{"id":22899,"date":"2026-05-14T13:26:28","date_gmt":"2026-05-14T13:26:28","guid":{"rendered":"https:\/\/ideainthebox.com\/index.php\/2026\/05\/14\/data-readiness-for-agentic-ai-in-financial-services\/"},"modified":"2026-05-14T13:26:28","modified_gmt":"2026-05-14T13:26:28","slug":"data-readiness-for-agentic-ai-in-financial-services","status":"publish","type":"post","link":"https:\/\/ideainthebox.com\/index.php\/2026\/05\/14\/data-readiness-for-agentic-ai-in-financial-services\/","title":{"rendered":"Data readiness for agentic AI in financial services"},"content":{"rendered":"
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Financial services companies have unique needs when it comes to business AI. They operate in one of the most highly regulated sectors while responding to external events that are updated by the second. As a result, the success of agentic AI in financial services depends less on the sophistication of the system and more on the quality, security, and accessibility of the data it relies on.\u00a0<\/p>\n

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\u201cIt all starts with the data,\u201d says Steve Mayzak, global managing director of Search AI at Elastic.<\/p>\n

Agentic AI\u2014systems that can independently plan and take actions to complete tasks, rather than simply generate responses\u2014holds enormous potential for financial services due to its ability to incorporate real-time data and optimize complex workflows. Gartner<\/a> has found that more than half of financial services teams have already implemented or plan to implement agentic AI.\u00a0<\/p>\n

However, introducing autonomous AI into any organization magnifies both the strengths and weaknesses of the underlying data it uses. To deploy agentic AI<\/a> with speed, confidence, and control, financial services companies must first be able to search, secure, and contextualize their data at scale. \u201cAgentic AI amplifies the weakest link in the chain: data availability and quality,\u201d says Mayzak. \u201cAnd your systems are only as good as their weakest link.\u201d<\/p>\n

Financial services companies, therefore, require a trusted and centralized data store that is easy to access, dependable, and can be managed at scale.<\/p>\n

The high stakes of quality information<\/strong><\/h3>\n

Regulation in the financial services sector requires a high degree of accountability for all data tools. As Mayzak says, \u201cYou can\u2019t just stop at explaining where the data came from and what it was transformed into: \u2018Here\u2019s the data that went in, and this is what came out.\u2019 You need an auditable and governable way to explain what information the model found and the logic of why that data was right for the next step.\u201d That is, you need to be able to see, understand, and describe the underlying processes.<\/p>\n

At the same time, financial services companies require speed and accuracy in order to meet customer expectations and stay ahead of competition. Markets are continually shifting, and risks and opportunities move along with them. If an AI model can parse natural language (unstructured data) from complex sources\u2014in addition to structured data in spreadsheets that are easier to analyze\u2014this gives users more relevant information.\u00a0<\/p>\n

In this environment, there is no tolerance for error, including the hallucinations that plagued early AI efforts. Agentic AI systems depend on rapid access to high-quality, well-governed data that is secure and accessible. In financial services, that data spans transactions, customer interactions<\/a>, risk signals, policies, and historical context. The task of preparing that data for AI should not be underestimated. \u201cNatural 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,\u201d says Mayzak.<\/p>\n

The data must be well indexed and consolidated across different locations, not locked in the silos of separate systems across the organization. Otherwise, AI agents lag, provide inconsistent answers, and produce decisions that are harder to trace and explain, undermining confidence among regulators, customers, and internal stakeholders.\u00a0<\/p>\n

As Mayzak says, \u201cThere are many different ways to describe how to execute a trade at a bank. In an agent-powered world, we need those descriptions to be deterministic\u2014to give the same results every time. Yet we\u2019re building on powerful but non-deterministic models. That\u2019s incredibly tricky, but not impossible.\u201d<\/p>\n

For a financial services firm, managing this can be very challenging. A Forrester study<\/a> found that 57% of financial organizations are still developing the necessary internal capabilities to fully leverage agentic AI. \u201c<\/strong>The data exists in many different formats, created over the course of a bank\u2019s history,\u201d says Mayzak. \u201cTake any bank that\u2019s been around for 50 years: They might have 60 different types of PDFs for the exact same thing. And at the same time, we want the output of these systems to be 100% accurate. In many cases, there is no \u2018good enough\u2019.\u201d That is, companies need to do it right, and the first time.<\/p>\n

Searching and securing results\u00a0<\/strong><\/h3>\n

An effective search platform is key to solving the problem of fragmented, poorly indexed, inaccessible data. Financial services companies that can readily sift through both their structured and unstructured data, keep it secure, and apply it in the right context will get the most value from agentic AI. This often requires designing AI systems with data access and utility in mind so they can work faster and yield more accurate results, as well as reduce risk. \u201cSearch is the foundational technology that makes AI accurate and grounded in real data,\u201d Mayzak says. \u201cSearch platforms have become the authoritative context and memory stores that will power this AI revolution.\u201d<\/p>\n

Once in place, these AI-enhanced searches and autonomous systems can serve financial services companies for a range of purposes. When monitoring client exposure, agentic AI can continuously scan transactions, market signals, and external data to detect emerging risks; platforms can then automatically flag or escalate issues in real time. In trade monitoring, AI agents can review trade workflows, identify discrepancies across different formats, and resolve exceptions step by step with minimal human intervention. In regulatory reporting, AI can gather data from across systems, generate required reports, and track how each output was produced. These applications of AI save time while supporting audit and compliance needs by being traceable and explainable.<\/p>\n

Although such capabilities already exist, they are often manual, fragmented, and difficult to scale. Agentic AI allows financial organizations to move toward more automated, efficient, and scalable processes while maintaining the accuracy and transparency required in their highly regulated environment. As Mayzak says, \u201cIt\u2019s not that different from how humans operate today, just done at a much faster pace and at scale.\u201d\u00a0<\/p>\n

Building an agentic AI ecosystem<\/strong><\/h3>\n

Launching agentic AI can be daunting, especially if other AI ventures have stalled internally. Mayzak\u2019s recommendation is to choose a manageable use case and allow it to grow over time. \u201cSuccess can build on success,\u201d he says. \u201cWhile companies may aim to automate a 70-step business process, they are discovering that you have to start somewhere. What is working in the market is tackling the problem one step at a time. Once you get the first step working, then you can take the next step, and the next.\u201d\u00a0<\/p>\n

The financial services organizations that lead among their peers will be those that integrate agentic AI into a broader ecosystem that includes strong security controls, good data governance, and effective management of system performance. As Mayzak says, <\/strong>\u201cDoing 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.\u201d By iterating on pilots and continuously improving, companies will build agentic systems that can be measured, managed, and scaled. This will transform agentic AI into lasting competitive advantage.<\/p>\n

Learn more about how Elastic supports financial services.<\/a>
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This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review\u2019s editorial staff. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.<\/em><\/p>\n<\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

Financial services companies have unique needs when it comes to […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"content-type":"","footnotes":""},"categories":[226],"tags":[],"class_list":["post-22899","post","type-post","status-publish","format-standard","hentry","category-technology"],"acf":[],"_links":{"self":[{"href":"https:\/\/ideainthebox.com\/index.php\/wp-json\/wp\/v2\/posts\/22899","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/ideainthebox.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/ideainthebox.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/ideainthebox.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/ideainthebox.com\/index.php\/wp-json\/wp\/v2\/comments?post=22899"}],"version-history":[{"count":0,"href":"https:\/\/ideainthebox.com\/index.php\/wp-json\/wp\/v2\/posts\/22899\/revisions"}],"wp:attachment":[{"href":"https:\/\/ideainthebox.com\/index.php\/wp-json\/wp\/v2\/media?parent=22899"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/ideainthebox.com\/index.php\/wp-json\/wp\/v2\/categories?post=22899"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/ideainthebox.com\/index.php\/wp-json\/wp\/v2\/tags?post=22899"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}