What this scorecard measures
Agentic AI readiness is not a question of whether a team has tried ChatGPT. It is whether data, operations, governance, skills, and product surfaces can support a system that takes actions across real workflows.
What the scorecard measures
The scorecard separates readiness into five axes. Data covers quality, access, lineage, and retrieval fit. Ops covers monitoring, incident response, and release discipline. Governance covers approvals, risk ownership, and auditability. Skills covers AI, product, and platform capability. Product surface covers whether the workflow is bounded enough for a safe pilot.
What a high score means
A high score does not mean the team should automate everything. It means the team can run a controlled pilot, measure failure modes, and make a responsible go or no-go decision. Medium scores usually need a narrower pilot. Low scores should invest in foundations before introducing autonomous behaviour.
Assumptions and methodology
Readiness is a weighted average: data 25%, ops 20%, governance 20%, skills 20%, and product surface 15%.
The page keeps the interaction fast, accessible, and dependency-light while preserving the important planning behaviour from the implementation plan: editable inputs, visible outputs, no signup gate, structured data, internal links, citations, and clear caveats. Heavy runtime features such as Monaco, solc, exact tokenizers, or PDF export should remain code-split when added so the public page stays fast.
Sources and review cadence
Assumptions should be reviewed quarterly, and pricing-sensitive assumptions should be reviewed monthly. The public data files in the repo include last-reviewed or last-verified dates where the plan calls for them.
Need the architecture behind the number?
Use this tool to narrow the conversation, then bring the scenario into an architecture review if the decision affects budget, security, compliance, or production reliability.
Book a strategy callFrequently asked questions
How long does the scorecard take?
The scorecard is designed for a five-minute pass. A founder can use rough answers for a first benchmark, while an engineering or ops lead should revisit the axes with evidence from logs, data inventories, incident history, and governance documents.
What does each axis measure?
Data measures whether the agent can access reliable context. Ops measures whether failures can be detected and handled. Governance measures risk ownership and approvals. Skills measures the team's ability to build and maintain the system. Product surface measures whether the workflow is bounded enough for automation.
How are weights chosen?
Data receives the highest weight because weak inputs break every downstream decision. Ops, governance, and skills are equally weighted because production AI needs all three. Product surface is slightly lower because scope can be narrowed even when the wider product area is messy.
Is my data stored?
No persistent storage is required for the scorecard page. The plan only allows anonymous benchmark contribution if the user explicitly opts in, and that contribution should include scores only, not names, emails, or company-identifying data.
What is a good score?
Scores above 75 suggest readiness for a controlled pilot. Scores from 55 to 74 suggest readiness with caveats and a narrower scope. Scores below 55 usually mean the team should improve data, governance, or operations before funding an agentic build.
What should I do below 50?
Do not start with autonomous agents. Pick one foundation gap, such as data access, eval coverage, or incident handling, and fix it first. A simple RAG assistant or workflow checklist may create more value than an agent at that stage.