AI Readiness Checklist
- James McGreggor
- Jun 12
- 5 min read
Introduction
Adopting AI is no longer just a technical milestone — it’s an enterprise-wide transformation that touches every layer of business, technology, and culture. Whether you're deploying copilots to support daily work or designing autonomous systems to optimize decision-making, your organization’s ability to succeed depends on far more than model performance. It hinges on clarity of purpose, architectural integrity, compliance confidence, change agility, and—above all—human readiness.
The following categories and questions serve as a comprehensive readiness framework to help teams critically assess their strategy, infrastructure, governance, and user experience. They are designed to expose blind spots, align stakeholders, and ensure that your organization can adopt AI in a responsible, scalable, and effective way. Each section reflects essential elements that must be considered early and revisited often throughout your AI journey.
While most questions are applicable to all organizations, be sure to tailor it to your specific organizational needs.
🔹 1. Strategic Alignment & Business Readiness
What are the defined business goals for AI integration, and how were use cases prioritized?
Have you defined a clear AI adoption roadmap with phased investment, team scaling, and review cycles?
Is there a defined RACI or decision framework for AI governance across business and tech teams?
How do you measure success, and are analytics in place to track impact on business outcomes?
What is your plan for launching and scaling AI (e.g., alpha, beta, pilot phases), and how is feedback collected and used?
Is your team trained to interpret AI-driven insights and convert them into actionable decisions?
Are you offering this AI solution as a service internally or externally, and if so, how is it being packaged?
🔹 2. Architecture, Infrastructure & Technical Design
What models are being used (public, fine-tuned, proprietary), and what criteria were used to choose them?
What overarching AI architecture are you using (e.g., agent-based, cognitive, hybrid), and why?
Are you using RAG, fine-tuning, or other techniques — and how are models being trained or updated?
How is the solution being deployed (e.g., AWS, Azure, GCP), and are there business constraints on platform choice?
Do you have a fall-back or degradation plan if your AI system becomes unavailable?
What communication models are being used (e.g., ACP, MCP), and are they optimized and maintainable?
How are you tracking and documenting architectural decisions and trade-offs (e.g., model size vs. latency)?
What is the overall system architecture, and how are components like LLMs, APIs, and storage integrated?
What does the data architecture look like (e.g., medallion, lakehouse, lambda), and is it appropriate for your needs?
What does your ML architecture look like (e.g., transformer, CNN, GAN), and why was it selected?
How are you handling non-text modalities (images, speech, tabular data) if the use case requires it?
🔹 3. Data, Compliance & Security
What compliance standards (e.g., HIPAA, SOC2, GDPR) are applicable, and how is compliance ensured?
Who owns the data, IP, and models, including after leaving your infrastructure?
How is responsibility for privacy, security, and model accuracy legally assigned — and are third-party contracts clear?
How are encryption and access controls managed, particularly in agentic systems or across external integrations?
How are you performing data lineage and traceability (from source to model inference)?
Is there a defined process for revoking or expiring data and outputs (e.g., due to data deletion or model updates)?
Where is your data stored, and how are multiple sources aggregated, cleaned, and maintained?
Is your data structured, well-governed, and labeled for effective model training and use?
Do you have a third-party audit strategy for your AI systems?
Are you using synthetic or mock data for testing, and how do you plan to transition to real data?
🔹 4. Development, Deployment & Change Management
What does the deployment pipeline look like — environments, approvals, and change control procedures?
How is versioning maintained across models, logic, interfaces, and data pipelines?
What is your plan for model drift, architecture updates, and infrastructure changes over time?
How are you monitoring model performance over time (e.g., accuracy drift, cost-per-response, response latency)?
Are humans-in-the-loop used for evaluation, and how is their feedback managed?
What tools and processes are used to protect against hallucinations — and do they evaluate internal consistency as well as outcomes?
Do you have a rollback and rollback validation process for hotfixes or failed updates?
How are user-generated feedback or behavior logs being used to improve the models?
What is the QA and testing process — are you using TDD, prompt-based validation, or interface contracts?
How are third-party integrations handled, and do interface contracts clearly define responsibilities?
Is documentation standardized, centralized, and accessible for future teams — or overly dependent on key individuals?
🔹 5. User Experience, Adoption & Governance
Do you have an AI Readiness Committee or cross-functional governance body that reviews and evolves these practices regularly?
Are you providing users with transparency on AI decisions (e.g., model confidence, explainability options)?
Is onboarding provided for users to understand what the AI does and doesn't do well?
Are you designing intuitive interfaces with consistent, accessible terminology for users at all levels?
Is there a feedback loop from users built directly into the interface (e.g., thumbs up/down, flagging)?
How is the user interface integrated — as a standalone layer or as a dynamic part of the AI experience (e.g., JIT UI)?
Does the user experience align with the AI feature's purpose — including visual, behavioral, and conversational cues?
How are you making the solution accessible to users, including those with disabilities or low technical fluency?
Are behavioral insights or adaptive learning included to improve interaction over time?
AI adoption isn’t just about building the right tool — it’s about building the right environment for that tool to succeed. The questions above offer a blueprint for readiness, helping you gauge whether your organization is equipped to integrate AI in a way that is technically sound, ethically grounded, and operationally sustainable.
By addressing these areas—strategic alignment, architecture, data and compliance, deployment, and user experience—you set the stage not only for smoother implementation but also for long-term trust, adaptability, and measurable value. The most successful AI initiatives are those rooted in clarity, governed by design, and centered on the people who use and manage them. Treat this readiness assessment not as a checklist to complete, but as an ongoing conversation to mature your capabilities and culture alongside your technology.
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