A&M helps companies establish effective, right-sized AI Governance programs based on their size, industry, organizational complexity and specific information risks they face. Whether a company is starting from ground zero or has a mature program that just needs some tuning, A&M brings a proven set of industry best practices to AI Governance program development, including:

• Charter development
• Roles and responsibilities definition
• Program framework design
• Target operating model development
• Organizational change management

A&M helps companies discover and document their use of AI across the enterprise, from on premises and SaaS systems, to third-party exposure, to the business use cases for AI. A&M brings a proven methodology for uncovering and documenting AI assets and activities, including:

• AI application inventory
• AI use case inventory
• AI vendor inventory

A&M helps companies navigate the complex patchwork of international and U.S. regulations relevant to their use of AI. In partnership with Corporate Legal and outside counsel, A&M documents the compliance requirements companies are subject to based on the AI assets and activities they have in place, including:

• AI regulatory crosswalk
• AI regulatory requirements definition
• AI governance workplan

A&M helps companies extend their existing security frameworks and protocols to their AI systems. In partnership with IT and Cybersecurity, A&M evaluates current security controls to determine where extensions are needed to ensure the security of AI systems, including:

• AI security assessment
• Incident response plan
• Data protection

A&M helps companies prepare their data for use in AI systems. In partnership with Corporate Legal and outside counsel, as well as corporate IT, A&M addresses the data management and compliance requirements for ingesting data, including:

• Data quality assessment
• Data infrastructure readiness
• Data lineage and compliance

A&M helps companies put governance in place to oversee and manage the technical soundness of AI. A&M defines the framework and processes for technically excellent AI, including:

• Infrastructure and deployment
• Model validation and control
• Technology integration and quality

A&M helps companies establish the framework for evaluating the results of AI from a business, risk and financial perspective. A&M establishes the ways of working for stakeholders to set the performance targets and measure them throughout the life of an AI system, including:

• Performance operating model
• Key Performance Indicators
• Performance monitoring