This momentum is changing not only how insights are generated, but also how decisions are prepared, governed, and executed. As Microsoft Build 2026 made clear, the next phase of agentic AI is not just better outputs. It is governed execution grounded in enterprise context, with systems designed to coordinate action across research, portfolio construction, risk, and compliance under human supervision. For asset managers, this shift is profound.

This is not simply a technology transition; it is a fiduciary moment. For asset managers, Microsoft’s Frontier Tuning points toward institution-shaped intelligence: models tuned not only on enterprise knowledge, but on the workflows, conventions, and decision patterns that define how a firm operates inside its compliance boundary. In a fiduciary setting, that is indispensable because trust depends not just on what AI knows, but on whether its outputs and actions align with approved controls, human oversight, and reproducible governance.

The critical challenge, then, is to maintain trust. AI cannot merely improve performance; its actions must be governed, explainable, and attributable in line with fiduciary duty.

From analytics to homeostasis: AI as a trust-control layer

In biology, homeostasis is how living systems stay stable by sensing change, making small corrections, and restoring balance. In asset management, agentic AI makes it possible to build a similar closed-loop layer across workflows defined by governance parameters, helping keep activity within agreed boundaries of risk appetite, policy, and intent even when markets move faster than humans can triage.

Historically, AI in asset management has centered on prediction, such as forecasting spreads, detecting anomalies, or scoring counterparties. Agentic systems add the ability to observe, decide, and act across workflows under human guidance, gathering evidence, reconciling sources, drafting outputs, escalating exceptions, and coordinating next steps. Used well, these systems can create a homeostatic effect, helping firms maintain operational equilibrium and resilience.

The shift to a homeostatic approach

AI is being used in capital markets not only to deliver new capabilities but to help strengthen trust as a cornerstone of infrastructure. Trust can be infused throughout the platform by design, helping to ensure that data and operations remain safe, responsive, and governed even in fast-changing conditions.

In trading workflows, for example, the LSEG and Microsoft partnership is integrating licensed market data into Microsoft 365 Copilot and agent experiences so that decisions can be informed with authoritative, permissioned context at the point of execution. This is increasingly the right pattern for agentic systems: not intelligence separated from governance, but intelligence grounded in trusted context and controlled inside the systems where work happens.

By combining licensed data with built-in governance capabilities, trust is embedded into how intelligence is delivered, orchestrated, and acted on across the workflow, rather than managed downstream.

Likewise, Moody’s has federated decision-grade credit intelligence directly into Microsoft 365 Copilot, Researcher, and Excel, grounding AI-assisted analysis in authoritative, auditable context so that trust can be enforced by the system, not retrofitted after the fact.² Similarly, Morningstar has embedded proprietary research as entitlement-aware context for copilots and agents, keeping advisor workflows reviewable and aligned to licensed sources and user permissions. UBS is also bringing together internal and market data into AI-assisted advisor workflows, enabling client advisors to access unified, permissioned insights in real time.

Further, at Nasdaq, AI is being applied directly into the boardroom experience while preserving the strict controls those workflows demand. Its Nasdaq Boardvantage platform helps members turn hundreds of pages of board materials into concise, decision‑ready insights. This reduces review time by up to 60% while ensuring that outputs are fully auditable, remain grounded in proprietary data, and are protected by design.

Together, these examples show that intelligence can scale when workflows are auditable, permissioned, and attributable by default.

Getting started: Three moves asset managers can make now

The path forward is not theoretical; it is operational. Asset management firms can begin by focusing on three priorities:

• Invest in data readiness. Start by unifying internal and external datasets into a governed data foundation that can support agentic AI. Platforms like Microsoft Fabric are increasingly designed not only to bring data together, but to create the shared business context that agents need to operate consistently across teams and workflows. That matters because the bottleneck in scaling agents is no longer just model capability. It is whether each system can access the same governed definitions, permissions, and operational data foundation needed to move from isolated experiments to production-ready agent systems.

• Operationalize governance and enterprise ontology. Define ownership, approval paths, and audit checkpoints directly into AI-enhanced workflows under human supervision. A broader contextual intelligence layer is now emerging to connect how people work, how the business is modeled, and how knowledge is grounded across enterprise systems. Within that picture, Microsoft Work IQ remains an important component, helping make sense of activity across emails, meetings, documents, and chats while honoring existing permissions, sensitivity labels, and governance controls. Applied consistently in AI-assisted experiences, this kind of shared context helps turn governance from a static framework into a living system embedded in everyday work.

• Enable model-agnostic intelligence. Explore the comprehensive catalog of AI models so that you can select the best ones as needs evolve. Microsoft Foundry increasingly represents the production layer for agents, bringing together models, hosted agent services, memory, observability, evaluation, and guardrails in one governed environment. That gives firms more flexibility as requirements change while helping them move from pilot use cases to durable, production-scale systems.

Once these foundations are in place, firms can layer agentic workflows across research, operations, and risk while keeping clear human ownership at every material decision point and standardizing evidence trails so material outputs are reproducible and traceable. The goal is not automation alone, but structural operating leverage that scales intelligence with observability, governance, and security.

Making trust measurable and operational

Trust is not abstract in capital markets; it is the product of repeatable controls including lineage, entitlements, validation, and oversight. In practice, this is increasingly operationalized within the systems where work already happens. The idea is to bring productivity, security, identity, and agent management into a more unified operational layer, so that trust-related controls are enforced upstream in the flow of work rather than applied after the fact. Microsoft 365 E7 for Enterprise is one example of this broader architectural direction.

Accordingly, many firms focus on AI-enabled workflows defined by a common set of “trust signals,” including:

• Traceability: Every output must link back to authorized data, policies, and model versions.
• Explainability: Decisions must be not just technically correct but contextually defensible.
• Human accountability: Ownership must be explicit: who reviewed, who approved, and who can intervene.
• Controls and monitoring: Guardrails must operate continuously, especially under stress conditions.
• Regulatory evidence: Every decision path must be reproducible when challenged.

These are not abstract ideals but key operational expressions of responsible AI practices, helping asset managers ensure that AI is compatible with fiduciary duty.

The bottom line: Improving operational leverage

As firms mature in AI adoption, the focus evolves from products to platforms and ultimately to systems. The difference is not incremental; it is structural.

Value is no longer created by isolated use cases, but by integrated intelligence loops running on trusted infrastructure that connect external market signals with internal data, workflows, and decision rights. When orchestrated well, AI can serve as a coordination layer for action at scale in line with fiduciary requirements.

That is what drives operational leverage. Instead of adding incremental tools or headcount, firms can embed intelligence directly into workflows so the organization can better align decision-making, sensing, and action. Trust is thus inherent within systems and is no longer merely a byproduct.

Take the next step

We work with institutions to move from experimentation to production by embedding governance into multi-agent systems and operations so that fairness, reliability, privacy, and accountability are built into the foundation where AI runs. Our end-to-end approach can help unify enterprise and market intelligence into a shared operating context that turns fragmented insights into coordinated action at scale. Because most financial markets professionals already use Microsoft tools, it can also help lower deployment effort and improve adoption while keeping human oversight at every material decision point.

This is a rare and urgent invitation to the custodians of fiduciary capital to architect a homeostatic system of intelligence. It marks the convergence of capital and cognition, redefining how enduring organizations create and sustain value over time.

Explore how to best adopt AI

To learn more about how your organization can adopt AI, start by engaging with your Microsoft representative or service provider and explore these resources:

• Read our blog for stories about how Frontier Firms are using agentic AI to gain an edge in capital markets.
• To learn how Frontier Firms in capital markets lead with AI, visit Microsoft for Financial Services.
• For a practical transformation framework, read our new ebook, Frontier Transformation in Capital Markets.

¹ EY, Unlocking strategic advantage: Generative AI in wealth and asset management, September 16, 2025.

² Moody’s, Moody’s advances decision-grade credit intelligence across enterprise AI workflows, powered by Microsoft 365 Copilot, April 21, 2026.

The post Trust as infrastructure: How agentic AI is rearchitecting asset management at scale appeared first on The Microsoft Cloud Blog.

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 The Microsoft Cloud Blog.

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 The Microsoft Cloud Blog.

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

Unlock business value and deepen customer relationships in the era of AI

Learn more

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 The Microsoft Cloud Blog.

In this blog—the third in a series following Bill Borden’s post on generative AI in banking and Sasha Sanyal’s perspective for insurance—I’d like to share our perspectives on generative AI garnered from our work with capital markets clients in recent months as they seek answers to essential questions, such as: What are the benefits and key use cases? What are the current constraints? Are my firm and my data protected? How do I even get started?

Reimagine capital markets firms

Enabling capital markets firms with a modern cloud platform to drive agility and growth.

Discover more

The promise of generative AI across the capital markets value chain

We are still in the early days, but the momentum across the board has been fascinating. A recent report by McKinsey illustrates that the technology could deliver value equal to an additional USD200 billion to USD340 billion annually if the use cases are fully implemented within the banking industry. There is also potential for double-digit percentage efficiency gains as it gets to deployment and scale for certain scenarios.¹

Just in the last few weeks there have been several news articles covering innovative examples of firms prioritizing AI to support internal and client objectives, such as: JP Morgan for trading signals (Interesting Engineering); Goldman Sachs for powering insights (Wall Street Journal); Morgan Stanley for unlocking the knowledge base for wealth management (OpenAI.com); and State Street for its potential to revolutionize the firm (Forbes). A news report from Bloomberg (courtesy Wealth Management) lists several other examples of Wall Street firms that are considering using AI to rewire the world of finance.

A recent survey by KPMG highlighted that, across industries, more than three-quarters of executives (77 percent) see generative AI as by far the most impactful emerging technology they will use, and 71 percent plan to implement their first generative AI solution within two years. And more than three-quarters of financial services executives see opportunities to power fraud detection, risk management, and client experiences.²

The art of the possible for generative AI in capital markets

Consider, for example, a typical “day-in-the-life” for key roles in a global markets firm, depicted as follows:

• Starting with the front office, at the genesis of the trade, content generation can be employed to draft pitchbooks and facilitate credit reviews. Semantic search can help clients and representatives find better and more accurate insights from vast amounts of research data. And summarization techniques can instantly produce highly accurate reports of client interactions, augmented with sentiment analysis and signal intelligence—all of which can be instrumental in building strategies that address client, portfolio, and internal parameters.
• Through trade support and intraday book management in the middle office, early detection of exceptions can reduce negative impacts and false positives; trade notifications can be optimized; and firms can implement enhanced position monitoring, outlining regulatory impacts and analyzing investment valuations or environmental, social, and governance (ESG) factors.
• Finally, in the back office and executive functions, firms can benefit from enhanced reporting to improve decision-making through increased speed and relevance both internally (to adjust strategy and for remediation) and externally (to improve client experience and regulatory reporting).

Driving AI innovation with solutions from Microsoft

To light up scenarios such as these for their businesses, many capital markets firms are exploring the compelling capabilities of Microsoft offerings such as Microsoft 365 Copilot and Microsoft Azure OpenAI Service, which combines advanced foundational models from OpenAI with the enterprise capabilities of Azure. Helping them build powerful new generative AI solutions, such as customized copilots and chatbots, is Azure AI Studio, a new Azure OpenAI capability announced recently. Azure AI Studio makes it easier to create AI models using a company’s own private data while maintaining control, compliance, privacy, and security. What’s more, to protect their data, firms can rely on the enterprise-grade security of the Microsoft Cloud, as well as capabilities such as those provided by Microsoft Purview for robust information protection, governance, and compliance—critical capabilities for our clients, who are operating in the most regulated markets.

Responsible AI by design: Helping capital markets firms adopt generative AI

As next-generation AI innovation gains momentum, we are optimistic about what it can do for people, industry, and society. Microsoft advancements in AI are grounded in our company mission to help every person and organization on the planet to achieve more. We’re committed to making the promise of AI real—and doing it responsibly. Our approach to AI is based on three principles: meaningful innovation, empowering people and organizations, and responsibility.

Accordingly, we’re dedicated to the responsible development of AI systems for the industry, ensuring they will function as intended and be used in ways that earn trust. We were one of the first major technology companies to call for thoughtful government regulation on facial recognition technology and are committed to creating responsible AI by design through our responsible AI standard. For more information, see “What is Microsoft’s Approach to AI?”

Capital markets firms building new AI solutions with Microsoft

The value of AI is fast evolving. We are already seeing firms making noteworthy progress in early innovations on the Microsoft Cloud—such as Swift, which is building an anomaly detection model to reduce financial crime, and Morningstar, which has built a new chatbot to surface and summarize their independent insights in a conversational format for investors and investment professionals.

Also, Moody’s has just announced a set of next-generation data, analytics, research, collaboration, and risk solutions as part of a strategic partnership with Microsoft. This includes an internal copilot that incorporates Moody’s proprietary data, analytics, and research, and will be used by the firm’s 14,000 global employees.

What’s next for capital markets and AI

With generative AI, we anticipate an incredible wave of innovation in capital markets that will not only transform how firms operate but also open new markets and revenue opportunities. It will comprise elements such as the following:

• Creating compelling experiences with copilots. New and existing applications will begin to incorporate generative AI with copilots likely to become indispensable to employees’ and clients’ day-to-day experiences. The copilot will be there to support them and work under their direction, with the human in charge.  
• Driving greater business impact with data. AI is expected to revolutionize the process of transforming data into insight and action. As a result, it promises to free employees to focus more on delivering value to their businesses and clients. 
• Enabling more sophisticated use cases. Plug-ins will also be key to the capital markets industry as developers and independent software vendors (ISVs) seek to provide solutions to augment large language models targeting use cases such as quantitative analysis. 
• Diversifying the client base and creating new revenue streams. By democratizing new ways of working with data and related services, firms can develop opportunities to deliver value-added solutions to new and mass markets.
• Realizing business value compliantly. With the help of soon-to-be-available tools such as Microsoft’s Responsible AI Dashboard and Azure AI Content Safety, firms will be able to build on their early trials and move to production by establishing appropriate guardrails needed to comply with internal and regulatory requirements.
• Innovating with text and image inputs. As we look even farther out on the horizon, we are excited to see how capital markets firms will take advantage of new multimodal model capabilities, whereby image and text inputs drive text outputs. Being able to reason over documents with text and images, diagrams, or screenshots and return insightful text responses could yield tremendous innovation in an industry where visualizations are so prevalent in representing data, trends, facts, and forecasts.  

Whatever your starting point or your ambition, our recommendation is to use a “crawl-walk-run” approach and framework, working with Microsoft and our partners—and think of generative AI as a way to deliver incremental gains from operating efficiencies to start with, evolving to more transformative plays over time.

Learn more—and join the revolution

There is much more to come, as firms across the industry continue their AI journeys and Microsoft delivers more new products and services. Watch this space in the coming months for more blog posts where I’ll share our new learning and insights.

In the meantime, to learn more about how capital markets firms can unlock business value, deepen client relationships, and manage risk with AI and the Microsoft Cloud, visit our website or get in touch with your Microsoft sales representative,

Finally, learn more about the era of generative AI across the financial services industry by reading other posts in this series:

• The era of generative AI: Driving transformation in banking
• The era of generative AI: Driving transformation in insurance

¹ McKinsey, The economic potential of generative AI: The next productivity frontier, June 14, 2023.

² KPMG, Generative AI: From buzz to business value, May 2023.

The post The era of generative AI: Driving transformation in capital markets appeared first on The Microsoft Cloud Blog.

For wealth management firms in particular, the mandate to embrace a “digital-first” strategy has never been more urgent. The competitive landscape is rapidly changing, as a new generation of clients demands new, highly personalized experiences. In the race to become the hub for all things financial, fintechs and non-traditional players have already changed the face of the industry with personalized client engagement models and new partnerships across the ecosystem.

In response, wealth managers need to quickly pivot to focusing on the client experience rather than on products. The challenge is significant for institutions with monolithic legacy infrastructures and more traditional customer bases. They need to innovate rapidly to meet clients in new ways through scalable, differentiated omni-channel solutions. That puts new demands on technology teams who need to do more with less, often with their budgets cut.

This is where the cloud comes in—and where the choice of cloud provider is essential. “Cloud has become a critical technology for institutions across all lines of business in the financial services industry,” says Jerry Silva, vice president for IDC Financial Insights. “But institutions need more than just the infrastructure to ensure the transformation to a scalable business. Institutions need to work with providers that can supplement infrastructure with industry-specific data frameworks and rich partner ecosystems to fulfill the promise of a digital future.”

Microsoft Cloud for Financial Services

Discover how Microsoft is driving innovation in financial services.

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Microsoft Cloud for Financial Services empowers wealth management firms and other financial services providers to implement the right digital strategies for their businesses. By bringing together the best of the Microsoft Cloud and solutions from our global partner ecosystem, Microsoft Cloud for Financial Services enables the delivery of personalized financial solutions at scale to all customer segments while also elevating the client experience. We do this through a combination of foundational privacy, security, and regulatory compliance, plus an industry-specific data model for applications designed to meet the specific needs of financial institutions globally.

Today’s announcement represents progress across several important fronts.

New features for wealth management

With today’s announcement, and building on our October 2022 release, we’re accelerating the value of our cloud for geographies and languages previously announced. Two new features for wealth management, now generally available, expand our investment across industry verticals:

• A new wealth management data model. We’re delivering an extension to the banking data model for wealth management that captures new attributes including financial goals and investment instruments specific to wealth management. This will power the development of new applications specific to wealth management scenarios and enable our partner ecosystem to plug in with their own vertical solutions.
• Unified client profile (UCP) for wealth management. This will foster deeper client relationships by delivering meaningful advice—for example, providing relationship managers with a deeper understanding of a client’s financial status, investment portfolios, financial goals, important life moments, and other personal attributes. This is a variation on the UCP feature for retail banking.

Customer experience updates in preview

To meet customers’ expectations and provide exceptional customer experiences, financial service providers must continuously enhance their offerings by creating stronger connections among people, processes, and systems. This requires a commitment to ongoing improvement in delivering the seamless and efficient services that customers expect, especially at the crucial points of intersection where customers or clients engage with the financial institution for the first time.

To further support our customers in this important endeavor, we are happy to announce the following preview updates for customers in the United States, the United Kingdom, and Australia:

• Onboarding application toolkit updates. We’re adding automated workflows, including a document intelligence feature that extracts key data from customer-submitted documents such as official identification required for onboarding scenarios. This also includes a new application queue feature and improvements to the application task manager that enables the cancellation of tasks.
• Intelligent appointments integration with Microsoft Teams Virtual Appointments. This enables the creation of a unique, branded, end-to-end experience for virtual meeting scenarios in Microsoft Teams (Teams Premium subscription required for some capabilities).

Additional enhancements

Finally, we’re announcing the following additional enhancements to our cloud:

• Landing zones. For both Microsoft Azure and Microsoft Power Platform, these are pre-configured, industry-specific architectures that speed time to value and reduce risk in Microsoft Cloud for Financial Services deployments (no subscription to Microsoft Cloud for Financial Services required).
• Australia, welcome to our previews. We’re delighted to welcome Australia to the list of countries participating in our previews. Our Aussie customers and partners join the United States and United Kingdom to accelerate cloud onboarding and facilitate additional customer feedback on product development.

And a wealth of partner solutions

Overall, today’s news represents an important continuation of our cloud journey for financial services. It also reflects the growing momentum and importance of our partner ecosystem, which is critical to delivering integrated solutions and support for customers. There are now 75 solutions available for our customers to use in extending the value of Microsoft Cloud for Financial Services, all from leading systems integrators (SIs) and independent software vendors (ISVs) from around the world. Some of our partners in this ecosystem include Accenture, ArganoArbela, ASC, Avanade, Avtex, Backbase, Bambu, BaseCap, BioCatch, Capgemini, EY, Finastra, KPMG, Mambu, Mortgage365, Publicis Sapient, PwC, Seismic, Thought Machine, VeriPark, Wealth Dynamix, and Zafin, who develop joint solutions built on our platform that deliver differentiated customer experiences, empower employees, and manage enterprise risk.

Together with our partners, we will continue to invest in improvements to Microsoft Cloud for Financial Services so that financial institutions will better be able to manage their data at scale and use it to improve the customer experience, coordinate engagements across stakeholders, drive greater operational efficiencies, and develop new products and business models.

Learn more

There’s more to come, so watch this space. You can always get the latest on Microsoft Cloud for Financial Services at our website. To find the latest on current release plans, visit Microsoft Cloud for Financial Services | Microsoft Learn. And to learn what our cloud can do for your business, get in touch with your Microsoft representative or trusted partner.     

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