As capital markets firms push toward the frontier, success increasingly depends on turning AI ambition into secure, repeatable operating impact at global scale. In this independent IDC guest blog, Thomas Shuster examines how agentic AI is reshaping capital markets operating models—and why firms are gravitating toward platforms and partners that combine technological leadership, deep industry expertise, strong governance foundations, and proven experience delivering AI value across the end-to-end value chain.

When capital markets leaders talk about Frontier Firms, it is important to recognize that the term’s definition has shifted. It is less about being first to experiment with new tools and more about translating AI investment into measurable, repeatable operating gains. That distinction matters as the operating environment tightens. Settlement cycles continue to compress, regulatory expectations change, and risk controls must remain effective as markets evolve. At the same time, technology teams are expected to modernize while continuing to support large legacy environments. In this context, agentic AI emerges as a practical marker of frontier operating models.

From tools to operating models

Early generative AI tools improved drafting, summarization, and search. These capabilities were helpful but not transformative or differentiated. The step change occurs when firms shift from task acceleration to workflow redesign, deploying AI agents to execute multistep processes across systems under bounded human oversight.

Frontier Firms focus on workflows characterized by high friction, frequent exceptions, and material costs when delayed. They redesign processes so agents perform the coordination and context gathering work that typically slows teams down: pulling data, checking policies, identifying breakpoints, proposing actions, and routing tasks to the right owners. Humans remain accountable for decisions but no longer act as the connective tissue that holds workflows together. This shift has important workforce implications because human effort moves away from manual orchestration and toward judgment, escalation, and decision-making.

By contrast, non-Frontier Firms often attempt to layer AI onto workflows still defined by manual handoffs and fragmented systems. These initiatives may succeed in pilots but frequently stall when exposed to real-world operational variability.

Integration, not intelligence, is the limiting factor

Many operational breakdowns in capital markets stem from fragmented information. Trade exceptions can span execution data, reference data, allocations, settlement instructions, and counterparty communications. Know your customer (KYC) refreshes depend on sanctions data, beneficial ownership structures, customer documentation, and policy interpretation. These are inherently cross-system and, increasingly, cross-organization challenges.

Frontier Firms treat data access as a core capability rather than a downstream integration problem. They invest in ecosystems that support secure, permitted access to internal and external data with auditability and clear economic and contractual rules. In practice, the operating framework often matters as much as the underlying technology. Questions of data ownership, computational rights, value sharing, and dispute resolution frequently determine whether an agentic use case can scale. Where these foundations are absent, teams compensate with manual workarounds that are slow, error-prone, and difficult to audit.

Governance as an accelerator

There is a persistent tendency in capital markets to defer governance until a use case has demonstrated value. That approach breaks down with agentic AI. Agents act within workflows and can trigger downstream consequences if controls are weak.

IDC’s research shows that only about 4% of financial institutions believe AI agents should operate with full autonomy. More than 75% rate transparency as very or extremely important, with the share rising to roughly 88% among Frontier Firms. How frontier organizations operationalize trust reflects these preferences. They define which decisions require human approval, log agent inputs and actions, establish clear escalation paths, and design workflows that make overrides straightforward. Many organizations also prefer to rely on platform-level governance capabilities rather than bespoke controls for each use case.

When done well, governance becomes an enabler rather than a constraint. It allows firms to deploy agentic workflows more broadly and with fewer surprises, aligning risk and innovation teams. Where governance lags, organizations often see the opposite outcome: Risk teams perceive AI as uncontrolled, innovation teams view governance as blocking progress, and value remains trapped in isolated proof points.

Where Frontier Firms pull ahead first

IDC finds that Frontier Firms adopt functional and industry use cases almost twice as much as their peers. Expectations for automation are also rising. In IDC’s resiliency and spending research, 87% of firms expect providers’ agentic AI capabilities to eliminate manual and semi-manual workflows within 18 months.

The gap widens most quickly where speed, exception handling, and control converge. In post-trade operations, many organizations still manage exceptions through email and informal handoffs, slowing resolution, and weakening auditability. Frontier Firms move toward agent-supported, structured case management. In onboarding and due diligence, event-driven regulatory expectations are making periodic refresh models brittle. While only about 10% of financial institutions used AI for regulatory compliance in the past year, nearly 90% plan to do so in the next 12 months. In research and intelligence functions, agents increasingly monitor sources, summarize changes, and map exposures, shifting human effort from aggregation to decision making.

AI is reshaping business models

The frontier advantage is not limited to efficiency. IDC’s research shows that organizations using agentic AI report a 2.3-time return on investment (ROI), with average payback periods of about 13 months. These attractive economics are accelerating investment. Building customized AI agents to automate business processes ranks as the top area of significantly increased IT spending among capital markets firms in 2026, which more than 80% of organizations have cited.

As these agents mature, firms are also reassessing their application strategies. In IDC’s survey, 84% of financial services firms agree that AI agents are emerging as a new layer of enterprise capability, prompting renewed scrutiny of investments in packaged applications.

Closing thought

Agentic AI is not a shortcut around complexity. It is a way to absorb complexity without scaling cost and risk linearly. Ambition alone does not distinguish Frontier Firms. Differentiating them are data access, governance discipline, operating model design, workforce readiness, and organizational habits required to turn agentic AI into a durable source of advantage.

Explore more insights on agentic AI in capital markets

• Read IDC’s Analyst Connection How and Where Frontier Firms Are Bending the Curve on Innovation and explore the infographic Expanding the Frontier: Agentic transformation in capital markets for deeper insights into the strategies shaping the future of capital markets.
• Watch Transforming Financial Services – Leaders in AI adoption and Innovation to see how leaders in capital markets are successfully using AI to transform complexity into opportunity.
• 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.
• Explore capital markets solutions and resources on Microsoft for Capital Markets.

The post How Frontier Firms use agentic AI to gain an edge in capital markets 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.

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.

The insurance industry stands at a pivotal moment in its digital transformation journey. With deep data reserves, a tradition of analytic decision-making, and a workforce skilled in actuarial and underwriting practices, insurers are uniquely positioned to benefit from the ongoing advances in AI.

However, despite early enthusiasm and pilot projects, only 7% of insurers have successfully scaled AI initiatives across their organizations.¹ The adoption of increasingly powerful AI agents—systems that can support autonomous tasks, help make decisions, and take action under human oversight—offers a promising path forward. By embedding intelligent agents into workflows, insurers can tackle legacy constraints, talent shortages, and operational inefficiencies to unlock transformative value.

Challenges in adopting agentic AI

The broad adoption of agentic AI in insurance is hindered by several entrenched challenges.

First, a persistent talent shortage affects specialized roles such as actuarial analysis and underwriting, which limits the industry’s ability to leverage data effectively. Adding to the challenge is legacy infrastructure, including outdated systems and fragmented data architectures, which can impede integration and scalability.

Financial strain across the insurance sector is another major factor, with catastrophe losses exceeding $100 billion annually for six consecutive years, making high-frequency property losses a structural issue.²

Organizational resistance also plays a significant role; siloed teams, unclear priorities, and a conservative corporate culture slow the pace of AI adoption.

Opportunities with agentic AI

Despite these hurdles, agentic AI presents transformative opportunities. Workforce augmentation is one of the most promising areas. For instance, Sedgwick’s Sidekick Agent, developed in collaboration with Microsoft, enhances claims processing efficiency by more than 30% through real-time guidance and decision support.³

AI also enables personalized customer experiences at scale, using embedded systems to tailor communications and support. Operational efficiency can be improved significantly in some implementations, with end-to-end redesigns yielding 30–40% gains in net efficiency.¹

Furthermore, agentic AI, under human guidance, can enhance quality assurance by improving consistency and helping insurers adhere to compliance processes, which is especially important as seasoned professionals retire and institutional knowledge can be lost.

With agentic AI, chatbots can also be improved to more effectively enhance customer experience. Instead of answering a question and handing a customer off to a queue, an agentic solution can help orchestrate a more end-to-end experience. Potentially, this can include everything from capturing first notice of loss, to requesting missing documentation, updating policy and billing systems, scheduling appraisals, and proactively notifying customers of next steps (all subject to insurer workflows and regulatory review, of course).

This “resolve, not route” approach is already showing measurable impact in claims operations: For example, according to McKinsey, one major insurer rolled out more than 80 AI models in its claims domain, cutting complex-case liability assessment time by 23 days, improving routing accuracy by 30%, and reducing customer complaints by 65%.⁴

For carriers, such outcomes translate into faster cycle times, higher customer satisfaction, and better loss-adjustment expense control—all while preserving necessary human oversight where judgment, empathy, and regulatory accountability are required.

Strategies for success with agentic AI

To successfully adopt agentic AI, insurers must align technology initiatives with customer needs and business goals.

Establishing an AI Center of Excellence (CoE) is a foundational step. A CoE provides governance, strategic direction, and technical expertise, helping organizations avoid fragmented AI adoption and scale responsibly.

Iterative testing is also essential, beginning with high-volume, repeatable tasks that help insurers refine models through feedback loops and production pilots.

Targeting scarce talent areas, such as fraud detection and underwriting, can maximize impact by augmenting roles that are difficult to fill.

Industry accelerators like Cognizant’s Agent Foundry offer prebuilt tools and frameworks that can help reduce implementation time and support compliance efforts. This platform-agnostic solution supports the full lifecycle of agent deployment, from discovery to scale, and integrates seamlessly with existing enterprise systems.

Finally, cultural transformation is critical. Since 70% of scaling challenges are organizational, insurers must foster a culture of change, accountability, and long-term thinking to fully realize AI’s potential.¹

Move to agentic AI with confidence

Agentic AI is not just a technological upgrade, it is a strategic imperative for insurers seeking to thrive in a rapidly evolving landscape. By addressing structural challenges and embracing AI as a catalyst for transformation, insurers can unlock new levels of efficiency, personalization, and resilience.

The path forward requires bold leadership, cross-functional collaboration, and a commitment to continuous learning. Those who invest in scalable frameworks, such as AI Centers of Excellence and industry accelerators, will be best positioned to lead the next era of insurance innovation.

Explore solutions for agentic AI in insurance

• Explore insurance solutions and resources on Microsoft for Insurance.
• To learn about how Cognizant and Microsoft are partnering to help insurance companies advance their cloud modernization journeys, see the Cognizant and Microsoft Partner Page.

¹ Insurance leads AI adoption. It’s time to scale

² 2025 marks sixth year insured natural catastrophe losses exceed USD 100 billion, finds Swiss Re Institute

³ Sedgwick optimizes claim workflows with AI application Sidekick and Microsoft integration

⁴ The future of AI in the insurance industry

The post Microsoft and Cognizant: Delivering on the promise of agentic AI adoption in insurance appeared first on Microsoft Industry Blogs.

Yet as organizations deepen their partnerships with major cloud and AI providers, regulators and executives alike are sharpening their focus on concentration risk, the concern that reliance on a relatively small number of technology providers might create critical business vulnerabilities. 

Rather than viewing cloud dependency as a threat, forward-looking leaders regard it as an important facet of modernization. The challenge is not to avoid concentration; it is to manage it intelligently, helping a firm maintain control, enhance resilience, and remain flexible amid changing conditions.  

For financial services firms in many jurisdictions, exit planning—a structured process to safely disengage from critical providers—has moved from a theoretical consideration to a regulatory expectation and an important component of operational resilience. 

Managing risk and exit planning in an evolving landscape 

Concentration risk has long been framed as systemic exposure (“What if a key provider fails?”), prompting regulators to mandate exit plans that assume full termination. In theory, this seems straightforward; in practice, it rarely is. 

Modern financial institutions operate in a deeply interconnected ecosystem where critical third-party providers are embedded in core operations and strategic innovation. These partnerships go beyond simple outsourcing; they often underpin transformation initiatives and are key to resilience when managed well by the organization. As a result, in highly integrated environments, full disengagement may be operationally complex and unlikely in practice, but firms are still required to maintain feasible, risk based exit plans. 

In this regard, Microsoft has introduced important capabilities (such as standardized architectures, diversified cloud regions, and built-in failover options) that customers can incorporate into their resilience and exit planning strategies. They can effectively reduce dependency risk for critical services and ensure continuity, but they stop short of enabling a full provider exit. Regulators increasingly acknowledge that perfect exits are not always technically or economically feasible. What they require are proportionate, well tested plans that reflect operational reality. The priorities are transparency, control over critical workloads, and pragmatic dependency management.  

Against this backdrop, regulators are recalibrating expectations, focusing on actionable, tested strategies rather than theoretical full exits. Two major frameworks illustrate this shift: 

• The European Union Digital Operational Resilience Act (DORA): Requires institutions to maintain tested transition plans that enable the removal or migration of contracted information and communication technology (ICT) services and data.
• The United Kingdom Prudential Regulatory Authority (PRA) SS 2/21 and the Critical Third Party (CTP) oversight regime: Requires firms to maintain documented and tested exit strategies for any “material” (such as critical and high-impact) outsourcing arrangement, with clear definition of roles, responsibilities, and continuity plans. 

Both frameworks emphasize proportionality, focusing on critical or important business functions, and integration into broader business continuity and resilience of governance.  

Integrating exit planning within a broader resilience strategy 

Exit planning is no longer optional, it is a compliance essential. Fortunately, given the complexity of today’s hybrid and multi-cloud environments, regulators do not expect “perfect” exit plans. Instead, they encourage risk-based, practical, and tested practices that dovetail with broader efforts.  

Exit planning should be embedded within a comprehensive, structured approach to strengthen operational resilience. To support such an integrated approach, Microsoft has developed a six-step resilience framework that aligns closely with the requirements of DORA: 

1. Update cloud risk governance: Systematically review policies and controls to ensure that cloud adoption aligns with business priorities, regulatory requirements, and risk tolerance.
2. Identify concentration: Specify critical third-party and indirect nth-party dependencies, such as a vendor’s suppliers, subcontractors, or technology partners. 
3. Assess alternatives: Evaluate potential providers and exit strategies—comparing cost, resilience, and compliance to ensure continuity and mitigate concentration risk before making final decisions. 
4. Design for resilience: Plan systems and recovery processes that can withstand disruptions from hardware failures and service outages, recover quickly, and maintain critical operations. 
5. Test business continuity plan: Prepare for loss of a data center or region, or long-term failures, with regular testing that identifies gaps and validates recovery procedures. 
6. Prepare exit plans: Develop and test detailed exit strategies—including timelines, resource allocation, and contingency measures—to ensure seamless provider transition and maintain compliance under stress scenarios. 

This integrated approach ensures that exit plans remain both practical and sustainable, and do not exist in isolation. Ultimately, exit planning is part of a larger system of controls and safeguards, evolving alongside the business’s cloud and AI innovation cycles. 

Enhancing exit planning with guidance and tools from Microsoft 

Recognizing the criticality of continuity, reversibility, and secure data transfer in financial services organizations, Microsoft has developed a comprehensive framework of contractual commitments, technical solutions, and support services to empower firms to manage exit scenarios with confidence and control. 

For example, if a regulator intervenes in a company’s operations, Microsoft is committed to granting the regulator full administrative control over the institution’s cloud environment. In cases of reorganization or acquisition, Microsoft enables the assignment or transfer of service rights to successor entities, ensuring that critical services remain uninterrupted. Importantly, Microsoft will not suspend or terminate services solely due to a transfer of rights, provided contractual obligations are met, and offers flexible service extensions to facilitate smooth transitions and data retrieval. 

Beyond contractual measures, Microsoft equips customers with a suite of advanced technical tools to support seamless data migration and workload portability. These include:  

• Azure Arc, a bridge that enables hybrid and multi-cloud management, letting firms extend Microsoft Azure services to on-premises or other clouds for flexible migration and reduced concentration risk.
• Containerization and portability: Using containers (such as Azure Kubernetes Service and Docker) and microservices makes applications portable—simplifying workload transfers between Azure and other environments.
• Automated data migration: Built-in tools like Microsoft Azure Data Factory automate extract transform-load (ETL) processes, streamlining bulk data migration during exit events.
• Microsoft 365 data management, provided with Microsoft Purview and other solutions, to provide key capabilities, including:
  • eDiscovery tools that can export emails, documents, and collaboration data in standard formats for easy transfer.
  • Backup solutions to create point-in-time snapshots, supporting reversibility and continuity.
• Hybrid, private, and sovereign cloud options for Microsoft Exchange, SharePoint, OneDrive, and Skype for Business enable migration across platforms. 

By combining clear contractual safeguards, advanced migration tools, and ongoing investment in hybrid cloud and open APIs, Microsoft empowers financial institutions to plan and execute exit strategies that align with regulatory mandates and business objectives. Exit planning then becomes a proactive process, one that safeguards business continuity and regulatory compliance at every stage of the cloud journey. 

Learn more 

• For an in-depth analysis, download our whitepaper, “Addressing concentration risk and building practical exit strategies for Microsoft Cloud.”
• Read more about our approach for managing operational risk in financial services on Microsoft Learn.
• Visit our blog for stories of how Microsoft for Financial Services helps firms accelerate business value.

The post Managing concentration risk and exit requirements: A framework for financial institutions appeared first on Microsoft Industry Blogs.

Today, financial services has the highest concentration of Frontier Firms—organizations that embed AI agents across every workflow to drive speed, agility, and scalable innovation. These are companies that have demonstrated greater business impact from AI by blending human judgement with AI agents. A November 2025 IDC study commissioned by Microsoft shows that Frontier Firms report returns on their AI investments roughly three times higher than slow adopters.¹

In my previous blog, I detailed a three-phase roadmap for banks to become Frontier Firms. Now, I’d like to share essential practices in AI adoption that we believe are predictors for financial services success in 2026. These are based on hundreds of conversations I’ve had with customers, industry leaders, and technologists worldwide over the past three months. Organizations aiming to maximize AI’s potential in 2026 and beyond should consider these practices as part of their approach to AI transformation.

The new essentials for AI success in financial services

In 2026, success won’t come from experimenting with AI, it will come from re-architecting core business processes to be human-led and AI-operated. Frontier Firms are already heavily invested, with 70% of organizations across industries saying that in the next 24 months, they plan to increase their budgets for generative AI and agentic AI.¹

In financial services, there is pressure from senior executives and boards to move at greater pace and scale—to differentiate their firms by infusing AI into the fabric of their business.

In response, our customers are eager to learn how Frontier Firms do it. What are they doing to drive real impact faster than others? How do they make the right investments? What can they not afford to miss?

Regardless of when and how a firm acts, we see five essential keys to success, not all of them purely technical.

1. Value creation will drive innovation in agentic AI

As the adoption of AI evolves, Frontier Firms are re-thinking how they measure value creation. The traditional approach to crafting business cases is giving way to a new, more dynamic model as Frontier Firms are now measuring the impact of AI as use cases are deployed. A/B testing (comparing two versions of a use case to understand which delivers greater impact) has helped organizations like Investec quantify meaningful benefits, including saving bankers up to 200 hours a year with Microsoft Copilot for Sales. Focusing AI enablement on customer-facing teams is a practical way to move beyond internal productivity gains and begin influencing top-line growth. 

Frontier Firms in financial services are now focusing on the measurable impact of AI—revenue growth, increased margins, and market share gain from new products, differentiated customer experiences, and empowered employee workflows. This goes well beyond use cases focused on efficiency. According to IDC, 36% of financial services firms are planning AI use cases in the next two years to boost revenue with new business models, products, or services.¹

To scale AI to more powerful, multifunction workflows, Frontier Firms are creating agentic operating models that embed AI more deeply across the business and the workforce. Working under the direction and oversight of humans, these new AI agents can reason, plan, and act across critical workflows.

Embedding agents correlates with value creation by aligning AI with core processes and key metrics. A good example is Generali France, a key player in the insurance sector in France. The organization is powering strategic use cases in customer relations and core business expertise with AI agents. In their helpdesk operations, they’ve developed a 24/7 voice assistant that’s capable of reassuring claimants before a human steps in. Nearly 1.3 million calls—representing 30% of requests—are resolved directly by clients, with no human intervention needed.

IDC predicts such adoption of agentic AI will triple in the next two years.¹ Winning firms will anchor their innovation to business outcomes that matter in financial services (such as safer payments, faster credit decisions, and decreased fraud, to name a few), and report outcomes in quarterly scorecards so teams see purpose, not just tools.

2. Skilling and AI fluency will maximize workforce value

Even the best technology will fall short if workers aren’t trained and supported to embrace it.

Successful transformation addresses the human aspect—ensuring everyone understands the benefits and feels part of the journey. Change management is both a top-down and bottom-up process. Leadership must set the vision while making sure that employees at every level are empowered to contribute.

Organizations should start with skilling, with a focus on “learning in the flow of work.” Leaders can foster learning by embedding it into daily tasks so that skills stick and compound over time. They should consider building learning pathways focused on AI fluency for all employees, plus specialized tracks for specific roles, then reinforce adoption with incentives that reward employees for integrating AI into everyday workflows.

Lloyds Banking Group offers a powerful example. Departments competed for a limited number of Microsoft 365 Copilot licenses with bids based on business cases. The firm built a network of 1,000 volunteer “flight instructors” and hosted weekly “promptathons” to share best practices. The impact: over 10,000 employees trained, with 93% daily usage among 30,000 licensed users.

3. Innovation will expand across business processes

Frontier Firms are quickly moving beyond single function use cases and innovating across seven business functions on average. Focused on expanding impact across the business, AI innovation in financial service will map to key functions such as research automation in capital markets, claims in insurance, and anti-money laundering or fraud in banking. Plus, more than 70% of firms are using AI in customer service, marketing, IT, cybersecurity, and product development.¹

This broad approach is delivering better outcomes for Frontier Firms on many critical fronts: top-line growth (88%), brand differentiation (87%), cost efficiency (86%), and customer experience (85%).¹ Interestingly, it also opens innovation to drive new opportunities, such as transforming support functions into revenue generators through new customer experiences.

The impact of advanced AI spans the financial services industry. In capital markets, it improves market research and analytics, personalizes the client experience across digital channels, and helps tailor services to individual needs. BlackRock, for instance, is transforming its investment lifecycle by embedding AI into its Aladdin platform, used by tens of thousands of users. Likewise, LSEG and Microsoft have built tools that let financial professionals quickly build custom agents leveraging 33-plus petabytes of trusted market data.

In banking, AI is equipping financial institutions with new tools for personalized service and stronger client relationships. AI will help to improve the effectiveness of targeted marketing campaigns, streamline lending and mortgage processes, and safeguard assets with advanced fraud analysis. AI agents will continue to proliferate, thanks to efforts such as Argentina’s Banco Ciudad, which launched a new AI Center of Excellence that delivered 10 agents in six months to improve customer service, workflow automation, and cross-team integration.

In insurance, AI will accelerate value by automating complex, high-value processes such as underwriting, claims management, and policy administration while improving risk modeling and compliance. It will become more effective in helping insurance agents better serve clients, and in helping customers make the best policy choices. Fraud detection and decision making will advance, thanks to innovations such as a new service from Shift Technology designed to help insurance companies by automating classification and extraction of unstructured data.

4. Responsible AI and regulatory readiness will be competitive advantages

The firms that lead in AI will also lead in governance. IDC predicts 1.3 billion AI agents will be in business workflows by 2028.² As they become part of the organization, business leaders need to think of them as employees in many ways. They will require identities, permissions, and oversight. They’ll need to be trained, monitored, and auditable.

Proactive compliance is now an imperative, if not a competitive advantage. In 2026, regulatory complexity will only intensify, meaning that trust must be the foundation for scale and innovation. Frontier Firms embed responsible AI frameworks into every stage of the lifecycle, from design to deployment and monitoring. Leaders will integrate data privacy, encryption, and access controls across AI innovation from day one.  

Bradesco’s Bridge is an example of how a bank can responsibly operationalize agentic workflows. Bridge uses Microsoft Azure AI to provide a governed API layer to enforce consistent policies and secure data access. The result is 83% resolution rates for digital service and a 30% reduction in tech costs.

Complementing this, Microsoft’s new Agent 365, announced at Microsoft Ignite 2025, addresses the critical need for control at scale. Agent 365 is a unified control plane that extends enterprise management and security to partner-developed agents and even those running outside the Microsoft Cloud. Integrated with Microsoft Entra, Microsoft Purview, and Microsoft Defender, it enforces identity, permissions, and data protection while surfacing telemetry for ROI and compliance. All agent activities are logged into Microsoft Sentinel and Purview audit logs, giving security operations teams a full audit trail to investigate incidents. It also minimizes the risk of “shadow AI” and helps ensure that agent deployment is compliant. 

5. Data strategy will unlock AI at scale

Perhaps the single most important requirement for success with agentic AI is data readiness. Without the right strategy to ensure data interoperability and real-time intelligence, an organization’s most important initiatives are destined to fall short. 

To derive the most value from AI, the first step is to unify all of the organization’s data. Fragmented systems—core banking, risk models, compliance archives, customer relationship management—create blind spots. The traditional approach to doing this—moving all of an organization’s data to a single location—has often proven costly and resource intensive. The new approach is to use a unified data platform, such as Microsoft Fabric, which connects data wherever it sits, giving organizations a single source of truth, even when the data resides on other platforms or systems. This approach empowers organizations to deliver faster insights, lower costs, and unify governance, accelerating their AI deployment.  

LSEG leveraged Microsoft Fabric to modernize their data infrastructure and accelerate time to market. LSEG uses Apache Spark on Fabric to process around 80,000 files daily, consuming approximately 280,000 capacity units per day. Usage is growing rapidly, with month-on-month consumption increasing by more than 50%, signaling the start of a transformative journey.  

Finally, organizations must embed governance and security. Identity-based access, audit trails, and adaptive risk controls are non-negotiable.

AI at scale is not just about models—it’s about the foundation: data, cloud, and governance. Microsoft delivers on all these imperatives: Microsoft Fabric IQ for unified semantics, Foundry IQ for contextual knowledge, Azure for scalable performance, and Microsoft Agent 365 for governance. The mandate is clear—modernize your data foundation today to avoid challenging consequences later.

Now is the time for agentic AI in financial services

Financial institutions have long pursued productivity gains to reinvest in growth, and that imperative remains. But AI is no longer just about cost savings—it’s about reinventing how organizations engage customers, redefine services, bend the innovation curve, and create competitive differentiation.

To empower our customers to accelerate to scale, Microsoft has built a full stack enterprise AI platform that is fully integrated. Our approach is architected around 3 objectives: 

• AI in the flow of human ambition
  Copilot can be used by end users to develop personal agents to execute tasks on their behalf. For low-code development, customers can use Microsoft Copilot Studio to build agents and assistants that can answer questions and execute workflows that integrate with their data and line of business systems. Both are powered by Work IQ—an intelligence layer that customers can use to give real-time insights into how teams are working across applications and processes and improve operational performance. 
• Ubiquitous innovation
  To enable ubiquitous innovation, Fabric is an AI-powered, end-to-end data and analytics platform that breaks down the data siloes and unifies data that resides in different places. It is powered by Fabric IQ, a semantic intelligence layer that enables customers to organize data around the language and meaning of their business (such as relationships, entities, and logic) so teams can build agents that are grounded in the same semantic understanding of the business.
  
  Ubiquitous innovation is also enabled by Microsoft Foundry, Microsoft’s unified Azure platform for building, deploying, and optimizing pro-code AI apps and agents—bringing models and tools together. Foundry IQ makes it easier for teams to build reliable agents and apps that use trusted governed knowledge. Foundry IQ is a knowledge grounding layer for agents: it connects AI apps and agents to content across many sources (like documents, policies, Microsoft SharePoint, OneLake, and external stores) using an Azure AI Search–powered knowledge base and a single grounding API, with permission-aware access. 
• Observability at every layer
  As organizations navigate the agentic era in financial services, the question isn’t just how to deploy AI agents—it’s how to do it securely, at scale, and in a way that positions the organization as a leader. This requires a connected, trusted, and scalable platform to orchestrate AI across the business, which Microsoft offers. With Agent 365 as a unified control plane, organizations gain a single governance layer that extends enterprise-grade identity, security, and compliance to every agent—whether it’s built in-house or running on external clouds. This means organizations can confidently embrace AI innovation without sacrificing oversight or regulatory alignment. Microsoft’s approach means organizations aren’t just adopting tools; they’re anchoring their business to the platform for the open agentic web—a trusted, interoperable ecosystem where agents and humans collaborate seamlessly.  

The firms that embrace this shift—modernizing data, embedding governance, and preparing their workforce—won’t just adapt; they’ll lead the next era of financial innovation.

Learn more

• Read the Becoming a Frontier Firm: AI in Financial Services e-book—your blueprint for scaling with AI, featuring case studies from banking, capital markets, and insurance.
• Watch Leading at the Frontier: AI Innovation in Financial Services to see real-world AI strategies from industry innovators and experts.
• Visit Microsoft for Financial Services to learn more about how Microsoft empowers financial services organizations to achieve more.

¹ IDC, What every company can learn from Frontier firms leading the AI revolution, sponsored by Microsoft, November 2025.

² IDC Info Snapshot, sponsored by Microsoft, 1.3 Billion AI Agents by 2028, May 2025 #US53361825.

The post AI transformation in financial services: 5 predictors for success in 2026 appeared first on Microsoft Industry Blogs.

As banks compete aggressively with AI, many are gravitating toward the concept of the Frontier Firm—a new kind of enterprise that doesn’t just use AI, but rearchitects itself around it. Frontier Firms view AI agents as digital colleagues, empower employees to act as agent bosses, and operate with intelligence on tap to work smarter, scale faster, and create new value.

Characteristics of the Frontier Firm in banking

The speed of transformation sparked by generative AI is unprecedented, leaving banks precious little time to make critical decisions. Across industries, 82% of leaders say 2025 is a pivotal year to rethink their organizational strategy, and 81% expect AI agents to be deeply integrated into their workforces within the next 12 to 18 months. In banking, 70% of companies that have adopted AI are realizing cost savings.² The challenge is to forge a strategy that delivers immediate ROI while also fostering long-term transformation.

The Frontier Firm concept brings clarity and a workable blueprint to this high-stakes challenge. It blends machine intelligence with human judgment, resulting in systems that are AI-operated but human-led. The Frontier Firm features the following characteristics:

• Intelligence on tap: AI capabilities are embedded across workflows and decision-making.
• Work chart versus org chart: Teams form around outcomes, not departments.
• Agent–boss mindset: Every employee manages and collaborates with AI agents.

With these principles in mind, banks can advance their innovation efforts without waiting for perfect conditions. Most banks are already well positioned to make quick strides.

The Frontier Firm in banking: Three-phase AI transformation journey

The journey to becoming a Frontier Firm plays out in three phases, each phase representing a deeper integration of AI into business operations. Organizations may operate in multiple phases at the same time, depending on function and maturity.

Phase 1: Human with assistant

In this phase, the goal is to empower employees with AI agents such as copilots and digital assistants that help improve productivity, generate efficiencies, and reduce drudgery.

Early innovation with generative AI in banking was limited, focused primarily on internal needs and designed to evaluate the technology and its impacts. These initial use cases quickly demonstrated both the immediate value and long-term potential of AI.

For many banks, an especially powerful productivity driver has been the adoption of Microsoft 365 Copilot, which embeds generative AI into everyday apps like Word, Excel, PowerPoint, Outlook, and Teams. In the UK, for example, with Copilot, Hargreaves Lansdown reduced the time required to record customer meeting notes from an average of four hours to just one. In Australia, Copilot helped Bank of Queensland cut the time required to produce drafts of internal manuals by 99%, marketing content by 88%, and human resource documents by 75%.

Complementing Microsoft 365 Copilot are a set of role-based agents that support specific job functions, one of which is Microsoft Copilot for Finance. Designed to help streamline operations and make faster decisions, Copilot for Finance connects with financial systems such as Dynamics 365 Finance ERP and even third-party platforms. At Microsoft, it delivered 22% in cost savings on reconciliation tasks for our corporate Treasury organization, doing in 10 minutes what previously took more than an hour. 

A key lesson here is that integrating AI into familiar productivity tools and daily workflows promotes quick adoption, as opposed to introducing new applications or interfaces. To amplify the impact, Copilot solutions can be enhanced to seamlessly work with a bank’s internal data and key partner connectors. For example, Wells Fargo built an agent for 35,000 bankers across 4,000 branches to help its employees find information to better assist customers. As a result, 75% of searches now happen through the agent, and query response times have gone from 10 minutes to just 30 seconds.³

Elsewhere, Barclays deployed Copilot to 15,000 users, and the results soon led to an expansion to 100,000 employees worldwide, incorporating the bank’s broad ecosystem of collaboration tools, portals and online resources. Likewise, using Azure OpenAI Service and Azure AI Search, Swiss bank UBS developed a set of “Smart Assistants” to help client advisors deliver more personalized insights by synthesizing 60,000 documents, plus a Legal AI Assistant (LAIA) that transforms how teams search a repository of 26 million multilingual legal documents.

An important counterpart to embedded copilots is an emerging class of personal agents within the Microsoft 365 Copilot suite, which can tackle deeper, domain-specific tasks. The Researcher Agent delivers in-depth insights by synthesizing internal and market data to support functions like strategic planning, compliance, and competitive analysis. The Analyst Agent works like a virtual data scientist, transforming raw data into forecasts, customer behavior visualizations, and automated reports.

Becoming a Frontier Firm: AI in financial services

See real-world examples of how Frontier Firms are leading with AI.

Read the e-book

Phase 2: Human–agent teams

In the second phase, AI agents take on specific tasks under human direction. Employees delegate tasks to them, review outputs, and intervene only for exceptions.

Some banks are now developing powerful agents to assist employees across a broad range of key tasks such as reconciling transactions, performing KYC checks, or conducting background verifications. Agents can also, help manage onboarding journeys, verify documents, and deliver training. The net effect is to free employees to focus on higher-value work at a greater scale.

For example, Dutch ABN AMRO Bank replaced its legacy chatbots with two new AI-powered assistants, Anna and Abby, that autonomously manage employee and customer conversations using Azure AI Language for intent recognition. Employees delegate routine tasks to the agents, which escalate to humans only for exceptions. This reduced drop-off rates, improved Dutch language accuracy by 7%, and now supports more than 3.5 million conversations annually.

Beyond customer service, banks are deploying agents to streamline complex, high-volume processes. In mortgage lending, agents can automate document verification, income validation, and regulatory checks—reducing cycle times and improving transparency. This helps lessen or eliminate bottlenecks caused by manual dependencies, accelerating approvals and improving customer satisfaction.

AI innovation in financial services is moving to a more powerful agent-based model in which AI serves as a collaborative tool. Financial services provider Virgin Money built a new contact center agent called Redi that triages customer inquiries, executes predefined journeys, and seamlessly escalates sensitive exceptions, such as bereavements, to human agents. Designed with input from customer center staff to emulate live interactions, Redi embodies the idea of “human-in-the-loop” governance, where AI handles the bulk of execution, but employees retain control over edge cases. Staff now view Redi as “another colleague” that supports them by triaging tasks and enabling them to focus on empathy and relationship-building.

Phase 3: Human-led, agent-operated

In this advanced phase, AI agents do more than assist or collaborate, they own and execute complete business processes. Humans provide direction, oversight, and exception handling, but day-to-day operations are managed by agents.

Agentic systems can reason, plan, and act independently to achieve goals. For example, an agent that can shop for clothing, plan a vacation, or buy groceries based on a consumer’s preferences and limits. This is the vision behind a new AI-powered platform that enables agents to “find and buy” on behalf of users.⁴ Features such as tokenized digital credentials, which confirm that an agent is authorized to act on a consumer’s behalf, foreshadow new ways agentic AI can deliver seamless, secure, and personalized experiences, and create new value.

Agentic AI-powered commerce and payments are driving a new suite of tools being developed by PayPal, designed to help developers build AI agents that can transact, manage invoices, and track shipments using PayPal’s APIs.⁵ As part of a unified platform for commerce, these tools let agents autonomously execute end-to-end commerce workflows using natural language.

Many banks are exploring innovation in agentic AI with scenarios spanning a broad range of business services, including advisory and customer service support; channels, like kiosks, online, social, and contact center; and operations, such as trading, payments, treasury, and more.

A key enabler of the Microsoft vision is the unique role of Microsoft’s global partner ecosystem, which encompasses an unparalleled range of independent software vendors (ISVs), global systems integrators (GSIs), and advisory firms. Our partners are building domain-specific Copilots, workflow agents, and Environmental, Social, and Governance (ESG) dashboards and regulatory compliance tools embedded within Microsoft AI, Microsoft 365, and Microsoft Azure. By incorporating agents into everyday tools and workflows, they are enabling the advanced real-time decision making, personalized customer engagement, and intelligent operations that make human-led, agent-operated AI a reality for banks.

The key role of governance in advanced AI

The promise of the Frontier Firm is to reimagine banking in ways that advance competitiveness and customer value. This can only happen with effective governance frameworks, which help ensure that all AI is developed and deployed safely and responsibly.

AI must be trustworthy, auditable, and aligned with corporate governance frameworks, so businesses can utilize its power without losing control. While many providers focus narrowly on compliance, Microsoft embeds governance into every layer of our AI stack, grounded in Responsible AI principles—fairness, reliability, privacy, inclusiveness, transparency, and accountability—and operationalized through centralized councils, cross-functional oversight, and tooling like Microsoft Purview.

For banks, this means AI systems that are not only compliant but also auditable, explainable, and aligned with regulatory expectations. We deliver governance at scale with a blueprint for responsible transformation and safeguards that protect customers, reputations, and regulatory standing.

Concurrently, a secure foundation is also critical. As your employees and customers interact with AI services, it is emerging as a new attack surface that requires world-class protection. Microsoft provides a comprehensive security platform, Microsoft Security Copilot with Microsoft Sentinel, which is fully integrated into our AI platform.

Learn more and explore Agentic AI

Helping banks chart their unique, comprehensive journey to becoming a Frontier Firm is central to our work at Microsoft. We build long-term relationships with customers, technology partners, data providers, and industry stakeholders to help banks create new value and customer relationships.

We have developed a five-step maturity journey and a set of foundational elements for any bank to become a Frontier Firm. To explore how your organization can move forward, start by engaging with your Microsoft representative or service provider.

• Visit Microsoft for financial services to learn more about how Microsoft empowers financial services organizations to achieve more. 
• To see how leaders in financial services are successfully using AI to transform complexity into opportunity, watch Leading at the Frontier: AI in Financial Services. 
• 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.
• Learn more about using Copilot in Financial Services and download the Financial Services scenarios kit.

¹ Forbes, The Future of AI in Financial Services, October 3, 2024

² KPMG, Intelligent banking report, February 2025

³Microsoft Work Trend Index Annual Report – 2025: The year the Frontier Firm is born, April 23, 2025

 ⁴ Visa, Find and Buy with AI, Visa unveils new era of commerce, April 30, 2025

⁵ PayPal Newsroom, PayPal brings together developers, AI leaders to power agentic commerce, April 29, 2025

The post The Frontier Firm in banking: A blueprint for advanced AI innovation appeared first on Microsoft Industry Blogs.

At Microsoft, we develop highly tailored, long-term technology partnerships with financial services firms around the world. Increasingly, this includes co-innovating with AI to help unlock new business value and deepen customer relationships. At present, enhancing research and analytics with AI is one of the primary transformation levers for investment banks, asset management firms, and financial data and analytics providers. In many cases, it is helping to solve longstanding challenges around deriving greater value from data and rapidly converting insights into competitive advantage. 

Realizing the promise of data-driven research through AI 

AI is rapidly changing the nature and value of advanced analytics in research. Traditional analytics have long helped firms understand what happened and why—but AI is helping them predict what will happen next and prescribe optimal courses of action in real time.

This shift from retrospective analysis to proactive intelligence can help firms unlock new sources of value and ultimately develop groundbreaking new products that redefine the competitive landscape. 

As innovative firms recognize the potential of AI, they also see the opportunity to address longstanding challenges that hinder effective research. Among these:  

• Data overload and complexity
  Financial markets are inundated with massive volumes of data from diverse, often siloed sources that can be difficult to integrate and synthesize. This makes it hard to access the right data at the right time, which can slow decision-making and heighten risk. As data requirements become more complex, solutions are needed that can unify, structure, and analyze data at scale to deliver timely, actionable insights.
• Fragmented workflows across user journeys
  Research analysts frequently struggle to navigate large volumes of disparate data housed in disconnected systems, tools, and formats, leading to time-consuming manual data compilation and synthesis. The increase in non-integrated tools, applications, and data structures disrupts business workflows and can lead to inefficiencies, duplication of effort, errors of omission, and delays in decision-making.
• Dependency on traditional data sources
  Many firms and analysts rely heavily on conventional market reference data, company fundamentals, industry reports, and databases, which often lack real-time insights and limit the speed and accuracy of market predictions. As new opportunities arise, firms need solutions that can extract more value out of existing sources while also making it easy to incorporate alternative and real-time sources—enhancing both predictive accuracy and responsiveness to market shifts.
• Information overload and time constraints
  Research and analyst professionals are always challenged to keep up with reports, emails, meetings, and chats. The overload tends to slow decision-making and increases the risk of missed opportunities. Stringent regulatory compliance requirements add additional demands.  

Five ways AI redefines the value of research in financial services 

AI gives financial services firms new solutions to these longstanding barriers and opportunities to use data in new ways that can differentiate their offerings. Here are five important areas where AI can change the game: 

1. Advance analysis with AI-powered analytics 

AI-powered analytics empower research analysts to cut through the noise of information overload and extract valuable insights with unprecedented speed and precision. The combination of AI with predictive analytics empowers researchers to analyze historical patterns more deeply, identify emerging trends, and make more informed investment decisions. This can ultimately help to improve engagement and win rates. 

A prime example of this is our partnership with Moody’s where we co-developed innovative solutions for research and risk assessment. Moody’s Research Assistant significantly increases productivity and effectiveness, with users reporting up to 80% time savings on data collection and 50% on analysis during the pilot phase.¹  

2. Accelerate operational efficiency through intelligent automation 

Traditional research processes—such as manual data compilation, synthesis, and report generation—are time-consuming and error-prone. AI-powered automation transforms them by integrating data sources, automating repetitive tasks, and promoting seamless collaboration across teams, which results in faster turnaround times, reduced operational costs, and improved operational efficiency.  

With tools like Microsoft Copilot, Researcher agent, and Analyst agent, firms can significantly boost productivity and operational efficiency. These AI-powered assistants can handle such tasks as summarizing investor reports and earnings calls, creating presentation-ready visualizations from raw data, and drafting research documents and client-ready insights quickly. This frees up valuable time for analysts to focus on higher-value activities, such as strategic analysis and client engagement. 

3. Deliver real-time insights 

To help meet the accelerating pace of business, AI-powered applications empower financial services firms to surface real-time insights from a variety of sources including market news, earnings reports, and social media.  

Bridging knowledge across platforms helps analysts identify emerging trends faster and develop better investment strategies. For example, AI can continuously monitor global news sources and sentiment signals to identify early indicators of market shifts and potential disruptions. Firms can then use this information to react swiftly and make proactive investment decisions ahead of competitors. 

Firms can build new AI-powered solutions that incorporate real-time data into advanced searches, personalization, and recommendations, using innovations like the powerful vector database built by KX—essentially, a specialized system that understands the meaning and context of a huge set of data types such as text, images, or PDFs. It aims to help financial institutions seize opportunities faster by turning real-time data into real-time action. 

4. Empower employees with high-value experiences 

AI-powered tools can transform how financial services professionals work with tools and solutions that support the most critical research functions, such as financial modeling and pitchbook preparation. Processes can be significantly streamlined while remaining interoperable, secure, and compliant.  

A good example of this is the innovation resulting from our long-term strategic partnership with LSEG (London Stock Exchange Group) to transform data with next-generation productivity and analytics solutions. One recent advancement is the launch of the LSEG Workspace Add-in, which integrates AI-powered insights into Excel and PowerPoint. With features like contextual data discovery and interactive charting, the add-in can help financial professionals work faster and more insightfully. 

Reducing the burden of manual tasks can also help boost job satisfaction. The integration of AI into daily workflows helps people focus on more intellectually stimulating activities, freeing up time for higher-value analysis and strategic thinking, and helping to attract and retain top talent. 

5. Deepen market understanding 

AI-powered analytics are transforming how analysts understand markets and convert insights into action. By processing vast amounts of financial data in real-time, AI can uncover complex patterns and correlations that were previously undetectable, such as market sentiment from news articles and social media or a real-time pulse on investor sentiment or market dynamics. Machine learning models can predict stock price movements with greater accuracy by integrating diverse data sources, including economic indicators and company performance metrics. 

A richer view of market forces and dynamics translates into better decision-making and sharper investment strategies. It helps firms anticipate emerging risks and opportunities sooner, enabling them to respond faster and more confidently in an increasingly volatile market landscape. 

Now is the time for agentic AI 

A new class of AI tools will soon deliver the ability to plan, reason, and take actions to achieve goals. In financial services, they will be able to gather, analyze, and contextualize information autonomously from diverse sources and proactively surface relevant insights—or even suggest strategic actions based on real-time developments. 

On the near horizon, advanced “orchestrator” agents will focus on new capabilities in distinct functional areas such as market intelligence, data aggregation, strategy simulation, reporting, and compliance. This holds the potential for powerful competitive advantages, helping analysts to stay ahead of market shifts, make more accurate predictions, and deliver higher-impact recommendations. 

Learn more 

• To learn more about how your organization can adopt AI, start by engaging with your Microsoft representative or service provider, or get in touch with a Microsoft representative today.
• Explore how Copilot can become the UI for AI in your business by visiting Microsoft 365 Copilot and learn about implementation at Microsoft Learn.
• For more on how to accelerate your company’s success with AI, read our whitepaper, Building a Foundation for AI Success: A Leader’s Guide.

Microsoft for financial services

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¹ Moody’s Investor Relations, “Moody’s Launches Moody’s Research Assistant,” December 2023.

The post 5 ways AI is supercharging research in financial services appeared first on Microsoft Industry Blogs.

When 70% of Microsoft 365 Copilot users report that the integration of generative AI into their everyday applications and tasks makes them more productive,¹ firms can see how AI can fundamentally revolutionize their businesses—starting by empowering the people who keep their companies running.

Historically, technology innovations have often not focused foremost on the needs of the average worker. Rather, they were often focused on empowering executives or driving loftier business goals such as enhancing competitiveness and profitability or powering new products and services. Generative AI is distinct in that it is tailored to benefit employees first. 

Helping banks and other financial institutions take full advantage of AI is central to our work at Microsoft. In the past two years, we’ve worked intensively with firms around the world to explore new avenues of AI innovation, with use cases that span an incredible range of opportunities. None have been more noteworthy than employee empowerment.

For many users, this happens initially with Microsoft 365 Copilot, which is embedded into apps like Word, Excel, PowerPoint, Outlook, and Microsoft Teams to integrate AI directly into everyday work tasks. 

Copilot removes drudgery and empowers employees 

When every employee has an AI assistant that helps them work better and faster, the sky is the limit on innovation. And it couldn’t come at a better time. According to the Microsoft 2025 Annual Work Trend Index, 53% of leaders say their company’s productivity needs to increase, yet 80% of the global workforce reports lacking the time or energy to do their job.¹

This “capacity gap” is why 82% of leaders expect to use digital labor to expand their workforce in the next 12 to 18 months. For many, the journey starts with Copilot and related cloud solutions that remove the drudgery of work and help people do the same work better and faster. 

4 ways Copilot is delivering immediate impact in financial services 

Enhanced productivity is the broad term for an important set of benefits that generative AI can deliver. For financial services firms, the specific use cases and benefits span many areas but we will concentrate here on four in particular: summarization, content creation, process optimization, and real-time insights.

1. Summarization
One of the most valuable features of Copilot is summarization—the ability to instantly produce a customized summary of anything from a recently conducted meeting to a transcript of a customer conversation, to summaries of whitepapers and PowerPoint presentations. In a fast-paced environment where analysts, advisors, and other professionals juggle multiple tasks, having a tool that can immediately extract key takeaways and follow-up actions is invaluable.

A good example is the experience of Hargreaves Lansdown, a leading United Kingdom financial services company that was early to embrace Copilot. Until recently, advisors had to manually take notes for customer meetings and later transfer them into a branded document, a process that could take up to four hours. With Copilot summarization and Microsoft Teams Premium, the process is being cut to as little as one hour through the automatic generation of meeting summaries, documentation, and action items.

Copilot not only speeds summarization, “it’s also good quality information,” says Systems Operations Manager Daniel Toman. “We know nothing is being missed.”

2. Content creation
The process of drafting emails, building presentations, and writing important client documents can be time-consuming and frustrating. Copilot eases that burden by identifying relevant source materials, using natural language processing to create messages and documents, and pulling information from across the Microsoft Graph (an API that connects all Microsoft 365 data, documents, and users.) The benefits for financial services include improved client engagement, scalable workflows, and AI-powered insights.

Content creation is delivering major benefits to Bank of Queensland (BQQ), which adopted Copilot to enhance collaboration and productivity. It has helped decrease the time required to draft internal manuals by 99%, marketing content by 88%, and human resource document drafts by 75%. Those gains are credited with improved customer service and operational efficiency—plus greater innovation in a competitive market.

“Copilot puts the power in the hands of the employee to be able to find efficiency.”

—Hayley Watson, Head of Enterprise Capability, BOQ

And to offer another customer example, in the United Kingdom, Floww, a financial infrastructure platform provider, increased employee efficiency by up to 20%, using Copilot to process massive quantities of data spanning technical documents, regulatory compliance requirements, and financial information, then condensing and delivering reports in easily accessible, shared formats.

3. Process optimization
Too many important processes in financial services are still dependent on manual tasks that can slow productivity and drain resources. Copilot solves this by automating processes and enhancing collaboration. The net benefits for firms include streamlined operations, fewer errors, and more time for employees to focus on high-value work.

For example, Dutch wealth management firm Van Lanschot Kempen wanted to help advisors focus more on personal connections with clients and found that too much time was being spent on unautomated tasks. So, they enlisted Copilot to improve common workflows and processes. Copilot now helps save them time by drafting emails (in multiple languages) in response to prompts, taking notes in meetings, and identifying and automatically assigning action points. An added benefit is that it reduces the language barrier and increases the quality of emails and documents.

“Having to take notes and structure action points and recaps accounted for around 40% of my time, Copilot is now my assistant during and after meetings.”

—Johanna Albert, Digital Adoption Specialist

Elsewhere, LGT, a Liechtenstein-based international private banking and asset management group, is using Copilot in their legal and compliance departments—for instance, to simplify reviews of lengthy contract documents. What used to take up to four hours can now be done in about 30 minutes. And global payments platform Paysafe cut the amount of time spent building technical documentation by up to 50%, automating meeting documentation, information retrieval, and document creation.

4. Real-time insights
Copilot is redefining how financial services professionals work by providing real-time insights that empower employees and enhance decision-making. Imagine, for example, a mortgages operation manager who needs to ensure efficient loan processing while maintaining compliance and optimizing customer satisfaction. Throughout the process, Copilot instantly retrieves and summarizes critical data, improving both speed and accuracy. Across firms and roles, real-time insights help with everything from research and predictive analysis to collaboration, workflow automation, and decision-making.

The scope of the transformation this represents is reflected in Microsoft’s strategic partnership with Moody’s to co-create new products and services for research and risk assessment. Built on a combination of Moody’s robust data and analytical capabilities and the power and scale of Azure OpenAI Service, the partnership creates innovative offerings that enhance insights into corporate intelligence and risk assessment. One early offering is a new copilot, “Moody’s CoPilot,” deployed to Moody’s 14,000 global employees, which helps drive firm-wide innovation and enhance employee productivity in a safe and secure digital sandbox. 

Beyond Microsoft 365 Copilot: A new breed of financial services agents 

Microsoft 365 Copilot is really just the start. Microsoft also offers a range of Copilot solutions tailored for businesses sectors, including Microsoft Copilot for Finance, which connects to financial systems such as Dynamics 365 and other leading financial management platforms to assist with tasks like analysis, reconciliation, collections, and communications.

For firms who want to do more, Microsoft Copilot Studio makes it easy to build custom AI agents without requiring extensive coding expertise. It also includes a powerful new Researcher Agent that helps with complex, multi-step research. It delivers deep insights with greater quality and accuracy than previously possible to advance important tasks like market analysis, opportunity identification, and key reporting. 

Set the stage for new waves of AI innovation 

Employees who use copilots are not just more productive, they’re more likely to be engaged and innovative. For many, their embrace of AI will become career accelerators. Empowered employees will help drive the reinvention of even the most established firms, and make endless contributions to the upstarts and even those we can’t imagine today.  

As the rapid evolution of AI continues, we will soon see the adoption of new classes of AI agents that won’t simply provide assistance but will execute tasks and orchestrate action autonomously. The future is approaching rapidly: our research suggests that global business leaders expect their teams will be training (41%) and managing (36%) AI agents within five years.¹  

Soon, human-agent teams will upend the org chart, and every employee will become an agent boss. Now is the time to get them up and running. 

Learn more 

• Visit Microsoft for financial services to learn more about how Microsoft empowers financial services organizations to achieve more.
• Learn more about using Copilot in Financial Services and download the Financial Services scenarios kit.

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¹ The 2025 Annual Work Trend Index: The Frontier Firm is born.

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