Are you optimistic about the transformational business outcomes that artificial intelligence (AI) can affect? Do you envision your organization moving from the basics of assisted intelligence with data-driven decisions into the next generation of augmented intelligence, where your organization designs machines to enhance human intelligence and interactions? It\u2019s all possible. But it\u2019s not possible without people. People drive culture.\nSet the vision. Enable cross-functional collaboration. Design for culture.\nVision. Collaboration. Culture. These are the pillars required to set the stage for a world-class AI center of excellence. Whether you\u2019re interested in standardization and integration, leveraging data assets, or measuring business value, a center of excellence (COE) can provide the governance or resource balancing necessary for organizational success.\nVision: A lesson in the centralization of value\nTo truly grasp the vision behind a center of excellence, let\u2019s go back to its roots. Centers of excellence originated way back in the Qin Dynasty of China in 221 BC. The COE is founded on the concept of centralization of authority that captures the systematic and consistent concentration of that authority. The advantages of such centralization include straightforward responsibilities and duties, clear decision-making, and the belief that centralized power more strongly promotes the interest of the individuals who hold that power. Following upon the heels of these advantages are the disadvantages, which include decisions not being made by those resources with the best knowledge of the problem, delayed execution due to slow information dissemination, and exclusion of certain resources that could have contributed to a better decision.\nDuring the Industrial Revolution, we started seeing new manufacturing concepts emerge\u2014specifically, a shift from the Domestic System (Putting-out System) to the Factory System. The Domestic System shipped materials to rural producers, who often worked out of their homes. This decentralized approach provided products of reasonable but inconsistently quality at a relatively high price. The Factory System centralized the production of goods on a massive scale using machines. This new approach decreased costs and increased worker efficiency. The adoption of the Factory System presented a new question: Where should we produce the goods?\nCollaboration: The parallels between AI COE structures and location theory\nThe degree of collaboration between the AI COE and the rest of the organization is directly affected by the AI COE\u2019s organizational design. Location theory concentrates on predicting the ideal geographic location for economic activity. In short, where should a business be located? Do we focus on variance costs? Is the friction of distance between production and distribution a problem? These concepts seem very embedded in the old world of manufacturing\u2014a world that today\u2019s business executives rarely talk about or reference.\nInterestingly enough, we can start to find parallels between designing the structure of pioneering COEs and location theory. Three fascinating location theories help us to understand the organizational-design considerations inherent to setting up an AI COE.\n\nAlfred Weber, a German economist, suggested that manufacturing plants should be located where costs are the least (least cost theory). The Weber Theory considered such costs as transportation, labor and agglomeration.\nHarold Hotelling, and American mathematical statistician and an influential economic theorist, developed the Hotelling Theory. This theory introduced the principle that the location of the industry can\u2019t be understood without reference to other industries of the same kind, and he recommended locating near customers (clustering). The idea of locational interdependence connects a business\u2019s location to its ability to operate and generate a profit.\nAugust L\u00f6sch, a German economist concentrating on regional and urban economics, studied the economics of location. L\u00f6sch\u2019s Theory explained that manufacturing plants should be in locations where they can maximize profit (zone of profitability).\n\nWeber\u2019s Theory, Hotelling\u2019s Theory and L\u00f6sch\u2019s Theory all generally agree that business should gravitate to where net profit is the greatest. At the surface, this seems like common sense. When applying this concept to COEs, we\u2019re less concerned with profit and more concerned with continuous value delivery (which may or may not immediately involve profit). We now are left to ponder where in our organization is the maximum value generated?\nCulture: finding a home for the AI COE\nHistory isn\u2019t a good predictor of future success. Yet, it doesn\u2019t hurt to understand it and take into consideration the mistakes made in the past so they\u2019re not repeated\u2014in original form\u2014by your organization.\nWe arrive at the following observations:\n\nCentralized techniques effectively distribute communications with minimal opportunity for message variation.\nDecentralized approaches help to generate worker buy-in due to their customized and distributed nature.\nLocation theory introduces the concept that businesses should be located where the value is maximized.\n\nExtrapolating these concepts, let\u2019s apply them to an AI COE. When we teleport from the COE of late to the present, we discover several useful lessons:\n\nCentralized management of a COE is most effective for consistently communicating the organizational vision.\nA decentralized model for employee engagement helps to co-create ownership and accelerate the COE\u2019s organizational adoption.\nCOEs should have functional reporting centralized (hardline), but it must be operationally (day-to-day or soft line) embedded where maximum value is generated (inside the business).\n\nThese concepts offer insightful principles when we design and construct our AI center of excellence for durability, resilience, and accelerated adoption. Specifically, intelligent organizational design is crucial for supporting an evolution in organizational values, beliefs, and behaviors to embrace a data-centric culture that supports an AI-first mindset.\nBuilding an AI-first mindset is all about culture and people and less about technology and data. Armed with an AI-first mindset, surrounded by the right people, and supported with an AI culture, is your AI COE able to measure its value?\nMeasuring the AI COE\u2019s value\nDefine success for your AI center of excellence by setting the vision. Identify principles that the AI COE will use to make future decisions. What are the expectations of the AI COE? What\u2019s the AI COE\u2019s essential value proposition? How will value be co-created to deepen shared ownership?\nOnce the vision is defined, shift your focus to how collaboration will be operationalized. How will the AI COE team collaborate with existing internal organizational constructs (teams, departments, and divisions)? When will the AI champions be identified and engaged? How is the AI community incentivized to participate?\nNow begin to design for cultural transformation. How is the \u201cdata-as-an-asset\u201d culture tipping point defined? What benchmarks show demonstrated culture shifts in behavior? How are individual performance plans linked to an AI-first mindset? Which communication and media forms will be leveraged for education and awareness?\nPlanning AI workshops, identifying AI pilots, and creating an AI lecture series can all advance the adoption and make AI real for your organization. However, it can be difficult to know when you\u2019re making progress. The following questions can help you evaluate if you\u2019re making a positive impact on value realization with your AI initiative:\n\nAre business leaders able to link AI programs to business results?\nDo existing business champions identify as part of the AI community?\nHave AI pilots quantitatively advanced value realization?\nCan organizational resources articulate how they\u2019re contributing to the knowledge of the AI community?\nDo resources access the organization and explain how their roles have changed in light of an AI-first mindset?\nAre randomly polled organizational resources able to clarify the value proposition of the enterprise AI COE?\nIs the AI COE integrated into existing business processes, or does it sit on the bench as an outsider to organizational value creation?\n\nYour technology partners are going to suggest you start the AI COE journey by taking data-asset inventories, reviewing the technology stack, and exploring sharing technologies across siloed areas. These will all be necessary in due time, but the success of an AI COE begins and ends with people, not data. Set the vision. Enable cross-functional collaboration. Design for culture.