A structured way for engineering design teams to identify, score, and track the risks introduced by AI-assisted work — from calculation errors to data exposure — modeled on the same risk-register discipline used for project risk management.
Why Treat AI Risk Like Any Other Project Risk
Engineering teams already maintain risk registers for schedule, budget, safety, and technical risk on projects — a structured log of what could go wrong, how likely it is, how bad the consequence would be, and what mitigates it. AI-assisted work introduces a new category of risk that deserves the same discipline rather than being treated as a novelty outside normal risk management. A risk register doesn't prevent AI errors by itself, but it forces a team to name specific failure modes in advance rather than discovering them after a mistake reaches a client.
Core Risk Categories for AI-Assisted Engineering Work
A useful register organizes risks into a small number of categories, each scored for likelihood and consequence:
- Calculation/technical accuracy risk: an AI tool produces a plausible-looking but incorrect numeric result, formula application, or code reference, and it isn't caught before reaching a deliverable.
- Data confidentiality risk: client-confidential drawings, specifications, or PII are submitted to a tool without adequate data-handling assurances.
- Overreliance/deskilling risk: staff, particularly junior engineers, lean on AI output without developing or exercising the underlying judgment needed to catch when the AI is wrong.
- Hallucinated-reference risk: an AI tool cites a code section, standard, or manufacturer spec that does not actually say what the tool claims, or does not exist at all.
- Bias/consistency risk: AI-assisted decisions (e.g., automated first-pass equipment selection) systematically favor certain vendors, methods, or assumptions in ways the team hasn't consciously chosen.
- Vendor/availability risk: a firm becomes dependent on a specific AI tool or vendor whose pricing, availability, or data policy could change unfavorably.
Scoring and Prioritizing Entries
Score each identified risk on the same likelihood × consequence scale a team already uses for other project risks (e.g., 1-5 on each axis). A hallucinated code reference in an early-stage concept document scores very differently than the same failure mode in a stamped calculation package headed to a permit reviewer — consequence depends heavily on where in the workflow the AI-assisted content sits and how much independent verification happens downstream.
Mitigation Column — What Actually Reduces Each Risk
For each entry, name a concrete mitigation rather than a vague one. "Have someone check it" is weaker than "engineer of record independently verifies any AI-suggested code reference against the actual current code section before it enters a calculation." Useful mitigations tend to fall into a few types: mandatory independent verification steps, restricting which tools can touch confidential data, requiring citations/sources for any AI-suggested code or standard reference, and training staff specifically on the failure modes most relevant to their discipline.
A Minimal Template Structure
A workable register needs only a few columns per entry: risk description, category, likelihood (1-5), consequence (1-5), resulting score, mitigation, owner, and review date. Review the register at the same cadence as other project risk reviews — quarterly for an ongoing program, or at each phase gate for a discrete project — rather than creating it once and letting it go stale.