Practical tools and templates for responsible AI adoption in enterprise and startup environments.
Maintained by Thomas C. Grow II
Fractional Chief AI Officer · AI Strategy & Governance · Digital Transformation
Certified Chief AI Officer (CAIO), World AI University
Charter Member & Teaching Faculty, World AI Council
Co-founder, MarketHack.ai
If you find this useful, ⭐ star this repo to follow updates — new frameworks and templates are added regularly.
This repository contains working frameworks, assessment tools, and reference documentation developed through fractional CAIO engagements and AI transformation projects. The goal is practical utility — not theoretical completeness. Every document here has been shaped by real organizational challenges.
These materials are appropriate for:
- Organizations beginning an AI adoption journey
- CTOs and engineering leads needing governance structure before scaling AI
- Fractional executives looking for a starting reference point to adapt for clients
All frameworks are informed by and consistent with publicly recognized standards including the NIST AI Risk Management Framework (AI RMF), the EU AI Act, and ISO 42001. They represent independent professional practice, not proprietary curriculum.
/assessment/ai-readiness-scorecard.md
A structured diagnostic for evaluating an organization's readiness to adopt AI across five dimensions: data infrastructure, talent, process maturity, governance, and risk tolerance. Produces a baseline score and prioritized action areas.
Use this before committing to any AI implementation roadmap.
/governance/ai-governance-policy-template.md
A customizable policy framework covering:
- Acceptable use of AI tools by employees
- Data privacy and third-party AI vendor requirements
- Human oversight requirements by risk tier
- Incident response for AI-related failures
- Review and audit cadence
Designed to be adapted, not adopted wholesale. Every organization's risk profile is different.
/strategy/ai-transformation-roadmap.md
A phased approach to AI transformation structured around three horizons:
- Horizon 1: Quick wins — automating existing manual processes with measurable ROI
- Horizon 2: Integration — embedding AI into core product and operational workflows
- Horizon 3: Transformation — AI-native operations, predictive capabilities, and competitive differentiation
Includes stakeholder alignment guidance and KPI frameworks for each phase.
/governance/risk-tiering-model.md
A practical model for classifying AI use cases by risk level (Low / Medium / High / Critical) with corresponding oversight, testing, and approval requirements. Adapted from the EU AI Act and NIST AI RMF.
/governance/vendor-ai-checklist.md
Due diligence questions for evaluating third-party AI tools and vendors before organizational adoption. Covers data handling, model transparency, bias testing, SLA commitments, and exit provisions.
These frameworks reflect a governance-first philosophy — not because governance slows things down, but because organizations that skip it consistently encounter costly remediation, trust failures, and regulatory exposure later. Good governance is a growth enabler, not a brake.
Key beliefs embedded throughout:
- Human oversight is non-negotiable for consequential decisions, regardless of model confidence
- Transparency over opacity — if you can't explain how an AI decision was made, you shouldn't be making it at scale
- Risk is contextual — a chatbot for internal FAQs carries different risk than AI-driven hiring or lending decisions
- Adoption without culture change fails — technical implementation is the easier half
All content in this repository is offered as a starting point. Fork it, adapt it, make it yours.
Licensed under Creative Commons Attribution 4.0 International (CC BY 4.0) — use freely with attribution.
These frameworks are a sample of the tools I bring to fractional CAIO engagements. If your organization is navigating AI adoption and needs strategic leadership without a full-time hire, I'd be glad to talk.
I work with venture-backed startups, digital product studios, and established enterprises to build AI strategies that are governance-first, practical, and built to scale.
📬 Connect on LinkedIn · github.com/WarpedMind
Planned additions:
- AI ethics review checklist for product teams
- Prompt governance guidelines for enterprise LLM deployments
- Board-level AI reporting template (for executives presenting AI strategy to leadership)
- Case study: Transforming manual marketing analytics into autonomous AI workflows (MarketHack.ai)
Last updated: March 2026