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Find Value in AI - A Guide for Banks and Credit Unions

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What You’ll Learn

Learn the best advice and insights from a panel on AI readiness that features Financial Brand Co-Publisher Jim Marous hosted Josh Nash, SVP and Director of IT, Camden National Bank, alongside data industry experts.

In an hour-long conversation, which can be viewed here, the group outlined strategies for successful planning and implementation of AI initiatives, identified roadblocks to adoption, and laid out use cases for immediate ROI.

How Banks and Credit Unions Can Get Value from Enterprise AI


This guide gives banking leaders a practical roadmap to AI adoption — moving beyond the hype to real strategies, use cases, and success stories. Inside, you’ll find insights from industry experts at Passerelle, Snowflake, AWS, Qlik, and Camden National Bank on:

  • Overcoming adoption barriers such as data silos, regulatory concerns, and data quality challenges
  • Proven frameworks for aligning AI initiatives with business value, ROI, and account holder experience
  • Actionable use cases ranging from fraud prevention and risk assessment to automation, personalization, and unlocking unstructured data
  • Real-world examples of how institutions like Camden National Bank have modernized their data estate to prepare for AI
  • Step-by-step guidance on building a foundation of trusted data, governance, and scalable architecture to support AI growth

Whether you’re just beginning your AI journey or looking to expand, this guide will help your institution cut through complexity and identify the fastest path to measurable business impact.

This Guide Features

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Table of Contents

Camden National Bank’s AI Readiness

A real-world example of how modernizing the data estate creates the foundation for AI success.

Barriers to AI Adoption in Banking

Key challenges like regulation, data quality, and legacy systems, along with strategies to overcome them.

Where Should Banks and Credit Unions Get Started with Enterprise AI?

Why should banks and credit unions embrace Enterprise AI, and what are the practical first use cases – including fraud prevention, automation, personalization, and unstructured data insights.

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More Resources

Case Study:
Camden National Bank

Case Study:
Metro Credit Union

Guide:
Modernization to Snowflake