Artificial intelligence (AI) has emerged as a transformative technology for businesses across industries. As per Gartner, the business value derived from AI is projected to reach $3.9 trillion in 2022. However, for many companies, unlocking AI‘s full potential remains challenging. Lack of technical skills, poor data quality, fragmented efforts, and the inability to scale promising pilots inhibit AI adoption and impact.
This is where AI Centers of Excellence (AI CoEs) come in. AI CoEs are cross-functional teams that provide centralized leadership for an organization‘s AI strategy and initiatives. By 2024, over 50% of enterprises are expected to have established centralized AI CoEs, up from less than 20% in 2021.
So what exactly are AI CoEs, what value do they provide, and what does it take to build one successfully? This comprehensive guide has all the answers.
What is an AI Center of Excellence?
An AI Center of Excellence (AI CoE) is an organizational unit dedicated to leading a company‘s AI strategy. The AI CoE team consists of both technical experts and business leaders who collaboratively identify, prioritize, and drive AI initiatives across the organization.
The primary objectives of an AI CoE include:
- Developing and executing the organization‘s AI strategy aligned with overall business goals
- Providing centralized oversight and coordination for all AI programs and investments
- Building internal capabilities – data, technology, talent – required for AI success
- Promoting adoption of AI solutions through enablement and advocacy
- Establishing frameworks, guidelines, and governance for responsible and ethical AI
- Fostering innovation through partnerships with startups, academia, and industry consortiums
Essentially, the AI CoE serves as the guiding force for scaling the impact of AI on processes, offerings, and business models. It sets the standards for how AI projects must be identified, developed, measured, governed, and sustained across units. This strategic, integrated approach is key to overcoming the common challenges of fragmented efforts, risk avoidance, lack of oversight, and inertia that plague many organization‘s AI ambitions.
Benefits of Developing an AI Center of Excellence
Investing in an AI Center of Excellence offers multifaceted value:
1. Centralized Leadership & Coordination of AI Efforts
An AI CoE with executive sponsorship and cross-department representation provides centralized, unified leadership of an organization‘s vision, roadmap, and investments for AI.
By serving as an enabling body that guides business units, the AI CoE helps connect disparate efforts and foster enterprise-wide synergies. It develops frameworks that enhance coordination and transparency of AI initiatives across units. Issues like duplicated efforts, misaligned priorities, or isolated impact can be avoided through this centralized oversight of AI efforts and investments.
2. Increased Efficiency & Business Impact from AI Investments
Companies often struggle to efficiently transition promising AI proofs-of-concept to full-fledged production systems at scale. As per Gartner, close to 85% of AI projects fail or deliver subpar business value.
The coordinated approach facilitated by an AI CoE leads to far greater success in enterprise-wide AI adoption. Standardized processes for evaluation, development, and deployment enables seamlessly scaling of solutions. The AI CoE also enables reuse of datasets, models, and other assets to accelerate development cycles. Continuous monitoring of existing solutions keeps them aligned to evolving business conditions.
These best practices propagated by the AI CoE significantly increase the business impact derived from AI investments.
3. Development of Internal AI Talent Pipelines & Capabilities
One of the major struggles companies face with AI adoption is the scarcity of specialized skill sets needed – from data scientists to AI ethics experts.
The AI CoE nurtures the company‘s internal capabilities – people, processes, technology infrastructure – that form the foundation for AI success. Through training programs, hiring initiatives, and new partnership models with external experts, the AI CoE strengthens and expands cross-functional AI talent. It fosters hands-on education for citizen data scientists from business units.
These talent development efforts pay rich dividends as a critical mass of capabilities gets built over time. Companies become far less constrained in the types of AI applications they can experiment with and deploy.
4. Standardization of Tools, Architectures, Policies & Metrics
The AI CoE institutes standards and reusable frameworks for AI development that increase organizational efficiencies. This includes:
- Common data analytics platforms and model development tools: Reduces redundant investments made across units, while enabling collaboration
- Modular architectures: Components like natural language processing pipelines and computer vision modules can accelerate new solution development
- Policies for ethics, model validation, documentation: Ensures consistency in the governance and responsible use of AI across the enterprise
- Standard metrics and benchmarks: Allows accurate monitoring of progress in the AI journey compared to industry benchmarks
These technology standards and policy playbooks propagated by the AI CoE prevent inconsistent, ad-hoc approaches to AI. They set the stage for scalable, trustworthy and measurable AI – the foundations for success.
Leading Practices for Building an AI Center of Excellence
The components of an effective AI Center of Excellence itself varies based on a company‘s size, industry, AI maturity and specific objectives. However, these leading practices apply broadly for shaping a successful AI CoE:
Assess the Organization‘s AI Maturity
Gartner defines a 5-stage model for assessing AI maturity:
- AI-unaware: No adoption of AI technology or initiatives so far
- AI-aware: Experimenting with AI proofs-of-concept but limited production deployment
- AI-focused: Early stage of AI adoption and deployment for narrow applications
- AI-enabled: Rapid expansion of AI adoption with measurable benefits
- AI-led: AI capabilities deeply embedded across organization and offerings
Understanding the current AI maturity level helps shape the appropriate scope, priorities and structure for the AI CoE.
For example, a company in the early "AI-focused" stage may need the CoE to concentrate more on fundamental enablement issues – data infrastructure, skill development, constant advocacy for getting buy-in across reluctant business units.
On the other hand, a more mature "AI-enabled" firm may need to focus the CoE on specialized applications, complex integration scenarios, continuous model improvements, and rapid scaling of solutions across regions.
Assemble a Cross-Functional Team
Successful AI CoEs comprise experts spanning technology, business operations, and corporate strategy. Key roles include:
- AI leader/evangelist (CXO level): Provides executive direction and promotion for AI strategy
- Data/AI scientists: Technical expertise to evaluate use cases, develop prototypes and accelerate adoption
- Business operations: Help align AI initiatives to business objectives and surface challenges to adoption
- IT: Ensures backend technology infrastructure can support AI solutions at enterprise scale
- Ethics experts: Establish governance practices for responsible AI across different applications
- Solution architects: Help map systems and data flow requirements for integrated AI solutions
- Domain experts: Provide in-depth insights on industry-specific AI opportunities and requirements
- HR: Enable recruiting and development of internal AI talent pipelines
This matrix team structure fosters both technical excellence as well as business relevance in AI pursuits. It provides comprehensive oversight of the needs, possibilities and implications of AI for the organization – enabling balanced, ethical decisions.
Maintain Alignment to Business Objectives & KPIs
The priorities and initiatives of the AI CoE need to tightly cohere with overall business goals. The executive leadership plays a key role here – laying out focus areas aligned to corporate vision and clarifying broad objectives for AI.
These objectives then translate to tangible key performance indicators – both for the AI CoE unit as well as the business impact of AI pursuits enabled by the CoE. Example KPIs include:
- AI CoE KPIs: Number of new AI use cases identified, pilots conducted, production solutions launched; frameworks and policies established; employees completing AI skill development programs
- Business Impact KPIs: Improvements in metrics like revenue, customer satisfaction scores, operational efficiency achieved through AI adoption across units
Continuously tracking progress on these KPIs ensures the CoE stays strategically aligned – not operating in a vacuum but enabling business value acceleration through AI.
Promote AI Education & Set Realistic Expectations
Unrealistic expectations on AI‘s current capabilities often lead to inflated budgets, disappointment from failed pilots, and distrust towards new initiatives. The AI CoE helps demystify AI through education and market positioning:
- Basic AI literacy programs for employees help overcome the "magic" misconception – building awareness of what AI can and cannot achieve with current technologies
- Immersive hands-on workshops enable citizen data scientists across business units to directly experience AI model building
- CIO forums and leadership sessions provide an avenue for the AI CoE to directly clarify and align stakeholder expectations
Ongoing promotion of realistic perspectives on market trends and internal capabilities seeds an environment where AI can thrive – without the overpromising and underdelivery that plague many companies.
Develop Reusable Capabilities & Platforms
The AI CoE team focuses on building shareable infrastructure, platforms, and components that business units can easily build on top of – accelerating their development cycles. These horizontal capabilities drive scale while lowering costs over time.
Examples include:
- Data lake and analytics platforms for easy, unified access to clean, curated datasets
- Model factories with standardized workflows for common tasks – NLP, text analytics, forecasting etc.
- Governance templates with built-in checkpoints for ethics, robustness, and documentation needs
- Cloud-based model serving platforms that handle deployment, monitoring and updates
Documented APIs and modular design principles enable seamless integration and reusability of these CoE-sponsored platforms across AI solution development.
Over time, they evolve into the technology backbone upon which the enterprise can rapidly scale a thriving ecosystem of AI applications.
Collaborate Through Partnerships & Consortia
An inward-looking approach often limits innovation. Successful AI CoEs stay actively connected to the external ecosystem – through partnerships with advisors, startups, academia and industry groups.
For example, involvement in nonprofit AI consortia allows the CoE teams to gain early insight into emerging best practices, ethical frameworks for new technologies like generative AI, and collective analysis of vendor solutions.
Strategic experimentation partnerships with AI startups helps experience cutting-edge innovations years before enterprise products integrate them. Collaborations with research universities yield a talent pipeline and IP for advancing proprietary capabilities.
These external networks seeded by the AI CoE enhance visibility and position the organization as an innovative AI leader in its industry.
Key Roles Within an AI Center of Excellence
While the structure and scope of responsibilities adapt based on specific organization contexts, these leadership roles often emerge within mature AI Centers of Excellence:
AI CoE Leader
The CoE leader provides executive-level oversight – conveying business priorities, mobilizing resources, and spearheading high-level decision making frameworks for AI strategy and adoption. With CXO level influence, they ensure alignment between the AI CoE and wider organizational goals.
Data/AI Lead
The data/AI lead provides technical direction across build, buy and partner decisions regarding AI platforms, infrastructure, and solutions. Hands-on guidance on solution engineering, skill development, and adoption roadmaps underpins their role.
Enterprise Architect
Mapping the interconnections between business processes, data systems and software applications is crucial for AI integration and scale. Enterprise architects within sophisticated CoEs take the lead in shaping modular, scalable AI architecture blueprints aligned to the corporate technology stack.
Ethics & Policy Lead
Specialized leadership that helps establish organizational standards and guard rails regarding sensitive issues like data transparency, algorithmic bias, user privacy, and human oversight requirements for AI systems. They serve as the moral compass for the CoE, seeding ethical foundations into all pursuits.
Organizational Models for an AI Center of Excellence
AI CoEs can take different forms based on the context:
Centralized vs Decentralized
Small organizations may only need a lean centralized team overlooking all AI efforts. Larger corporates often require both a central strategic core that defines frameworks, as well as decentralized domain-focused AI teams within business units tailored to niche industry applications.
Evolutionary Stages
Early on, AI CoEs tend to focus on building fundamental capabilities – data infrastructure, skill development, identifying low hanging fruit applications. As AI maturity grows across the enterprise, the CoE shifts more towards continuous improvement initiatives, specialized use cases, and propagating best practices for scaling AI responsibly.
Physical vs Virtual
While it is ideal for core AI CoE members to be co-located, several rotational members from IT, analytics COEs or business units may only virtually intersect – collaborating on specific initiatives without full time association. Appropriate collaboration tools and meeting rhythms help drive productivity for such hybrid models.
These alternative models illustrate how AI Centers of Excellence adapt to enterprise contexts. There is no "one size fits all"; rather the CoE structure should stay strategically aligned and morph based on the evolving AI adoption story across the organization.
Key Challenges in Building an AI Center of Excellence
While critical for scaling AI‘s business impact, shaping successful AI Centers of Excellence poses several difficulties that demand proactive mitigation:
Cultural Resistance
The disruption brought by new technologies like AI inevitably creates skepticism and change inertia. Without voluntary participation, AI CoEs struggle to permeate solutions across siloed business units. Promoting participative decision making, highlighting benefits compellingly, and targeting easy early wins can help gradually cultivate engagement across the enterprise.
Budget & Ownership Ambiguity
Unclear budget allocation and conflicting priorities between shared CoE resources vis-a-vis dedicated business unit teams can bog organizations down in counter-productive power struggles. Defining shared cost/Shared benefit models upfront backed by leadership endorsement helps maintain alignment.
Talent Shortages & Attrition
AI talent remains scarce despite best hiring initiatives. Simultaneously, churn is common given work complexity, emerging remote models, and wage inflation. Securing executive commitment for talent-friendly incentives and retention programs right from the beginning helps the AI CoE remain robust over time.
Solution Sprawl & "Shadow AI"
Well-meaning users across units attempting small-scale experiments or workarounds also presents risk of fragmented efforts, costly duplication and non-transparent systems. Codifying "lines of control" for what business units can and cannot prototype independently is important, along with checkpoints and incentives promoting cross-team collaboration.
With foresight, patience and proactive change management, these impediments blocking AI Center of Excellence success can be overcome – unleashing transformation powered by coordinated AI deployment at enterprise scale.
The Time for AI Centers for Excellence is Now
AI Centers of Excellence represent a vital catalyst for companies seeking to extract transformational business value from AI investments in the coming decade. They provide the missing glue – leadership, talent, reusable technology building blocks, economies of scale – for elevating AI solutions from promising one-off successes to stable enterprise-grade systems generating lasting impact.
While the structure and scope must align with business context, the underlying imperative remains consistent – centralized coordination and scaled enablement of AI activities is pivotal for success. The time for experimentation is winding down; delivering AI that makes real business impact is taking center stage. Forward-looking companies are establishing mature AI Centers of Excellence right now to hit the ground running on this competitive frontier – one poised to massively reshape industries in the 2020s. Will you lead or be left behind?
