Why an AI center of excellence?

New technology is not good or evil in and of itself. It's all about how people choose to use it. - David Wong

Technology selection is the just the beginning, the implementation is a different beast itself. One of the most important aspect of technology implementation is the structured approach. However, more often this aspect lacks necessary thought process and weight-age which eventually results in failed implementations.

Good, bad or indifferent, if you are not investing in new technology, you are going to be left behind. - Philip Green

On the wave of technology adoption, the organizations typically embark because of the need to survive or to meet business goals or peer or industry pressure. This has been true for any technology trend came in past or even the recent surge of AI adoption.

The first step of AI adoption begins with lot of excitement and inquisitiveness but what lacks is a clear direction to convert into a large-scale enterprise program as part of a long, committed journey.

AI or any other new technology often faces a common challenge during adoption and implementation is the absence of enough guidance and support to implement at large scale.

Many top executives are concerned about their ability to best integrate the AI tools and technology with their current technology landscape and it's usefulness to meet business goals. As executives, they cannot dig deep into the intricacies of such tools and technologies. For them, it is crucial to understand the possibilities and opportunities these technologies bring to the table—what they offer, how they can help or what are the limitations, how they can transform business models, and the resources it’ll need to successfully deploy.

At this juncture, the organization requires a beacon of light to provide right direction, required support with right resources and a path to follow. This is where the Center of excellence (CoE) plays a pivotal role to help them to navigate the technology shift.

With every advancement in technology or arrival of a new trend it is an obvious organizational behavior and response to perform some initial proof of concept and pilot programs. The same approach applies to AI adoption as well. But is it enough ? what next?

Making the most of AI adoption is every company’s vision but the real challenge is about primarily answering following questions

1.      What – What are the real use cases for AI application?

2.      How – How to adopt the right approach of AI implementation?

3.      Who – Who are right set of people to implement and skills required?

4.      When – when to begin implementation? Now? or certain organization preparation required before adoption and implementation?

These are all valid and expected questions for an organization which hasn’t tasted the AI implementation.The CoE can help in addressing these questions and drive the adoption to meet the goal behind implementing AI.

• Positioning – While the AI CoE must have a separate mandate and charter. The underlying fact needs to be understood that AI tools and technology should be used to supplement the existing work. It is important to ensure that they are not positioned to become a competition for the existing Digital initiatives such as RPA, ITPA analytics etc..
• Governance - Devise the policies, procedures, and standards for use case identification, model development and deployment methodologies to meet audit, regulatory, information security, and compliance requirements. Another important aspect to optimize the Governance structure itself so that unnecessary overload of meetings, communication and updates can be avoided.
• Strategy – In the initial days, the CoE should be focused on informing, persuading, enforcing or innovating products and services, but the goals should evolve towards measurement, monitoring, course correction and research as the usage matures.
• Knowledge management and collaboration – CoE as the name suggest is generally a central structure within the organization (though there are other models of CoE deployment). It places the CoE in the right position to be an effective communicator and collaborator of AI work happening across organization. The CoE should build a central repository of AI solutions that are being used to address business problems and enable re-usability.
• AI skill and talent development – Continuous and engaging efforts to identify, train and map skilled resources to projects are part of basic structure of a CoE.
• Business requirement alignment – Continuous engagement is imperative with Business to understand the goals, influence of ongoing AI programs on business, improving awareness in business about the capabilities developed and how those could be aligned to business goals.
• AI outcomes – Outcomes decide if the program is successful or not. Isn’t it? Like any other program, the AI program too have some expected outcomes. The CoE can play crucial role in deciding the right outcome for a program and track the progress.
• Data availability – CoE should envision to become the golden source of data for various business functions. AI implementation is naturally dependent on availability of vast amount of data and any limitation in the data availability will reduce the effectiveness of AI implementation. Having said that, the CoE also need to ensure that data secrecy and integrity policies, exception and approval processes are established to expose appropriate data only to right audience.

To summarize, the CoE is the driving force which helps the organization to launch the AI journey with right speed and direction and helps to maintain the trajectory of Digital transformation journey.