- Workflow and action boundaries
- Representative evaluation cases
- Approved integration access
- Human review and exception routing
- Change ownership and an operating runbook
An AI demonstration can prove an idea is worth exploring. It does not, by itself, define how the application will behave across representative tasks, changing data or failed integrations. Those questions belong in the implementation plan.
Define the workflow boundary
Describe the input, the proposed action and the person accountable for the result. Separate information retrieval from actions that change records or contact people. Each boundary needs its own permissions and success criteria.
Evaluate representative work and difficult exceptions
Build a small, maintainable set of evaluation cases from authorized examples. Include missing information, conflicting sources and failed tools. Record what is checked automatically and what still needs human judgment; a single success score can hide important failure modes.
Design human intervention before launch
An application needs a clear path when it cannot complete a task or when a decision exceeds its authority. Define what the reviewer receives, what action they can take and how the decision returns to the workflow.
Give changes an operating owner
Model updates, source changes and connector changes can affect behavior. Assign an owner for evaluation, change review, cost and incident handling. Keep a versioned baseline so the team can understand what changed.
Scope delivery and operations together
An implementation partner can help close the gaps between the prototype and the operating environment. The handover should include responsibilities, known limitations, checks and a prioritized improvement backlog.
An original operational perspective from LyndonAI. Scenarios are illustrative; this article does not describe a customer result or provide legal advice.
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