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Franklin Templeton is leveraging OpenAI’s Codex to enhance equity research workflows, focusing on risk analysis and diverse research perspectives.
Franklin Templeton has announced the development of AI-driven workflows to enhance its investment research processes, starting with equities. The initiative aims to assist portfolio managers in comparing diverse research views and analyzing investment risks, leveraging tools provided by OpenAI, including its Codex platform.
AI-enhanced equity research
The firm’s current research process is human-led, involving sequential steps such as stock analysis, earnings call reviews, and competitive assessments. However, Franklin Templeton is now incorporating AI to streamline and improve these workflows. According to Katrina Dudley, senior vice president of Public Market Investments, the initial approach of simply connecting AI models to a database did not yield satisfactory results due to the models’ inability to understand relationships within the data. To address this, the team introduced a semantic layer, which structures the data to reflect its meaning and interconnections.
The enhanced system integrates trusted connectors, portfolio context, and workflow context to build a reusable intelligence foundation. This approach is designed to combine research, risk, compliance, and trading perspectives while exposing portfolio managers to diverse opinions and explicitly highlighting risks associated with investment ideas. Human judgment remains central to the decision-making process.
Role of OpenAI’s Codex
Franklin Templeton has utilized OpenAI’s Codex platform, which was originally launched on May 16, 2025, as a cloud-based software-engineering agent. Codex is powered by the codex-1 model, an OpenAI o3 variant trained through reinforcement learning on real-world coding tasks. While the specific version of Codex used by Franklin Templeton has not been disclosed, the firm has employed the tool in hackathons that pair technical experts with investment practitioners. These experiments are supported by the firm’s Investment AI champions, who work to transition prototypes into production-ready workflows through rigorous security reviews and data controls.
This initiative represents a significant shift in Franklin Templeton’s research workflow design, emphasizing the integration of AI to enhance productivity and decision-making. However, the firm has not disclosed metrics such as contract value, deployment headcount, or quantified performance improvements, focusing instead on the structural changes to its research processes.
