FactSet

What role does AI play in balancing speed with accuracy in investment workflows?

For investment teams, AI is heightening expectations around analytical speed, yet faster output holds value only when it preserves the accuracy, traceability and control on which financial decisions depend.

FactSet’s platform is architected for that reality. Its distinction lies in combining trusted data, interoperability and governance to make AI effective within financial workflows. Instead of sitting apart from the workflow, AI is embedded directly into the environments where research is conducted, portfolios are evaluated and decisions are shaped. This allows firms to accelerate analysis while maintaining the visibility, control and evidentiary standards that informed financial decision-making requires.

Built on open architecture and AI Building Blocks, FactSet also gives technology teams the flexibility to build applications tailored to firm-specific research methods, analytical processes and institutional priorities while preserving privacy, security and governance.

“A truly client-centric approach is fundamental to our story,” says Patrick Starling, SVP of product management, AI solutions. “Our clients look to us for solutions, an informed perspective on what is working across the industry and guidance on building best-in-class infrastructure to support AI-ready data.”

Increasing Analytical Yield inside the Investment Process

How does FactSet improve efficiency in research and portfolio analysis through AI?

The most immediate value appears in the daily mechanics of investment work. FactSet reduces the manual burden that slows research and portfolio analysis by applying AI to complex data retrieval, report generation and insight surfacing at speed. Time is reclaimed from repetitive information assembly and redirected toward interpretation, conviction formation and portfolio judgment.

Within research workflows, AI-powered tools can distill earnings transcripts, filings and internal research in seconds while generating customizable comparison grids across companies and reporting periods, all with source visibility preserved. Portfolio managers, meanwhile, can access concise narratives explaining attribution, tracking error and the market forces influencing performance directly within portfolio analytics. Analytical context is delivered closer to the point of decision, reducing the need to reconcile fragmented systems and allowing investment teams to act with greater fluency.

To support clients working across a broader mix of AI and technology platforms for research, analytics and decision support, FactSet partners with leading providers to embed trusted data and advanced analytics directly into those external systems. This gives clients access to curated, structured and unstructured datasets spanning fundamentals, estimates, prices and more within the environments already supporting their work. The result is greater continuity across platforms, less friction across procurement, licensing and integration and stronger research productivity overall.

Producing AI Output that can Withstand Scrutiny

Why is governance and data lineage critical for trustworthy AI-generated financial outputs?

FactSet supports that standard through governance, metadata precision, entitlement controls and data lineage. Its Model Context Protocol (MCP) solution operates as a managed context layer, translating prompts into precise, entitlement-aware API calls so large language models reach the correct datasets under the appropriate access conditions.

Standardizing how large language models interact with FactSet’s financial data, MCP creates a more exact retrieval model for financial AI. Responses improve because the model is working from a governed context rather than loosely assembled information. Auditability becomes more robust because metadata governance and entitlement logic are embedded directly into the interaction layer. Access remains bounded by authorization, which is indispensable in regulated and institutionally complex environments.

Retrieval-Augmented Generation (RAG) adds a further layer of evidentiary strength. Many financial clients cannot rely on a model’s broad world knowledge, while open internet search can introduce inconsistency, irrelevance or weak sourcing. FactSet grounds generated statements in its own verifiable financial data foundation, reducing hallucinations and giving users a more reliable basis for evaluation.

That difference becomes critical when AI output is used to support investment judgment. Without grounded retrieval, a generalized AI system comparing dividend histories across multiple equities might produce a plausible response while introducing inaccuracies. With RAG in place, each statement can be validated against FactSet’s data foundation, complete with precise citations. For investment professionals, that marks the difference between output that merely appears credible and output that can support a real decision.

“Employing techniques like RAG, we ground every AI output in factual, verifiable financial data to eliminate guesswork,” says Starling.

Traceability also carries through to the user experience. Generative outputs are linked to primary sources so users can trace each insight back to its origin rather than accept a black-box conclusion. FactSet also applies version controls and signals when content has been synthesized or inferred. Ongoing client feedback continues to shape how those signals are refined in practice.

Scaling Adoption without Weakening Oversight

In what way does FactSet scale AI adoption while maintaining security and oversight controls?

As AI reaches more users, desks and workflows, trust is determined as much by oversight and confidentiality as by transparency.

FactSet has structured its AI model to broaden usability without relaxing governance. Robust authentication, encrypted access and role-based controls protect AI-enabled workflows, while privacy-by-design architecture ensures sensitive information remains within secure boundaries. Continuous auditing and automated monitoring strengthen firms’ capacity to respond to shifting threats and compliance demands with greater precision.

Human judgment remains integral to that control structure. FactSet combines expert review, automated validation and client feedback to identify bias, catch errors and preserve output integrity as usage expands. Data specialists and AI engineers set the boundaries of autonomy, establish guardrails, investigate anomalies and intervene where ambiguity demands closer scrutiny. Training programs further equip teams to recognize bias exposure and apply mitigation measures before efficiency begins to outrun discernment. Scale is achieved, but not by relaxing evidentiary standards.

As the next phase unfolds, FactSet sees AI evolving beyond reactive prompt response into a more anticipatory role in financial decision support. The company envisions copilots that can recognize data requirements earlier, identify emerging risks and improve coordination across functions within a secure, open and interoperable ecosystem. Within that framework, AI does not replace human expertise. It extends its reach and sharpens its effect.

Continual exchange with clients, partners and the broader data science community informs how that model evolves. FactSet advances its technology through iteration, shared intelligence and applied learning, refining solutions against live institutional demands rather than abstract technical aspiration. Interoperability, governance and verifiable financial data remain central to that progression, reinforcing its recognition as the AI-powered Financial Data Automation Tool of the Year 2026.

Embedding AI within trusted workflows and grounding every output in verifiable financial data, FactSet gives investment professionals a faster path to insight while reinforcing confidence in the result.