Most fail because teams treat implementation as model selection rather than lifecycle engineering. Common gaps: poor data quality, no evaluation harness, cost and latency discovered late, and no human-in-the-loop path to sustain quality over time.

Retrieval-Augmented Generation retrieves relevant documents at inference time. Fine-tuning adjusts model weights on domain-specific data. RAG is usually faster to deploy and easier to update; fine-tuning yields deeper specialisation but needs more ML skill and budget. Many mid-sized teams get the specificity they need from RAG first.

Leading organisations often reach production in roughly 90 days; typical enterprises may take around nine months. The gap is process: clear success metrics, data readiness before model choice, and evaluation infrastructure built from the start outperform late bolt-ons.

Patterns span synchronous review before delivery, asynchronous flagging after delivery, and confidence-threshold routing so only uncertain outputs hit humans. Higher stakes favour synchronous review; higher volume often suits routing or flagging. Corrections should feed evaluation and retraining.

Analysts cite that many GenAI proofs-of-concept stall or are abandoned owing to weak data, inadequate risk controls, rising cost, or unclear ROI - framing why lifecycle discipline, not a bigger model, is the lever.