Real-Time Data Gaps Are Hindering AI Scale in Banking, Finds Info-Tech Research Group

The banking industry is accelerating AI initiatives in fraud detection, personalization, and risk analytics, yet many institutions are confronting structural limitations within legacy data environments. New insights from global research and advisory firm Info-Tech Research Group reveal that continued reliance on structured, historical data is constraining AI scalability. The firm’s recently published blueprint, Modernize Your Data Strategy to Enable AI/ML in Banking, outlines a framework designed to help financial institutions modernize data foundations while maintaining strong governance and regulatory discipline.

ARLINGTON, Va., Feb. 27, 2026 /PRNewswire/ – Financial institutions investing heavily in artificial intelligence and machine learning are discovering that scaling AI requires more than incremental data improvements. As AI use cases move from pilot programs to enterprise deployments, banks must process dynamic data streams, behavioral signals, and unstructured digital interactions at scale. Architectures built around static reporting and batch processing introduce execution risk and limit the ability to operationalize predictive and prescriptive insights across business units.

In its Modernize Your Data Strategy to Enable AI/ML in Banking blueprint, Info-Tech Research Group finds that many institutions underestimate how significantly AI reshapes enterprise data requirements. Without deliberate alignment between business objectives, governance models, and evolving data capabilities, AI investments risk underperforming against executive expectations.

“AI is fundamentally transforming how banking capabilities are delivered,” says Mitchell Fong, Research Director at Info-Tech Research Group. “Financial institutions that fail to modernize their data architecture will struggle to convert AI investment into measurable business value.”

Info-Tech Identifies Core Steps to Modernize Banking Data Strategy for AI

In its Modernize Your Data Strategy to Enable AI/ML in Banking blueprint, Info-Tech details a structured five-step approach designed to help financial institutions expand data strategies, reduce AI implementation risk, and align evolving data capabilities with enterprise objectives:

Identify Corporate Objectives and Initiatives
Executive leadership, including the CEO, business unit heads, and CIO, should reassess enterprise priorities through the lens of AI-driven transformation. Objectives that once focused primarily on efficiency and regulatory reporting must now incorporate intelligent automation, real-time risk mitigation, personalized engagement, and AI-enabled innovation.

1. Gather the Inputs for the Strategy
   The Chief Data Officer and CIO, working with enterprise architects and business data owners, should define the full spectrum of data required to support AI capabilities. This includes structured transactional data, real-time streams, third-party sources, behavioral signals, and unstructured digital interactions.
2. Ideate on How to Increase Business Value From Data
   Data and analytics leaders, in collaboration with business and risk stakeholders, must define how AI-driven insights translate into measurable outcomes. The emphasis should shift from static reporting to predictive and prescriptive use cases such as fraud prevention, dynamic credit assessment, and personalized services.
3. Rationalize Priorities That Enable Business Goals
   The CIO and Chief Data Officer, alongside finance and risk leaders, should sequence initiatives based on strategic impact, regulatory exposure, data readiness, and architectural maturity. Clear prioritization ensures AI use cases are supported by appropriate governance and infrastructure.
4. Finalize the Business Data Strategy
   Led by the Chief Data Officer and endorsed by executive leadership, the finalized strategy should formalize expanded data requirements, real-time accessibility standards, explainability expectations, and enterprise governance controls. Clear alignment between modernized data foundations and measurable business value is essential to scale AI effectively across the organization.

By following the structured approach outlined in Info-Tech’s blueprint, IT leaders in financial institutions can modernize traditional data strategies to support AI-driven capabilities without compromising governance or regulatory discipline. The framework provides a practical roadmap to align business objectives, data modernization efforts, and executive accountability, enabling banks to scale AI initiatives with greater confidence and measurable impact.

For exclusive and timely commentary from Info-Tech’s experts, including Mitchell Fong, and access to the complete Modernize Your Data Strategy to Enable AI/ML in Banking blueprint, please contact [email protected].

About Info-Tech Research Group

Info-Tech Research Group is one of the world’s leading and fastest-growing research and advisory firms, serving over 30,000 IT, HR, and marketing professionals around the globe. As a trusted product and service leader, the company delivers unbiased, highly relevant research and industry-leading advisory support to help leaders make strategic, timely, and well-informed decisions. For nearly 30 years, Info-Tech has partnered closely with teams to provide everything they need, from actionable tools to expert guidance, ensuring they deliver measurable results for their organizations.

To learn more about Info-Tech’s HR research and advisory services, visit McLean & Company, and for data-driven software buying insights and vendor evaluations, visit the firm’s SoftwareReviews platform.

Media professionals can register for unrestricted access to research across IT, HR, and software, and hundreds of industry analysts through the firm’s Media Insiders program. To gain access, contact [email protected].

For information about Info-Tech Research Group or to access the latest research, visit infotech.com and connect via LinkedIn and X.

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SOURCE Info-Tech Research Group