How to Build an AI Center of Excellence

Artificial intelligence is transforming life as we know it. The COVID-19 pandemic has further accelerated automation and increased enterprise investment in AI across the globe. The global AI market size is expected to grow to USD 309.6 billion by 2026, at a Compound Annual Growth Rate (CAGR) of 39.7%. While there is greater adoption of AI worldwide, scaling AI projects is not easy. According to Gartner, the staff skills, the fear of the unknown, and finding a clear starting point for AI projects are some of the most prominent challenges faced by enterprises in their AI journey. To overcome these challenges and launch new AI initiatives, executives are setting up dedicated AI knowledge platforms or AI Center of Excellence (AI CoE) within their organization. As per a Harvard Business Review article, 37% of U.S. executives from large firms that use AI have already established a COE in AI.

What is an AI Center of Excellence (AI CoE)? 

An AI Center of Excellence (AI CoE) is a centralized knowledge group or team that guides and oversees the implementation of organization-wide AI projects. An AI CoE brings together the AI talent, knowledge, and resources required to enable AI-based transformation projects. It brings together all the AI capabilities needed to address the challenges of AI adoption and prioritize AI investments. AI COE essentially serves as an internal centralized counsel to identify new opportunities for leveraging AI to solve various business problems such as controlling costs, improve efficiency, and optimize revenue. The key objective of setting up an AI CoE is to build and support the AI vision of the company and serve as an internal counsel to manage all AI projects.

Why should you build an AI CoE?

An AI CoE plays a vital role in developing AI talent and driving innovation within the company. The team acts as an internal counsel for guiding the company on all AI initiatives from prioritizing AI investments to identifying high-value use cases for implementation. By providing a robust framework for AI implementation, the CoE helps in building future-ready engineering capabilities to manage high volumes of data, improve efficiency, and drive innovation. Here are the key benefits of creating an AI CoE:

  • To consolidate AI resources, learnings, and talent in one place.
  • To create and implement a unified AI vision and strategy for the business.
  • To standardize the platforms, processes, and approach to AI within the organization.
  • To speed up AI-led innovation and identify new revenue opportunities.
  • To scale data science efforts and make AI accessible to every function within the company.
  • To drive AI-enabled initiatives such as cost reduction, churn prevention, and revenue maximization to stay ahead of competitors.

How to build an AI CoE? 

Technology is constantly evolving. Enterprises must continuously adapt their AI roadmap to deliver the highest business value. With scattered data science teams, resources, and legacy systems, it becomes hard to know where to start. To set up an AI innovation center, business leaders must take a holistic approach encompassing all the factors that contribute to its success.

The key pillars of an AI Center of Excellence 

A CoE or innovation center is built on the following four primary pillars:

1. Strategy: Strategy helps in clearly defining the business goals, identifying the high-impact use cases, and prioritizing the AI investments. When you are starting with AI, while it is okay to think big, it is wise to start with small and smart, achievable, realistic AI goals. This will allow you to progress quickly and gauge the ROI from the AI initiatives.

2. People: The people-oriented pillar helps define the data-driven culture and the strategies for managing the teams that use AI and data within the organization. It is important to clearly define the roles, data owners, and structures for driving AI-led innovation. The people strategy also helps in hiring the right AI talent and fostering a collaborating, innovation-driven culture.

3. Processes: The process-oriented pillar helps define the methods to sustain continual AI innovation within the company. The more iterative and agile the process is, the easier it is to learn fast and adapt to fast-changing business needs.

4. Technology: The right technology stack is crucial for building robust AI capabilities. The tech strategy should clearly define the process for evaluation, and adoption of new tools that match the organizational needs and work well with the existing IT infrastructure.

Best practices and tips to build an AI CoE 

If executed with leadership commitment and a very well-planned strategy, AI has the power to reward organizations with non-linear rewards, however, if not operationalized with the right fundamentals and critical mass scale, it could become a bottomless pit of investments with no significant returns. Here are some best practices and tips to set up an AI CoE:

1. Set AI vision and measurable goals 

Leaders need to identify the key business objectives they want to achieve with AI such as improving conversion, reducing churn, etc. These core goals help in prioritizing the AI investments to be made and in identifying the most high-impact use cases to be implemented first. You also need to develop a transparent and compressive system to track the progress and measure the benefits of their AI initiatives. By capturing the benchmarks and the KPIs for the AI experiments, enterprises can gauge the value generated and do course corrections early on if required.

2. Assemble the right team and set up governance

People are the core strength of an AI CoE. Once you have identified the business problems to solve, you need to onboard the right talent to the core innovation team. Roles and responsibilities will have to be clearly defined. Leaders will also have to set up governance to oversee the development of the CoE.

3. Get your data ready for AI 

AI is only as smart as the data used to train it. Enterprises must invest in robust data collection, cleaning, storage, management, and validation mechanisms to ensure that the data used is reliable and ready for AI.

4. Standardize and create reusable AI assets

Based on the existing systems, the business objectives, and high-value AI use cases, companies must invest in the necessary tools and infrastructure required to apply AI. By creating scalable, flexible, and reusable AI assets, enterprises can apply their AI solutions to multiple scenarios and derive more value.

5. Democratize AI and collaborate with no-code platforms

Good ideas can come from anywhere. To drive AI-led innovation, you need a collaborative, data-driven work culture and the right AI tools. A no-code AI platform can enable anyone in the organization to use AI and apply their perspective to solve complex business problems. In addition to democratizing AI, these platforms help in standardizing AI operations within the company. Leaders must also initiate AI and data science education across functions to nurture an AI-first culture.

The final verdict

In today’s competitive market, AI has become a necessity and a key enabler for growth. No-code AI platforms such as HyperSense can accelerate the adoption and democratization of AI across the organization. It puts the power of AI in the hands of the non-technical business user, eliminating the traditional challenges of misaligned objectives, skill shortage, and siloed operations.

Flexible and modular AI platforms play a vital role in broadening organization-wide AI adoption. A dedicated AI CoE and the right AI platform can facilitate the launch of AI initiatives, accelerate AI implementation, improve efficiencies, and ultimately help enterprises to achieve their long-term AI goals.

For more details on HyperSense AI platform, please email us at or visit our website, www.

Make Better Decisions with Quantifiable Data-driven Evidence

Request Demo

Get started with Subex
Request Demo Contact Us
Request a demo