As AI begins to mark its territory as a key tool in financial markets, buy-side firms are increasingly exploring how they can best leverage the technology, however, the obstacle of fragmented data appears to be hindering effective adoption.
Specifically, 70% of buy-side firms are now actively deploying AI solutions, yet 63% of firms still lack unified data across their front-, middle- and back-offices, indicating that widespread preparation for AI across the buy-side is not yet consistent, according to a recent report from SimCorp.
This problem appears to be particularly pertinent for firms in the EMEA region, with 51% experiencing challenges with a lack of unified data, and a further 61% referring to difficulties in sourcing and integrating unstructured data sources when looking to adopt AI tools into their workflows.
“Nearly two-thirds cite a lack of a unified, real-time data layer to support investment, operations, and client management processes, which perhaps is inevitable given the wider challenges of data fragmentation, duplication, and manual reconciliation,” said Joe Morant, global head of asset and wealth management at Alpha FMC.
“These issues underscore the operational complexities resulting from siloed systems and labour-intensive workflows, which are further compounded by the increasing complexity of private asset data, the demand for multi-asset total portfolio views, and the intensification of information demands from clients […] Firms eager to embrace advanced analytics will remain constrained by fragmented data, legacy systems, and cultural inertia.”
Despite these challenges, the study still indicates that the buy-side is keen to keep up new AI and technological advancements.
Of the survey respondents, 58% are consolidating their technology platforms and vendors, as they seek out effective execution strategies that will allow them to adapt to this innovation shift.
Predictive analytics at the fore
As highlighted in the report, a key focus for the buy-side appears to be how AI-driven tools can enhance data and analytics capabilities, with 66% of respondents stating that they expect the most value from AI will be gained from predictive analytics and forecasting over the next two years.
In addition, AI-powered data quality, such as continuous monitoring and anomaly detection, was also an area expected to bring significant benefits for buy-siders integrating this technology into their workflows.
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Speaking in the report, Tanguy de Grandpré, director of product front office and AI innovation at SimCorp, said: “Predictive analytics and forecasting depend on data quality, as reliable predictions require trustworthy data. AI is becoming central to both. The transition from static thresholds to contextual monitoring is crucial, particularly for alternative investments.
“Unstructured data in this asset class lacks the built-in validation of exchange-traded instruments, requiring firms to identify anomalies based on evolving patterns rather than fixed rules. When anomalies surface, understanding data provenance helps distinguish genuine exceptions from quality issues.”
With AI becoming increasingly prominent across buy-side workflows and trading desks, it appears that addressing issues of data fragmentation is essential to ensuring the greatest benefits can be reaped from these tools, and those that ensure these challenges are tackled will gain the most value.
To collate SimCorp’s ‘2026 InvestOps report’, WBR interviewed 200 buy-side leaders across APAC, EMEA and North America during Q4 2025.