AI Readiness Pilot

Start with the work, not the model. A useful AI pilot usually appears inside a repeated workflow: preparing a proposal, summarizing a customer call, triaging support tickets, drafting a report, comparing research notes, checking a document, or moving context from one person to another.

The best signal is visible friction. Look for delays, repeated copy-paste work, inconsistent quality, missing context, senior people answering the same questions, or customer-facing work that depends too heavily on one person's memory.

A workflow map should name the trigger, inputs, steps, handoffs, decisions, outputs, owner, current pain, and the standard for good work. Without that map, AI adoption becomes opinion-based because nobody can compare before and after.

Do not select the most exciting use case first. Select the workflow where the team can explain the current routine, provide examples, name the risk, and tell whether the result became better.

The right pilot is narrow enough to run and meaningful enough to matter. If it is too broad, nobody owns it. If it is too small, nobody learns anything the company can reuse.

Score each candidate on five dimensions: business value, workflow clarity, available context, risk, and team energy. A strong first pilot has visible value, a clear routine, available examples, manageable risk, and people who want to try.

Avoid pilots where the output cannot be judged. If the team cannot say what a good answer, draft, summary, or decision looks like, AI will only make the ambiguity faster.

A pilot decision memo should also explain what was not selected. This prevents the team from treating every possible AI use case as urgent at the same time.

AI readiness depends on operating design. The important question is not whether AI can produce output, but how people will request, inspect, improve, and use that output inside the real workflow.

Every pilot needs quality gates. Decide what AI can draft or suggest, what a human must review, what must be escalated, and what should never be automated. Write these rules before the pilot starts.

The loop should be visible: input, AI assist, human review, final decision, feedback, and improvement. If feedback disappears, the workflow will not get better because the team only sees isolated outputs.

Use examples, not only instructions. Show one weak input, one better input, one acceptable output, and one output that must be rejected. This makes quality teachable.

A pilot becomes useful when it touches live work. Synthetic examples can help training, but they rarely reveal the missing context, unclear ownership, tone problems, and edge cases that block adoption.

Choose a work item with enough realism to matter and low enough risk to recover from mistakes. The first run should create learning, not pressure to prove the whole AI strategy.

Review the result in three layers: input quality, AI output quality, and human decision quality. Often the problem is not the model; it is unclear context, weak examples, or missing review criteria.

Capture changes immediately. If the team waits until the end of the month, the details that would improve the routine will be forgotten.

Measurement should start before the pilot begins. Capture the current baseline so the team can distinguish real improvement from novelty, enthusiasm, or a one-off good result.

Use a small scorecard. Measure speed, quality, adoption, confidence, and business effect. Not every pilot needs a full ROI model, but every pilot needs a decision rule.

The point of 30 days is learning. At the end, decide whether to scale the workflow, redesign the loop, narrow the use case, improve the knowledge base, or stop because the evidence is weak.

Make the decision visible. Teams lose trust when pilots drift. A clear review turns experimentation into operating discipline.