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.

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.

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.

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

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

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

Maximize business value with AI

Discover a practical framework and real-world examples

Become a Frontier Firm

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

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

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

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

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

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

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

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

How agentic AI is benefiting insurers worldwide

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

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

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

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

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

The journey to becoming a frontier insurer starts now

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

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

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

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

Next steps on your journey to agentic AI

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

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

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

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

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

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

What utility leaders reinforced: Keeping pace with change

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

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

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

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

Trusted data as the foundation for AI in operations

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

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

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

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

From siloed AI solutions to agentic operations

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

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

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

Partner innovation making modernization deployable

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

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

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

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

Security and resilience built into modernization

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

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

What comes next

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

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

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

Turn insight into action

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

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

After decades of digitization—and years of rapid advances in AI—the industry now stands at the threshold of a far more profound shift: the rise of agentic AI. 

Unlike earlier forms of automation, agentic AI goes beyond task assistance. Intelligent AI agents can plan, reason, and act autonomously collaborating alongside clinicians, care teams, researchers, developers, and all workers from the back office to the front lines. When embedded into everyday workflows, agents transform intelligence from something accessed on demand into something continuously at work—embedding subject matter expertise with human ambition to achieve our highest aspirations.  

But if pervasive agentic intelligence is the destination, how far along is healthcare on the journey?

Measuring healthcare’s readiness for agentic AI 

To answer that question, Microsoft, in collaboration with The Health Management Academy, conducted original research published in the January 2026 issue of the New England Journal of Medicine. Based on surveys and in-depth interviews with senior healthcare executives across provider organizations in the United States, the research offers a grounded, reality-based view of how health systems are progressing along the agentic AI maturity curve—from early experimentation to enterprise level optimization. 

What the research reveals 

1. Agentic AI remains early—but strategic interest is rising
   While enthusiasm is growing, adoption remains nascent. 43% of respondents report piloting or testing agentic AI, yet only 3% have deployed agents in live workflows. At the same time, one-third of respondents indicate no plans to explore agentic AI within the next one to two years—highlighting the gap between experimentation and operational readiness. 
2. Confidence in long-term impact is strong
   Despite limited deployment today, belief in agentic AI’s future impact is clear. 60% of respondents agree or strongly agree that agentic AI will meaningfully improve or disrupt the provider–patient experience, with similar optimism around productivity gains (57%). Nearly half anticipate deeper human–AI collaboration within the next three to five years—reinforcing the view that agents will augment, not replace, clinical and operational roles. 
3. A catalyst for workforce, productivity, and experience transformation
   More than three quarters (77%) expect AI agents to improve backend productivity, while 60% believe they will fundamentally reshape the patient–provider experience. Yet this transformation will require change: 60% cite reskilling and upskilling as a top challenge as ecosystems of AI models and agents expand. 
4. A clear gap between belief and deployment
   Qualitative interviews reveal that leaders increasingly view agentic AI as a strategic end state—one that depends heavily on progress in workforce readiness, governance, and data infrastructure. Moving from promise to sustained value will require deliberate, coordinated investment across all three. 

Why this moment matters: A leadership imperative

The publication of this research marks a shift in the future of work. The question is no longer if agentic AI will reshape healthcare—but how intentionally health systems choose to shape that transformation. 

Healthcare has a rare window to define the role of agentic AI before patterns harden, and expectations are set. Success will be determined not by technology alone, but by how effectively organizations prepare their foundations and empower their people to work alongside digital colleagues in a hybrid workforce. 

Building strong governance frameworks, establishing a trusted data foundation, and developing an AI ready workforce are no longer optional—they are prerequisites for leadership in the organizations on the frontier of the next era of transformation. 

• Read the New England Journal of Medicine article.
• Read the full report: Agentic AI and the Next Frontier of Transformation.
• Explore how other healthcare organizations are already using AI to make tangible impacts.

From Vision to Value: AI Use Cases Transforming Healthcare

Organizations are using AI to increase efficiency, accelerate innovation, derive insights from their data, and empower their workforce.

Get the whitepaper

The post Assessing healthcare’s agentic AI readiness: New research from Microsoft and The Health Management Academy appeared first on Microsoft Industry Blogs.

It’s the day before a friend’s birthday. You need a gift under 50 dollars that arrives tomorrow. Instead of opening a dozen tabs, you ask an AI assistant. In a single conversation, it understands your context, evaluates constraints, and recommends a choice you can act on with confidence and feel good about giving.

That moment illustrates a shift underway in retail. ¹Bain & Company estimates that 30% to 45% of US consumers already use generative AI to research and compare products. Shopping is increasingly happening within AI-guided conversations, where agents move beyond listing options to interpret intent, evaluate tradeoffs, and guide decisions in real time.

For CMOs, this is not simply another channel to optimize. It represents a fundamental change in how influence is earned and how brands compete for loyalty, margin, and growth.

What is agentic commerce and how is it changing the shopper experience

For years, the front door to retail was a homepage, a search box, or a category page. Growth came from capturing keywords, driving traffic, and optimizing funnels, often measured through last click attribution.

Agentic commerce changes that. The new front door is the conversation.

Instead of navigating pages and filters, shoppers now express intent in natural language: “I need a sustainable gift under fifty dollars for a coworker who loves cooking. It has to arrive by Friday and feel premium, not generic.” The conversation itself becomes discovery, surfacing unexpected options and sparking new ideas. Through continued dialogue, those possibilities sharpen into confident decisions.

What matters is not just what is recommended, but how that recommendation is shaped using context in the moment of decision.

How AI shopping agents generate insights that drive business growth

The following video shows agentic commerce in action. It demonstrates how a single request can generate value both for the shopper and the brand.

A shopper asks for a coat for a trip to Aspen in February. The agent immediately goes to work, considering weather data, the shopper’s style preferences, available inventory, pricing, reviews, and more.

At the same time, that request also produces actionable insights for the brand. The marketer sees intent signals combined with internal retail data and broader market trends. Patterns emerge, showing rising demand for winterwear. An AI agent recommends a targeted promotion, the marketer approves it, and the offer is delivered back to the shopper.

The shopper receives curated recommendations with a timely promotion, makes a confident choice, and heads to the slopes in the right jacket.

This is agentic commerce in action. The conversation delivers value in the moment while simultaneously generating learning that informs future business decisions. This creates a feedback loop that strengthens with every interaction.

What sets this apart from previous shifts in the industry is when influence occurs. It is no longer applied only before a decision through advertising or merchandising. Influence is now shaped during the decision itself, as real-time signals flow between shopper and brand.

What makes the agentic commerce shift different

Retail has gone through major transitions before from physical stores to websites, from desktop to mobile, and from owned commerce to marketplaces. Each wave opened new channels of demand.

Agentic commerce goes further. It changes how decisions themselves are made. It introduces a decision layer between shoppers and brands. Behind every interaction sits a learning layer, where signals flow back to merchants, informing decisions that shape recommendations, promotions, assortments, and experiences.

The scale of this shift is substantial. ²McKinsey projects that by 2030, the US business-to-consumer retail market alone could see up to $1 trillion in orchestrated revenue from agentic commerce, with global projections reaching $3 trillion to $5 trillion. This is not an incremental growth. It represents a reconfiguration of how products are discovered, evaluated, and purchased.

For CMOs, strategy now centers on guiding decisions at the moment of choice and capturing the intelligence those interactions produce. Brands that earn influence in the moment and compound what they learn over time are better positioned to build lasting advantages in pricing, loyalty, and lifetime value.

How to build competitive advantage in agentic commerce

As agentic commerce scales, two imperatives are emerging, and leading brands are pursuing both.

Ensure discoverability wherever shoppers ask

AI assistants such as Microsoft Copilot are becoming common starting points for product discovery. When a shopper asks, “What’s the best running shoe for marathon training?” or “Find a sustainable laptop bag under $200,” AI agents interpret intent and surface recommendations.

To compete in these moments, brands must ensure their products, attributes, and value propositions are accurately represented in AI platforms. Success depends on being discoverable when consumers ask questions, not just when they type keywords into a search bar.

Build owned agentic experiences that capture learning

Discovery on third party platforms creates awareness. Owned conversational experiences create advantage. When brands deploy AI agents on their own properties, they capture the context behind each decision. That intelligence feeds merchandising, pricing, inventory, content strategy, and personalization. Just as importantly, brands can use these insights to enrich product catalogs with the language, attributes, and use cases that improve discoverability on third-party AI platforms, strengthening AEO and GEO performance over time.

Trust plays a critical role here. According to Bain & Company, while consumers increasingly use AI for research, they currently trust brands’ on-site agents three times more than third-party agents. That trust advantage makes owned conversational experiences more effective at driving conversion.

Why the winning move is doing both

The question for CMOs is not whether to participate in agentic commerce. The shift is already underway. The real question is whether your brand will appear only in third-party recommendation engines, or whether it will also own the intelligence that turns interactions into durable advantages in pricing, loyalty, product development, and lifetime value.

Four actions retail leaders can take now to prepare for agentic commerce

Agentic commerce is taking shape, and the path to readiness is clear.

1. Optimize for AI discoverability. Most product data was built for search filters, not AI conversations. Brands need structured attributes, descriptions that reflect real use cases and brand voice, and accurate information for pricing, availability, fulfillment, and policies. This practice, often called AI engine optimization (AEO) or generative engine optimization (GEO), helps ensure AI can accurately represent your products when shoppers ask questions across search engines and conversational platforms.
2. Launch owned conversational experiences to learn fast. Deploying AI shopping assistants on owned surfaces creates a feedback loop. Each interaction and conversion generates insight into customer intent, preferences, and friction points. Marketers can use these insights to continuously improve product catalog data, strengthening AEO and GEO performance across all platforms.
3. Design for openness and portability. Agentic commerce spans websites, apps, stores, partner channels, and third-party platforms. Product intelligence and brand logic need to travel across surfaces so brands can participate in new ecosystems without losing differentiation or starting over each time an interface evolves.
4. Govern measurement so learning compounds into growth. As AI agents mediate more of the shopper journey, brands risk losing visibility into how decisions are made. Clear expectations around signal sharing, experimentation, and insight ownership helps ensure every interaction strengthens the business.

Trust and privacy run across all four moves. Transparent recommendations, responsible data use, and alignment with brand values shape long term loyalty.

A new growth era for retail CMOs

Agentic commerce does not replace marketers or merchants. It elevates their role, bringing intelligence, context, and action closer to the moment of choice. For CMOs, this is a leadership moment. It is an opportunity to shape how customers discover and choose products, turn data into conversational intelligence, and build trust as a durable source of differentiation.

Those who define how shoppers discover, trust, and choose in an AI mediated world will define the next era of growth in retail and consumer goods.

Prepare your brand for agentic commerce

Optimize your product data and brand content so AI agents can accurately represent and recommend your products across platforms. Access our guide on AEO and GEO to advance your business for AI-driven shopping.

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¹Bain & Company estimates that 30% to 45% of US consumers already use generative AI to research and compare products.

²McKinsey projects that by 2030, the US business-to-consumer retail market alone could see up to $1 trillion in orchestrated revenue from agentic commerce, with global projections reaching $3 trillion to $5 trillion

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Today’s business environment demands more with less. Organizations must deliver better personalization, higher volume, and increasingly complex insights while operating with greater efficiency. The gap between stakeholder expectations and what teams can realistically deliver continues to widen. 

Microsoft’s recent insights on Frontier Transformation address these challenges by embedding AI into the core of operations. Frontier Firms are organizations that treat AI as a foundational capability and are already transforming how they work. 

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Frontier Firms don’t simply automate; they adapt. By adding adaptive intelligence to existing systems, they unlock three advantages: 

• Awareness: Systems perceive conditions in real time. 
• Reasoning: They prioritize tasks based on business needs. 
• Interaction: They communicate in natural, intuitive ways. 

Early adopters see small improvements compound quickly. These include faster service, more accurate recommendations, fewer equipment surprises, and clearer insights into peak times and bottlenecks. As agentic AI matures, companies can offer guidance and assistance that feels intuitive. Employees gain more time for high-value work, and leaders gain deeper visibility into operations. 

Frontier Transformation is more than a technology upgrade. It represents a shift in operating model. Organizations that treat AI as a foundation will lead the next wave of business innovation. 

Agentic AI is reshaping customer experience 

This shift is already visible in retail, where agentic robots are transforming customer experience and improving operational performance. Customers expect fast, personalized service, yet retailers often face staffing constraints, training gaps, and unpredictable demand. 

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Industry studies show: 

• 75% of consumers are more likely to purchase when recommendations feel relevant. 
• Nearly 40% of in-store complaints relate to wait times. 
• Inventory inaccuracies account for 4–8% of lost sales. 

These challenges reflect a broader pattern across frontline-heavy industries. Customer expectations continue to rise, and employee workloads grow more complex. 

Microsoft’s Work Trend Index reinforces this dynamic. Frontline employees say AI tools that reduce repetitive tasks, surface real-time information, and streamline customer interactions have the biggest impact on satisfaction and performance. As organizations integrate adaptive intelligence into daily workflows, these benefits build on each other and help accelerate Frontier Transformation. 

Recent industry research shows that retail and consumer packaged goods organizations are generating significant business value from generative and agentic AI, with early deployments consistently delivering multi-times ROI and accelerating impact across frontline operations.

Agentic AI creates new possibilities for stores. Instead of relying on rigid automation, it blends environmental awareness, adaptive reasoning, and conversational interaction to help teams respond in real time. 

ADAM: From beverage service to customer care 

Richtech Robotics’ ADAM beverage robot illustrates how quickly agentic systems can enhance the customer experience. Richtech, based in Las Vegas, designs and commercializes autonomous robotic solutions for hospitality, retail, logistics, and manufacturing. Through a close, hands-on collaboration between Richtech’s engineering team and the Microsoft AI Co-Innovation Labs, the two companies jointly developed new adaptive intelligence for ADAM—transforming it into a conversational, context-aware assistant powered by Microsoft Azure AI. These enhancements enabled ADAM to move beyond routine beverage preparation and support richer customer interactions.

Today, ADAM: 

• Adjusts recommendations based on weather, time of day, and promotions. 
• Responds naturally to customer requests like “less sweet,” “extra ice,” or “what’s seasonal?” 
• Notifies staff about ingredient or equipment issues before problems occur. 
• Uses vision models to maintain speed and quality during busy periods. 

Retailers report smoother operations and better customer feedback. ADAM is context aware, conversational, and reliable—qualities customers consistently reward and areas where AI has historically struggled. 

While ADAM is a retail example, the pattern extends far beyond beverage automation. Across logistics, healthcare, hospitality, and manufacturing, Frontier Firms are adding ambient intelligence and agentic workflows to physical operations and seeing meaningful gains as a result. 

Unlocking retail transformation at scale 

Once retailers see how intelligence enhances a single customer interaction, the next question naturally follows: where else can this help? Building on the advancements made with ADAM, Richtech Robotics is extending these capabilities through its Agentic Store initiative. By applying vision, voice, and agentic reasoning to common in-store tasks, the initiative helps retailers address friction points that slow down the shopping experience. 

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Examples under development include: 

• Robots that guide customers to products.
• Systems that detect empty shelves or misplaced items.
• Voice-enabled, in-aisle assistance.
• Real-time adjustments based on foot traffic or local events.

This approach does not require heavy hardware investments. These workflows are software-driven and build on existing store infrastructure. It reflects how Frontier Firms drive transformation by spreading intelligence across the ecosystem rather than upgrading a single process at a time. 

Retailers gain clearer visibility into peak demand, customer behavior, product movement, and service quality without increasing manual tracking. As one store manager described it, “it feels like having a second set of eyes that never gets tired.” 

Convenient, high-quality service becomes a blueprint for store-wide intelligence. In the coming years, a clear difference will emerge between retailers that treat AI as a tool and those that treat it as a foundation. The latter will set the pace for the industry. 

Steps toward Frontier Transformation 

Agentic AI gives retailers a practical and achievable path forward. It elevates customer experience, reduces operational strain, and creates the foundation for smarter and more adaptive stores. Organizations that embrace Frontier Transformation position themselves as Frontier Firms, ready to scale faster, work more intelligently, and unlock new value through the combination of human judgment and AI-driven insight. 

The journey begins with small, strategic steps and a bold vision for what is possible. To explore the broader business impact of AI across frontline and customer-facing roles, review Microsoft’s Work Trend Index: The year the Frontier Firm is born.

Explore how organizations are transforming with AI, and learn how you can build your own generative AI proof of concept with the Microsoft AI Co-Innovation Labs.

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