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From data to decisions: how LSEG is scaling trusted AI
Scaling insight across one of the world’s most complex data ecosystems
London Stock Exchange Group (LSEG)(opens in a new window) sits at the heart of financial markets. A leading global financial markets infrastructure and data provider, it supports more than 40,000 customers and 400,000 end users across approximately 190 markets.
For years, LSEG had invested heavily in AI and machine learning to power financial models and analytics. But the emergence of generative AI introduced a fundamentally new opportunity: not just improving systems, but transforming how people interact with data, generate insight, and make decisions.
The challenge was clear. Despite advanced infrastructure, knowledge work across the organization still involved manual synthesis, fragmented workflows, and time-intensive processes that slowed insight generation and limited scalability.
“AI is a step change. But the real transformation comes when you rethink how you solve problems—not just how you execute them.”
—Emily Prince, Group Head of Enterprise AI, LSEG
At that moment, OpenAI became a natural partner—bringing powerful models, intuitive interfaces, and an ecosystem already being adopted by LSEG’s customers.
Inside the rollout
LSEG approached generative AI with a deliberate strategy: start with real problems, and scale responsibly.
The company selected OpenAI based on model quality, enterprise readiness, and alignment with customer demand. Many LSEG clients were already using ChatGPT, creating a natural opportunity to integrate LSEG’s trusted data directly into those workflows.
“That created a natural partnership,” says Max Grigoryev, Group Director for AI Products. “We could improve how we operate internally while helping customers use our data in the environments where they already work.”
At the same time, LSEG embedded governance from the outset. This included model evaluation frameworks, human-in-the-loop review for critical outputs, and strict data privacy and security controls.
Original source
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