AI Use Case Intake & Risk Triage Workflow

The more I worked with AI systems, the more I began to notice a familiar pattern.
Ideas moved quickly, but accountability often lagged behind.

Teams were eager to deploy new AI-powered features, yet there was rarely a consistent moment where someone paused to ask the harder questions.
What data are we using?
Who is affected?
What happens if this goes wrong?

I built this workflow to create that pause.

Not as a barrier to innovation, but as a point of intention. A way to bring clarity, structure, and shared responsibility into how AI use cases are approved and governed.

This project reflects how I approach emerging technology.
With curiosity, discipline, and a deep respect for the downstream impact AI systems have on people, products, and the organizations that deploy them.

This workflow turns AI governance from a reactive review process into an intentional decision point.

  • I designed this workflow to help organizations adopt AI thoughtfully, without slowing innovation or losing sight of risk, accountability, and trust.

    It provides a clear, structured way for teams to propose AI use cases, assess risk early, and make informed approval decisions that stand up to regulatory and executive review.

    • In practice, inconsistent security review triggers can slow deal cycles, frustrate sales teams, and introduce unnecessary risk. This intake and triage model reflects patterns I developed while supporting client-facing and vendor agreements in a high-volume enterprise environment, where security decisions needed to be made quickly, consistently, and with clear escalation paths.

    • Similar triage principles were applied during periods of organizational change, including divestitures, where review thresholds and escalation paths needed to be clearly defined to maintain operational continuity.

    • Intentional AI Adoption: A single, consistent intake process that brings business, legal, security, and compliance into the conversation early

    • Risk-Aligned Oversight: AI use cases are classified by risk level, with governance and controls scaled appropriately—not one-size-fits-all

    • Clear Accountability: Decisions, approvals, and conditions are documented to support auditability and long-term oversight

  • This intake and triage model is designed to sit at the front of the AI lifecycle, ensuring that use cases are consistently classified and routed to the appropriate level of review. By introducing risk-based triage early, organizations can reduce friction for low-risk use cases while ensuring that higher-risk systems receive the appropriate security, legal, and governance oversight.

  • This approach is designed for regulated environments, such as financial services and healthcare, where AI systems must be reviewed carefully before deployment, especially when they impact customers, patients, or critical decisions.

  • NIST AI Risk Management Framework · EU AI Act (risk tiers) · ISO/IEC 42001 · Enterprise GRC practices

  • AI governance works best when it feels supportive rather than restrictive.
    This workflow creates space for innovation while ensuring AI systems are introduced responsibly, transparently, and with the right level of care.

  • View the supporting artifacts on GitHub → https://github.com/adarian-dewberry/ai-governance-portfolio/tree/main/ai-use-case-intake

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