Financial services firms prioritise data readiness for agentic AI deployment
New analysis reveals that while adoption is accelerating, most institutions are still building the internal capabilities required to manage fragmented data and ensure deterministic outcomes.
Financial services companies are placing data quality, security, and accessibility at the forefront of their agentic AI strategies, rather than focusing solely on system sophistication. According to Steve Mayzak, global managing director of Search AI at Elastic, the efficacy of autonomous AI in this highly regulated sector depends less on the complexity of the models and more on the underlying data infrastructure’s ability to search, secure, and contextualise information at scale.
Agentic AI, defined as systems capable of independently planning and executing tasks rather than merely generating responses, offers significant potential for optimising complex workflows and incorporating real-time data. However, introducing autonomous agents into an organisation amplifies both the strengths and weaknesses of the data they utilise. Mayzak notes that agentic AI effectively magnifies the weakest link in the chain, which is typically data availability and quality.
The industry faces substantial challenges in managing fragmented and unstructured data while ensuring deterministic outcomes to meet strict regulatory accountability. Financial institutions must provide auditable and governable trails that explain not only the input and output of a model but also the logic behind the data selection. This is particularly difficult given that natural language data is often messy and historical records may exist in dozens of different formats within legacy systems.
Market data indicates a disparity between ambition and capability. Gartner reports that more than half of financial services teams have already implemented or plan to implement agentic AI. Conversely, a Forrester study found that 57% of financial organisations are still developing the necessary internal capabilities to fully leverage these systems, highlighting the gap between strategic intent and operational readiness.
Experts recommend that firms begin with manageable use cases rather than attempting to automate complex, multi-step processes immediately. By utilising robust search platforms to consolidate data, institutions can create traceable, explainable, and scalable applications for risk monitoring, trade oversight, and regulatory reporting. This approach allows companies to build confidence and iterate on pilots, ultimately creating an AI feedback loop that supports long-term competitive advantage.
