Leaders are under pressure to “do AI” without clear use cases. The wins are usually narrow: support triage, document extraction, internal search, or drafting—not replacing entire departments overnight.
Anchor on a measurable workflow
Pick a process with volume and clear success metrics—minutes saved, error rate, or cost per ticket. Run a four-week pilot on real samples.
RAG beats generic chat for proprietary knowledge
Index policies, manuals, and tickets in a vector store with access controls. Answers should cite sources and refuse when confidence is low.
Keep humans in the loop
High-stakes decisions need review queues, audit logs, and escalation paths. Automate drafts, not accountability.
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Cost controls for LLM workloads
Cache frequent queries, cap tokens per request, and route simple tasks to smaller models. Monitor spend by team and feature flag expensive experiments.
Evaluation beyond demos
Build golden sets from real tickets and documents. Track precision, refusal rate, and citation accuracy for RAG. Regression tests should run on every prompt or index change.
Organizational readiness
Name executive sponsors and domain experts who can judge output quality. Without ownership, pilots stall after the proof of concept.
Cost controls for LLM workloads
Cache frequent queries, cap tokens per request, and route simple tasks to smaller models. Monitor spend by team and feature flag expensive experiments.
Evaluation beyond demos
Build golden sets from real tickets and documents. Track precision, refusal rate, and citation accuracy for RAG. Regression tests should run on every prompt or index change.
Organizational readiness
Name executive sponsors and domain experts who can judge output quality. Without ownership, pilots stall after the proof of concept.