Scorecard: Is your AI pilot ready to launch?

The excitement around AI is real, but excitement alone does not ship a working pilot. Most teams stall because they skip the readiness questions: Is the use case specific enough? Is the data accessible? Does someone own the outcome?

A scorecard forces the conversation before money moves. It replaces gut feel with a structured evaluation that surfaces gaps early, when they are cheap to fix.

This is not about gatekeeping AI adoption. It is about making sure the first project lands well so the next ten get funded.

We evaluate readiness across five dimensions: use-case clarity, data availability, stakeholder alignment, technical feasibility, and success criteria. Each dimension gets a score from one to five.

Use-case clarity asks whether you can describe the workflow the AI will improve in one sentence. If the answer is vague, the pilot scope will be vague too.

Data availability checks whether the information the AI needs already exists in a digital, accessible format. PDFs in a shared drive count. Tribal knowledge in someone's head does not.

Stakeholder alignment measures whether a decision maker has committed time, budget, and a definition of done. Pilots without executive sponsorship tend to drift.

Technical feasibility looks at whether the AI capability you need is mature enough to deploy in weeks, not months. Chat and RAG are mature. Fully autonomous decision-making usually is not.

Success criteria ask whether you have a number you will measure. Reduction in handle time, increase in first-contact resolution, hours saved per week. Without a metric, you cannot prove the pilot worked.

Rate each dimension from one to five. A one means you have not started thinking about it. A five means you have documented evidence or a working prototype.

Add up your total. A score of twenty or above means you are ready to move. Fifteen to nineteen means you have one or two gaps to close first. Below fifteen, you need a discovery workshop before committing to a build.

The scorecard is not pass-fail. It is a prioritization tool. If data availability scores a two but everything else is a four, you know exactly where to focus before kicking off.

Choosing a use case because it sounds impressive rather than because it solves a measurable problem. The scorecard forces specificity.

Assuming data is ready because it exists somewhere. The scorecard distinguishes between data that exists and data that is accessible, clean, and structured enough to use.

Launching without a success metric and then struggling to justify the next phase. The scorecard makes you commit to a number before you start.

Skipping stakeholder alignment and discovering mid-pilot that the person who needs to approve the rollout was never briefed. The scorecard catches this early.

If you score twenty or above, you are ready to scope an AI Jumpstart engagement. Bring your scorecard to the kickoff—it accelerates discovery.

If you score fifteen to nineteen, block one week to close the gaps. That usually means documenting the use case, pulling a data sample, or getting a stakeholder to commit.

If you score below fifteen, consider a paid discovery workshop. We will help you identify the right use case, audit your data, and build the business case so the pilot has the best chance of landing.