Artificial intelligence holds enormous promise for financial services, but delivering real value from AI strategies depends on something far more fundamental: the quality and accessibility of underlying data.
That was the central message from industry leaders at the Microsoft AI Tour in London, with LSEG sharing insights from its own data transformation journey.
Despite encouraging signs of progress, significant work remains. A McKinsey survey found that 63% of financial services firms have reached at least a level three maturity for “responsible AI” in data and technology, ahead of the 55% average across all industries, LSEG explained.
Yet maturity ratings alone do not translate into business outcomes. Poor data quality produces inaccurate AI results, creating financial, compliance and operational risks, and demanding greater human oversight to correct errors.
The challenge runs deeper still. A separate survey found that 83% of senior business leaders believe AI adoption at their organisation would accelerate if stronger data infrastructure were in place.
For many firms, the obstacle is legacy technology, with sprawling data stacks built up organically over years, riddled with siloed datasets, duplicated infrastructure, limited interoperability, and vendor complexity.
The prescription, according to LSEG, is consolidation. Moving away from fragmented data repositories towards a single, organisation-wide data lake creates a unified source of truth where data quality, permissions, and metadata are consistent across the enterprise.
LSEG group head of enterprise AI Emily Prince said, “When organisations move away from having segregated data sets sitting in garden sheds and under floorboards, and instead in a single location that everyone can access, the capacity for AI to transform the business grows exponentially. When we did this at LSEG, we started to have a view on information that was incredibly powerful in terms of the insights that it could yield.”
LSEG has walked this path itself. The exchange and data group partnered with Microsoft to build an ecosystem capable of supporting AI use cases from initial concept through to deployment.
Its data now sits within a suite of Microsoft solutions, including Microsoft Foundry for AI, Microsoft Defender for security, Microsoft Purview for governance, and OneLake, with data rights embedded throughout, enabling users to understand their permissions and discover datasets more intuitively.
The scale of what has been unlocked is considerable. LSEG Everywhere, which includes deployment of the Model Context Protocol (MCP) and partnerships with Microsoft, Claude, ChatGPT, Snowflake and Databricks, gives firms access to more than 33 petabytes of licensed, AI-ready financial content, including proprietary datasets stretching back decades.
Prince said, “Bringing data to people in a turn-key way, that enables them to ideate and experiment, is extremely powerful. Now, put that together with Microsoft and MCP, and firms can now work with over 33 petabytes of trusted data that they can really lean into.”
For financial services firms still wrestling with fragmented infrastructure, the opportunity is clear. Richer historical data, including records from periods of financial distress, can sharpen stress testing and scenario analysis.
Broader access to news, reference data, and pricing data can improve the accuracy of AI-driven decisions. And democratising access to high-quality data across the whole organisation, rather than restricting it to specialist teams, can drive productivity gains and innovation at scale.
Prince said, “We are co-developing with customers, and some of the things that we are able to see, and build are so exciting” says Prince. “Historically, there are so many possibilities that the financial services industry hasn’t modelled, hasn’t captured. Put another way, there is so much opportunity.”
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