Guide / AI at work
AI operations evaluation checklist
Use this to evaluate an AI operations idea before you fund a build. It keeps the conversation on workflow, data, quality, and monitoring instead of model brand names. If you cannot describe the workflow and the failure mode, you are not ready for production automation yet.
Who this is for
Operations and product leads who want automation that survives contact with real tickets, documents, and staff workflows. Useful when a vendor demo looked impressive but nobody owns evaluation, guardrails, or monitoring after launch. Also for teams that already have messy PDFs or support queues and need a sober go/no-go before buying more model spend. Share this guide with legal and operations early so privacy and ownership debates happen before a vendor demo locks the narrative.
What to evaluate before you build
If you cannot describe the workflow, the data, and the failure mode, you are not ready for production AI. This checklist keeps the conversation concrete. Fill what you know; mark unknowns honestly. Unknowns are useful signals, not a reason to skip the brief.
The workflow in one paragraph
Who starts it, what arrives (email, PDF, ticket), what a correct result looks like, and who acts on exceptions.
Volume and peak times
Daily or monthly volume, and whether spikes matter (campaigns, month-end, weekends).
Tools already in use
CRM, helpdesk, ERP, drive folders, messaging. Automation must land where people already work.
Data you can share
Sample documents, redacted tickets, or schemas. Note privacy or compliance limits up front.
Quality bar
Acceptable error rate, when a human must review, and what a bad answer costs the business.
Guardrails
Topics the bot must not answer, PII rules, and escalation paths to a person.
KPI for success
Time saved, accuracy, cost per case, or customer wait time. Pick one primary metric.
Rollout path
Pilot group, full rollout owner, and how you will monitor the first weeks in production.
How Haikotek approaches AI ops
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.
What a useful first scope includes
- 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.
After evaluation
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.
Common mistakes to avoid
- 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.
When this guide is a 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. 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.
Common questions
- 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.