Just as the internet revolutionized commerce and mobile changed how we work, AI is a seismic shift that touches every corner of the enterprise. It redefines workflows, customer experiences, and decision-making. As businesses across industries look to integrate AI into their core processes, many ask the same question: Where do we start? If your organization is large enough, you may want to consider creating an internal AI Center of Excellence (CoE).
In this blog, we will cover the basics of an AI CoE, how to create one, who should be in it, and how to measure success.
Role of an AI Center of Excellence
A successful transformation requires a coordinated structure that can scale with the business. This is where a Center of Excellence (CoE) comes in.
A Center of Excellence is a specialized group of SMEs that drives innovation, collaboration, and best practices across an organization in a specific domain (e.g., AI). It becomes the bridge between executive intent and execution and ensures that pilots don’t stay pilots, that tools are adopted responsibly, and that every initiative contributes to meaningful business impact. It also functions as a governing and support hub, a source of strategic direction to align with company goals and a resource for standardization, guidance, and continuous improvement.
A successful CoE doesn’t just support implementation—it creates the repeatable processes, governance, and culture needed to sustain innovation. It also helps an organization ensure its technology strategy evolves with emerging trends. In the case of AI, an AI Center of Excellence would be responsible to:
1. Strategy & Alignment
| 2. Governance, Risk & Ethics
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3. Technology & Infrastructure
| 4. People & Change Enablement
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Start with mission before models
Before starting an AI CoE, organizations must anchor their AI journey in their core mission. That means asking themselves why your business exists. This will help them align their AI goals with business outcomes.
Once the company knows its AI goals, it can build a charter for their AI Center of Excellence. A charter is a foundational document that articulates the purpose, scope, and strategic objectives of the CoE. It should outline why the CoE exists, what problems it aims to solve, and how its efforts will create measurable business value.
It should also be tightly aligned with the broader organizational strategy, whether driving innovation, improving operational efficiency, enhancing customer experience, or enabling data-driven decision-making.
A well-defined charter also sets expectations for governance, accountability, and collaboration across departments. This will help to ensure that the CoE operates as a strategic enabler rather than a siloed technical function. Ultimately, it becomes the north star that guides decision-making, investment, and execution throughout the AI journey.
Identify focus areas
Once the charter is in place, the next step is to identify the core focus areas where the AI Center of Excellence will drive impact. This involves selecting specific domains and technologies that align with the organization’s strategic objectives and current level of AI maturity. The organization can explore two key lanes of AI adoption:
- Commodity AI—These are capabilities built into existing tools, like Microsoft 365 Copilot or AI-powered features in Salesforce or LEAi by LearnExperts. They can often be quickly adapted to improve department productivity.
- Mission-driven AI – These initiatives are highly tailored to a company’s business model and value chain. They might involve building custom machine learning models for supply chain optimization, customer behaviour prediction, or operational forecasting. These projects require deeper investment but often yield higher ROI and competitive differentiation.
Both lanes are essential. A mature AI strategy will integrate commodity tools for broad enablement and mission-driven systems for targeted transformation. For example. focus areas might include generative AI for content creation and automation, agentic AI for building intelligent systems that can take autonomous actions, and predictive AI for forecasting and decision support.
In addition to adopting tools, the AI CoE may also consider adding the following focus areas:
- Prompt engineering to leverage large language models (LLMs) effectively
- Academic research to stay at the forefront of innovation and contribute to the broader AI community
- Applied machine learning to ensure real-world use cases are tackled with scalable, production-ready solutions.
By clearly defining these technical and functional domains, the CoE can allocate resources, design training pathways, and build expertise that supports sustainable, high-value AI adoption across the organization.
Who should be in AI Center of Excellence
A high-performing AI CoE brings together a cross-functional team that blends technical expertise, business insight, and operational leadership. Below are the key roles to consider including in an AI CoE. Remember: Multiple roles can be filled by a single individual if they have the right skills, expertise and experience!
Technical staff
Data scientists lead the charge by developing machine learning models, testing hypotheses, and generating insights that drive decision-making. Machine learning engineers often support them, who take models from notebooks to production by building scalable pipelines and integrating them into applications.
Data engineers are essential for preparing, cleaning, and orchestrating data, ensuring high-quality and timely inputs for model training. For organizations working at the forefront of innovation, AI researchers can explore emerging algorithms and push boundaries on complex problems. Prompt engineers are increasingly vital in a generative AI environment, crafting well-structured prompts that improve performance, accuracy, and user experience with LLMs like ChatGPT or Microsoft Copilot.
AI systems are only as strong as the infrastructure they run on. Cloud or IT architects provide the backbone by designing secure, scalable environments—often in platforms like Azure, AWS, or GCP—that support data-intensive AI workloads. Security and compliance officers ensure that AI development meets data protection regulations, cybersecurity standards, and internal governance policies. Their role becomes especially critical when sensitive data, such as personal health information or customer records, is involved.
MLOps or DevOps engineers round out this group by building automated systems for deploying, monitoring, and updating models. They ensure that AI initiatives don’t stall after the pilot phase but continue to deliver value in production.
Academic researchers or external advisors can provide advanced technical insight or contribute to long-term R&D partnerships.
Business staff
Business analysts and domain experts are critical in surfacing high-value use cases, providing real-world context, and translating business problems into AI opportunities. Product owners or managers help shape the direction of projects, define success metrics, and act as liaisons between technical teams and business units. They ensure that the solutions developed are usable, scalable, and deliver ROI.
Meanwhile, innovation or digital transformation leaders help ensure AI initiatives are embedded into broader strategic goals. Change managers and training leads guide organizational learning and support the cultural shifts required for widespread AI adoption.
Strong leadership and clear governance are vital for sustaining AI efforts. Every AI CoE should have an executive sponsor—often a CTO, CIO, or Chief Data Officer—who champions the initiative, secures funding, and ensures alignment with enterprise strategy.
An AI governance lead is also essential for crafting policies around responsible AI use, including fairness, transparency, accountability, and regulatory compliance. This role becomes increasingly important as organizations scale AI and face more scrutiny around ethical use. Finally, a program manager ensures that projects are well-coordinated across teams, deadlines are met, and stakeholders remain aligned and informed throughout the AI project lifecycle.
Depending on your organization’s goals and maturity, additional roles can add significant value. Legal counsel helps navigate data privacy, intellectual property, and AI compliance risks, particularly in regulated industries like finance or healthcare.
Build a maturity model
To guide your AI CoE’s journey, building a maturity model that outlines clear stages of AI adoption and operationalization is essential. This model helps track the transition from decentralized experimentation, where individual teams or departments run isolated AI initiatives, to a center-led, fully integrated approach where AI is embedded in core business processes and aligned with enterprise strategy.
Early-stage maturity may include proof-of-concept (POC) efforts focused on testing feasibility and generating initial ROI. This then evolves into a minimum viable product (MVP) phase, where solutions are deployed for limited use and business value is validated.
The final stage is a fully operationalized environment, where models are production-ready, monitored, and continuously improved. Each phase should be evaluated against key attributes such as data readiness, model performance, deployment infrastructure, monitoring systems, and business adoption.
To quantify maturity, use a scoring system that assesses progress across core components like data pipelines, model lifecycle management, governance, and user engagement. Introducing a practical scoring rubric (ranging from 0 to 7) to evaluate these areas will allow teams to identify gaps and prioritize next steps objectively.
This structured approach ensures that the AI CoE scales efficiently and evolves responsibly, with a clear view of what “better” looks like at each journey stage.
How to measure success and benefits
An AI Center of Excellence is not a one-time project—it’s an evolving capability that should drive ongoing value across the organization. Measuring success starts with the objective outlined in the charter, which will help you build quantitative metrics and qualitative impact. Here are examples of dimensions to evaluate the success and benefits of your AI CoE:
- Best practice adoption – One of the core functions of an AI CoE is to create standards, playbooks, and reusable frameworks. Success can be measured by the number of best practices published and actively adopted across teams. This includes templates for prompt engineering, ethical AI guidelines, and standardized model deployment processes.
- Innovation pipeline – A thriving CoE should act as a catalyst for AI innovation. Track the volume and quality of AI use cases and ideas from business units. Are new opportunities being surfaced regularly? Are those ideas being validated and prioritized through a formal pipeline?
- Strategic impact – Is the CoE influencing big-picture thinking? Consider how AI initiatives inform strategic decisions, unlock new business models or enable differentiation. Regular executive reviews should assess how aligned the CoE is with enterprise goals and how it’s helping to translate vision into action.
- Deployment speed and model performance – Evaluate the speed and quality of AI model development and deployment. Metrics here might include time-to-production for machine learning models, success rates of pilots moving to production, or improvements in model accuracy, precision, and business relevance.
- Internal adoption of AI tools – Measure how widely and effectively internal AI tools (e.g., copilots, chatbots, or recommendation engines) are used. Track usage metrics, employee satisfaction, and support requests to gauge adoption and usability. Low adoption may indicate a need for better enablement or user training.
- Process standardization and efficiency gains – Look at the CoE’s role in streamlining AI workflows. Are data pipelines more reliable? Are models easier to maintain? Has MLOps maturity improved? The more standardized and repeatable your AI processes become, the more scalable your impact.
- Training and upskilling outcomes – AI transformation is ultimately a people transformation. Track how many employees have been trained in AI-related tools, how many have adopted prompt engineering or LLM literacy, and how skill levels are improving over time. Monitor completion of training programs and tie outcomes to business capability.
Foster continuous learning and innovation
Beyond technical execution, an AI CoE creates the space and structure to explore new technologies, democratizes access to AI tools across the enterprise, and helps employees build the skills needed for the future of work. As markets shift and technologies evolve, your CoE becomes the agility engine, enabling your organization to adapt, respond, and lead confidently.
Ultimately, the value of an AI CoE isn’t just in the models it deploys—it’s in the mindset it instills, the standards it sets, and the transformation it makes possible. By institutionalizing best practices, empowering cross-functional collaboration, and focusing relentlessly on impact, your AI CoE becomes a launchpad for lasting innovation and competitive advantage.
FAQ about xAPI
What is xAPI used for?
xAPI is used for tracking and recording learning experiences—especially those that happen outside traditional Learning Management Systems (LMSs). It’s commonly used in e-learning, training programs, and corporate learning environments. xAPI is used to:
Track learning activities online and offline (e.g., reading a PDF, watching a video, attending a webinar, or using a mobile app).
Record experiences in the format of “Actor – Verb – Object” (e.g., “John completed Safety Training Module”).
Send these records to a Learning Record Store (LRS), which stores and manages the data.
Support interoperability between different learning platforms.
Common use cases are:
Monitoring compliance training (e.g., OSHA, HIPAA).
Measuring engagement across different learning tools.
Gathering insights on learner behavior and performance.
Integrating with virtual reality (VR), simulations, or mobile apps.
Replacing or enhancing older standards like SCORM.
What are xAPI examples?
Common xAPI use cases are:
Monitoring compliance training (e.g., OSHA, HIPAA).
Measuring engagement across different learning tools.
Gathering insights on learner behaviour and performance.
Integrating with virtual reality (VR), simulations, or mobile apps.
Replacing or enhancing older standards, like SCORM.
Is xAPI still relevant?
In the age of artificial intelligence, xAPI is not only relevant but increasingly important. Its core strength lies in its ability to collect detailed, structured data about learning experiences across a wide range of environments—online, offline, formal, and informal. This granular data becomes the fuel that powers AI systems in education and training.
xAPI is also uniquely suited to support the interoperability required in today’s fragmented digital learning ecosystem. AI-powered learning tools are now available across various platforms, including Learning Management Systems (LMSs), mobile apps, simulations, chatbots, and video learning platforms. Because xAPI can track and unify data from all these different tools into a single Learning Record Store (LRS), it enables AI algorithms to analyze a learner’s complete journey, not just isolated events within a single platform.
AI also enhances the value of xAPI by interpreting its data in more meaningful ways. While xAPI captures what happened (e.g., “Jordan completed a module”), AI can explore deeper questions such as why a learner may be struggling or what kind of content would help them progress. This synergy supports predictive analytics, intelligent feedback, and personalized learning paths—capabilities that are quickly becoming expected features in modern learning environments.
Finally, by feeding xAPI data into AI models, platforms can adjust the difficulty, pacing, or type of content in real-time to suit each learner’s needs. This dynamic approach is far more effective than static content delivery, making learning more engaging and effective.
What is xAPI vs SCORM?
| Feature | SCORM | xAPI (Tin Can API) |
|---|---|---|
| Release Year | 2001 (SCORM 1.2/2004) | 2013 |
| Data Captured | Limited (course completion, pass/fail, score, time spent) | Extensive (any learning activity or experience) |
| Tracking Scope | Only in-browser & LMS-based content | Tracks activities anywhere (online, offline, apps, simulations, etc.) |
| Offline Tracking | ❌ Not supported | ✅ Fully supported |
| Device/Platform Flexibility | Web-based, desktop LMS only | Any device: mobile, VR, simulations, etc. |
| Learning Record Storage | LMS only | Learning Record Store (LRS), separate from LMS |
| Interoperability | Limited to LMS and SCORM packages | Highly flexible across systems |
| Verb Structure | None | Actor–Verb–Object (e.g., “Alex completed module”) |
| Extensibility | Very limited | Fully extensible and customizable |
| Modern Use Case Fit | Basic eLearning | Adaptive learning, analytics, AI integration |
When to choose SCORM over xAPI?
Choose SCORM if you’re working in a traditional LMS environment with simple eLearning courses, and you don’t need to track anything beyond completion or quiz scores.
Choose xAPI if you want to support learning beyond the LMS, track diverse types of learning experiences (including offline), enable analytics, integrate with AI, or support adaptive/personalized learning.
LEAi for successful AI Center of Excellence
LEAi by LearnExperts is as a powerful enabler, particularly in the realm of learning and development. Here’s five reasons why it aligns with and supports the core responsibilities of an AI CoE:
- LEAi empowers organizations to rapidly develop training content by transforming existing materials, such as documents, presentations, and videos, into structured learning programs. This capability facilitates the swift dissemination of AI knowledge and best practices and promotes widespread adoption of AI.
- With its built-in learning frameworks, LEAi ensures that all training content adheres to established instructional design principles so the AI CoE can deliver consistent and high-quality AI training programs.
- LEAi’s AI-driven approach enables the rapid creation of comprehensive training materials to foster continuous learning and upskill employees in AI competencies.
- By automating the transformation of subject matter expert content into training, LEAi ensures that as new AI projects are developed, corresponding training materials can be quickly produced to support implementation and adoption.
- LEAi’s structured approach to content creation integrates assessment tools and learning objectives. This allows the AI CoE to measure the effectiveness of training programs.
LEAi is a strategic tool within an AI Center of Excellence. It streamlines the development of AI training programs, ensures consistency and quality, and supports the organization’s broader AI adoption and governance objectives. Contact us to learn more about how LearnExperts can support your AI Center of Excellence.
