The past two years have reshaped the expectations placed on investment teams. AI adoption, rising explainability requirements, tighter governance, and evolving research workflows are redefining what institutional investors need from their technology stack. These themes took center stage at the 2025 AI for Finance Symposium, where leaders from BlackRock, Northern Trust, JPMorgan, and Fidelity shared how AI is influencing research, risk, operations, and compliance across the industry.
The message was consistent:
AI will not replace investors. But teams that integrate AI into a modern, connected investment process will outperform those that don’t.
That is where EDS is uniquely positioned.
The Challenge: AI is Powerful, but Most Firms Can't Operationalize It
Despite significant investment in AI initiatives, 85–95% of pilots in finance never reach production. Symposium speakers pointed to several recurring barriers:
• Limited explainability and regulatory acceptance
• Output drift and nondeterministic results
• Siloed research, risk, and execution workflows
• AI systems that scale analysis but not accountability
AI alone isn’t the issue. The gap lies in the absence of a unified system of record that makes AI safe, auditable, explainable, and directly tied to real investment decisions.
How EDS Enables Modern Investment Teams
1. A Connected Investment Workspace
AI only reaches its potential when integrated into real workflows, not isolated as another standalone tool. EDS unifies the core components of the investment process:
• Research management
• Portfolio construction and rebalancing
• Factor risk and performance attribution
• ESG and fundamental analysis
• Performance reporting and insight pipelines
The result is a single environment where teams can work, analyze, and decide – without the fragmentation of spreadsheets, homegrown tools, or disconnected systems.
2. Explainability and Risk Built for Institutional Standards
Transparency is now a baseline requirement. EDS incorporates:
• Point-in-time audit trails
• Integrated factor models
• Explainable attribution and scenario analysis
• Decision records tied to exposures and outcomes
Risk isn’t an add-on. It’s embedded into the platform’s foundation.
3. AI That Enhances (Not Replaces) Fundamental Investing
Analysts are inundated with unstructured information. EDS uses AI to turn that information into structured, repeatable insight:
• Summaries of filings and broker research
• Auto-tagged and standardized insights
• Research linked directly to positions and theses
• AI assistance embedded inside workflows
The goal isn’t automation for automation’s sake – it’s helping analysts scale their capabilities.
4. Designed for How Real Investment Teams Work
Firms don’t want AI making decisions for them. They want AI that enhances judgment, accelerates understanding, and removes friction.
EDS is built to support that philosophy, helping teams work faster, stay organized, and maintain accountability without disrupting their investment approach.
Final Takeaway
AI won’t determine competitive advantage — how teams operationalize AI will.
Investment teams that adopt platforms like EDS will be able to:
• Make faster, more informed decisions
• Scale fundamental research
• Strengthen transparency and risk oversight
• Convert AI outputs into measurable performance
Those who don’t will simply fall behind.