Business AI readiness assessment

Score your organization’s readiness for useful, secure AI adoption.

Use this evaluation to identify strengths, gaps, risks, and next steps before investing in AI tools, agents, automation, VPS-hosted LLMs, or onsite local LLM systems.

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How to score

Rate each item from 1 to 5.

Use 1 for not ready or unknown, 3 for partially ready, and 5 for strong readiness. The assessment produces a directional score, not a substitute for a full technical and security review.

1 Not ready2 Early3 Partial4 Strong5 Excellent

1. Strategy & Leadership

Does the organization understand why it wants AI and who will own adoption?

Leadership has clear business goals for AI adoption.

There is an internal owner or sponsor for AI initiatives.

The business can prioritize AI projects based on value and risk.

2. Workflow Opportunity

Are there practical workflows where AI can reduce time, cost, errors, or bottlenecks?

Teams can identify repetitive or document-heavy tasks that slow them down.

The organization has customer service, reporting, research, sales, operations, or admin workflows that could benefit from AI assistance.

There are clear measures of success such as hours saved, faster response times, or better accuracy.

3. Data Readiness

AI becomes more useful when business information is accurate, accessible, and organized.

Important documents, procedures, and knowledge are stored in known locations.

Data quality is reliable enough for decision support, search, or automation.

The organization understands which data is sensitive, confidential, regulated, or unsuitable for public AI tools.

4. People & Training

AI adoption succeeds when people know how to use it safely and effectively.

Employees have basic awareness of AI capabilities and limitations.

Teams are willing to learn new workflows and provide feedback during pilots.

The business has or is willing to create usage policies for AI tools.

5. Technology & Integration

AI tools often need to connect with existing systems, files, CRMs, email, or databases.

The organization knows which systems AI may need to integrate with.

Current software systems have APIs, exports, or other integration paths.

The business has access to technical support for deployment and maintenance.

6. Security & Privacy

Security is central when AI may interact with business data, customers, internal documents, or decisions.

Access controls and permissions are defined for key systems and data.

The organization has considered whether public AI, VPS-hosted LLM, or onsite local LLM deployment is appropriate.

There is a plan for monitoring AI usage, outputs, data exposure, and user access.

7. Governance & Risk

AI needs practical rules for quality, accountability, review, and escalation.

The business has guidelines for what AI can and cannot be used for.

Human review is planned for important, sensitive, customer-facing, or financial decisions.

The organization can document AI workflows, decisions, prompts, or system behavior when needed.

8. Implementation Capacity

Successful AI adoption requires pilot discipline, support, iteration, and ownership.

The business can start with a focused pilot rather than trying to automate everything at once.

There is budget or time available for training, configuration, testing, and support.

The organization can measure outcomes and improve the AI solution after launch.

Not scored yet

0/100

Answer the questions to generate a readiness score and recommendation.

Recommended Next Step

Start by scoring the assessment. Low scores usually call for readiness planning and training. Mid-range scores are good candidates for pilot projects. High scores may be ready for secure AI agents or private LLM implementation.

Category Notes

Scores will appear after you begin rating questions.

Save Results to CRM

Capture the company and assessment score so it appears in the admin dashboard.