The operating model
Centrally governed, decentrally delivered
The CoE builds and governs the reusable components once. Domain teams self-serve the agent development lifecycle inside those guardrails.
The path
What your CoE owns in the first six months
The platform components already exist. The work is sequencing which standards you set centrally and when domain teams take over delivery.
Day 90: standards that hold
Approved models, prompts, tools and agents registered centrally. Routing rules, guardrails and PII redaction applied to every request that matches, not reviewed project by project.
What is an AI Center of Excellence?
A central team that sets the standards, tooling, and guardrails for AI across the organization, so domain teams do not each solve the same problems from scratch. The common failure mode is that it becomes an approval queue rather than a set of reusable components other teams build on.
Should our CoE be centralized or decentralized?
Both, on different axes. Governance, infrastructure, and reusable components are centralized so standards hold. Delivery is decentralized so the teams closest to the domain build the use cases. Orq.ai is the control plane that lets you run both at the same time.
How do we stop the CoE becoming a bottleneck?
Give teams self-service access to approved components instead of an approval queue. When models, prompts, tools, and agents are registered centrally and callable by any team, the CoE reviews the guardrails once rather than reviewing every project.
How do we get shadow AI under control?
Route every request through one gateway. Once traffic runs through a single governed API, you can see which teams use which models, what it costs, and where policy is being breached, without asking teams to self-report.
What does the CoE own, and what do domain teams own?
The CoE owns the gateway, observability, governance, and the registries of approved agents, tools, prompts, and skills. Domain teams own building, deploying, and optimizing their own use cases on top of those components.
How long does it take to stand this up?
The platform components are already built, so the technical work is connecting teams to the gateway and turning on observability. Orq.ai is designed to be operational in weeks, not quarters. The longer work is organizational: agreeing which standards are set centrally and which decisions stay with the domain teams.
How do we measure the ROI of an AI Center of Excellence?
Measure the platform, not the pilots. Track time from idea to production agent, the share of AI spend running through the gateway, reuse rate of registered components, and cost per successful task. A CoE that cannot show those numbers ends up being judged on individual use cases instead.
What KPIs should an AI CoE track?
Five that survive a board review: time to first production agent, percentage of AI traffic under governance, number of teams shipping without CoE involvement, policy breach rate, and cost per successful task. The gateway and observability layer report the last four directly.
Do we need an AI CoE if we already have a Cloud Center of Excellence?
Extend the one you have rather than standing up a second. The operating model carries over; the control problems do not. Models change weekly, outputs are non-deterministic, and cost scales with usage rather than instances, so you add an AI-specific control plane on top of the cloud governance you already run.
Who should be on the AI CoE team?
Smaller than most expect. A named lead with executive sponsorship, one or two platform engineers who own the gateway and observability, someone accountable for governance and risk, and rotating domain experts from the teams actually shipping use cases. Delivery capacity belongs in the domain teams, not the CoE.
How does this map to ISO 42001 and the NIST AI RMF?
Both ask for the same primitives: an inventory of AI systems, documented risk assessment, logging and traceability, human oversight, and periodic review. Running every request through one gateway produces the inventory and the audit trail as a by-product, which is the part teams usually assemble by hand.
Should we build our own AI control plane instead?
Building your own gateway, observability and governance is one to two quarters of platform engineering before the first use case ships, then permanent maintenance as providers and regulations change. Buying makes sense when your differentiation is the use cases rather than the plumbing.
How do we prioritize AI use cases across business units?
Run one intake with consistent criteria: business value, data readiness, technical feasibility and risk class. Publish the backlog so teams can see where their request sits. The CoE scores and sequences; the business owns the value case. Without a single intake, priority goes to whoever escalates loudest.
What does it cost to run the CoE on Orq.ai?
Usage-based. The platform meters what you actually run, and because every team goes through one control plane you get cost attribution by team, model and use case, which is usually the first number a CoE is asked to produce.
How does this fit with our EU AI Act obligations?
The EU AI Act requires teams to understand which AI systems they run, how they are used, and how risks are managed. By routing usage through one governed control plane, teams can maintain a clear inventory, apply consistent policies, retain audit trails, and assign ownership across the lifecycle.
Is Orq.ai certified?
Orq.ai’s security and compliance information is maintained in our Trust Center. It covers the platform’s current controls, supporting documentation, and the latest status for teams completing vendor reviews.
