Whitepaper
A Framework for Managing a Company Wide AI Program
Whitepaper

A Framework for Managing a Company Wide AI Program

An enterprise-grade framework for rmanaging AI deployment across corporations involving leadership, stakeholders, and portfolio management.

Whitepaper
A Framework for Managing a Company Wide AI Program
Dr. Brian Scott Glassman
15 minutes
September 2025
13 pages
AI Program Management Corporate AI Strategy AI Leadership Stakeholder Management AI Portfolio Management Risk Management Change Management

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Whitepaper
A Framework for Managing a Company Wide AI Program
Author: Dr. Brian Scott Glassman
Reading Time: 15 minutes
Published: September 2025
Pages: 13

Details for the Article

Core Take Aways

Three-layer framework covering stakeholders, AI leadership, and AI portfolio management

Four-phase AI portfolio approach: use case exploration, pilot projects, test deployments, and company rollouts

Structured methodology for managing AI initiatives from conception to enterprise-wide implementation

Comprehensive guidance on AI governance, ethics, risk management, and regulatory compliance

Executive Summary

This framework provides executives, VPs, AI practitioners, and stakeholders with a comprehensive approach to managing corporate AI programs. The visual framework integrates three key layers: stakeholders (C-suite, VPs, department heads, customers, partners), AI leadership (Head of AI, research, ethics, risk management, compliance), and the AI portfolio spanning from use case exploration to company-wide rollouts.

The document emphasizes the critical need for structured AI program management due to the unprecedented pace of AI evolution and its transformative potential. Drawing parallels to the internet's disruptive impact, it argues that companies must establish AI leadership and frameworks now to avoid obsolescence.

The four-phase portfolio approach ensures systematic risk mitigation while maximizing learning opportunities. Each phase builds upon the previous one, from initial use case validation through pilot testing to limited deployments and finally enterprise rollouts, with clear decision points and success criteria throughout the process.

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