Creation lanes that already pay off
First drafts of specs, customer emails, test data, UI copy variants, and boilerplate in languages your team already reviews carefully. Pair with human edit passes; treat outputs as starting points, not signatures. For engineering, inline assistants that suggest patches inside your editor reduce context switches versus greenfield “write me a microservice” prompts that ignore your standards.
Administration traps
Auto-summarizing meetings nobody needed, generating forms nobody reads, or classifying tickets into seventeen buckets when five would do—these multiply work. Before automating, ask whether the process should exist. Often the humane fix is fewer steps, aligned with workload discipline.
Governance without paralysis
Document allowed use cases, data classes that must not enter public models, and retention expectations. Red-team prompt injection paths for customer-facing assistants. Keep vendor claims proportional to verification effort—see security proportionality.
Ownership and portability
Prefer providers and contracts that let you export prompts, fine-tunes, and evaluation sets. Your differentiation is not the model weights—it is the curated context and review habits around them. Data control still applies.
Further reading
- NIST AI Risk Management Framework — structured approach to trustworthy AI.
- OWASP Top 10 for LLM Applications.
- ISO/IEC 42001 — AI management system standard (overview from ISO).
- W3C Verifiable Credentials Data Model — adjacent identity patterns for high-assurance workflows.
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