AI Centers of Excellence accelerate adoption of AI industry

The age of AI and digital has accelerated the pace of innovation so quickly that some organizations just can’t keep up. In fact, senior executives and CEOs around the world are “extremely concerned,” as a recent PwC survey, “The Anxious Optimist in the Corner Office,” noted, about how their business will compete. in a continually volatile economic environment. climate stimulated by technological advances. In addition, a McKinsey survey found that only 20% of companies were able to achieve advanced analytics capabilities, of which 50% are building AI and about two-thirds of these are establishing an AI center of excellence. Furthermore Gartner study found that 80% of companies will fail to maximize the full potential of AI due to a lack of data scientists. As a result, companies have started to focus on AI Centers of Excellence (CoE), which are led by a group of business experts to enable rapid execution of organizational goals such as upgrading. adoption scale and advice to stakeholders. AI centers of excellence are essential in the digital age while keeping pace with innovation and benefiting from growing knowledge and best practices. All of this drives the technological transformation of the business while providing critical use cases.

Think of an AI center of excellence as a foundational knowledge platform in your organization. This knowledge platform contains lessons learned from past AI initiatives and a clear vision for using AI in your business strategy. It allows teams to constantly provide solutions that are consistent with the needs of your business. It can generate revenue, generate savings, improve customer experience, and give you a competitive advantage.

According to Gartner research, 95% of organizations with a dedicated Cloud Center of Excellence in 2021 are expected to deliver true transformational success in the cloud. Additionally, a Gartner report titled “Pick the Right Center of Excellence for Your Artificial Intelligence Strategy” found that 50% of organizations with more than three AI initiatives underway will create an AI Center of Excellence from by 2022. In addition, a Harvard Business School study “How to set up an AI center of excellence” demonstrated that “37% said they have already set up such an organization. Deutsche Bank, JP Morgan Chase, Pfizer, Procter & Gamble, Anthem, and Farmers Insurance are among the non-tech companies that have created centralized AI watch groups. This information suggests that there appears to be a correlation between the number of AI-related initiatives launched by companies and their tendency to implement a center of excellence. The same Harvard Business Review study alluded to change management and the technical aspects of the CoE.

It is important to note that there are several functional and operational models that companies adapt when it comes to CoE. The change management model emphasizes the forward-looking innovation that artificial intelligence can bring to the organization’s business players. The education and training of managers and business units are at the heart of this model. In addition to change management, the Sandbox approach is another central model, in which the CoE acts as the innovation and R&D center of the company. This model emphasizes proofs of concepts and different emerging technologies. The key is the alignment of the business units around the POCs and the responsibility for the initial launch and development of the use cases by topic. Finally, the Launchpad model for CoE leverages and builds on the capabilities of existing data scientists, engineers and developers. The CoE deploys the best subject matter experts across all departments to provide hands-on training and education and define early-stage business solutions.

A practical application of the CoE of different models would be a data science center specifically designed to scale data science initiatives. State-of-the-art technological infrastructure / capabilities in addition to expert teams are required to successfully leverage data science. Often times, it is very costly for companies to dedicate funds to data science teams in all departments of the organization, as significant costs are incurred due to the redundant nature of having to provide each department with its own team of data. data science. In this case, the CoE solves this problem for the organization by forming a centralized data science unit that generates business value throughout the organization.

There are already several successful examples of corporate CoE adoption. For example, Cisco Systems has partnered with universities to create centralized units of data science and AI training programs for employees to turn them into experts. Anheuser-Busch InBev Belts Program is a perfect example of a large enterprise application of a center of excellence to accelerate business goals. The program has trained thousands of employees around the world to achieve Lean Six Sigma certifications, which has enabled the company to scale up its data science and AI initiatives. In addition, Germany’s largest private media company, ProSiebenSat.1 Media, leveraged a CoE model that strategically places its data analytics teams within its digital and IT departments to streamline its model. sales force and strengthen its skills in terms of return on investment in AI. All of these practical use cases have enabled these large companies to drive business transformation and adoption of artificial intelligence. Another example, NTT Data, a multinational systems integration company, has established an AI Center of Excellence, geared towards training in AI data science and engineering in key areas of AI.

While many companies are embracing IA CoE, there are also significant challenges that could hamper the effectiveness of IA CoE. These challenges include democratizing AI for businesses, finding real talent, and aligning with responsible AI standards. As companies move to decentralized models, the AI ​​CoE could face issues with employee buy-in. Based on talent, without the right expertise, it is difficult to establish an AI CoE that reflects the core capabilities of the business and its ability to innovate. Finally, without including responsible AI, AI centers of excellence could face costly backlash and damaged reputations with other industry players.

Along with practical examples in the business, it is important to highlight the key best practices involved in building an AI center of excellence.

1. Start with a basic business problem. Describe your vision, strategy and governance, and describe the main stakeholders that you want your AI solution to solve for the business. Clearly describe the benefits of implementation and adoption. Next, assess the financial requirements and feasibility of the AI ​​center of excellence, understanding the current rate of adoption, AI knowledge and know-how, and organizing a committee to oversee the development of the center. of excellence in AI. Finally, set up governance and gain buy-in from all key stakeholders – CTO, CEO, CIO and all key business unit leaders. Introduce governance and the role leaders will play. Describe the role of the Chief AI Officer within the framework of governance.
2. Data must become the most important asset of the business. Every business needs to think critically about how it collects, stores, governs and manages data assets, as well as their accessibility and quality. Determine the methods of collecting, storing, and annotating the data, as well as the ability to train the data repeatedly. Get reliable, high quality, clean, complete and reliable data.
3. Identify and display use case libraries from POCs, flagships, implementation of standard business units to Moonshot projects. Determine which use cases AI is both effective and pragmatic for.
4. Build your talent pool and initiate AI Education. This applies to products, solutions, engineering, product management, machine learning, and data analysis. The entire business community needs to be requalified and honed to create an AI-ready mindset.
5. Build an appropriate infrastructure. Support for post-production delivery pipeline modeling and management with AI and analytics capabilities.
6. Determine the maturity model, best practices and implementation phase of COE AI: This includes significant efforts such as designing the AI ​​center of excellence, creating dedicated team units that will boost the innovation rate of the organization, monitoring test environments, creating a process. and a governance structure, analyzing areas to maximize efficiency, allocating COE AI assets and building a financial model. It is important to develop scalability with larger pilots and projects.
7. Benchmarks and layout metrics such as KPIs, metrics of success for each AI initiative, demonstrating how to save money and time, generate revenue, or improve efficiency.
8. Marking and marketing the COE AI: Once the COE is structured and established, the organization needs to focus on marketing its hub to showcase unique AI use cases and stay relevant and competitive in the digital economy.

As the pace of technological innovation and AI continues to accelerate, AI CoE will become not only an essential necessity, but also a key operational function for companies to maintain a competitive advantage in the marketplace. AI as well as continue to innovate and increase revenue opportunities.