The excitement around AI, and its ability to impact almost every function across the enterprise, is forcing a lot of CEOs to consider revising their corporate governance structures. As you can imagine, the org charts vary dramatically ranging from centralized hierarchical oversight to decentralized distributed innovation. The emergence of the Chief AI Officer (CAIO), AI task forces, divisional ownership by business unit leaders, and SVP-level committees are all common approaches. Here’s what we’ve been seeing.
Divisional Ownership and Business Unit Leaders
Some CEOs are handing the AI reins to business unit leaders or divisional presidents, a move that aligns AI initiatives with specific business outcomes. This strategy promotes accountability and customizes AI strategies to divisional needs. Yet, it’s a double-edged sword; while promising focused innovation, it risks creating silos that could stifle enterprise-wide synergies.
AI Task Forces and Committees
AI task forces comprising SLT members or SVP-level committees can help shape a unified AI strategy across the enterprise. These groups facilitate cross-functional collaboration, set overarching AI policies, and address ethical considerations ensuring a cohesive approach to AI governance that leverages diverse perspectives and expertise. In a well-formed committee, business leaders identify specific business outcomes, tech leaders ensure that the proper tech is deployed, and corporate leaders oversee the legal, ethical, and cultural aspects of AI projects. For our clients, AI task forces and committees are the most common practice.
The CAIO
The debate between centralized AI leadership, a Chief AI Officer (CAIO), versus decentralized models highlights the tension between the need for unified strategic oversight and the desire for innovation at the edges of the organization. Decentralized models, where individual teams lead AI projects within overarching policy frameworks, offer agility but require mechanisms like project registration and approval processes to balance freedom with control. But centralizing governance through a CAIO (with technical expertise and the authority to make enterprise-level decisions) can work wonders inside a large organization.
Operationalizing AI Across the Enterprise
One of the roadblocks to smooth AI deployment is a lack of a comprehensive corporate AI policy. This is understandable. It’s early days. The most successful organizations are breaking their AI policies into key areas of focus (legal, ethical, operational, etc.) and contemporaneously working on them while allowing the units to innovate. This approach drives risk managers crazy, but it also empowers an organization to compete with its corporate peers.
One practical way to keep tabs on internal projects is to establish internal technical guidelines for AI (way easier than the legal and ethical considerations). This approach promotes a culture of innovation while ensuring adherence to standards. Variations include open internal tooling for all employees versus controlled environments where AI projects must be registered and approved, reflecting a spectrum of governance models aimed at fostering responsible AI use.
Building Your First Synthetic Employee
I just finished leading a day-long workshop with over a hundred global tech leads for one of our multinational clients entitled, “Building Your First Synthetic Employee.” This exercise, thinking through the combination of both internal and external AI models and capabilities required to accomplish this business outcome highlights the importance of creating and constantly evaluating a comprehensive corporate AI policy that includes clear guidelines for data governance, data hygiene, and data privacy.
There’s No Right Way
The governance and leadership of AI initiatives are critical to maximizing AI’s benefits while minimizing its risks. There is no one-size-fits-all solution; effective AI governance requires a tailored approach reflecting each company’s unique circumstances, culture, and objectives. As we navigate the AI revolution, thoughtful governance and leadership will be key to harnessing AI’s transformative potential responsibly and effectively.
We’re Here To Help
You’ll find a bunch of free resources at shellypalmer.com/ai, but feel free to reach out if you’d like to learn more about how we can help you craft your AI governance policies.
Author’s note: This is not a sponsored post. I am the author of this article and it expresses my own opinions. I am not, nor is my company, receiving compensation for it. This work was created with the assistance of various generative AI models.
ABOUT SHELLY PALMER
Shelly Palmer is the Professor of Advanced Media in Residence at Syracuse University’s S.I. Newhouse School of Public Communications and CEO of The Palmer Group, a consulting practice that helps Fortune 500 companies with technology, media and marketing. Named LinkedIn’s “Top Voice in Technology,” he covers tech and business for Good Day New York, is a regular commentator on CNN and writes a popular daily business blog. He's a bestselling author, and the creator of the popular, free online course, Generative AI for Execs. Follow @shellypalmer or visit shellypalmer.com.
