Modernize Your Data Strategy to Enable AI/ML in Banking

Your current data strategy is struggling because:

Your strategy is based on traditional banking operations. The emergence of AI is changing how banking operates. Your strategy does not incorporate new AI-based business capabilities and the data required to support and enable them.
It has primarily focused on existing static data/data at rest. Your strategy has not evolved to include real-time and unstructured data being generated.
It has typically been focused on “traditional” structured data and doesn’t include unstructured data from conversations, digital agents, etc.

Our Advice

Critical Insight

Banks are struggling with AI-based data strategy because:

They are historically focused on enabling only traditional banking processes and capabilities.
There is a lack of acknowledgement of the impact that AI is having on traditional banking capabilities.
There is a failure to recognize the dramatic impact AI has on data requirements and capabilities.
Traditional approaches place a heavy focus on the existing relatively static structured data. AI increasingly uses unstructured data in real-time and the new requirements present unique challenges.

Impact and Result

Your new AI-based data strategy should:

Consider the impact that AI is having across many elements of your business strategy and business capabilities.
Reflect the changing nature of data consumed by AI to drive business value with a focus on unstructured data sources.
Capture new data requirements that AI-powered business capabilities require.
Seek to transform your data to be real time and easily accessible with the goal of enabling AI-powered internal and external capabilities.

Modernize Your Data Strategy to Enable AI/ML in Banking Research & Tools

1. Modernize Your Data Strategy to Enable AI/ML in Banking Deck – Expand on your existing data strategy by incorporating the impact of AI/ML.

Expand on your existing data strategy by incorporating the impact that AI/ML is having on how banks deliver their products and services.

The changes in business capabilities caused by AI/ML has an associated change in data requirements and how it is consumed.

This research will help to expand your traditional data strategy to incorporate the impact of AI/ML.

2. Data Strategy for Banking C-Suite Presentation Template – Tailor this example of a complete data strategy to your organization.

This in-depth presentation template provides an example of a complete data strategy, ready to be tailored to your organization and presented to your executive leadership to gain support for your strategy.

3. Business Context Interview for Banking Guide Template – Identify key organization leaders and questions needed to understand top priorities.

This template identifies key organization leaders and questions needed to understand top priorities.

Use this template as a starting point to interview your organization's leaders to elicit the right context, extracting business goals, organizational priorities, and key initiatives that will play a critical role in building your data strategy.

4. Data Value Mapping for Banking Tool – Understand how to realistically deliver tangible value from data.

This holistic value mapping tool guides you through a step-by-step process to understand how to realistically deliver tangible value from data that your executives will understand, care about, and be willing to support.

Use this tool to document and assess potential data initiatives, prioritize them against the organization's needs, assess key risks, and develop an indicative timeline for your key initiatives.

5. Data Initiatives & Strategy Ideation for Banking Primer – Transform the way your bank makes decisions.

Use this primer to help connect the dots between what the organization needs and what is technically possible for your data team.

Your bank is rushing to adopt AI/ML; modernizing your data strategy is critical for success.

About Info-Tech

Info-Tech Research Group is the world’s fastest-growing information technology research and advisory company, proudly serving over 30,000 IT professionals.

We produce unbiased and highly relevant research to help CIOs and IT leaders make strategic, timely, and well-informed decisions. We partner closely with IT teams to provide everything they need, from actionable tools to analyst guidance, ensuring they deliver measurable results for their organizations.

What Is a Blueprint?

A blueprint is designed to be a roadmap, containing a methodology and the tools and templates you need to solve your IT problems.

Each blueprint can be accompanied by a Guided Implementation that provides you access to our world-class analysts to help you get through the project.

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Get the help you need in this 4-phase advisory process. You'll receive 8 touchpoints with our researchers, all included in your membership.

Guided Implementation 1: Understand Your Corporate Objectives and Initiatives

Call 1: Identify your bank’s vision and goals and their impact of your AI-centric data strategy.
Call 2: Explore and document the impact that AI capabilities can have on your bank’s objectives and capabilities.

Guided Implementation 2: Gather Your Key Inputs for Your Strategy

Call 1: Evaluate line-of-business capabilities and data requirements. Discuss AI/ML capabilities impact on your bank.
Call 2: Identify critical risks to your data strategy as well as blockers to AI/ML adoption.

Guided Implementation 3: Ideate on How to Increase Business Value From Data

Call 1: Evaluate line-of-business data gain and pain-relieving initiatives. Give specific attention to AI/ML.
Call 2: Capture gain and pain-relieving initiatives.

Guided Implementation 4: Rationalize Priorities That Enable Business Goals

Call 1: Consolidate your data initiatives and establish your top data priorities.
Call 2: Finalize your data strategy/initiatives and assign individuals to execute project.

Author

David Tomljenovic

Search Code: 109130
Last Revised: December 16, 2025

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