RACI Matrix

A RACI matrix is a responsibility assignment tool with activities, deliverables, or decisions as rows and organizational roles as columns. Each relevant intersection identifies a role as Responsible, Accountable, Consulted, or Informed. Responsible roles perform the work. The Accountable role owns the outcome and has authority to accept it or ensure that it is completed. Consulted roles provide input through two-way communication, while Informed roles receive updates without becoming required reviewers.

A practical default is one Accountable role and at least one Responsible role for every defined activity. Several roles can contribute to execution, but their tasks should be distinguishable. A role can be both Responsible and Accountable when appropriate. If a row appears to require several accountable owners, split it into more precise decisions or deliverables. For example, technical release approval and acceptance of business risk may require separate rows with different owners.

For an AI initiative, build the matrix with business leadership, data science, engineering, operations, security, legal, compliance, and relevant domain specialists. Include activities such as approving the use case, authorizing data use, evaluating model quality and bias, reviewing privacy obligations, approving deployment, monitoring production behavior, and responding to incidents. Representation should follow the risks and affected stakeholders rather than treating every department as a mandatory approver for every task.

Consider a cloud-hosted customer support assistant. The machine learning team might be Responsible for evaluating answer quality, the AI product owner Accountable for accepting the evaluation against agreed business criteria, legal and domain experts Consulted, and support leadership Informed. A separate data-use approval activity could make the data owner Accountable and the data engineering team Responsible for documenting sources and access controls. Legal consultation does not transfer the organization's regulatory obligations or automatically constitute legal approval.

The matrix also supports business readiness assessments. An unassigned Responsible role can reveal missing skills or delivery capacity; an Accountable role without decision authority indicates a governance gap. Missing review criteria suggest a process gap, while an assigned monitoring team without telemetry or evaluation tools indicates a technology gap. These findings should feed hiring, training, process improvements, tooling investments, and funding decisions. Naming an owner alone does not resolve a capability shortfall.

When scaling beyond pilots, an AI center of excellence can own shared evaluation standards, reusable platform patterns, and workforce enablement. Product teams can remain accountable for their business outcomes and production services. Define the boundary explicitly, including exception handling, escalation paths, review deadlines, and handoffs. This avoids making the center of excellence either an approval bottleneck or an advisory group with no clear mandate.

In cloud operations, connect RACI assignments to deployment approvals, runbooks, incident procedures, and evidence collection. However, RACI is not an access-control mechanism, an organizational chart, or a substitute for the cloud shared responsibility model. IAM policies enforce permissions; the matrix explains organizational duties. Approval workflows and separation-of-duties controls must be implemented separately. Keep the matrix focused on meaningful outcomes, publish it where teams work, and review it when staffing, architecture, risk, or operating responsibilities change.