What Is an AI Center of Excellence (CoE)? How to Establish One?

An AI center of excellence (CoE) is a central hub, consisting of solutions architects, technologists, subject matter experts, and business managers, from which enterprises develop an overarching AI strategy and set a framework for the intelligent adoption and use of AI technologies. Vendors of AI-based systems and applications can also establish CoEs to help their customers deploy and optimize their latest AI advancements. Read on to learn about AI CoE benefits and the steps organizations can take to build one.

What Is an AI Center of Excellence (CoE)?

An AI center of excellence is a dedicated unit within an organization that centralizes AI expertise, resources, process oversight, and standards to drive the responsible adoption of AI across the enterprise. It consists of a cross-functional team that helps plan, build, and scale a variety of AI-based technologies. CoEs also set strategy, implement guidelines, and help the organization comply with relevant regulations and mitigate risks.

In addition, an AI CoE can oversee security and privacy controls, manage relationships with AI vendors, oversee staff training, help with the development of prompts, track and report on the latest AI advancements, and measure the impact of AI investments.

AI Center of Excellence Explained

Looking at just one industry, healthcare, a 2025 report from Menlo Ventures found that 22% of organizations had implemented domain-specific AI tools, a 600% increase over 2024. The rapid growth of AI adoption, also common in other industries, suggests that organizations need a central group to help ensure the technology is implemented efficiently, productively, and safely.

An effective AI CoE acts as both a hub and a coach. As a hub, it can provide common building blocks, such as reference architectures, large language model (LLM) catalogs, data sets, evaluation methods, and tools for the development, deployment, and monitoring of AI applications. As a coach, it helps teams across the enterprise identify opportunities to conduct pilots, move promising solutions into production with the appropriate controls, measure results, and ultimately add business value.

Without a central function, different departments often spend a lot of time resolving the same problems—model selection, access controls, privacy reviews, and the like. Team leaders can make decisions about AI use or usage restrictions that don’t align with other departments’ decisions. The CoE reduces duplication of effort and sets consistent guidelines for safety, quality, and performance. It also defines how AI agents will operate, which processes they can touch, what data they can read, and how people oversee and approve AI-driven actions.

For example, a hospital’s AI CoE could establish the strategy and framework for using AI agents to capture patient-clinician discussions and generate accurate draft notes, as well as propose best practices for reviewing those notes and protecting patient data.

Why Is an AI Center of Excellence Important?

An AI CoE helps organizations pursue AI opportunities consistently and transparently, with the goal of managing risk and ensuring that all stakeholders have visibility into what they can—and cannot—do with the technology. It creates shared standards for quality, security, and compliance, consolidates AI tool selection to reduce cost and complexity, and delivers consistent training. These centers also work with organizational leaders to determine which processes would benefit most from the use of AI agents, how the agents will be monitored, and how outcomes will be tracked and assessed.

Benefits of Establishing an AI Center of Excellence

The main benefits of establishing an AI CoE can include faster adoption of the technology, a greater return on investment, and minimized costs and risks. Here we lay out the potential benefits in more detail:

• Faster time to value. Reusable components, templates, and design patterns can help teams move from idea to pilot to production more quickly.
• Strong data governance and security. Setting up controls for privacy, security, data residency, and regulatory compliance can help reduce risk.
• Higher quality and reliability. Standardized evaluations, such as test sets and red teaming, can improve model performance over time. Test sets are subsets of data used to evaluate machine learning models. Red teams simulate hostile attacks on an AI system to uncover security weaknesses.
• Cost control. Centralized contracts, consolidated platforms, and shared inference capacity (which refers to an AI system’s ability to use its learned knowledge to generate increasingly relevant outputs) can reduce overall spending and improve performance.
• Talent development. Role-based training and the ability to work with people who share a focus on the best uses of AI can raise skill levels across the entire organization, including IT, sales, marketing, finance, supply chain, HR, and business operations.
• Better decision-making. The ability to compare various AI proposals against each other helps organizations prioritize use cases with a strong ROI and acceptable risk profile while avoiding duplication of effort.
• Safe use of AI agents. AI agents should inherit the data protection and identity management measures already in place for a given application. Single sign-on, role-based access controls, and logging and audit capabilities can help limit risk and prevent unintended actions.

When to Establish an AI Center of Excellence

Most organizations will experiment with AI before they establish an AI CoE—for example, by encouraging staff to use generative AI to draft job descriptions and performance evaluations. The next step typically is to identify high-value use cases that require executive buy-in. Here are five signs that your organization might need an AI CoE:

• You have multiple AI pilots competing for resources, each with unclear standards.
• Your organization is subject to strict regulatory and/or data residency requirements.
• Your organization’s teams need a secure way to build AI tools and agents.
• Leadership wants a single view of AI investments, their value, and their potential risk.
• Your organization plans to expand its AI use from discrete proofs of concept to enterprise-scale capabilities.

How to Build an AI Center of Excellence?

Defining the vision and goals for an AI CoE is only the first step. Here are the issues to consider in setting one up and the key responsibilities and skills that CoE team members need to have.

1. Executive sponsorship and clear mandates
   Without executive support and clear direction, an AI CoE has little chance of success. Lay out the proposed scope of the organization’s AI use, how different departments should collaborate on AI projects, what kinds of training staff will need to undergo, and how results will be tracked and measured. Furthermore, establish the CoE’s level of authority, including the role of employees across the organization in implementing and using AI.
2. AI adoption policies
   Define areas of responsibility for AI security, privacy, legal issues, risk measurement, and data management. Set up a working group to develop policies for AI investment and adoption. Some of the issues that the working group should consider include model and data use, data retention, third-party tool selection, and procedures staff can follow if an AI-related incident, such as a privacy breach, occurs. Make sure that all staff are aware of their responsibilities.
3. Reference architectures and platform strategies
   The CoE is responsible for establishing best practices for developing and deploying AI models, monitoring the consumption of AI resources, and setting and enforcing standards for API and SDK use. Security, regulatory compliance, and data privacy are all components of a robust platform strategy capable of supporting multiple AI workloads and business use cases. Integrating AI capabilities into existing IT systems can streamline data flow, simplify governance, and improve operational efficiency. The system architects responsible for this effort need access to LLMs, retrieval-augmented generation (RAG) technologies, and machine learning operations (MLOps) tools.
4. AI models and prompt frameworks
   To get consistent results from their AI models, organizations can document the characteristics of the models they choose, and weigh trade-offs such as accuracy, the time needed to produce results, costs associated with training, and how well the model processes data. Meantime, establishing prompt libraries helps AI users save time and generate consistent output from their queries. Strong guidelines can be used to guard against prompt injection risks, whereby a malicious prompt is used to trick the AI into performing unintended or harmful actions.
5. Data management practices
   It’s a best practice to inventory and map all data sources that will be used by an AI tool and classify their sensitivity, from public to highly restricted. Collect and retain only the data needed for a specific purpose to reduce data exposure risk. When selecting data for AI training, consider replacing original data with realistic but fictional values—using generic customer names instead of real ones, for example.
   
   Especially where regulations dictate, give customers control over how their personal data is used in AI applications. Establish clear policies to maintain data quality, consistency, and traceability throughout the AI lifecycle. Cataloguing data assets and changes that occur as the data is affected by user requests can help organizations ensure that their AI activity can be audited reliably.
   
   CoEs can help their organization develop robust data practices around AI to protect sensitive information and comply with privacy regulations. Proper data management helps ensure that AI models are accurate and reliable. It also supports transparency and accountability for auditing and troubleshooting. Demonstrating responsible AI and data management practices can enhance a company’s reputation with customers, partners, and regulators.
6. Project intake and prioritization
   AI CoE teams need to establish a process for employees to submit ideas for AI projects and to assess those proposals. Require a business case that lays out the challenge to be solved, anticipated outcomes, how those outcomes will be measured, and the use case’s potential risk profile. Assess opportunities based on their potential ROI and alignment with organizational goals, the resources they’ll require, and their level of risk. Track the progress, outcomes, and best practices for each initiative, as well as challenges encountered and overcome.
7. Policies and tooling for AI agents
   CoE members can set up frameworks defining how an AI agent will operate, which agents will connect to other agents, and how they will work together to perform tasks. They also can set up least-privilege access to enterprise systems, a practice that helps ensure that an agent can access only the minimum set of systems needed to perform its tasks. Meantime, the CoE can establish rules that will help ensure the organization’s use of AI is safe and aimed at meeting organizational goals. Real-time guardrails can block harmful content, halt misuse, and track spending to prevent cost overruns.
8. MLOps and LLMOps implementation
   MLOps (machine learning operations) is the process of streamlining and automating machine learning workflows so AI developers can create scalable deployments with consistent model performance. LLMOps (large language model operations) outlines the processes, tools, and workflows for managing the LLM lifecycle.
   
   A component of MLOps and LLMOps is the development of continuous integration/continuous delivery (CI/CD) pipelines for AI models. For example, the AI CoE can establish best practices for AI model versioning, monitoring and feedback loops, and prompt testing and evaluation. Other components are model registries, where AI developers can centralize and manage version control for the complete ML lifecycle, including model metadata and audit trails.
9. Role-based employee training
   Going forward, it’s likely that every employee will develop, implement, or use AI tools. Training and change management programs apply to every role, including senior executives, the data scientists and engineers who create AI models, and the end users and support staff who need to be able to recognize the signs of AI misuse or AI drift, which occurs when an AI model loses its original context or priority. Offering users hands-on labs and reference implementations can help standardize behavior and encourage adoption.
10. AI value measurement
    Piloting AI models gives teams the opportunity to prove the value of a specific solution in a controlled setting by measuring results. Successful models can be scaled up for broader use. Pilot programs need to align with high-value business use cases and have a strict time frame to prevent feature creep. Project postmortems allow CoE leaders to determine whether an AI solution should be rolled out to the broader organization or abandoned.
    
    Reusable assets such as prompts and prebuilt models can reduce costs and streamline model behavior. CoEs can help assess whether an organization’s AI use is meeting broader corporate objectives. Finance people on such teams can measure ROI, developers can track model lifecycles and output quality, and managers can oversee model adoption and risk incidents.
11. Change management and communications strategies
    Once the CoE has established the organization’s overall AI strategy and the steps to drive that strategy, communicating with employees is the next step. Employees need to understand the motivation behind using AI, the benefits the organization expects to gain, and what they can—and cannot—do with AI tools. With the introduction of any new technology, it’s important to educate users on responsible use, including the technology’s limitations and how to protect personal data.
12. Procurement and vendor management
    Create a centralized process for evaluating third-party vendors and their tools, focusing on security, privacy, where data will reside, who owns any IP generated by the tool, and any restrictions on how the tool can be used within the organization. Develop standardized contract terms.
13. AI cost tracking and management
    The AI CoE needs to track and manage the costs of AI use. Those include the cost of the tools themselves, the cost of developing or licensing AI models and training them, the cost of the underlying computer processing and data storage, and the costs associated with monitoring AI systems and their performance. Showback financial models are used to make teams aware of the financial impact of their resource consumption. Chargeback models are used to bill teams directly for their AI usage.
14. Risk and incident programs
    This step can’t be an afterthought. CoE teams need to make it easy for AI users to identify and report risks quickly and without fear of consequences. Five of the most common risk categories are data leakage, in which an AI system inadvertently exposes private information; safety violations, which are risks that can cause harm to users, such as incorrect diagnoses or treatment recommendations made by a healthcare AI agent trained on inaccurate or incomplete data; model drift, wherein an AI tool’s performance degrades over time; supply chain risks, which are vulnerabilities introduced by third-party tools or data; and agent misbehavior, which can occur when autonomous AI agents operate in unanticipated ways or engage in deceptive behavior.
    
    CoEs can run tabletop exercises, which simulate real-life security incidents, to assess the strength of an organization’s plans for responding to AI-related problems, such as security breaches or intellectual property infringement.

Practical Considerations

Consider the following when building an AI CoE:

• Pick the right first use cases, with a focus on promising business opportunities with measurable outcomes, rich sources of data, and low or moderate risk.
• The CoE can set and communicate overall organizational goals, while product teams are empowered to deliver products that meet those goals.
• Keep people in control. Especially when implementing AI agents, there should be clear approval processes for high-impact actions and safety mechanisms that allow humans to pause or override an agent’s actions.

Accelerate AI Innovations with Oracle

The Oracle AI Center of Excellence for Healthcare brings together Oracle’s AI, secure cloud, and data management expertise and the expertise of top healthcare consultants, other healthcare technology providers, and some of the world’s largest medical centers. As organizations look to maximize the benefits of AI and reduce risk, the center can help them implement practical solutions for enhancing patient care, improving operational efficiency, driving better workforce productivity and satisfaction, and automating financial processes.