High-Value Use Cases
Strong operational AI use cases include document extraction, triage/routing, anomaly detection, forecasting support, and summary generation for leadership reporting.
Choose use cases where AI augments a constrained team and where output quality can be validated with deterministic checks.
Risk Model
Treat AI systems as probabilistic components. Risks include hallucination, data leakage, model drift, and uncontrolled prompt changes in production.
Mitigate with scoped context, permission boundaries, output validation, and full audit logs.
Reference Architecture
A reliable architecture separates orchestration, policy enforcement, model calls, and deterministic business logic. This keeps core process control outside the model.
Use queue-based processing for expensive tasks, fallbacks for model failures, and confidence thresholds that trigger human review.
Implementation Plan
Pilot for 30–60 days with one use case, then scale only after performance and governance criteria are met.
Track accuracy, exception rate, time saved, and user trust metrics together. A fast model is not valuable if teams do not trust outcomes.
FAQs
Is AI ready for critical operations?
Yes for bounded tasks with strong validation and human fallback. No for unbounded decision-making without controls.
What is the first AI use case we should pilot?
Start with repetitive information processing where outputs can be checked against known rules.