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AI Center of Excellence (AI CoE): What it is & how to build in '24

Written by
Cem Dilmegani
Cem Dilmegani
Cem Dilmegani

Cem is the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per Similarweb) including 60% of Fortune 500 every month.

Cem's work focuses on how enterprises can leverage new technologies in AI, automation, cybersecurity(including network security, application security), data collection including web data collection and process intelligence.

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The adoption of artificial intelligence (AI) is increasing as companies try to capture value from enterprise AI applications. However, challenges such as:

  • Limited AI expertise,
  • Increasing data complexity,
  • And lack of tools for AI development

hinder AI adoption for enterprises, according to a 2021 IBM survey.

To overcome these obstacles and to minimize the chances of AI project failures, it is important to have a dedicated business unit that would coordinate and oversee all AI initiatives within the organizations. This is where the AI Center of Excellence (CoE) comes in, a unit already established in 37% of the large companies in the US. In this article, we focus on what an AI CoE is, its benefits, and the best practices for establishing one.

What is an AI Center of Excellence (CoE)?

An AI Center of Excellence (CoE) is a team consisting of technical experts that advises, guides, and oversees AI projects in an organization. An AI CoE bridges the gap between executive decision making and AI implementation by:

  • Identifying AI use cases to solve different business problems,
  • Determining and enabling the development of the necessary infrastructure for these AI use cases,
  • And building implementation roadmap for AI projects, from which tools and technologies to be used, responsibilities, as well as setting targets and KPIs.

What are the benefits of building an AI CoE?

Key benefits of building an AI CoE are:

  • Constituting a dedicated center that coordinates AI efforts across the organization,
  • Creating a unified vision for AI within an organization which helps consistent and efficient communication between stakeholders,
  • Creating a set of standardized practices and processes for AI. This makes scaling AI efforts easier,
  • Managing relationships with external parties such as start-ups and universities. This enables companies to benefit from external expertise but also to identify investment opportunities,
  • And acquiring and developing AI talent within the organization for long-term success.

What are the best practices for building an AI CoE?

Assess the AI maturity level of your organization

There is no single best formula for establishing an AI CoE, as organizations differ from one another. One key difference between organizations is their AI maturity level, which is the readiness of an organization to take advantage of artificial intelligence technologies. For instance, Gartner’s AI maturity model is as follows (Figure 1):

Figure 1. Gartner’s framework for AI maturity.

Source: Gartner

Your organization’s maturity level can determine the structure and composition of your AI CoE team as well as the next steps required.

Assemble a multi-disciplinary team

An AI CoE should contain technical experts, such as, data scientists and engineers, as well as business executives and leaders from departments that will adopt AI use cases. Moreover, IT and cybersecurity experts are also crucial to help integrate new technologies into existing structures and to ensure the security of new systems.

There are also other key personnel that can help coordinate AI efforts across an organization. For instance, project managers or procurement specialists are also vital depending on whether you will build in-house solutions or work with third-party AI vendors.

Evaluate the impact of the center periodically

Establishing KPIs and other metrics for AI initiatives would enable organizations to measure the impact of the CoE. It is important to be able to link AI initiatives and their business impact in terms of organizational efficiency, revenue, time and cost savings.

Such assessments would help organizations evaluate their progress with AI against the above metrics and specifically pinpoint the areas that need improvement.

Provide education to stakeholders

AI is transforming industries and business functions, but the expectations of what AI can achieve can become unrealistically high. It is important that team members in an AI CoE are educated about AI technologies and their potential business benefits. This will inform stakeholders about what AI can and cannot do for their departments.

We have an article dedicated to the myths and misconceptions about AI that businesses should be aware of. Feel free to check it out.

Consultants can help you establish centers of excellence in your business. Feel free to check our data-driven lists of AI consultants and data science consultants.

If you have other questions about enterprise AI, feel free to ask:

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Cem Dilmegani
Principal Analyst

Cem is the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per Similarweb) including 60% of Fortune 500 every month.

Cem's work focuses on how enterprises can leverage new technologies in AI, automation, cybersecurity(including network security, application security), data collection including web data collection and process intelligence.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Cem's hands-on enterprise software experience contributes to the insights that he generates. He oversees AIMultiple benchmarks in dynamic application security testing (DAST), data loss prevention (DLP), email marketing and web data collection. Other AIMultiple industry analysts and tech team support Cem in designing, running and evaluating benchmarks.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

Sources:

AIMultiple.com Traffic Analytics, Ranking & Audience, Similarweb.
Why Microsoft, IBM, and Google Are Ramping up Efforts on AI Ethics, Business Insider.
Microsoft invests $1 billion in OpenAI to pursue artificial intelligence that’s smarter than we are, Washington Post.
Data management barriers to AI success, Deloitte.
Empowering AI Leadership: AI C-Suite Toolkit, World Economic Forum.
Science, Research and Innovation Performance of the EU, European Commission.
Public-sector digitization: The trillion-dollar challenge, McKinsey & Company.
Hypatos gets $11.8M for a deep learning approach to document processing, TechCrunch.
We got an exclusive look at the pitch deck AI startup Hypatos used to raise $11 million, Business Insider.

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