The financial services sector operates in a league of its own, characterized by stringent regulations and a constant barrage of real-time market shifts. These unique pressures mean that when it comes to leveraging advanced technologies like business AI, the success story isn’t just about sophisticated algorithms. Instead, it critically hinges on the quality, security, and accessibility of the underlying data.
As Steve Mayzak, global managing director of Search AI at Elastic, aptly puts it, “It all starts with the data.” This foundational truth is especially pertinent for agentic AI, a revolutionary type of system designed to independently plan and execute tasks, moving beyond mere response generation.
The Data Imperative: Why Quality is Non-Negotiable for Financial AI
Agentic AI holds immense promise for financial services, particularly in its capacity to process real-time information and optimize complex workflows. In fact, Gartner reports that over half of financial services teams are already deploying or planning to implement these autonomous systems. However, introducing such powerful AI magnifies both the strengths and weaknesses of the data it consumes.
To deploy agentic AI with speed, confidence, and control, financial institutions must first master the art of searching, securing, and contextualizing their vast data reserves at scale. Mayzak warns, “Agentic AI amplifies the weakest link in the chain: data availability and quality. And your systems are only as good as their weakest link.” This highlights the urgent need for a trusted, centralized data store that is not only dependable and easy to access but also scalable across the enterprise.
Regulation in the financial services sector demands a high degree of accountability for every data tool and process. It’s not enough to simply show data input and output; you need an auditable and governable way to explain the model’s findings and the logic behind its decisions. Understanding and describing these underlying processes are crucial for compliance and stakeholder trust.
Financial firms also require unparalleled speed and accuracy to meet customer expectations and maintain a competitive edge. Markets evolve constantly, and with them, risks and opportunities shift moment by moment. The ability for an AI model to parse complex natural language (unstructured data), in addition to traditional structured data, provides a significant advantage by offering more relevant and timely insights.
In this high-stakes environment, there is absolutely no room for error, including the “hallucinations” that sometimes plagued earlier AI models. Agentic AI systems rely on rapid access to high-quality, well-governed data that is both secure and readily accessible. This data encompasses everything from transactions and customer interactions to risk signals, policies, and historical context, underscoring the monumental task of preparing it for AI.
Building Trust: Secure, Accessible, and Contextualized Data
The sheer messiness of natural language data, compared to its structured counterpart, makes organizing and cleaning it incredibly challenging yet profoundly important. Data must be meticulously indexed and consolidated across various locations, rather than remaining locked away in the isolated silos of disparate organizational systems. Otherwise, AI agents will lag, provide inconsistent answers, and produce decisions that are difficult to trace and explain, eroding confidence among regulators, customers, and internal teams.
“There are many different ways to describe how to execute a trade at a bank,” Mayzak explains. “In an agent-powered world, we need those descriptions to be deterministic—to give the same results every time. Yet we’re building on powerful but non-deterministic models. That’s incredibly tricky, but not impossible.” Managing this complexity is a significant hurdle, with a Forrester study revealing that 57% of financial organizations are still developing the internal capabilities needed to fully leverage agentic AI.
As Mayzak points out, a bank with decades of history might possess 60 different PDF formats for the exact same document. Despite this inherent data fragmentation, the output from these AI systems must be 100% accurate, as “in many cases, there is no ‘good enough’.” This means companies must get it right the first time, every time.
An effective search platform is the cornerstone for overcoming the challenge of fragmented, poorly indexed, and inaccessible data. Financial services companies that can effortlessly sift through both structured and unstructured data, keep it secure, and apply it with the right context will extract the maximum value from agentic AI. This often involves designing AI systems with inherent data access and utility, leading to faster, more accurate results and reduced risk.
“Search is the foundational technology that makes AI accurate and grounded in real data,” Mayzak emphasizes. “Search platforms have become the authoritative context and memory stores that will power this AI revolution.” By grounding AI in reliable, searchable data, firms can ensure decisions are traceable, transparent, and compliant.
Unleashing Agentic AI: Real-World Applications in Finance
Once properly integrated, these AI-enhanced search capabilities and autonomous systems can revolutionize operations across a range of financial services functions. Consider these powerful applications:
- Client Exposure Monitoring: Agentic AI can continuously scan transactions, market signals, and external data to detect emerging risks, automatically flagging or escalating issues in real time.
- Trade Monitoring: AI agents can meticulously review trade workflows, identify discrepancies across different formats, and resolve exceptions step-by-step with minimal human intervention.
- Regulatory Reporting: AI can efficiently gather data from disparate systems, generate required reports, and precisely track how each output was produced, supporting critical audit and compliance needs.
While many of these capabilities exist today, they are often manual, fragmented, and notoriously difficult to scale. Agentic AI allows financial organizations to transition towards more automated, efficient, and scalable processes, all while upholding the accuracy and transparency demanded by their highly regulated environment. “It’s not that different from how humans operate today,” says Mayzak, “just done at a much faster pace and at scale.”
Charting a Course: Practical Steps for Adoption
Embarking on the journey of launching agentic AI can feel overwhelming, especially if prior AI initiatives have faltered. Mayzak’s practical advice is to begin with a manageable use case and allow it to evolve organically over time. “Success can build on success,” he notes, suggesting that instead of trying to automate a complex 70-step business process all at once, companies should tackle the problem one step at a time.
Once the initial step is working effectively, the next can be addressed, and so on. The financial services organizations that truly lead their peers will be those that integrate agentic AI into a comprehensive ecosystem, complete with robust security controls, strong data governance, and effective system performance management. This holistic approach ensures resilience and reliability.
According to Mayzak, “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.” By continuously iterating on pilot programs and striving for ongoing improvement, companies will build agentic systems that can be measured, managed, and scaled effectively. Ultimately, this transforms agentic AI from a technological ambition into a lasting competitive advantage.
Source: MIT Tech Review – AI
