Building an AI Organization

The boom of artificial intelligence (AI) is transforming every aspect of business, from product development to customer service. But how can organizations make sure their AI initiatives align with their business goals, delivering value to their company? It all starts with choosing the right AI organizational model.

The advent of generative AI (GenAI) technologies like Chat-GPT has led to an explosion of AI usage and adoption over the past two years. Before this, researchers and visionaries developed AI technology either in academic settings or richly backed corporate research and development (R&D) labs. Given the limitations of off-the-shelf AI tools, businesses focused on AI often developed these technologies in-house.

In the R&D factory model, companies would appoint a chief AI officer or head of AI to lead an AI lab, often under a chief technology officer, general manager, or sometimes CEO. This leader, usually equipped with a PhD and academic background, would hire other AI specialists to build custom solutions. While this model is still common among top tech firms and startups, it comes with high costs and intense competition for skilled AI talent, which poses significant downsides for many companies.

Korn Ferry's research reveals that over 82% of CEOs and senior leaders expect AI to have a significant to extreme impact on their business, making a strong case for a solid AI organizational design. To learn more, Korn Ferry interviewed top executives across various industries and geographies about their AI team structures and the pros and cons of their approaches. Many leaders recommended a “build and buy” approach, combining off-the-shelf solutions with a broader AI strategy. From these conversations, we identified three main types of AI organizational models:

1. The AI Center of Excellence (CoE). Usually with a Head of AI, this design utilizes a centralized AI organization that crafts and executes the company's AI strategy, selects and implements AI technologies, and evangelizes AI capabilities across the enterprise. This model is suitable for companies that want to have a clear and consistent AI vision and leverage off-the-shelf AI solutions. And in theory, it sounds great to many, but this model can be a bit rigid and pull team members away as other priorities pop up.
2. Embedded AI. In this decentralized approach, the organization embeds AI capabilities directly into lines of business or functions. This model is suitable for companies that want to have a nimble and agile AI approach and solve specific business problems with AI, but problems can arise with no single leader or dedicated team when it comes to messaging and decision-making.
3. The AI Governance Board. This model includes a cross-functional body of senior leaders that drive the firm's AI agenda, control the budget and strategy for AI, and ensure governance and alignment. Pulling pieces from both the Embedded AI and the AI CoE model, the Governance Board is suitable for companies that want to have a holistic and collaborative AI approach and balance the needs of various stakeholders for the time being. Many opt to use this design as a starting point to eventually turn to the Embedded AI model as the setup typically works best for initiatives rather than long-term solutions.

Of course, there is no blanket approach when it comes to AI organizational design. The best model depends on several factors such as current AI capabilities, business goals, budget, leadership, and organizational culture.

Before choosing an AI model, organizations should assess their current situation, set goals, and evaluate the benefits and challenges of each design. Preparing for AI involves simplifying, standardizing, and automating processes, as well as hiring or developing the right AI talent, to bring the potential impact of this technology to life—turning aspirations into actions.

To find out how Korn Ferry is helping clients embrace AI, learn more about our Technology and Digital capabilities.