AI Adoption Checklist for Financial Institutions: 8 Steps for Successful AI Integration

By The Numbers

75% of knowledge workers have used generative AI, with 30% using it weekly

46% of these users would not give up AI usage, even if it were banned

55% of employees have used unapproved generative AI tools at work

In the last 12 months, the Federal Reserve, OCC, FDIC, CFPB, NCUA, SEC, CFTC, and White House have all issued alerts regarding the use of AI in financial services

AI Adoption Checklist

Governance Structure
- Establish an AI governance committee to develop necessary policies
- Identify compliance requirements (OCC, CFPB, NCUA, etc.)
- Develop a written corporate AI governance policy for administering AI tools
- Create a data classification policy identifying sensitive and restricted information
- Establish protocols for identifying and addressing potential AI misuse
- Create and document incident response protocols for AI-related issues
- Develop a written set of user guidelines for AI usage and incorporate these guidelines into relevant employee handbooks
Technology Evaluation
- Compile a list of desired AI assistants and AI tools
- Compile corporate IT input
- Compile end user input
- Evaluate the list of desired AI assistants and tools against the corporate AI governance policy
- Assess options for administering tools via access control and single sign-on (SSO)
- Compare capabilities across major AI assistants (OpenAI ChatGPT, Microsoft Copilot, Anthropic Claude, Google Gemini, etc.)
- Analyze security and privacy features for data protection
- Assess total cost of ownership across solutions
- Evaluate deployment options (cloud-based, on-premises, hybrid)
Risk Management
- Implement audit mechanisms for AI use, including prompt logs and access history
- Implement realtime sensitive data detection and redaction
- Implement notification system for prompts that violate policy
- Restrict AI tool usage to authorized users via access controls
- Establish regular review processes for AI risk assessment
- Maintain inventory of data assets and related policies
Employee Training
- Survey end users to assess current AI literacy levels
- Establish AI champions across departments
- Develop role-specific training programs
- Provide training on data handling and safe prompting
Initial Deployment
- Select use cases and users for pilot deployment
- Implement governance controls for the pilot
- Establish data preparation protocols
Monitoring and Optimization
- Create dashboards for monitoring AI usage and prompt logs
- Establish a review cadence for insights and adjustments
- Collect end user feedback
- Develop performance benchmarks
Scaling Adoption
- Develop an organization-wide AI roadmap
- Establish provisioning with identity management tools (Entra ID, Azure AD, Okta, etc.)
- Implement group- and policy-based access control
- Develop training for an expanded user base
- Establish cross-functional support teams
Continuous Evolution
- Review AI policy effectiveness regularly
- Monitor regulations and update policies
- Conduct regular ethics reviews
- Audit AI tools and data-handling processes
- Evaluate and adopt emerging AI capabilities
- Measure and communicate business impact
- Establish a center of excellence for knowledge sharing
- Create ongoing skills development programs
- Sunset underperforming AI tools