Artificial intelligence has evolved from a novel technology to a critical strategic component for businesses across various industries. Despite significant investments in AI, many organizations have yet to fully realize the transformative potential of their AI initiatives. A common stumbling block is that many AI Centers of Excellence (CoEs) operate more as centers of experimentation rather than strategic hubs that drive business value. This blog post will explore the necessary steps to transition AI CoEs from experimental stages to becoming integral parts of a business’s strategic operations, thereby unlocking their full potential.
The Current State of AI CoEs
AI, data, and analytics are now central to most strategic business imperatives. Organizations aspire to embed these technologies into their business decisions, aiming to become more intelligent and efficient. According to Deloitte’s State of AI in the Enterprise study, 94% of business leaders agree that AI is critical to their success over the next five years. However, despite increasing investments and a growing number of use cases, the outcomes from AI investments have been varied. Many companies have struggled to achieve the anticipated impact from their AI initiatives.
The core issue often lies in the approach to AI adoption. Business leaders frequently adopt an anecdotal, need-based approach to AI, rather than a holistic, all-encompassing strategy. This fragmented approach limits the ability of AI to deliver sustained and broad-based success.
Transforming the Role of AI CoEs
To transition AI CoEs from experimentation to strategic impact, organizations need to redefine the role of these centers. An AI CoE should not be an isolated unit focused solely on experimentation. Instead, it should be deeply embedded within the business, driving continuous and measurable outcomes. Here are key steps to achieve this transformation:
- Executive Sponsorship and Alignment: Executive sponsorship is crucial for the success of an AI CoE. Without strong leadership support, AI initiatives often face low adoption rates and limited impact. Executives must champion the AI CoE, aligning it with the organization’s strategic goals and ensuring that it has the resources and authority needed to drive significant change.
- Holistic Approach to AI: AI should be integrated into the core business processes, rather than being treated as a separate or experimental entity. This involves creating a clear plan for embedding AI into the business’s strategic framework. The focus should be on observable business impact, ensuring that AI initiatives are designed to deliver tangible results.
- Comprehensive Technology Stack: A successful AI CoE requires a robust technology stack that supports data and analytics functions. This includes modernized infrastructure, advanced analytics tools, and scalable AI platforms. A comprehensive view of the technology stack helps in addressing the interconnections among different business functions, facilitating seamless data integration and insight generation.
- Synergistic Evolution: The AI CoE should evolve in synergy with the business. This means that as the business grows and changes, the AI CoE must adapt and scale accordingly. This iterative growth helps in continuously improving AI capabilities and ensuring that they remain aligned with the business’s evolving needs.
Examples of Successful AI CoEs
Several companies have successfully transitioned their AI CoEs from experimental stages to strategic hubs. Here are a few illustrative examples:
- Global Pharmaceutical Company: This company’s AI CoE delivered over 20 advanced machine learning (ML) services, more than 50 conversational AI bots, and hundreds of robotic process automation bots. These AI capabilities were integrated into core business functions, driving significant improvements in operational efficiency and customer experience.
- Quick-Service Restaurant Chain: By leveraging AI-powered pricing strategies, the AI CoE helped this restaurant chain realize over $500 million in incremental margins. This example highlights how AI can directly contribute to the bottom line by optimizing key business processes.
- Major Life Sciences Company: The AI CoE at this company operationalized ML algorithms to support a multi-geography omnichannel strategy. This initiative influenced $800 million in global sales, demonstrating the strategic impact of AI on business outcomes.
- Technology Company: A thought-leading technology company utilized its AI CoE to build and operationalize AI models that enhanced trust and safety, provided operational insights, and improved customer experience. This example shows how an evolved AI CoE can drive innovation and create new opportunities for the business.
Centralized vs. Federated AI Models
A critical consideration for AI CoEs is whether to adopt a centralized or federated model. In a centralized model, AI and analytics resources are pooled together, allowing for standardized processes and tools, which enhances collaboration and efficiency. However, this approach can sometimes lack the flexibility needed to address specific functional or industry nuances.
In contrast, a federated model allows different business units to develop their AI capabilities independently, tailored to their unique needs. While this approach offers greater flexibility, it can lead to inconsistencies and siloed operations. The key is to strike the right balance, combining the benefits of both models to optimize AI efforts across the organization.
Transitioning an AI CoE from a center of experimentation to a strategic hub requires a comprehensive and integrated approach. By securing executive sponsorship, aligning AI initiatives with business goals, building a robust technology stack, and fostering synergistic evolution, organizations can unlock the full potential of their AI investments. As AI continues to become more integral to business operations, the focus should be on driving strategic impact and delivering measurable business outcomes through well-structured and effectively managed AI Centers of Excellence.
