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is looking for.” While commitment to this innovation shift appears to be strong across the industry, with SimCorp’ s report revealing that 58 % of buy-side firms are consolidating technology vendors and platforms, 63 % still lack unified data across front-, middle-, and back-office.
Without this unified data, the findings indicate that AI readiness across the industry may be undermined, as predictive models may amplify or make mistakes, and systematic failures can become increasingly common.
The importance of ensuring AI is built off a foundation of strong, accurate data was reflected by McIntyre, who affirms:“ The challenge with AI often isn’ t the AI itself, it’ s the underlying data and the number of handoffs involved.”
McIntyre continues:“ For example, if data exists in three different places, AI agents can normalise and reconcile it to produce a consistent output. But
if the source data is wrong, the problem remains. Different agents may process or normalise that same data in different ways, creating multiple points of failure. Over time, those failure points can compound, which is a concerning risk for any organisation.”
In addition, McIntyre emphasises that engaging with robust data for effective AI output is not just important for efficiency, but is also critical to align with compliance and regulatory standards.
Estimates from the London School of Economic and Political Science in September 2025 indicated that between 60 % and 70 % of all trades are now conducted electronically, with this number likely continually increasing as automation becomes more prevalent within the industry.
While this brings significant benefits, reliance on data must be treated cautiously, with memories of
“ I think it’ s really a double-edged sword when you implement these kinds of systems or solutions. If AI starts doing too much, people risk becoming intellectually lazier rather than smarter.”
TIMOTHEE CONSIGNY, CHIEF TECHNOLOGY OFFICER, H20 ASSET MANAGEMENT
34 // TheTRADE // Q2 2026