Use-case and value framing
Identify who is assisted, the decision or task being supported, what a useful response looks like, which errors matter, and what evidence would justify continuation.
AI & Intelligent Operations
Put AI inside governed workflow—not beside it.
Design, configure, and deliver AI-enabled ServiceNow workflow with bounded data, meaningful human review, evaluation, escalation, audit, adoption, and a controlled disablement path.
Decision evidence
Inspect the human-review boundary before enabling assistance.
Use the artifact to make role scope, staff authority, human disposition, escalation, audit, and the manual path inspectable before a workflow uses AI assistance.
Staff authority, human disposition, escalation, audit, and the manual path remain visible in the full diagram.
Opportunity and architecture
Turn an AI idea into a bounded operational use case.
Begin with the user need, workflow step, available knowledge, expected benefit, failure mode, reviewer, and stop condition. That framing keeps the work attached to service delivery instead of becoming a detached AI experiment.
Workflow and operational integration
Place the assistive step in the ServiceNow work pattern with routing, task ownership, exception paths, and role permissions. App Engine, Workflow Studio or Flow Designer, and IntegrationHub may support the designed flow where appropriate.
Knowledge and data boundaries
Specify approved sources, retrieval scope, sensitive-data exclusions, retention expectations, access controls, and how a user can recognize incomplete or stale context.
Platform visibility
Define operational questions, reviewer activity, exceptions, and adoption signals that may be represented through Platform Analytics or reporting without treating a dashboard as proof of performance.
Visual control loop
Assist → Review → Decide → Audit
Each stage leaves the next role with a clear responsibility, a visible decision cue, and evidence that can be inspected.
- AssistPrepare a bounded suggestion, summary, or draft from approved context.
- ReviewA qualified person checks accuracy, context, policy fit, and missing information.
- DecideThe accountable role accepts, revises, rejects, or escalates before consequential action.
- AuditPreserve the decision, source context, exception, feedback, and control outcome.
Environment qualification: Relevant products, entitlements, models, data controls, and operating responsibilities are confirmed for each client environment.
Governance and operations
Make review, escalation, and disablement operational.
The control design should be understandable to platform owners, service teams, reviewers, risk stakeholders, and the people expected to use it.
Human review before consequential action
Assign the reviewer, define the evidence they inspect, make revision or rejection possible, and keep decision authority with the accountable human role.
Evaluation and monitoring criteria
Define task-specific test cases, quality thresholds, drift indicators, feedback sources, review cadence, and the owner responsible for interpreting signals.
Escalation and complaint handling
Provide a visible route for users to challenge a result, report harm or poor quality, request correction, and reach an accountable service owner.
Auditability and feature disablement
Record material configuration, decision, exception, and change history. Define who can pause or disable the feature and how affected work returns to a manual path.
Connect platform mechanisms to operating responsibilities.
ServiceNow mechanisms may include governed AI capabilities, Generative AI Controller, AI Control Tower, App Engine, Workflow Studio or Flow Designer, IntegrationHub, and Platform Analytics/reporting when they fit the approved architecture.
Adoption and role-based enablement
- Prepare role-specific scenarios and review responsibilities.
- Teach users how to question, revise, reject, and escalate output.
- Give platform owners an operating cadence for evidence and exceptions.
- Connect change, release, support, and disablement responsibilities.
Evidence and next action
Bring the use case, workflow, boundaries, and decision roles into one working session.
Review the Responsible AI checklist, inspect a governance proof, or start with an editable business-level summary.
Governed AI state model
Keep assistance, human authority, and prohibited actions distinct.
AI governance leaders can inspect where automation may assist, where human review is mandatory, and where the workflow must stop.
Bound the assistance
Define the approved data, task, user population, output purpose, confidence limits, and fallback before enabling AI in a workflow.
Require meaningful human review
Present the source context, uncertainty, exceptions, and correction path to the authorized reviewer before action.
Keep authority with an accountable role
A named person accepts, edits, rejects, or escalates the recommendation; the model does not become the decision owner.
Preserve control after launch
Record inputs, outputs, human actions, model or configuration changes, exceptions, and a tested disable path.
Stop prohibited use
Do not allow unreviewed high-impact decisions, uncontrolled sensitive-data use, undisclosed authority transfer, or operation outside approved policy.
AI may support a bounded workflow, but authorized human review, accountable decisions, auditable controls, and explicit prohibited states govern every action.