Franklin Templeton uses OpenAI for research

What happened?

On November 5, 2026 OpenAI published a case study on Franklin Templeton (OpenAI). The asset manager is building AI workflows starting with equity research so portfolio managers can compare research views and examine investment risks.

Codex is one of several tools investment teams use to develop those workflows. Katrina Dudley, Senior Vice President of Public Market Investments, says the unlock is merging continuous intelligence with human judgment: AI no longer only automates the old human-led process, it also surfaces intelligent insights.

Connected intelligence

Early attempts put data in a database and wired models to it. That failed because the models did not understand how the data fit together. Adding agents and a semantic layer (a structured model of what the firm’s data means and how it connects) changed the results.

Research, risk, compliance, and trading offer different perspectives. Franklin Templeton wants a governed, reusable intelligence foundation that combines trusted connectors, portfolio and workflow context, and agentic capabilities for analysts and portfolio managers.

Role-specific workflows

The firm has used Codex in hackathons with experts alongside investment practitioners. Investment AI champions turn experiments into production-ready workflows, with security review, expert support, and data controls.

Why asset management is different

Many tools reinforce user preferences: favor one research provider and you see more of it. Franklin Templeton wants the opposite: diversity of research and opinion, and AI that highlights investment risks so managers make more informed decisions.

Practical takeaways

• Leadership encourages innovation inside clear risk guardrails; every experiment should teach something.
• Get new tools into people’s hands quickly with practical labs.
• Help teams understand the power of the technology, not just install it.
• Security review, expert support, and data agreements on the path from experiment to production.

Why it matters

This is not a new model launch. It is a large asset manager rebuilding research around OpenAI and Codex: connected data, agents, and risk-centered insights. If you build financial or enterprise AI workflows, the semantic layer, bias-avoidance stance, and production controls are the reusable parts.