Capital markets firms run on decisions and trust, functioning as ecosystems in which value is created collectively, while each individual organization retains the economic value of its competitive intellectual property (IP) and skills. As the industry begins to use AI for building real competitive advantage, the winning firms will re-think where to create durable value, how to protect proprietary insight, and how to build distribution advantage.
Most AI initiatives in investment management begin with point solutions for research, due diligence, reporting, document review, portfolio analysis, or client service. However, these applications represent only the first stage. The larger opportunity is to redesign the investment lifecycle itself so that information, decisions, controls, and execution can flow continuously across the full ecosystem, built around persistent context.
Nowhere is this truer than in capital markets, where the explosive growth of private markets and the convergence of public and private asset distribution have created a complex, multi-stakeholder ecosystem characterized by intense data dependencies, operational complexity, and regulatory scrutiny.
Major global capital markets participants—from large asset managers to investment banks, sovereign wealth funds, and wealth managers serving high-net-worth clients—all seek integrated technology solutions that can unify data, accelerate AI-powered decision support, and help collapse fragmented workflows while ensuring regulatory compliance. Agentic AI can help orchestrate work across these stages, reducing manual handoffs and enabling more intelligent, responsive business processes.
As policymakers and market participants explore broader access to private-market investments, the industry faces a significant scale challenge. Legacy approaches to capital formation, allocation, and distribution will not support a global alternatives market projected to reach $32 trillion in assets under management (AUM) by 2030 unless firms transition to integrated, scalable operating models.1
Moving beyond isolated AI use cases
As AI becomes embedded across capital markets, value increasingly comes from connecting people, data, and processes across the broader ecosystem. This is why the strategic challenge is not deploying more AI tools, but rather creating an end-to-end, integrated platform that enables firms to leverage shared value while protecting core intellectual property.
The firms that succeed will treat AI as a platform for coordination, rather than isolated intelligence. By enabling secure collaboration across participants while preserving governance and accountability, institutional knowledge compounds, which strengthens each insight and decision over time.
What an AI-enabled investment platform should support
At its core, an AI-enabled investment platform should do three things:
- Connect governed data and work context across portfolio data, market intelligence, research, operating metrics, client information, risk analysis, legal documentation, and internal knowledge. Bringing this context together in a governed environment can reduce fragmentation and create a more consistent foundation for decision support. It can also help teams understand how a conclusion was reached, what sources informed the work, and where human review remains necessary—an approach LSEG is putting into practice by using Microsoft Fabric to unify 30 systems and 1,200 datasets in a governed platform, improving data quality and reducing new-product development timelines from years to months.
- Support reusable expertise and workflows. Organizations develop advantage through their investment processes, institutional knowledge, relationships, and accumulated experience. Reusable AI-assisted capabilities can help teams scale that expertise more consistently across research, portfolio management, investor engagement, compliance, and distribution activities, while keeping people responsible for material decisions.
- Maintain trust, accountability, and control. As AI becomes more deeply embedded in investment workflows, identity, permissioning, auditability, data protection, lifecycle management, and policy controls need to move alongside information rather than being added later as separate layers. Suitability and explainability of investment decisions and advice must be built in by default.
This is how Microsoft’s AI platform approach becomes relevant. Microsoft IQ can provide governed context that agents can readily access within applicable permissions, while Microsoft 365 Copilot brings intelligence into familiar workflows. Microsoft Foundry and Copilot Studio help organizations build and manage reusable AI agents, matching each task with an appropriate model and cost profile. For regulated industries, that flexibility must be matched by strong controls. Microsoft Purview, Microsoft Entra, and Agent 365 support the governance, identity, security, and observability requirements needed to deploy agents responsibly at scale.
Three examples of operating model redesign
1. From origination to investment committee
Investment context is often recreated as opportunities move through sourcing, diligence, risk and legal reviews, and investment committee preparation. An intelligence layer helps maintain a governed, permissioned view of information across that lifecycle, bringing together investment data, research and diligence materials, proprietary firm knowledge, and approved external market data as shared context rather than isolated artifacts.
AI-assisted capabilities surface in the flow of work such as in Microsoft 365 Word or Excel, rather than compelling users to switch applications. Copilot then becomes the orchestration surface: a deal-screening agent can pull private-company signals from PitchBook, while a valuation-review agent can cross-check against prior assessments, identify supporting evidence, and prepare materials for review. Investment decisions remain with people, but teams spend less time assembling information and more time evaluating it.
Genworth demonstrates the potential in investment operations, using Microsoft 365 Copilot to reduce the time required to construct complex portfolio trades from days to hours while strengthening decision support within existing workflows. The result is better decision-readiness, fewer manual handoffs, and a clearer institutional record that can be reused when similar questions, risks, or opportunities arise.
2. Creating a stronger organizational learning loop
The investment lifecycle does not end when a decision is made. Portfolio performance, advisor feedback, investor demand, compliance reviews, operating results, and market developments all generate valuable intelligence, but many firms struggle to make those signals reusable across future decisions. Important insights often remain buried within meeting notes, presentations, email threads, and individual workflows.
With a foundation of intelligence and shared context, decisions that traditionally took weeks to propagate through investment, risk, operations, and distribution functions can become immediately available. Over time, that can make the firm’s experience more durable: patterns from prior investments, recurring client questions, risk signals, and operational lessons can become part of the next workflow rather than remaining isolated in the last one. Morningstar illustrates this model by extending trusted research into reusable, governed agents within advisor workflows, reducing hours of preparation to minutes while keeping intelligence grounded in approved data.
As Martina Cheung, CEO of S&P Global, noted in her recent LinkedIn post, with S&P Global’s data, insights and analytics in Microsoft 365 Copilot workflows, an analyst, for example, can ask Microsoft Copilot for a company’s latest EBITDA trends, market capitalization or peer comparisons, with answers grounded in cited S&P Global intelligence. From there, that data can flow into everyday workflows, whether that’s building a comps table in Excel or preparing a competitive landscape deck in PowerPoint.
3. Collaborating without compromising confidentiality
Investment due diligence relies on coordination across teams. That collaboration requires access to information, but it also requires strong controls around confidentiality, entitlements, licensing requirements, and regulatory obligations. In practice, AI-assisted workflows can help drive scale while respecting organizational policies and access boundaries. That is especially important when workflows span internal teams, external partners, and data from different licensing or confidentiality contexts. As firms scale AI adoption, this balance between collaboration and control becomes increasingly important.
Making collaboration possible without weakening control depends on governance that travels with the information. Identity, permissioning, data protection, auditing, and policy enforcement should ensure that people and AI systems can access only the data they are authorized to use. Nasdaq Boardvantage demonstrates this principle in a particularly sensitive context: it uses AI to support confidential board workflows while preserving established security, access, and governance controls. The approach also delivers measurable efficiency gains, reducing directors’ reading time by up to 60% and overall board preparation time by 25%.
The next competitive advantage
Capital markets organizations have always competed through expertise, relationships, judgment, and privileged information. In the AI era, those advantages will increasingly depend on how effectively firms connect proprietary data, institutional memory, governance, and human expertise. These characteristics make them distinctive while creating value across the broader ecosystem.
Achieving that requires a platform that connects data, workflows, controls, and institutional knowledge without compromising accountability or trust. The goal is not to replace investment professionals, but to give them better context, stronger governance, and greater leverage across increasingly complex public and private markets. The organizations that lead in the AI era will be those that combine trusted data, human expertise, agentic workflows, and strong governance to improve decision-making across the enterprise.
In this future, the AI-enabled investment platform is more than infrastructure. It is the operating foundation through which investment knowledge is created, governed, and applied at scale.
Learn more
- For a practical roadmap on how to turn isolated AI initiatives into enterprise-wide impact, explore Frontier Transformation in Capital Markets.
- For insights on bringing context into flow of work, read our blog, “AI in financial services: Bringing trusted data into the flow of work.”
- To learn more about scaling agentic AI in asset management with “Trust as infrastructure: How agentic AI is rearchitecting asset management at scale.”
1 Preqin, “Private Markets in 2030: Data Driving the Future of Alternatives,” October 16, 2025
