immense amount of data processing and reporting. It’s not a lack of need, knowledge, or even willingness that’s holding development back, but rather the inability to progress efficiently and consistently through rigorous permitting and development processes.

Engineers can spend thousands of hours drafting, cross-referencing, formatting, searching, reviewing, and reworking materials. They have to identify and fix inconsistencies across tens of thousands of pages. It is little wonder that plants have been notorious for construction delays and cost overruns.

To break this infrastructure bottleneck, we need to move away from highly customized engineering towards repeatable, reference-based delivery—while maintaining regulatory standards and engineering accountability.

With AI, we can identify tiny documentation inconsistencies and resolve them quickly. By unifying data and simulation across the lifecycle, we ensure complex work remains:

• Traceable: Every engineering decision is digitally linked to the evidence and regulations that back it up.
• Audit-Ready: The system keeps a perfect “paper trail,” ensuring that regulators can verify safety instantly.
• Secure: High-level intelligence is applied within a governed, protected environment.
• Predictable: High-fidelity simulations map time and cost, catching delays before they happen in the real world.

This isn’t just about speed; it’s about trust. Engineers and regulators are freed to focus on what matters most: building a safe, secure, high-capacity, carbon-free power source that’s on-time and on-budget.

Here is how AI and Digital Twins can carry a project from the initial phases to efficient operations:

• Design and engineering: Digital Twins and high-fidelity simulations enable faster iteration. Engineers can reuse proven patterns and instantly see how a tiny design change impacts the entire model, creating a validated plan before breaking ground.
• Licensing and permitting: Generative AI handles the heavy lifting of document drafting and gap analysis. It unifies all project information, ensuring comprehensive applications aligned with historical permits. This allows expert regulators to focus their time on safety judgments rather than reconciling thousands of pages of text.
• Construction and delivery: While traditional 3D models only map physical space, 4D (time scheduling) and 5D (cost tracking) simulations can virtually construct the plant before shovels hit the dirt. AI and Digital Twins allow developers to track physical progress against the digital plan in real-time, catching potential delays and preventing the schedule collisions that lead to expensive rework.
• Operations and maintenance: AI-powered sensors and operational digital twins detect anomalies early, ensuring higher uptime and predictive maintenance that keeps the grid stable with human operators firmly in control.

By unifying data, traceability, and simulation across phases, AI accelerates design validation with high-fidelity 3D models and Digital Twins, improves licensing consistency through AI-assisted document workflows, and connects design assumptions to operational performance—giving operators, regulators, and stakeholders clearer, continuous visibility.

Accelerating delivery: How Aalo Atomics, Idaho National Labs, and Southern Nuclear are deploying AI for nuclear

The proof is in the progress. Our collaboration is already changing the pace of nuclear delivery.

Aalo Atomics

Aalo Atomics has reduced the time-intensive permitting process by 92% using the Microsoft Generative AI for Permitting solution, saving an estimated $80 million a year. For Aalo, the value of the Microsoft and NVIDIA collaboration isn’t just speed—it’s confidence.

“Two things matter most: enterprise-scale complexity and mission-critical reliability. We’re deploying something complex at a scale only a company like Microsoft really understands. There’s no room for anything less than proven reliability.”

—Yasir Arafat, Chief Technology Officer, Aalo Atomics

Southern Nuclear

Southern Nuclear has developed and deployed agents using Microsoft Copilot across its fleet, including engineering and licensing, to improve consistency, reuse knowledge faster, and support better decision-making in key workstreams.

Idaho National Laboratory

When it comes to the public sector and specifically United States Federal, Idaho National Laboratory (INL) has become an early adopter of AI for nuclear technology. By using the AI capabilities to automate the assembly of complex engineering and safety analysis reports, INL is streamlining the review process and creating standard methodologies for regulators to adopt these tools safely, further speeding deployment.

Expanding the ecosystem: How Everstar and Atomic Canyon are operationalizing AI for nuclear on Microsoft Azure

Microsoft is actively expanding this secure ecosystem. Everstar—an NVIDIA Inception startup—brings domain-specific AI for nuclear to Azure to modernize how the industry manages project workflows and governed data pipelines.

“The nuclear industry has been bottlenecked by documentation burden and regulatory complexity for decades. This partnership means our customers get the secure, scalable cloud deployments they demand. It’s a significant step toward making nuclear power fast, safe, and unstoppable.”

—Kevin Kong, Chief Executive Officer, Everstar

We are also excited to highlight Atomic Canyon, whose Neutron platform is now available in the Microsoft Marketplace, allowing nuclear developers to deploy these capabilities with consistency and control through trusted procurement pathways.

Progress at the pace this moment requires

AI is enabling the energy industry to deliver more power, faster, and safely. This Microsoft and NVIDIA collaboration provides the path to do exactly that for advanced developers, owners, and operators. By turning fragmented, high-variance workflows into governed, auditable systems, we can compress timelines without compromising rigor. By unifying data, simulation, and evidence across design, permitting, construction, and operations, we are accelerating the deployment of firm, carbon-free power while strengthening regulatory confidence and operational resilience.

The AI for nuclear operations collaboration brings together NVIDIA Omniverse, NVIDIA Earth 2, NVIDIA CUDA-X, NVIDIA AI Enterprise, PhysicsNeMo, Isaac Sim, and Metropolis with Microsoft Generative AI for Permitting Solution Accelerator and Microsoft Planetary Computer to create a comprehensive, AI-powered digital ecosystem for nuclear energy on Azure.

Microsoft, NVIDIA, and Aalo Atomics will be presenting this AI-lead industry perspective at CERAWeek 2026 in a session entitled “A Digital Age for Nuclear: Aalo Atomics, NVIDIA, and Microsoft.”

Discover more

Ready to move from ambition to delivery? See how the Microsoft and NVIDIA nuclear for AI collaboration can drive change within your organization.

Contact us to learn more.

The post AI for nuclear energy: Powering an intelligent, resilient future appeared first on Microsoft Industry 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.

The post Manufacturing at the 2026 inflection point: How Frontier companies are entering the agentic era appeared first on Microsoft Industry 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 Industry Blogs.

Research we conducted with senior healthcare executives in the United States, published in the New England Journal of Medicine, revealed a growing readiness divide. As some systems build governance, security, and workforce models to scale AI safely, others are still in proof-of-concept mode. The result? Diverging outcomes in productivity, workforce strain, cost-to-serve, and resilience.

The question is no longer whether AI belongs in healthcare. It’s how quickly organizations can operationalize it—safely, responsibly, and at scale.

Microsoft works with more than 170,000 healthcare customers globally to move from pilot to production with enterprise-grade security, privacy, and compliance.

So what does Frontier Transformation actually look like? The following examples show how healthcare organizations are embedding AI into core workflows—moving beyond pilots to deliver real, scalable impact with the governance and trust required in clinical environments.

Accelerating discovery and clinical development with AI

Frontier organizations are reinventing discovery by treating AI as an always-on research partner. It compresses the time it takes to find, synthesize, and act on evidence across functions. The result isn’t just faster tasks; it’s faster decisions and a more scalable path from insight to impact. As these capabilities become table stakes, organizations that can’t industrialize knowledge of work will fall behind in speed-to-trial, speed-to-market, and ultimately speed-to-patient.

UCB: Scaling agent-based AI with a secure internal platform

UCB built SKAI, a secure internal platform on Microsoft Azure for generative and agent-based AI, helping teams apply knowledge faster and operationalize AI with governance built in.

Syneos Health: Streamlining complex data to bring therapies to patients faster

Syneos Health is using AI to help teams analyze large, complex data sets across the clinical development lifecycle. With faster, more consistent synthesis of study inputs and operational signals, biopharma customers can make decisions with greater speed and confidence. Syneos Health reported reducing time for clinical trial site activation by about 10%, helping remove friction from a critical step in getting lifesaving therapies to patients. Enhanced predictive modeling and forecasting tools also allow teams to identify risks earlier, model scenarios, and engage customers and clinical partners more effectively.

Advancing care delivery with AI in the flow of clinical work

In care delivery, transformation happens when AI shows up in the flow of work. It reduces cognitive and documentation load and gives time back to clinicians. Frontier organizations use AI to shift capacity toward patients, not screens, while improving consistency and quality. As patient expectations rise and workforce shortages persist, the ability to deliver more care with the same (or fewer) resources is quickly becoming a differentiator.

Intermountain Health: Rehumanizing care by reducing documentation burden

Intermountain Health adopted Microsoft Dragon Copilot to reduce the administrative load that can pull clinicians away from patients. By supporting clinical documentation and automating routine tasks, clinicians at Intermountain Health reported experiencing a 27% reduction in time spent on notes per appointment, reducing cognitive burden and enabling more meaningful patient engagement by incorporating AI as a core part of their clinical workflow.

Cooper University Health Care: Giving clinicians time back in the flow of care 

Cooper University Health Care is using AI-powered clinical documentation to reduce the administrative burden that pulls clinicians away from patients. By embedding AI directly into clinical workflows, clinicians at Cooper reported saving more than four minutes per patient visit on documentation, experiencing less burnout, and engaging more meaningfully with patients—demonstrating how AI optimized workflows can rehumanize care at scale.

Mercy: Bringing ambient AI to nursing workflows

Nurses are at the center of care delivery and often at the center of documentation burden. Mercy has been using AI capabilities to transform nursing care. By capturing and structuring information in the flow of work, Mercy reported 8 to 24 minutes saved per shift for high-use nurses, a 21% reduction in documentation latency and a 4.5% increase in patient satisfaction from their initial rollout.

Streamlining operations and experiences across the healthcare organization

Frontier Transformation requires more than point solutions. It takes an AI-ready operating foundation that connects people, processes, and data across the organization. Frontier organizations use copilots and agents to standardize work, automate routine interactions, and deliver more consistent experiences at scale. Those that treat AI as isolated experiments often find themselves outpaced by peers who can improve service levels while bending the cost curve.

Bupa APAC: Building an AI-ready foundation to improve customer experiences

Bupa APAC is streamlining operations, automating routine processes, and making customer experiences more seamless thanks to AI. With an emphasis on AI readiness—skills, governance, and secure access to information—Bupa APAC upskilled its workforce with Microsoft 365 Copilot and GitHub Copilot, generating more than 410,000 lines of AI-assisted code, initiating more than 30,000 Copilot chats, and accelerating more than 100 AI use cases to improve care.

CareSource: Scaling compassionate service with cloud and AI

CareSource is applying AI to support operational scale while keeping a human touch. By modernizing platforms and automating processes that can slow service delivery, CareSource reduced documentation time by 75%, saved over USD125,000 on automation, and boosted developer productivity by up to 30%, helping their teams focus on the needs of members, providers, and communities.

Strengthening cyber resilience with AI

Cyber resilience is a transformation prerequisite. As care becomes more digital, AI must help defenders move at machine speed while maintaining trust and compliance. Frontier organizations use AI to triage, investigate, and report faster—reducing risk and freeing experts for the threats that matter most. In a sector where disruption can compromise patient safety, lagging security maturity can erase hard-won gains in digital transformation.

St. Luke’s University Health Network: Saving nearly 200 hours per month with AI-powered security agents

As healthcare expands its digital footprint, cyber defense becomes inseparable from patient safety and trust. St. Luke’s University Health Network is using Microsoft Security Copilot agents to accelerate phishing alert triage and to generate incident reports in minutes instead of hours. The organization reported saving nearly 200 hours per month, freeing security teams to focus on higher-value investigations and improving speed to response across its environment.

Act now to lead the future

If you’re looking at these examples and wondering where to start, focus on a few moves that help you learn quickly and scale safely.

• Start with workflows, not technology: Identify the highest-friction moments (such as documentation, imaging backlogs, complex data synthesis, member service, and security triage) and design AI interventions that measurably reduce time, effort, and risk.
• Get your foundation right, early: Prioritize secure access, identity, and data governance so copilots and agents have the right context, without compromising privacy or compliance.
• Make it real, and make it stick: Operationalize responsible AI (like oversight, evaluation, and human-in-the-loop), measure quality and safety, and invest in change management so adoption scales beyond early enthusiasts.

Start your Frontier Transformation today

These organizations show what Frontier Transformation looks like in practice—embedding intelligence across clinical, operational, and administrative work to deliver faster insights, reduced burden, strengthen security, and create better experiences at scale. The competitive bar is moving quickly. Waiting to act can mean higher costs, slower throughput, and greater strain on already-stretched teams. With deep healthcare experience and a global customer base, Microsoft can help organizations scale AI responsibly from the first workflow to redesign to enterprise-wide adoption.

The post What Frontier healthcare leaders are doing differently with AI appeared first on Microsoft Industry 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-powered 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 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 Industry 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 Industry Blogs.

Public sector leaders face a pivotal moment: complex regulation, workforce constraints, and rising expectations for digital-first, no-wrong-door service experiences, while AI accelerates what’s possible. Forrester’s new Industry Cloud evaluation is timely because it looks beyond generic enterprise software categories to assess mission-ready platforms built for government realities: transparency, adaptability, and the ability to modernize with AI-native mission delivery while improving the reliability, responsiveness, and continuity of critical services.

Industry clouds for public sector: Where agentic AI meets mission-ready platforms

Public sector leaders aren’t just modernizing apps—they’re modernizing mission delivery. Microsoft empowers government mission leaders with an agentic AI platform: a secure, interoperable foundation where AI can move beyond answering questions to reasoning and acting across workflows with the transparency, policy controls, and compliance agencies require. 

At the center is Microsoft’s intelligence layer connecting the two oceans of government data (structured systems of record and unstructured documents, email, and operational knowledge) to close the intelligence gap that holds AI back. Work IQ brings context on how people and teams work across Microsoft 365; Foundry IQ grounds agents in authoritative policies, procedures, and institutional knowledge; and Fabric IQ unifies mission data into consistent meaning so agents can take action with governed, real-time insight. Together with Microsoft Azure, Dynamics 365, and Power Platform, and built-in governance through Microsoft security and compliance, agencies can harness their data, build mission centric agentic AI workflows, and scale agents that deliver outcomes.

• Deliver constituent service by connecting intake, contact centers, and case work with Copilot and agents that summarize, recommend next steps, and automate routine updates.
• Modernize permitting, licensing, and benefits with configurable workflows and low-code automation in Power Platform, grounded in authoritative policy through Foundry IQ.
• Strengthen compliance and oversight with governed data, auditable processes, and security controls that help agencies adopt AI responsibly while meeting sovereignty and regulatory requirements.
• Coordinate mission operations across safety, justice, and resilience by unifying signals from people, systems, and knowledge so agents can support faster decisions and cross-agency collaboration.

What government agencies value most: Trust and measurable outcomes

Across government, the signal we’re hearing is consistent: leaders want modernization that earns trust and delivers measurable results fast—without forcing frontline teams to change everything at once. The strongest platforms are the ones that reduce administrative drag, connect data and workflows end to end, and make secure AI usable in the reality of public sector operations. 

Instead of copying and pasting, I enter information once and it shows up everywhere I need it. We can map our days faster, reduce human error, and get case plans in motion sooner.

Jayna White, Subject Matter Expert, Washington, DC Child and Family Services Agency (CFSA)

We also hear that AI only becomes transformative in the public sector when it can be deployed in a controlled environment, aligned to existing security protocols, and scaled quickly to the people doing the work. Sandia National Laboratories’ approach reflects a growing pattern: deliver the capabilities of modern models while keeping sensitive work protected within the agency’s boundaries.

We wanted the value of popular AI models, but we wanted to deploy that value in a very secure Azure environment. With this tool, we could take a snapshot of each new model released and give that to our employees without having to actually connect to a public model.

John Zepper, Information Engineering Executive Director and Chief Information Officer, Sandia National Laboratories

Together, these stories highlight the outcomes public sector organizations are prioritizing right now: 

• Faster, better-informed decisions with unified, person-centric case and mission data.
• Real time returned to frontline teams by eliminating re-entry, manual handoffs, and repetitive searches.
• Security-first AI adoption that fits government risk frameworks and protects sensitive work.

Extend mission outcomes with trusted partners

No single provider solves every government scenario out of the box, so extensibility matters. 

Microsoft provides a foundation across cloud, productivity, workflow, data, and AI—while partners add industry depth, localization, implementation services, and specialized workloads (including emergency response and dispatch scenarios). 

An open, interoperable posture—aligned to public sector standards—helps agencies avoid scenario-level lock-in while gaining the benefits of a governed platform. 

A platform path to responsible AI modernization

For government leaders evaluating industry cloud strategies, this recognition is a practical signal: 

• You can pursue AI-enabled modernization responsibly—with trust requirements front and center. 
• You can adopt a platform approach that scales across agencies and missions, instead of rebuilding the same capabilities program by program. 
• You can modernize in a way that supports compliance and sovereignty needs, while still enabling interoperability and innovation. 
• You can choose an approach that supports incremental adoption, meeting agencies where they are—whether starting with productivity and collaboration or expanding into data, low-code workflows, and mission systems. 

We appreciate Forrester’s recognition of Microsoft as a Leader in this inaugural evaluation, and we welcome the ongoing dialogue with public sector leaders on how to modernize services with AI—securely, transparently, and with the trust citizens expect. 

To explore the full evaluation, read The Forrester Wave™: Industry Cloud Solutions For Public Sector, Q1 2026.

Forrester disclaimer: Forrester does not endorse any company, product, brand, or service included in its research publications and does not advise any person to select the products or services of any company or brand based on the ratings included in such publications. Information is based on the best available resources. Opinions reflect judgment at the time and are subject to change. For more information, read about Forrester’s objectivity here.

The post Microsoft is named a Leader in The Forrester Wave™: Industry Cloud Solutions For Public Sector, Q1 2026 appeared first on Microsoft Industry Blogs.

AI is delivering real, measurable returns for telecom

AI is already delivering measurable business impact across industries, and telecom is among the leaders. A recent IDC study shows operators are achieving 2.8 times return on generative and agentic AI investments, with many leading companies reaching up to 5 times return. Frontier telecoms are realizing even greater returns from AI by making it foundational to how their business operates—from employees and core workflows to the end‑to‑end value chain. These leaders are moving beyond incremental efficiency by using an end-to-end AI platform and unified data approach that embeds AI into everyday operations, enabling faster decisions, tighter execution, and continuous performance improvements across their organization.

With more than 80% of the Fortune 500 building active AI agents, Microsoft Copilot is rapidly becoming essential to how employees think, collaborate, and deliver results. As AI proves its value, telecoms are moving beyond pilots to connected intelligence that elevates customer experiences, replaces manual workflows with autonomous operations, hardens and self‑heals networks, and drives new revenue opportunities. Connected intelligence will differentiate fast-moving telecoms across every area of business operation.

Read more about Frontier telecoms here.

Return on intelligence and trust

For telecoms, achieving value from scalable AI depends on two factors: intelligence and trust. Built from three complementary IQ elements—Work IQ, Fabric IQ, and Foundry IQ—Microsoft IQ is the intelligence layer that connects AI, data, and context across the business. It gives AI agents deep awareness of how people work, how the business operates, and how decisions are made. This intelligence layer accelerates decisions, improves customer experiences, automates operations and networks, and unlocks new ways to monetize AI‑based services. Trust is built through Microsoft’s carrier‑grade control plane, which provides built‑in monitoring and governance across the entire AI platform, including AI agents from our partner ecosystem, to allow telecoms to innovate responsibly, support regulatory compliance, and scale AI with confidence.

At MWC 2026, Microsoft is announcing new technologies that will help telecoms move forward with AI and use intelligence to drive the business.  Microsoft delivers this through a single platform that brings AI, unified data, trust, and governance together to enable telecoms with connected, actionable insights to accelerate innovation and growth.

Building the sovereign, AI-ready edge for telecom

For telecoms, thriving in the era of agentic intelligence begins with a resilient foundation. Today, we’re advancing Microsoft Sovereign Cloud with fully disconnected operations, extending cloud capabilities and AI-ready infrastructure deeper into operator networks than ever before. As demand accelerates for low-latency services, real-time processing, and stronger assurances around data sovereignty and regulatory compliance, the edge has become a critical extension of telecom networks and foundational layer of modern digital infrastructure. This is especially true for regulated industries and mission-critical scenarios where operational resilience and control over data are paramount.

To support these confidential environments, Azure Local offers full stack capabilities that support customers across connected, intermittently connected, and fully disconnected modes. This is essential for sovereign environments where uninterrupted access to operational, network, or customer-facing systems is non-negotiable. Azure Local disconnected operations keeps critical services running securely without connectivity to the cloud. At the same time, Foundry Local will be able to offer modern infrastructure and support for large AI models. Using the latest graphics processing unit (GPU) infrastructure from partners like NVIDIA means customers with sovereign needs will now be able to run models locally on their own hardware, inside strict sovereign boundaries enabling powerful, local AI inferencing in fully disconnected environments.

Customers can deploy and govern workloads inside their own datacenters, using familiar Microsoft Azure experiences and consistent policies, without depending on continuous connection to public cloud services.

AT&T uses Azure to support its cloud and edge strategy, enabling consistent operations across a distributed network footprint. By applying Azure’s management and governance capabilities across environments, AT&T can bring compute and data processing closer to where services are delivered while maintaining strong security and operational oversight.

“As we expand our network edge capabilities, Azure plays a key role in helping us apply cloud-native principles across our distributed infrastructure. The scalability and flexibility of Azure’s adaptive cloud approach allow us to deploy services closer to our customers, maintaining control while providing the reliability and performance they expect from AT&T. This long-standing partnership enables us to innovate and deliver next-generation experiences at the edge.”

—Sherry McCaughan, Vice President, Mobility Core and Services

Azure’s cloud-native management capabilities and global platform enable organizations like AT&T to modernize and scale edge environments, supporting next-generation services while maintaining consistent governance, security, and operational control. Native management capabilities and a global platform enable organizations like AT&T to modernize and scale edge environments, supporting next generation services while maintaining consistent governance, security, and operational control. 

We’re also investing in multi-rack deployment capabilities for Azure Local, extending scale points to support large-scale infrastructure for the most demanding, mission-critical workloads. Customers will be able to expand from single-node and cluster deployments to multi-rack environments designed for high availability, fault isolation, and operational simplicity at scale. Multi-rack deployment on Azure Local is currently in preview and will be available in the coming months.

Microsoft is collaborating with telecom operators to deliver sovereign cloud platforms and managed services that combine hyperscale innovation with local control, enabling enterprises to meet data residency, regulatory, and security requirements while accelerating trusted digital and AI transformation.

Agentic customer experiences that drive growth

With this foundation in place, telecoms can move beyond isolated use cases to scale intelligence across experiences and growth models. The same agentic capabilities that transform customer engagement also unlock new ways to monetize services, reduce cost to serve, and create differentiated value.

Today, telecom customer journeys are fragmented. Customers often switch channels to complete tasks, driving abandonment and cost. AI agents turn customer intent into end‑to‑end action across systems. Microsoft is now offering a telecom agentic store reference framework to replace click‑based journeys with natural‑language interaction. Coordinated AI agents handle discovery, sales, service, billing, and partner offers in the background—customers state their goal and agents deliver the outcome. The result is higher digital completion, faster resolution, and better experiences. This framework also creates a new monetization platform, enabling federated AI marketplaces with built‑in identity, billing, and sovereign deployment for trusted ecosystem commerce at scale. Telecoms are already working with Microsoft and system integrators to adopt this architecture—unifying sales and service, reducing cost‑to‑serve, and creating a scalable foundation for partner‑led innovation. 

FiberCop modernizes edge cloud and contact center

FiberCop runs Italy’s most advanced, far-reaching and pervasive digital network infrastructure. FiberCop recently announced that it has integrated Azure Local into its network, transforming the access infrastructure into an edge cloud platform capable of delivering cloud-native services, virtualized network functions, and advanced workloads while meeting sovereignty and compliance requirements. Today, FiberCop announces that it is accelerating its agentic transformation, moving to an AI‑first contact center model where autonomous AI agents, Copilot, and human expertise work together. By adopting Dynamics 365 Contact Center, FiberCop has begun modernizing customer engagement with unified data, intelligent routing, and AI‑powered self‑service and assisted service that delivers more efficient operations and better customer experiences at scale.

Introducing Ericsson Enterprise 5G Connect to reimagine customer experience

Ericsson announces ongoing collaboration with Microsoft introducing the Ericsson Enterprise 5G Connect solution—validated on Microsoft Surface 5G Copilot+ PCs and built on top of Windows 11’s Enterprise Cellular Managed Connectivity (ECMC) capabilities. This new offering enables enterprises to centrally manage secure, seamless 5G connectivity for mobile and hybrid employees, using automatic network switching and robust policy enforcement to enhance productivity and security. IT teams gain scalable management and control, while end users benefit from uninterrupted, AI-powered experiences across private and public 5G networks. The solution is currently being piloted by Ericsson and is in private preview. To learn more, visit our Windows IT Pro blog.

Intelligent business operations, built for telecom

Delivering connected customer experiences depends on what happens behind the scenes. Telecom operations require trusted, governed access to network and customer data. That’s why operators are moving from legacy data warehouses to a modern lakehouse that unifies business and network data.

Microsoft Fabric provides a single, policy‑governed data foundation for real‑time, operational, and analytical data to speed AI insights at scale. Building on this foundation, today we’re announcing Azure Databricks Lakebase will be available in March 2026, giving telecom operators a managed PostgreSQL environment with next generation separation of storage and compute for transactional data, providing instant availability, instant clones, and scale-to-zero. This brings online transaction processing (OLTP) capabilities to the Databricks Data Intelligence Platform on Azure designed for developer performance with low total cost of ownership (TCO), eliminating the traditional gap between operational systems and the lakehouse.

Partners are building on this data foundation as well. For example, Nokia integrates its data suite with Fabric to securely unify network telemetry and reduce AI use case development time by up to 80%.

MTN transforms fraud prevention with AI

In today’s rapidly evolving digital landscape, identity theft and first-party fraud are escalating at alarming rates, posing significant risks to individuals and businesses across South Africa. MTN has made a bold move to transform its fraud management approach by harnessing advanced Microsoft technologies. Shifting from traditional, reactive methods to a proactive, AI-powered ecosystem, MTN is not only protecting its customers and strengthening revenue defenses but also reinforcing national digital resilience and contributing to a safer, more secure digital economy for all.

Amdocs powers intelligent business operations

Amdocs is making several announcements with Microsoft that deepen integration to deliver next-generation solutions. The first is AI-powered application modernization through the Amdocs Agentic Services platform, embedding Microsoft AI solutions such as Azure OpenAI and Microsoft Foundry into end-to-end modernization and migration to Azure. Second, Amdocs Cognitive Core platform built on amAIz, offering prebuilt agent libraries, cross-domain insights, and telecom-specific AI that integrates with any business or operating system stack and runs securely on Azure. Colt Technology is working with Amdocs and Microsoft to streamline operations and accelerate service delivery with real-time insight.

To learn more about transforming the OSS/BSS with agentic AI, read this blog.

Power autonomous networks with built-in trust and control

As intelligence is embedded across data and operations, the next frontier is the network itself. Agent-driven operations enables networks to move from reactive management to autonomous executions that respond faster, reduce risk, and improve resilience at scale.

To help operators move from pilots to production at scale, Microsoft is evolving its network operations agent (NOA) reference architecture—a proven framework shaped by real world deployments, industry collaboration, and learnings from Microsoft’s NetAI program.

NOA is built for today’s telecom realities: exploding event volumes, rising complexity, and persistent skills gaps. The latest evolution deepens integration with Microsoft AI and collaboration platforms, strengthens alignment with open standards, and delivers a modular, production-ready path to autonomy—without compromising telco-grade safety, governance, or human oversight. Operators engage AI directly through Microsoft 365 Copilot and Microsoft Teams, while Microsoft Foundry and the Microsoft Agent Framework provide a governed, observable runtime for multi-agent orchestration at scale. Expanded support for TM Forum Open APIs helps ensure interoperability across existing business and operations support systems, making NOA an open, secure foundation for autonomous networks. Read more about NOA.

Leading operators such as Far EasTone Telecom and Vodafone are already applying this blueprint to modernize network operations, reduce human error, accelerate recovery times, and enable engineers to focus on higher value work.

Far EasTone Telecom (FET) is turning agentic AI into real operations impact 

FET exemplifies how leading operators are turning this architecture into real operational impact. FET is using the NOA framework to redefine cloud native network operations by embedding agentic AI across its NOC and change management workflows. Today, nearly 60% of its NOC operations are AI-assisted, with about 10,500 operational tasks executed per month, including incident summaries, automated ticket closure, network checks, and proactive voice notifications. AI agents now handle largescale alarm correlation and root cause analysis in seconds, supporting nearly 7,000 monthly operational queries with an average response time of 16 seconds, and enabling most maintenance actions to complete within one minute. This shift has significantly reduced human error, accelerated recovery times, and allowed engineers to focus on higher value work.

Vodafone’s journey toward intelligent network operations

Vodafone is working with Microsoft to apply this proven AI‑powered blueprint for autonomous network operations across transport infrastructure and field‑force management. The collaboration combines Vodafone’s deep network expertise with Microsoft Foundry and the NOA framework to modernize how large‑scale telecom networks are operated.

This blueprint is built on Microsoft’s own experience running autonomous agents across its global Azure transport network, where AI continuously monitors performance, identifies root causes, and autonomously manages more than 65% of fiber‑break field dispatches—improving time to repair by up to 25% and accelerating root‑cause analysis by 80%. By applying these proven capabilities to Vodafone’s transport network, the two companies are accelerating the shift toward intelligent, automated transport network operations across the telecom industry.

“By working with Microsoft, we’re combining deep network expertise with proven AI‑powered operations to create something greater than either could achieve alone. Together, we’re building intelligent, automated transport network operations that empower our teams and deliver faster, more resilient connectivity networks for our customers.”

—Alberto Ripepi, Chief Network Officer, Vodafone

Other operators, including AT&T, T-Mobile, Telefónica, and MEO, are adopting Microsoft Foundry as a blueprint for scaling agentic AI across complex, multi-vendor networks. 

Today, Kenmei announces it is collaborating with Microsoft to help operators accelerate their path toward autonomous networks by combining Kenmei’s telecom intelligence offer with Azure and Microsoft Fabric to enable scalable analytics and agentic AI–powered operations. Already in use at leading operators like Telefónica and Etisalat (e&), this collaboration brings proven deployments into a broader cloud and AI ecosystem designed to reduce manual effort, speed decision‑making, and unlock new levels of network automation.

Microsoft achieves global milestone to extend internet access with Starlink

As telecoms scale intelligence across networks, operations, and experiences, connectivity remains the starting point. Because AI only delivers impact where access exists, expanding internet access is foundational to an intelligent and inclusive telecom future.

Today, 2.2 billion people around the world remain unconnected.¹ To help overcome this barrier, Microsoft pledged to bring access to 250 million people by 2025. We are pleased to share that we’ve expanded internet access to 299 million people through the power of technology and partnerships in communities around the globe. But we know there is more work to do to support unconnected communities and enable global participation in the AI economy.

In support of this on-going effort, we are unveiling a new collaboration with Starlink designed to bring Microsoft’s experience with governments, local operators, and community partners together. With more than 9,000 satellites in low-Earth orbit, Starlink will extend digital infrastructure to rural, agricultural, and hard-to-reach communities. You can read more about how we met this milestone and are continuing to extend AI-enabled connectivity aligned with community needs.

Join us at MWC 2026 to learn more

Frontier telecoms are already proving what’s possible when AI, data, trust, and governance come together on a single platform to power faster operations, autonomous networks, intent-driven engagement, and real return on intelligence.

Join Microsoft At MWC 2026 to see how operators and partners are moving from AI promise to production through real deployments, live demos, and customer stories.

¹Facts and Figures 2025, ITU.

The post Microsoft accelerates telecom return on intelligence with a unified, trusted AI platform appeared first on Microsoft Industry Blogs.

That’s why I’m excited to share that Microsoft Azure has completed an independent, industry‑led GxP supplier audit conducted through the Joint Audit Group managed by Ingelheimer Kreis (IK).

GxP refers to regulations that ensure quality, safety, and data integrity in highly regulated environments, particularly in life sciences. This milestone provides independent validation that Azure’s systems and processes meet the standards required to support regulated workloads in the cloud, giving organizations greater confidence to accelerate their AI transformation and scale innovation responsibly.

“Overall, the audit observed strong organizational maturity, robust processes, and effective governance structures. Microsoft demonstrated a high degree of transparency, collaboration, and readiness to address regulatory expectations. Furthermore, Microsoft demonstrated strong maturity in quality, security, compliance, engineering, and operational processes. The organization showed strong commitment from leadership and robust operational controls.”

As quoted by the Joint Audit Group managed by Ingelheimer Kreis

This milestone builds on Azure’s longstanding commitment to compliance, reinforcing trust across life sciences and other highly regulated industries while helping accelerate broader cloud and AI adoption.

Raising the bar for cloud trust in life sciences and beyond

IK conducted a GxP-aligned supplier audit of selected aspects of Microsoft’s cloud service operations within an agreed scope. The sessions provided insight into governance, security and software engineering practices, and operational processes that may impact regulated GxP use of Microsoft Azure and related services. The audit was performed using a spot-check approach and reflects the information presented by Microsoft during the sessions. The IK audit results provide IK members with assurance regarding the Azure controls environment, enabling members to work to remove compliance blockers, accelerate their adoption of Azure services, and obtain confidence and trust in the security and sovereignty controls of Azure.

The joint GxP audit provides pharmaceutical and life sciences organizations with a higher level of confidence that Azure’s operational, security, and compliance practices meet industry expectations for validated GxP workloads. By having a coalition of major pharmaceutical manufacturers audit Microsoft’s cloud controls, customers gain assurance that Azure’s change management processes, evergreen update model, and underlying operational rigor align with the standards historically required in on-premises validated environments. This independent industry assessment reduces longstanding adoption barriers for regulated workloads and gives customers a basis for trusting Azure as a compliant, reliable platform for GxP relevant applications.

Microsoft Azure is designed to meet stringent requirements for data residency, privacy, and compliance. With Microsoft, organizations can keep sensitive data within defined geographic boundaries and under local jurisdictional control.

Microsoft offers a comprehensive set of compliance offerings to help organizations comply with national, regional, and industry-specific requirements. Backed by more than 100 compliance certifications—including ISO, HIPAA, and HITRUST, Azure meets rigorous security and privacy requirements across global and industry frameworks.

Securing the future: a collaborative approach

Security and compliance in the cloud is a shared responsibility, and the division of those responsibilities between the cloud service provider and customer depends on the cloud offering utilized. Microsoft works to ensure that we are compliant with industry and international standards, and customers are responsible for ensuring their data within the Microsoft Cloud is protected in a manner that is compliant with the standards and regulations imposed on the customer.

Azure integrates with services such as Microsoft Purview Compliance Manager and Defender for Cloud to provide organizations with visibility into their compliance posture and enable proactive governance across cloud environments.

We also provide clear guidance and detailed, auditable evidence through the Microsoft Trust Center and the Service Trust Portal. These tools exist to give customers transparency and confidence, pairing high‑level trust principles with concrete proof customers can use to meet their own regulatory and assurance needs.

With independently audited controls now recognized by leading multinational pharmaceutical companies, Azure gives life sciences organizations the confidence to run their regulated workloads in the cloud—so they can focus on what truly drives value: discovering new therapies, accelerating R&D, scaling clinical operations, and manufacturing medicines reliably at global scale. Instead of diverting resources toward duplicative cloud platform audits, customers can trust that Azure’s underlying operational rigor, change management processes, and security practices meet GxP expectations.

The audit strengthens the foundation that lets life sciences innovators move faster, modernize safely, and keep their focus on bringing breakthrough medicines and devices to patients. For more information on the audit, contact the team.

Empowering our customers

Microsoft remains committed to meeting today’s compliance, security, and regulatory standards. Across our cloud platforms and services, we maintain rigorous and independently validated controls, adhere to applicable laws and industry requirements, and continually strengthen our frameworks to protect the confidentiality, integrity, and availability of customer data. This commitment is reinforced by foundational company policies, a robust global compliance program, and active oversight from senior leadership—ensuring that every Microsoft offering is built on trust, transparency, and responsible innovation.

By working with industry leaders and regulators to shape compliance frameworks and advance sovereign cloud capabilities, Azure supports the next era of regulated AI innovation. By upholding these standards, we empower organizations in regulated industries to operate confidently, knowing their workloads run on a platform designed to meet stringent expectations today and evolve alongside emerging regulatory guidance, validated by independent experts and experienced by customers every day.

More on our approach to trust and compliance

• Dive deeper into how leading life sciences organizations are building readiness and resilience with trusted cloud platforms. Explore the e-book, Reshaping Manufacturing and Supply Chain in Pharma and MedTech, to discover actionable strategies for compliance, innovation, and operational excellence in regulated manufacturing.
• Explore From Vision to Value: AI Use Cases Transforming Healthcare to learn how healthcare and life sciences organizations are applying AI today on trusted cloud platforms.
• To learn more about Microsoft’s approach to trust and compliance, explore resources at the Microsoft Trust Center.

Connect with us at upcoming industry events to see how Azure can help your organization achieve more with confidence.

The post Microsoft Azure achieves GxP milestone, reinforcing trust for regulated workloads appeared first on Microsoft Industry Blogs.