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Generative AI, and increasingly AI agents, are quickly taking center stage in financial services.
What was formerly limited to experimentation has since evolved into systems capable of data analysis, real life action and large-scale decision making.
Head of Financial Services EMEA & Strategic Customers at Databricks.
Many businesses are already feeling the effects of this transformation; according to KPMG research, more than half (51%) of the financial sector say AI is reshaping their business. On the other hand, almost three-quarters (72%) are concerned about data quality.
This is when strategic risk creeps in, stemming from fragmented or poorly governed data, that ultimately delays the transition from pilot to production.
Financial institutions must shift their focus if they want to see true value from AI. With solid data foundations that are backed by robust infrastructure and unified governance they will then be in a better position to implement AI safely and successfully.
The true challenge now is not what AI can accomplish, but rather how businesses can take the right steps to enable it to operate at enterprise level.
Building the foundations for enterprise-scale AI
Most AI pilots fail both because the data beneath them are fragmented, poor quality or locked away in silos and because their AI agents do not have a focus on measuring and improving quality and accuracy. In order to successfully deploy AI, the infrastructure must be set up correctly to harness results.
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For leaders in the financial sector to close the AI adoption gap, a structured roadmap should be in place to enable their business to move from experimentation to scaled impact.
The first step is to unify data silos under a single platform to eliminate duplications, reduce inefficiencies and build reliable, trusted models from a single source of truth.
From there, governance must be embedded to manage lineage, access and audit trails. For AI agents, governance is far more than a mere compliance exercise. A unified governance model treats agents with the same rigor as human staff, applying robust access controls and security measures.
Prioritizing explainability is equally crucial. In a highly regulated market, businesses need accessible, transparent models that demonstrate how results are produced.