The banking sector is ramping up AI efforts in fraud detection, personalization, and risk analysis, but numerous institutions face structural constraints in their legacy data systems. New findings from global research and advisory firm Info-Tech Research Group show that ongoing dependence on structured, historical data is limiting AI’s ability to scale. The firm’s recently released blueprint, Modernize Your Data Strategy to Enable AI/ML in Banking, outlines a framework to help financial institutions update their data foundations while maintaining strong governance and regulatory compliance.
ARLINGTON, Va., Feb. 27, 2026 – Financial institutions investing heavily in artificial intelligence and machine learning are finding that scaling AI requires more than minor data improvements. As AI use cases move from pilot programs to enterprise-wide rollouts, banks must process dynamic data streams, behavioral signals, and unstructured digital interactions at scale. Systems built for static reporting and batch processing introduce execution risks and limit the ability to put predictive and prescriptive insights into action across business units.
In its blueprint, Info-Tech Research Group finds that many institutions underestimate how drastically AI reshapes enterprise data needs. Without intentional alignment between business goals, governance models, and evolving data capabilities, AI investments risk falling short of executive expectations.
“AI is fundamentally changing how banking services are delivered,” says , Research Director at Info-Tech Research Group. “Financial institutions that don’t modernize their data architecture will struggle to turn AI investments into measurable business value.”
Info-Tech Identifies Core Steps to Modernize Banking Data Strategy for AI
In its blueprint, Info-Tech details a structured five-step approach to help financial institutions expand their data strategies, reduce AI implementation risks, and align evolving data capabilities with company goals:
Identify Corporate Objectives and Initiatives
Executive leaders—including the CEO, business unit heads, and CIO—should reevaluate company priorities through the lens of AI-driven transformation. Goals that once focused mainly on efficiency and regulatory reporting must now include intelligent automation, real-time risk reduction, personalized engagement, and AI-powered innovation.
- Gather the Inputs for the Strategy
The Chief Data Officer and CIO, collaborating with enterprise architects and business data owners, should outline the complete range of data needed to support AI capabilities. This includes structured transactional data, real-time streams, third-party sources, behavioral signals, and unstructured digital interactions. - Ideate on How to Increase Business Value From Data
Data and analytics leaders, working with business and risk stakeholders, must define how AI-driven insights translate into measurable results. The focus should shift from static reporting to predictive and prescriptive use cases like fraud prevention, dynamic credit assessments, and personalized services. - Rationalize Priorities That Enable Business Goals
The CIO and Chief Data Officer, along with finance and risk leaders, should order initiatives based on strategic impact, regulatory risk, data readiness, and architectural maturity. Clear prioritization ensures AI use cases have the right governance and infrastructure support. - Finalize the Business Data Strategy
Spearheaded by the Chief Data Officer and approved by executive leadership, the final strategy should formalize expanded data requirements, real-time access standards, explainability expectations, and enterprise governance controls. Aligning modernized data foundations with measurable business value is key to scaling AI effectively across the organization.
By following the structured approach in Info-Tech’s blueprint, IT leaders at financial institutions can modernize traditional data strategies to support AI-driven capabilities without sacrificing governance or regulatory compliance. The framework provides a practical roadmap to align business goals, data modernization efforts, and executive accountability—helping banks scale AI initiatives with more confidence and measurable impact.
For exclusive, timely insights from Info-Tech’s experts—including Mitchell Fong—and to access the full blueprint, please reach out to .
About 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 globally. As a trusted leader in products and services, the company delivers unbiased, highly relevant research and industry-leading advisory support to help leaders make strategic, timely, and informed decisions. For nearly 30 years, Info-Tech has partnered closely with teams to provide everything they need—from actionable tools to expert guidance—to ensure they deliver measurable results for their organizations.
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SOURCE Info-Tech Research Group