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Business & StartupsOpenAI News · June 10, 2026

From data to decisions: how LSEG is scaling trusted AI

LSEG has partnered with OpenAI to integrate generative AI into its global data platform, aiming to accelerate insights, innovation, and product release cycles. This collaboration enables LSEG employees and customers to leverage AI for faster data analysis, prototyping, and decision-making while maintaining strict governance and data privacy.

Author: Morein.ai Editorial

LSEG, a leading global financial markets infrastructure and data provider, has historically invested in AI and machine learning to power its financial models. The emergence of generative AI, however, presented a new opportunity to transform how users interact with data and make decisions.

The company recognized the challenge of manual synthesis and fragmented workflows that hindered insight generation despite its advanced infrastructure. OpenAI became a natural partner, offering powerful models and intuitive interfaces already adopted by LSEG's customers.

LSEG strategically deployed ChatGPT Enterprise and OpenAI APIs, enabling thousands of employees to use AI for drafting reports, synthesizing market data, and streamlining internal workflows. This resulted in significant time savings and increased efficiency across various teams.

Governance was embedded from the outset, including model evaluation frameworks, human-in-the-loop review for critical outputs, and strict data privacy controls. This approach allowed for rapid adoption driven by employee enthusiasm while ensuring compliance and safety.

Looking forward, LSEG plans to expand beyond individual productivity gains to more deeply embedded, workflow-level AI applications. A key focus is combining OpenAI models with LSEG’s trusted data through systems like its Model Context Protocol, allowing customers to access precise and verifiable information within AI workflows.

This initiative aims to empower LSEG's global workforce and customers to fully leverage AI in their decision-making processes, ultimately accelerating time to insight.

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