How to evaluate an AI training pilot before expanding

A pilot needs an exit decision: expand to another team, repeat with changes or stop. Define who decides, which task should improve and what evidence they will receive. If the selected group already knows the tool, its results do not describe what will happen with beginners.

Our corporate AI training guide covers formats and provider selection. This article addresses a later purchase: reviewing a completed pilot before buying the next stage. The initial proposal should separate the workshop, subsequent evaluation and ongoing support. Do not assume follow-up is included unless it was agreed.

The ILO/Cinterfor evaluation guide distinguishes learning, workplace transfer, impact and financial return. Match the evidence to the claim. Learning a technique does not yet demonstrate that it is used at work or generates a monetary benefit.

Choose a recognizable task, such as preparing an operations report. Record permitted inputs, expected output, reviewer and errors that require stopping. Use comparable samples before and after: similar documents, difficulty and acceptance criteria.

The record can include participant role, task, material version, approved tool, date, total time, corrections, final quality and owner approval. Include preparation and review in total time. A fast draft that creates more rework does not demonstrate a better process.

Keep evidence in an approved company workspace with agreed access. To discuss training with a provider, initially describe tasks and constraints. There is no need to send confidential documents or credentials.

This matrix is a Duotach working proposal for reviewing a pilot. It is neither a certified scale nor a result measured in client work. Fill each row with available evidence and identify what remains unmeasured.

Keep the type of work but change the content used in class. Participants should select relevant materials, frame the task, review the output and explain what they would reject. Repeating the instructor demonstration alone leaves autonomy unproven.

For the report example, create fictional inputs with a missing figure and conflicting versions of another value. An acceptable output identifies these limitations and requests review instead of inventing a number. This is an illustrative exercise without our own effectiveness measurements.

Define the rubric before reviewing submissions: fidelity to sources, detection of insufficient information, required format and human review. Do not offset a critical privacy failure or an invented figure with a high average score. Agree which failures are critical for this process.

Expansion makes sense when the agreed tasks meet accepted quality, subsequent use is evidenced and the receiving team can operate under the same conditions. Before purchasing more groups, verify permissions, tools and reviewer availability. A volunteer pilot does not guarantee company-wide adoption.

Adjust when people learn but cannot continue because access is missing, material is outdated or the task is unsuitable. Record the obstacle and correction before buying another stage. Lack of practice may call for targeted reinforcement; a missing integration requires a separate technical scope.

Pause expansion if quality cannot be checked, critical failures persist or nobody owns review. An evidence gap should leave the decision pending with an owner and a condition for reconsideration. Missing data is no basis for promising a return.

Usage records show activity. Operational improvement requires accepted outputs and a consistent comparison. Record other changes, such as a new template or reduced workload, that may explain the difference. A before-and-after comparison alone does not isolate the effect of training.

A primary report from Coca-Cola Andina dated May 20, 2026 describes an AI training evaluation and separates application evidence from financial return. The company says it did not calculate direct financial ROI because monetary conversion was not sufficiently robust. This is an external reference, not a Duotach client or result.

If Finance needs an estimate, agree how improvement will be valued and include preparation, training, licenses, practice and review costs. Distinguish released capacity from actual cash savings. Without a defensible basis, report observed learning and application without assigning ROI.

Summarize the task tested, participants and exclusions, accepted evidence, unresolved failures and proposed decision. Identify whether each obstacle needs practice, internal management or development. This helps compare the next scope without purchasing a larger program to solve a different problem.

Duotach offers Claude training for companies using exercises related to team tasks. Ask about a bounded training stage by describing the area, current experience, task and objective. Preparation, evaluation and follow-up are agreed in the proposal. The inquiry does not include a diagnosis built with your data.