CRU’s Chief Product Officer, Vlasios Voudouris, joined industry leaders at the AI for Finance Summit 2026 in London last week to discuss the foundations required to deploy artificial intelligence (AI) responsibly and effectively in markets.
Speaking on the panel, The Foundation Layer: Data and Infrastructure Behind AI in Markets, Mr Voudouris explored why trusted, well-governed data must sit at the centre of AI-enabled market intelligence – particularly in complex commodity markets.
Below is the key summary of his discussion at the panel.
AI is changing the value of market intelligence
As AI becomes more deeply embedded in research and decision making, the focus is shifting from simply accessing information to ensuring the intelligence behind an answer is accurate, explainable and fit for purpose.
For commodity markets, this is particularly important. Price assessments, supply-demand balances, cost curves and asset-level data must be supported by clear methodology, precise timing and specialist market context. A response that appears credible but relies on incomplete or unverified information can introduce significant risk.
Proprietary data is increasingly important
Large language models (LLMs) can make complex information easier to search, interpret and use – but they are not, on their own, a reliable source of market truth. The strongest AI applications will combine natural language capabilities with governed, point-in-time data and transparent methodology.
This also changes the role of market intelligence providers. Traditionally, data and analysis have been delivered through platforms built around individual users. Increasingly, clients will want to integrate trusted intelligence directly into their internal models, research environments and AI-enabled workflows. This makes proprietary market intelligence – and the expertise that underpins it – more important, not less.
Connecting the numbers with the insights
Public information may be increasingly easy for AI to retrieve and synthesise. However, it cannot replicate expert interpretation and what it means for specific use cases without deep domain expertise. This is build over decades of primary market coverage, asset-level analysis, established methodologies and specialist understanding of physical supply chains – all of which CRU provides.
The greatest value will come from connecting the what with the why – pairing structured market data with analysis, policy developments, market signals and expert judgement that give those numbers meaning.
In summary, the key takeaway from the AI for Finance Summit 2026 was that the future of AI in markets will be determined by the quality, governance and context of the data that powers it.
For CRU, this means making proprietary commodity intelligence easier to integrate into client workflows, while maintaining the methodological rigour and market expertise on which clients depend.
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