The estimated global spending on AI in 2024 is $110 billion with 75% of the enterprises adopting AI/ML in their workflows. The lack of the right talent and uncertainties around implementation make AI adoption difficult for organizations. This makes it essential to establish dedicated teams that drive innovation through AI and can build and implement a clear AI innovation strategy. These teams form the Center of Excellence (CoE). AI CoEs are crucial to keep pace with the technology and grow with knowledge and best practices. With Quantiphi’s deep tech expertise in leveraging Vertex AI, we have been able to implement AI CoE for our customers to create value and help them achieve their AI goals.
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AI CoE is a pool or a team that assists/consults, educates, and oversees the incorporation of AI projects/developments across the organization. This dedicated team has the required skill sets, expertise, domain knowledge, and experiential learnings from the past AI adoptions to leverage AI-driven solutions to transform the business. The AI CoE solves business problems resulting in an increase in cash flows, improved operational efficiency, reduced downtime, and lower costs while boosting the competitive advantage.
The AI CoE team plays an important role in fostering AI innovation in an organization that needs to optimize its processes/functioning. The team identifies the most relevant and value-generating projects to maximize the business outcomes.
The AI CoE team establishes the foundation for AI implementation and consistently works on developing advanced solutions to drive innovation. The organizational foundation to establish an AI CoE consists of visibility into the need for AI innovation and the projects needed to drive that innovation. The organization needs to consider all the aspects and their interrelation to build a strong and efficient centralized team to drive AI innovation.
Vision and Strategy
A comprehensive vision is vital to building a robust AI CoE team. The advantages that a clear vision offers include – defined common goals, a smooth decision-making process, dedicated roles and responsibilities, and improved operational efficiency. Strategizing helps to envision achievable goals pertaining to the selection of use cases and AI investments. There are certain important aspects to be considered while developing this strategy which includes talent, budget, business goals, and other resources. A proper strategy and its effective implementation speed up the innovation process and achieve ROI faster.
Engineers/Stakeholders and Alliance
Once the vision and strategy are defined, we need the talent – engineers and stakeholders – to take charge of this vision. It becomes imperative to have the right talent with well-defined roles. For example, Data Engineer, Machine Learning Engineer, and so on. In addition, the nature of the collaboration between the CoE members and the rest of the organization is crucial to ensure the proper functioning of the developed strategy. Hiring the right team with niche technical skills and building a collaborative culture strengthens the organization.
Operations and Execution
The operations and appropriate execution lead to continuous innovation in an organization. Streamlining the processes and operational strategies and making them lean helps in achieving an ideal CoE. Execution of these strategies paves the way to achieve maximum value from the CoE. The execution framework should be designed with the following aspects taken into consideration:
The actual implementation of the use cases/projects eventually boils down to the technology used in the development. Hence, it is necessary to choose the right tech stack that suits the needs of the applications. The tech stack strategy should align with the requirements of the roadmap and should be compatible with the existing tech infrastructure. It should be flexible in order to adapt with the evolution of the organization.
The goal of setting up an AI CoE is to initiate and drive innovation and improve the existing processes. The AI CoE creates a framework that can experiment, measure, and foster a collaborative culture to the path to excellence. This framework leads to standardized practices that can be leveraged to scale the projects and move towards organization-wide AI innovation. These best practices also make it easier to be adopted by other functional units within the company.
A leading US-based producer of computer memory and computer data storage approached us to build a Model Training Flow section for the customer’s data science team that wanted to perform image-related analysis. Quantiphi developed a fully-automated MLOps pipeline using Vertex AI in Google Cloud Platform (GCP) in two phases to perform analysis of over two million images per day, resulting in faster analysis and accurate defect detection in the images.
Foreseeing the success of the earlier engagements, the customer wanted help in setting up an AI CoE for advisory on their ongoing projects. In addition, the customer also wanted bespoke training for their teams across the US and APAC region.
Quantiphi deployed a dedicated pool of SMEs to provide advisory support and technical guidance to the customers’ team for the development of use cases along with consultative support and thought leadership aiding enterprise-wide AI adoption. The dedicated CoE team assisted the customer in the development and prioritization of the POC/pilot and production-grade use cases involving varying degrees of complexity. This helped the customer to achieve maximum efficiency and ROI metrics meeting their long-term business goals. The CoE team also conducted tailored workshops on the acquired skill sets and recommended best practices to streamline current and future workloads.
The AI CoE and Quantiphi aim to conduct monthly and quarterly workshops for the customer-focused on key learnings from fundamentals to advanced concepts and best practices featured from the 2 phase engagement of MLOps using Vertex AI. Additional training sessions will also be covered based on the feedback received from the AI-CoE team for current and future workloads.
In today’s digital era and competitive market, AI has become a vital tool and a key enabler for growth. A dedicated AI CoE helps businesses prioritize initiatives, select the right AI systems, accelerate AI implementation, and enable enterprises to achieve business goals.
Get in touch with our experts to drive growth with an AI CoE.