Franklin Templeton builds AI workflows to link research and risk for portfolio managers
Franklin Templeton is developing AI-driven workflows to help portfolio managers compare investment research and assess risks, starting with equity analysis. The project, detailed in a case study by OpenAI, aims to address gaps in the current process where analysts, risk teams, compliance, and traders work in silos. Katrina Dudley, senior vice president for public market investments, said the…
Key points
- Franklin Templeton uses AI workflows with **Codex** and semantic layers to link research and risk for equity analysis
- Project aims to replace siloed analyst, risk, and trading inputs with a unified, governed system for portfolio managers
- Diversity of research perspectives and explicit risk exposure are core design priorities to avoid bias and narrow insights
The initial approach—storing data in databases and connecting models—failed because models couldn’t interpret relationships between data points. Franklin Templeton now uses tools like Codex alongside agents and a semantic layer (a structured model of data meaning and connections) to surface actionable insights. The system prioritizes diversity in research perspectives and explicit risk exposure, ensuring analysts see differing opinions alongside downside analysis. Development involves hands-on testing through hackathons and practical labs, with guardrails for security, expert oversight, and data agreements to transition experiments into production. Dudley emphasizes balancing automation with human judgment, aiming to make connected intelligence widely accessible while encouraging analysts to spend more time gathering firsthand insights from private companies.
Franklin Templeton Develops AI Research Workflows to Highlight Risks
Unite.AI · 6 November 2026
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