The integration of agentic AI into financial services presents a dual challenge: harnessing its potential while managing the complexities of data quality and accessibility. As financial institutions strive to implement systems that can autonomously plan and execute tasks, the focus is shifting from technological sophistication to the reliability of the underlying data. "It all starts with the data," says Steve Mayzak, global managing director of Search AI at Elastic.
The financial sector, marked by strict regulations and the need for real-time decision-making, underscores the importance of high-quality, secure data. According to Gartner, more than half of financial services teams are either using or planning to adopt agentic AI solutions. However, the introduction of these advanced AI systems highlights both the strengths and vulnerabilities linked to the data they depend on. The demand for effective data management has never been greater, as Mayzak notes: "Agentic AI amplifies the weakest link in the chain: data availability and quality."
The Necessity of a Centralized Data Repository
For financial services, a trusted and centralized data store is essential for effective AI deployment. This repository must be easily accessible and manageable at scale, especially given the regulatory landscape that requires transparency and accountability. Mayzak stresses that organizations must go beyond simply tracking data lineage; they need to provide an auditable and governable methodology to explain the logic behind data usage. In high-stakes environments, financial institutions must demonstrate both the origins of their data and the transformations it undergoes.
As markets evolve rapidly, financial firms need agile AI systems capable of processing both structured and unstructured data. The ability to interpret natural language data from various sources enhances relevance and accuracy, addressing customer expectations and competitive pressures. However, the complexity of cleaning and organizing unstructured data is significant. Mayzak points out, "Natural language is way more messy than structured data, and that makes the process of organizing and cleaning it up that much more important and also that much harder."
Addressing Fragmentation and Inaccessibility
Fragmented data, often found in disparate systems, presents considerable challenges for financial services. Mayzak notes that many organizations are still developing the necessary capabilities to fully leverage agentic AI, with a Forrester study indicating that 57% of financial firms are in this stage of development. As companies aim for 100% accuracy in their outputs, existing data formats—often inconsistent and numerous—impede efficiency and effectiveness. For instance, a bank with decades of history may have up to 60 different types of documents for similar transactions.
To successfully implement agentic AI, firms must prioritize establishing a stable search platform that improves data accessibility and security. An effective search system enables organizations to sift through structured and unstructured data seamlessly, ensuring that AI applications produce accurate and contextual results. As Mayzak states, "Search is the foundational technology that makes AI accurate and grounded in real data."
A Pragmatic Approach to Implementing Agentic AI
Launching agentic AI requires a strategic and phased approach. Mayzak advises organizations to begin with manageable use cases and scale from there. "Success can build on success," he explains. By starting with small, achievable projects, firms can gradually expand their AI capabilities, iterating on pilots to refine and enhance their systems.
The future of financial services hinges on the successful integration of agentic AI within a comprehensive ecosystem that emphasizes strong security measures, effective data governance, and systematic performance management. Companies that can achieve this integration will not only improve their operational efficiency but also create a feedback loop that provides valuable insights for future investments. As Mayzak concludes, "Doing this well will create an AI feedback loop, where executives gain new signals from these systems to assess the effectiveness of their investments and generate reliable, actionable insights."
As the financial services industry continues to evolve, the capacity to manage and utilize high-quality data will remain a key factor in the successful implementation of agentic AI. The journey is complex, but with the right strategies in place, firms can transform these challenges into a competitive advantage.
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