Generative AI moved from novelty to budget line in three years. Microsoft Copilot, Google Gemini for Workspace, Salesforce Einstein, ServiceNow Now Assist, and a long list of vertical players now embed large language models into the tools where work already happens. The shift from “open a chatbot tab” to “ask the CRM record a question” is the difference between a curiosity and a productivity feature that survives the next budget review.
Beneath the demos, the engineering pattern is consistent: retrieval-augmented generation against company data, a model that does not see customer data outside an explicit query, and human review on anything that leaves the building. Hallucinations remain the dominant risk for legal, financial, and medical workloads, which is why serious deployments lean on extractive citations rather than free-form generation, and on evaluation harnesses that catch regressions when models silently change behind a vendor’s API.
Procurement has caught up with the hype. Where does customer data go during inference, how long is it retained, can the vendor produce a model card with training-data disclosures, and what happens to fine-tuned weights if the contract ends? Buyers who skip these conversations end up either repeating them under a regulator’s deadline or unwinding deployments that failed an audit. The teams extracting the most value in 2026 picked two or three high-volume workflows and instrumented them carefully, rather than rolling out AI to everyone and measuring nothing.