AI Center of Excellence
The key to unlocking the full potential of gen AI
A majority (85%) of organizations drive business insights and efficiencies with generative AI, but only 36% have optimized its use.
The key to unlocking generative AI’s full potential lies in the formation of an AI Center of Excellence (CoE). An AI CoE acts as a central hub that breaks down team silos and fast-tracks AI’s positive business impact.
“Organizations with disjointed practices, multiple data science teams working independently and a lack of value sharing indicate a need for an AI CoE,” says Ryan Rascop, Senior Business Development Manager at Insight.
“Large data science and data engineering teams working on AI projects without alignment and standardization can lead to inefficiencies and increased costs,” says Rascop.
AI Center of Excellence (CoE) is a dedicated team or division within an organization that leads the strategy, development and implementation of AI initiatives. It serves as the linchpin of AI expertise and governance to accelerate innovation, promote best practices and drive AI adoption in the organization. AI CoE benefits:
Streamlines processes
Consolidates knowledge
Delivers sustainable business value
Bridges knowledge gaps between departments
Maintains alignment with the organization’s overarching goals
Yes, and it’s fully operational
We are actively building one
Still considering it
We have no plans to create one
Establishing an effective AI CoE requires assembling a multidisciplinary team. Executive sponsorship for investment and support is also a must.
The core team should include roles such as Data Science, DevOps, FinOps, and Platform Engineering and Developer Experience (DevX) Experts. Each team plays a specialized role in developing, managing and optimizing AI applications:
Data Science & DevOps Team: Specialists in data management and process automation. Creates insightful analytical models and institutes Continuous Integration/Continuous Deployment (CI/CD).
Financial Operations (FinOps) Role: Controllers of the CoE’s budget. Manages and optimizes spend across storage, cloud services and platform resources to maximize cost-effectiveness and judicious use of financial resources.
Platform Engineering & Developer Experience (DevX) Experts: Architects of the foundational platforms. Ensures the Data Science and FinOps teams have curated paths that accelerate their journey with efficient and streamlined operations.
For companies in the early stages of this journey, the process begins with defining policies and goals. These include standards, along with compliance and security measures.
“The ultimate goal of a CoE is to drive organizational change and standardize practices, processes and compliance,” says Rascop. “It’s important to choose an AI infrastructure that supports the business and can scale as needed.”
The next step involves hiring and training a unified team. “This often includes building prototypes and Minimally Viable Products (MVPs) with them and accelerating development of their practices to show early ROI,” says Rascop. “But it also includes the other side of things like managing AI compliance, governance and security to offer them continued success as they go down the path.”
An environment that is both resilient and flexible forms the basis for building a successful AI CoE. As Rascop explains, “Regardless of what solution stack you land on, starting with a solid infrastructure and standardization avoids the need for major infrastructure overhauls and ensures long-term success across the enterprise.”
What’s surprising is that, despite the long-term strategic benefits of building an AI CoE, most organizations have yet to begin the process.
Ryan Rascop, Insight Senior Business Development Manager, spoke with the “AI in Business Podcast” by Emerj. Find out what Rascop had to say about building your own high-performing AI CoE.
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Only 47% of business leaders have launched an AI CoE. This reveals a significant opportunity for organizations to demonstrate leadership and sustain a competitive edge in a market where the designation of “industry leader” is still up for grabs.
Following best practices is the surest step to achieving that edge. Beyond following the steps outlined here, Insight experts have created an “AI Center of Excellence Best Practices” so that you can be confident in building an AI CoE with repeatable frameworks that accelerate your AI initiatives.
“It’s about establishing an AI governance framework that supports your strategic goals while managing risks,” says David McCurdy, Chief Enterprise Architect and CTO at Insight. “A well-functioning AI CoE provides a solid foundation for AI initiatives to flourish in a controlled and efficient manner.”
In the current landscape — regardless of industry — a high-performing AI CoE can act as an oasis of clarity in an otherwise convoluted web of AI deployment. Through knowledge transfer and strategic alignment, AI CoEs have the power to consolidate disjointed practices and dispel uncertainties about whether AI projects are, in measurable fact, adding value to your organization.
