Why demos stall
Most AI pilots fail for operational reasons, not model quality. A prototype that looks impressive in a slide deck often has no path to identity, permissions, systems of record, or on-call ownership. Teams then treat the demo as progress — while the workflow that actually creates value stays manual.
Production AI starts with a job to be done: a specific workflow, a success metric, and a clear owner. Model selection comes after that framing, not before.
Map the workflow before you pick a model
Write down the steps humans perform today, where data enters and leaves, and where judgment is required. Identify the handoffs that create delay or error. Only then decide whether GenAI assistance, classification, extraction, agents, or classical automation is the right lever.
- Define inputs, outputs, and acceptance criteria in business language
- Name the systems of record and who owns data quality
- Separate “nice to have assistant” from “must-not-fail automation”
- Decide where a human must approve before an action lands
Build evaluation and oversight into the path
If you cannot measure output quality, you cannot operate the system. For language features, that means retrieval grounding, known-bad prompts, and review samples. For automation, that means idempotency, rollback, and audit trails. Treat evaluation as part of engineering — not a later QA add-on.
Integrate where work already happens
Value shows up when AI lands inside existing tools and processes — ticketing, CRM, document flows, internal apps — not in a parallel portal nobody opens. Integration boundaries (APIs, identity, tenancy, rate limits) should be designed in Discover and Strategize, before build scales.
Know when to stop
Sometimes the honest recommendation is that AI is not the right move yet — because data quality, process clarity, or ownership is missing. A deliberate stop after a scoped discovery is cheaper than a year of pilot theater. Production workflows reward teams that can say no as clearly as they can ship.