Generative AI pilots fail for predictable reasons: ungoverned data, no clear owner for outputs, and no integration path into daily workflows. This article walks through a practical readiness checklist — data grounding, approval workflows, and a realistic first use case — before you commit budget to a full rollout.
Data GroundingClean, RBAC-filtered knowledge base
Governance LayerHuman-in-the-loop review process
Targeted Use CasesMeasurable, narrow pilot scope
The Three Critical Readiness Questions
Before jumping straight to vendor selection or LLM fine-tuning, enterprise leaders should challenge their engineering and business leads with three key structural questions:
"An LLM without strict data retrieval constraints doesn't give you enterprise intelligence—it gives you plausible-sounding risk."
- 1. Is your internal data structured for retrieval (RAG)? AI models are only as accurate as the context they consume. Unindexed PDFs, duplicated documents, and unclear access controls will inevitably bleed into AI outputs.
- 2. Who owns the final generated output? Every AI workflow requires clear operational oversight. Without defined ownership and approval checkpoints, generated drafts rarely transition into business actions.
- 3. Does it fit natively into existing software systems? Portals that force staff to leave their daily tools (ERP, CRM, ticketing) see rapid drop-off after initial novelty wears off.
