Start with a workflow, not a model
The teams seeing returns from generative AI did not begin with “which model is best?” They began with a painful, measurable workflow — support triage, proposal drafts, invoice matching, or internal knowledge search — and asked how generation could shrink cycle time without lowering quality.
When the use case is vague, models become expensive demos. When the use case is concrete, you can score accuracy, latency, and cost against a baseline human process.
Where assistants create ROI
High-value assistants sit next to structured systems of record. They draft, classify, and summarize — then a human or rule engine confirms before writes hit production data.
Strong patterns include: summarizing tickets for support leaders, generating first-pass product copy for marketers, explaining dashboards for non-technical stakeholders, and scaffolding boilerplate for engineers who still own the final merge.
Where budget gets burned
Budget burns when every request hits a frontier model, prompts are unversioned, and there is no evaluation set. It also burns when AI is bolted onto broken process — automation that accelerates a bad workflow just fails faster.
Treat prompts, retrieval sources, and guardrails like product code. Log outcomes. Cap spend. Prefer smaller models for classification and reserve larger models for synthesis.
What to build next
If you are exploring AI inside a SaaS product, start with one assistant that removes a weekly hour of manual work for a defined role. Instrument it. Expand only after the first loop is trusted.
Devsair helps teams ship AI features that attach to real product workflows — not slideware. If you want a scoped MVP, we can map the workflow and stack with you.