Look at your organizational set-up
Along with establishing who gets the work done, it's important to revisit the "how." Think about the kind of physical set-up that will help you achieve your business goals and integrate AI most effectively. For instance, do you need geographically dispersed business units and AI tools or a more centralized structure?
Our research suggests that a centralized organizational model may be the most effective, with Strategic Scalers saying they now use this approach.
Another variant is "hub-and-spoke," a model that includes both a centralized cross-functional AI group (i.e., the "hub," sometimes called a Center of Excellence) and separate autonomous AI teams (i.e., the "spokes") that sit within business units. Finally, a "distributed" model also exists. Highly autonomous AI teams are housed within each business unit or function, with a delivery focus specific to that business unit or function.
Be guided by your business aspirations, and define a way of working that best supports those goals and your level of AI maturity.
Mind the gap
There can be a gap between the CEO's understanding of AI—what it can do and how—and what the people actually implementing the AI believe. The CEO's perspective will naturally be influenced by the topline strategic intent of the company, what her peers are telling her, and what her long-term aspirations are for the organization. The AI leads doing the work might not always be aligned with the realities and focused goals of the C-suite—but they need to be! In fact, our research indicates that leadership's limited understanding of AI's potential can be one of the top challenges companies face when scaling AI. Strategic Scalers "mind the gap"—they reduce the distance between the goals and understanding of the C-suite and the practitioners when it comes to how AI can and should be applied to change the world, and their world.
Time to implement? Look outside your organization
We are now firmly in the "era of implementation" with an explosion of investment in AI capabilities coming from well beyond Silicon Valley.2 These days, there are myriad tools which are proven, low-cost and academically rigorous. And there are varied and flexible ways to get your hands on AI: open source code, application programming interfaces (APIs), and small and medium-sized enterprise (SME) vendors to name just a few. As AI becomes mainstream, solution price points will also continue to drop.
Now you can reuse, partner or buy to implement and scale AI capabilities before you even need to consider building new proprietary technologies in-house. Take advantage of what's out there for success at speed and scale.
So how do you decide when to reuse, buy, partner or build? This is a full topic in and of itself (we published an entire article on it), but the simple answer is almost always reuse, buy or partner to take advantage of the investment other companies have already made—and get started quickly.
1Accenture, "The big disconnect: AI, leaders and the workforce," July 12, 2018.
2 Dr. Kai-fu Lee, AI Superpowers: China, Sillicon Valley and the New World Order. Houghton Mifflin Harcourt, 2018.