# AI CoE Setup Checklist: 32 Steps to a Center of Excellence That Ships > Source: https://netray.co/tools/ai-center-of-excellence-checklist > Publisher: Netray (netray.co), AI and integration engineering for ERP and manufacturing systems. > Section: Free Tools ## Short answer A 32-point checklist for standing up an AI Center of Excellence that delivers working systems instead of slide decks, covering charter, funding, platform, governance, and adoption. ## Category ai-automation ## Tool **Kind:** checklist **Groups:** #### Charter and mandate **Items:** **Text:** A written charter states what the CoE decides, what it advises on, and what it does not touch **Critical:** true **Text:** An executive sponsor at C-level is named and meets the CoE lead at least monthly **Critical:** true Success metrics are defined in business terms, not model or activity metrics The operating model is chosen deliberately: centralized, federated, or hub and spoke Boundaries with IT, data, security, and business units are documented and agreed A published intake path tells the business how to bring an idea to the CoE A sunset or evolution plan defines what the CoE looks like once capability is distributed #### Funding and portfolio **Items:** **Text:** The CoE has a standing operating budget separate from individual project funding **Critical:** true Use cases are scored on value and effort using a consistent, published method A portfolio view balances quick wins, platform investments, and longer-horizon bets Run cost for every deployed system has a named owner and an ongoing budget line Realized benefits are tracked after go-live, not just forecast in the business case A stage-gate exists to stop use cases that are not working, with a documented kill criterion #### Platform and engineering **Items:** **Text:** A reference architecture defines the approved pattern for building AI solutions **Critical:** true **Text:** Shared components exist for retrieval, evaluation, logging, and authentication **Critical:** true Model hosting decisions are made explicitly: cloud, on-prem, or air-gapped per data class Prompts, model versions, and configurations are under source control with staged releases An evaluation harness with golden datasets runs automatically on every change Environments are separated into development, test, and production with promotion controls Cost per use case is measured and visible to the teams that generate it #### Governance and risk **Items:** **Text:** AI risk tiering is defined with proportionate controls mapped to each tier **Critical:** true An inventory of all AI systems in use is maintained and refreshed quarterly Data classification rules for AI inputs are enforced technically, not only in policy Security review is embedded in the delivery path rather than bolted on at the end Regulatory obligations are mapped, including EU AI Act, NIST AI RMF, and sector rules like ITAR or CMMC AI incident categories and response runbooks are defined and rehearsed #### Talent and adoption **Items:** **Text:** Core roles are filled: product owner, data engineer, AI engineer, and domain expert **Critical:** true **Text:** Business domain experts are formally allocated time, not borrowed informally **Critical:** true A skills plan covers hiring, upskilling, and partner augmentation with named gaps Champions are identified in each business unit and are practitioners rather than managers Role-based enablement exists for end users, builders, and leaders separately Adoption is measured per deployed system and reviewed with the business owner **Scoring Note:** Check an item only if it is genuinely in place today, not planned. The eight critical items are the ones whose absence causes CoEs to stall within their first year: an unclear mandate, no standing budget, no reference architecture or shared components, no risk tiering, and no dedicated core team. Above 75% with all critical items checked usually indicates a CoE that will ship rather than advise. ## Content **Intro:** This free AI CoE (AI Center of Excellence) setup checklist gives CIOs, transformation leaders, and newly appointed AI leads thirty-two concrete steps to stand up a CoE that delivers working systems rather than strategy decks. The items are grouped into charter and mandate, funding and portfolio, platform and engineering, governance and risk, and talent and adoption. Eight are marked critical because they are the failure points that stall a new CoE within its first year. Work through the list, mark only what is genuinely in place today, and use the unchecked critical items to sequence your first ninety days. **Sections:** #### Why most AI CoEs stall in year one **Body:** New AI functions rarely fail on talent. They fail on mandate and money. A CoE that can advise but not decide gets routed around by business units in a hurry, while one that decides everything becomes a bottleneck teams design their way past. Funding is the second trap: when the CoE has no standing budget, every activity must attach itself to a project, so nothing gets built that serves more than one use case. Platform investment is exactly that kind of work. The third trap is measuring activity, counting pilots launched and people trained, rather than measuring deployed systems that a named business owner still uses six months later. #### Choosing an operating model **Body:** Three operating patterns dominate in practice and each fits a different kind of organization, depending on how much engineering maturity already exists inside the business units. Pick one deliberately and write the choice into the charter, because ambiguity about who builds and who decides produces most of the friction a new CoE experiences during its first year. **Bullets:** - Centralized: the CoE builds everything. Fastest to establish standards, becomes a bottleneck as demand grows. - Federated: business units build with CoE-set standards. Scales well but requires real engineering maturity in the units. - Hub and spoke: the CoE owns the platform and hard problems while embedded practitioners deliver locally. The most common landing point. - Whichever you choose, the platform components must be centrally owned or every team rebuilds retrieval, logging, and evaluation. #### Sequencing your first ninety days **Body:** Do not attempt all thirty-two items at once. In the first month, close the charter, sponsor, and budget items, because everything else depends on them and they take the longest to negotiate. In the second, stand up the reference architecture and the first shared components while delivering one real use case, so the platform is shaped by actual delivery rather than by theory. In the third, formalize intake, risk tiering, and adoption measurement using the evidence the first use case produced. A CoE that has shipped one genuinely useful system by day ninety earns the credibility to govern; one that has produced only frameworks does not. #### How Netray helps stand up an AI capability **Body:** Netray builds AI Centers of Excellence the practical way: by delivering a first production use case alongside your team and extracting the platform components, standards, and governance from real delivery. We bring the reference architecture, evaluation harness, retrieval and logging components, and the security patterns already proven in ITAR and CMMC environments, so your team is not designing them from a blank page. For manufacturers on Infor SyteLine, Infor LN, M3, ServiceMax, or Salesforce, we connect the platform to the systems where the value actually sits. The goal is explicit: your people run it without us within a defined period. **Cta:** Talk to Netray about standing up your AI Center of Excellence with a working first use case inside ninety days. **Faq:** #### How do you set up an AI CoE? **Answer:** Start with the mandate items: a written charter, a C-level sponsor, and a standing operating budget, because everything else depends on them. In the second month, stand up the reference architecture and the first shared components while delivering one real use case. In the third, formalize intake, risk tiering, and adoption measurement from that delivery. Choose centralized, federated, or hub and spoke deliberately and write it into the charter. The checklist above covers all 32 items, and the eight marked critical are the ones that stall a new CoE. #### How large does an AI Center of Excellence need to be? **Answer:** Smaller than most organizations assume. A functioning core is typically four to six people: a product owner, a data engineer, one or two AI engineers, and dedicated domain expert time. What matters more than headcount is that the time is genuinely allocated rather than borrowed. CoEs staffed with people who each contribute twenty percent of their week alongside their day job consistently underdeliver, because AI delivery requires sustained focus through data work that resists part-time attention. #### Should the CoE build solutions or enable others to build them? **Answer:** Both, in sequence. Early on the CoE must build, because standards written before anything has been delivered are guesses and because credibility comes from working systems. Once two or three use cases are live and the platform components exist, shift toward enablement so business units deliver on the shared foundation. The signal to shift is when demand exceeds CoE capacity and the reference architecture has survived contact with more than one real problem. #### How should we measure whether the CoE is working? **Answer:** Measure outcomes, not activity. Useful metrics include the number of AI systems in sustained production use with a named business owner, realized benefit tracked against the original business case, time from intake to production for a low-risk use case, and reuse rate of shared platform components. Avoid counting pilots launched, people trained, or ideas in the backlog, since all three can rise indefinitely while nothing reaches production. ## Search terms AI center of excellence checklist, how to set up an AI CoE, AI CoE operating model, enterprise AI team structure, AI center of excellence charter, AI CoE governance, building an internal AI capability --- Talk to an engineer: https://netray.co/contact?intent=erp_ai | Machine catalog: https://netray.co/agent/catalog.json