The AI Workflow Triage Checklist

Start with one named workflow

A useful triage starts with a specific workflow. Not accounting, operations, customer service, or reporting as a whole. One repeated piece of work with a beginning, an end, inputs, outputs, and people involved.

The narrower the workflow, the easier it is to evaluate. A weekly report draft, vendor quote comparison, job note summary, invoice exception review, or internal document search process is much easier to assess than a vague goal like make operations more efficient.

Score the pain before discussing tools

The first question is not whether AI can help. The first question is whether the pain matters. A workflow may be annoying without being worth automating. Good candidates are frequent, measurable, and connected to time, errors, delays, risk, or customer impact.

• How often does this work happen?
• How many people touch it?
• How much time does it consume each week?
• What errors or delays does it create?
• What does the business lose when it goes wrong?
• What would prove that a fix worked?

Identify the data and document reality

Many AI ideas fail because the team talks about the desired output before looking at the actual inputs. Are the inputs spreadsheets, PDFs, emails, notes, database exports, screenshots, or inconsistent files from multiple systems?

The input reality determines the implementation. Clean structured data may need SQL, validation rules, or normal automation. Messy text and repeated document review may be better suited for AI assistance. Some workflows need both.

Decide where AI belongs in the workflow

AI is often useful in the middle of the work, not at the final decision point. It can summarize, classify, draft, compare, extract, explain, or flag exceptions. A human should review the output before it is trusted or sent downstream.

This keeps the system useful without pretending that a model should silently approve work, spend money, make commitments, or become the official source of truth.

• Use deterministic code for rules, calculations, and validation.
• Use AI for messy language, summaries, comparisons, and drafts.
• Use humans for approvals, exceptions, and judgment-heavy decisions.
• Use measurement to decide whether the workflow is worth expanding.

The triage decision

A good triage ends with a decision, not a vague recommendation. The workflow should be marked as a good AI-assisted candidate, a normal automation candidate, a process cleanup candidate, or a bad idea for now.

That decision is valuable because it prevents wasted effort. Sometimes the best AI strategy is saying no to the wrong project and choosing one small workflow that can actually be measured.