GitHub - tinyopsstudio/ai-readiness-governance-kit: Practical templates for AI use-case prioritization, governance gaps, vendor review, and a 60-day action plan. · GitHub
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60-day-action-plan.csv
60-day-action-plan.csv
AI_READINESS_QUESTIONNAIRE.md
AI_READINESS_QUESTIONNAIRE.md
LICENSE
LICENSE
README.md
README.md
ai-use-case-register.csv
ai-use-case-register.csv
governance-gap-checklist.csv
governance-gap-checklist.csv
vendor-scorecard.csv
vendor-scorecard.csv
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AI Readiness & Governance Starter Kit
A practical, vendor-neutral starting point for teams deciding where AI belongs, which use cases deserve investment, and what controls should exist before rollout.
The kit is designed for an internal working session. It does not certify compliance, replace legal or security review, or guarantee that a use case is safe to deploy.
Included
AI_READINESS_QUESTIONNAIRE.md: 30 discovery questions across strategy, people, process, data, technology, and governance.
ai-use-case-register.csv: a structured register for comparing candidate AI use cases.
governance-gap-checklist.csv: evidence-oriented checks for ownership, data boundaries, approvals, monitoring, and incidents.
vendor-scorecard.csv: a weighted scorecard for comparing AI vendors and tools.
60-day-action-plan.csv: a sequenced template for turning findings into accountable work.
15-Minute Start
Choose one real workflow, not a broad goal such as "use AI."
Add it to ai-use-case-register.csv and identify the accountable business owner.
Answer only the questionnaire sections relevant to that workflow.
Mark each governance check Yes, Partial, No, or Not applicable, with an evidence link or named evidence owner.
Score candidate vendors from 1 to 5 for each criterion. Keep unknowns visible instead of treating them as acceptable.
Put unresolved decisions into the 60-day plan with an owner, output, and decision gate.
Use-Case Scoring
Score each dimension from 1 to 5:
Business value: time saved, service improvement, revenue protection, or risk reduction.
Readiness: process stability, data quality, ownership, and integration feasibility.
Delivery effort: implementation, change management, testing, and maintenance burden.
Risk: impact of errors, sensitive data exposure, legal obligations, and reversibility.
Evidence quality: strength of the baseline and the ability to measure a pilot.
Do not collapse the scores into one number until the team has discussed hard-stop risks. A high-value use case with unclear data rights or irreversible decisions should not outrank a safer pilot simply because its arithmetic score is higher.
Evidence Standard
For every important answer, capture one of:
a policy or contract section;
a system configuration or audit record;
a named owner who can confirm the process;
a measured baseline;
a test result;
a dated action to obtain missing evidence.
"We believe" is a prompt for more evidence, not a completed control.
Need an External Review?
TinyOps Studio LLC offers a fixed-scope AI Readiness & Governance Diagnostic for $2,500. It covers up to 50 staff and eight candidate use cases, with a readiness snapshot, prioritized register, governance gap memo, vendor scorecard, and 60-day action plan.
Purchase and fulfillment are handled through the TinyOps Whop checkout.
License
MIT. See LICENSE.
About
Practical templates for AI use-case prioritization, governance gaps, vendor review, and a 60-day action plan.
tinyopsstudio.com/ai-readiness-governance-sprint
Topics
governance
responsible-ai
ai-strategy
ai-governance
ai-readiness
vendor-risk
Resources
Readme
License
MIT license
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