Start with process clarity, safeguards and measurable outcomes—not the model alone. The sections below outline a useful way to evaluate the work, choose sensible next steps and avoid expensive assumptions.
Pick a workflow before picking a model
Teams often start with an AI demo and only later ask which problem it should solve. Reverse that order. Write down the process, its owner, the inputs involved, the decisions being made and the point at which a human needs to approve an action.
Build a baseline you can compare against
Measure time per task, error rates and exception volume before making a change. A workflow that already runs reliably may not benefit from a large AI layer. Conversely, repetitive document classification or internal knowledge lookup may be a reasonable pilot if errors are detectable and reversibility is built in.
Separate suggestions from irreversible actions
An assistant drafting a response has a different risk profile from an agent issuing refunds or editing production records. Start with read-only access, draft recommendations and supervised approvals. Apply least privilege, record decisions and define what happens when the model is uncertain.
Design for messy source information
Business data often contains duplicates, inconsistent names and missing context. Retrieval quality, clear ownership and data hygiene can matter more than prompt tricks. Include citations to original records where possible and provide a way to correct the result.
Pilot, evaluate and expand carefully
Choose a limited use case and success measure, such as reduced handling time without increased correction rates. Review failures and user feedback. Broaden access only after the pilot is secure, observable and genuinely useful to employees.