How to Set-up an Artificial Intelligence Center of Excellence in Your Organization?
By: Yogesh Malik
Tags: Artificial Intelligence, Machine Learning, Startup, Coe, Center Of Excellence

Strategy for Artificial Intelligence Center of Excellence

You need to bring in research orientated tech expertise and establish a platform that can catalyze the growth of the deep technology ecosystem and present the stimulus for innovation in the fields of data science and artificial intelligence.

This new team should function like an extended team to the external units to deliver excellence by scaling up existing/new product/project activities build around artificial intelligence technology.

From value creation to value realization, this new AI COE should deliver minimum viable product (MVP) with original ideas by experimenting with emerging technologies. A key step towards preparing COE for its success in the organization is to build prototypes with a long-term view and enhance ecosystems and partnerships to promote purposeful artificial intelligence

The people, process and technology must be in alignment for any new venture to be competent and holistic. These three components are the key steps towards a successful organizational transformation

Executive sponsorship: Senior management people play a significant role in the organization and an effective sponsorship from them is required so that you can improve collaboration across the organization. This will benefit you stay aligned on the strategic priorities. Once you secure their commitment sponsors can function as a point of escalation — but make clear you don’t engage them for day-to-day tactical issues

Finding the right team: This could be your biggest trouble. Getting people on-boarded for the right skills could make or break your center of excellence strategic plan. How do you source these people? Internal hire or external, local to your COE region or a mobile/remote worker from some another country’s office

Capture benchmark that can demonstrate value: If you can’t measure it you can’t manage it. Your leadership want to see measured progress and you need to have a dashboard to represent internal performance. Make it transparent and comprehensive.

Finding the right problem: Be it your organization internal problem or customer use case, you need to research multiple launches. You need a design thinking before you act on building an AI platform. Talk to sales, interview clients, know their business pain points that can be solved by the adoption of artificial intelligence. Problem need to be thoroughly investigated so that you can apply the right AI tool to iron out, and present COE value proposition even for the early pilot launch.

Leveraging the data to solve it: The importance of data availability and data quality are of pivotal value. Artificial Intelligence provides a whole different meaning to the importance of data. The collection, distribution, and validation of data are thus important issues in the formulation of solutions involving AI

Build a repeatable AI solution: A repeatable solution that can be offered to others in the company or marketed to the customer, drives value to the business. A template approach that can be applied to the different situations is invariably favorable. Center of excellence leaders need to hook up with numerous product owners, designers, business analysts in building horizontal or industry specific offering solutions