# AI Readiness Evaluation Guide Prepared for use by Vermont AI Systems Purpose: Evaluate whether a business is ready to adopt AI safely, practically, and profitably. ## How to Use This Evaluation This evaluation is designed for business owners, leadership teams, operations managers, IT leaders, and department heads who want to understand where AI can help and what must be prepared before implementation. It can be used as a self-assessment, a guided discovery call, or the first step in a formal AI readiness engagement. Score each question from 1 to 5. 1 means the organization is not ready, has not considered the issue, or lacks the necessary information. 2 means the organization is in an early stage and may have informal ideas but no reliable structure. 3 means the organization is partially ready, with some practices in place but important gaps remaining. 4 means the organization is mostly ready and can likely support a focused AI pilot. 5 means the organization is highly ready, with strong ownership, useful data, practical workflows, security awareness, and implementation capacity. The maximum score is 120 raw points across 24 questions. To convert to a 100-point readiness score, divide the raw score by 120 and multiply by 100. ## Readiness Levels ### 0–39: Foundation Needed The organization should focus on AI education, workflow discovery, data organization, and security planning before major implementation. Recommended next steps include leadership training, staff awareness sessions, basic AI policy creation, and a short list of low-risk use cases. ### 40–64: Pilot Ready The organization likely has practical AI opportunities but needs structure before broader rollout. Recommended next steps include selecting one focused pilot, defining success metrics, reviewing data sensitivity, training the users involved, and choosing a safe deployment method. ### 65–81: Implementation Ready The organization appears ready for targeted AI agents, automation, or internal knowledge tools. Recommended next steps include solution design, integration planning, access control review, testing, monitoring, and support planning. ### 82–100: Advanced / Secure Scale The organization may be ready for broader AI adoption, private knowledge systems, VPS-hosted LLM environments, or onsite local LLM solutions. Recommended next steps include architecture planning, role-based access, retrieval-augmented generation over business knowledge, logging, monitoring, backup, and support processes. ## Category 1: Strategy & Leadership This section evaluates whether the organization understands why it wants AI and who will own adoption. 1. Leadership has clear business goals for AI adoption. Score: ____ 2. There is an internal owner or sponsor for AI initiatives. Score: ____ 3. The business can prioritize AI projects based on value and risk. Score: ____ Category subtotal: ____ / 15 Discovery prompts: What business problem is AI expected to solve? Who will make decisions about scope, budget, security, and approval? How will leadership decide which AI projects come first? ## Category 2: Workflow Opportunity This section evaluates whether the organization has practical workflows where AI can reduce time, cost, errors, or bottlenecks. 1. Teams can identify repetitive or document-heavy tasks that slow them down. Score: ____ 2. The organization has customer service, reporting, research, sales, operations, or administrative workflows that could benefit from AI assistance. Score: ____ 3. There are clear measures of success such as hours saved, faster response times, better accuracy, or improved customer experience. Score: ____ Category subtotal: ____ / 15 Discovery prompts: Which tasks are repeated every week? Which tasks require employees to search through documents, emails, tickets, spreadsheets, or systems? Where do delays or handoffs create frustration? ## Category 3: Data Readiness This section evaluates whether business information is accurate, accessible, and organized enough for AI use. 1. Important documents, procedures, and knowledge are stored in known locations. Score: ____ 2. Data quality is reliable enough for decision support, search, or automation. Score: ____ 3. The organization understands which data is sensitive, confidential, regulated, or unsuitable for public AI tools. Score: ____ Category subtotal: ____ / 15 Discovery prompts: Where does important company knowledge live? Are files current and organized? What information should never be pasted into a public AI tool? Are there customer records, financial data, employee data, health information, legal information, or trade secrets involved? ## Category 4: People & Training This section evaluates whether employees are prepared to use AI safely and effectively. 1. Employees have basic awareness of AI capabilities and limitations. Score: ____ 2. Teams are willing to learn new workflows and provide feedback during pilots. Score: ____ 3. The business has or is willing to create usage policies for AI tools. Score: ____ Category subtotal: ____ / 15 Discovery prompts: Who is already using AI at work? Are they using approved tools or personal accounts? Do employees understand hallucinations, data exposure, prompt quality, and human review requirements? Which teams need training first? ## Category 5: Technology & Integration This section evaluates whether AI can connect with current systems, files, applications, CRMs, email, databases, or internal tools. 1. The organization knows which systems AI may need to integrate with. Score: ____ 2. Current software systems have APIs, exports, automations, or other integration paths. Score: ____ 3. The business has access to technical support for deployment and maintenance. Score: ____ Category subtotal: ____ / 15 Discovery prompts: What software does the business rely on every day? Which systems contain the information AI would need? Are APIs available? Who manages accounts, permissions, backups, and integrations? ## Category 6: Security & Privacy This section evaluates whether the organization can protect business data when adopting AI. 1. Access controls and permissions are defined for key systems and data. Score: ____ 2. The organization has considered whether public AI, VPS-hosted LLM, or onsite local LLM deployment is appropriate. Score: ____ 3. There is a plan for monitoring AI usage, outputs, data exposure, and user access. Score: ____ Category subtotal: ____ / 15 Discovery prompts: What data can AI see? Who can access the AI system? Should the business use public tools, private hosted infrastructure, or local onsite models? Is logging needed? Are there compliance, contractual, insurance, or customer confidentiality requirements? ## Category 7: Governance & Risk This section evaluates whether the organization has practical rules for quality, accountability, and oversight. 1. The business has guidelines for what AI can and cannot be used for. Score: ____ 2. Human review is planned for important, sensitive, customer-facing, or financial decisions. Score: ____ 3. The organization can document AI workflows, decisions, prompts, or system behavior when needed. Score: ____ Category subtotal: ____ / 15 Discovery prompts: Which AI uses are allowed, restricted, or prohibited? Who reviews outputs before they reach customers? What happens if AI gives a wrong answer? Does the organization need audit trails or approval workflows? ## Category 8: Implementation Capacity This section evaluates whether the organization can execute a pilot, support it, and improve it over time. 1. The business can start with a focused pilot rather than trying to automate everything at once. Score: ____ 2. There is budget or time available for training, configuration, testing, and support. Score: ____ 3. The organization can measure outcomes and improve the AI solution after launch. Score: ____ Category subtotal: ____ / 15 Discovery prompts: What is the smallest useful pilot? Who will test it? How will success be measured? Who supports users after launch? How often should the solution be reviewed and improved? ## Score Summary Strategy & Leadership: ____ / 15 Workflow Opportunity: ____ / 15 Data Readiness: ____ / 15 People & Training: ____ / 15 Technology & Integration: ____ / 15 Security & Privacy: ____ / 15 Governance & Risk: ____ / 15 Implementation Capacity: ____ / 15 Raw total: ____ / 120 Readiness score: ____ / 100 ## Recommended Action Plan Template Based on the evaluation, the recommended next step is: Readiness level: _______________________________ Top three strengths: 1. _____________________________________________ 2. _____________________________________________ 3. _____________________________________________ Top three gaps or risks: 1. _____________________________________________ 2. _____________________________________________ 3. _____________________________________________ Recommended first AI pilot: ________________________________________________ Training needs: ________________________________________________ Security and deployment recommendation: Public AI tools acceptable for low-risk use: Yes / No / Limited Private VPS-hosted LLM should be considered: Yes / No / Later Onsite local LLM should be considered: Yes / No / Later Implementation notes: ________________________________________________ ## Suggested Vermont AI Systems Follow-Up Services After the evaluation, Vermont AI Systems can support the business with a leadership briefing, staff AI training, AI usage policy development, workflow automation planning, AI agent design, private knowledge assistant implementation, VPS-hosted LLM deployment, onsite local LLM planning, integration support, monitoring, documentation, and ongoing improvement.