AI operations evaluation checklist

Operations and product leaders who want AI automation that survives contact with real support tickets, internal documents, and staff workflows. Useful when a vendor demo looked impressive, but nobody owns evaluation, guardrails, or post-launch monitoring.

• —Workflow Definition: Who initiates it, what input arrives (email, PDF, ticket), what a correct result looks like, and who handles exceptions.
• —Volume & Peak Times: Daily/monthly volume and peak spikes (campaigns, month-end, weekends).
• —Tool Integrations: Existing stack (CRM, helpdesk, ERP, drive folders) where automation must land.
• —Sample Data & Privacy: Redacted documents or schemas with clear privacy limits defined up front.
• —Quality Bar: Acceptable error rates, human review thresholds, and the business cost of a bad response.
• —Guardrails & Escalations: Restricted topics, PII masking rules, and clear escalation paths to humans.
• —Primary Success KPI: Time saved, accuracy rate, cost per case, or customer wait time.
• —Rollout Path: Pilot group, full rollout owner, and post-launch monitoring plan.

We design the workflow, connect data sources, ship the model or retrieval stack, run evaluation, and leave light monitoring. Discovery sizes effort and risk before a larger build. Demo-only pilots without an operations path are not the goal. Expect to share sample documents or redacted tickets early; without data, evaluation is theatre. Human review steps are designed in where error cost is high, not bolted on after a bad launch.

If the use case is sound, work continues under Pragmatic AI. If data quality or process ownership is the real blocker, we say so early. Performance or delivery work may come first when the underlying system cannot support automation yet. Keep the KPI and guardrail notes; they become acceptance criteria for the first production milestone. Bring the checklist to the kickoff so evaluation criteria are not renegotiated mid-build.

☷ Good Fit: Use it when a workflow already exists on paper or in people's heads and you need to know whether automation will reduce cost or create new failure modes. It fits document OCR, support routing, and forecasting tied to tools staff already open every day.

☷ Not A Fit: Skip it if you are shopping for a generic chatbot with no workflow owner, or if legal will not allow any sample data into an evaluation environment. Pragmatic AI is the build path; this checklist is the filter that keeps demos from becoming unpaid production support. Cross-link to Performance or Delivery when the underlying system is too fragile to automate yet.

☷ A single workflow with clear users, inputs, and outputs, written so a new stakeholder can understand it without a meeting.

☷ Evaluation criteria and a plan for human review where needed.

☷ Integration assumptions, guardrails, and a monitoring checklist after launch.

☷ Starting with a chatbot when the real pain is document intake, routing, or forecasting.

☷ Skipping evaluation criteria and then arguing about "quality" after the pilot.

☷ Leaving PII and escalation rules undefined until legal reviews the launch week.

☷ Running a demo with no owner for monitoring, prompts, or model cost in the first weeks of production

Do you only build chatbots?

No. Common work includes document OCR, support automation, and forecasting workflows tied to your tools. Chat is only one interface pattern, and often not the highest ROI starting point.

Will this stay a demo?

Only if you insist on a demo without data, owners, or monitoring. Our default is a path to production with evaluation and guardrails. If legal blocks samples, we discuss synthetic or on-prem options before promising timelines.

How do we start?

Send the checklist via Contact with the AI workflow intent, include one sample workflow and any privacy constraints. We reply with fit, discovery shape, and whether Performance or Delivery should come first