AI Readiness Assessment

Why use an AI readiness assessment?

AI tools are incredibly powerful, but they need to be configured, integrated, and implemented properly for your team to get the full benefits. Without laying a firm foundation, you can end up with several types of issues: 

• Cyber security and data security problems
• Broken permissions that expose sensitive data to users
• Inaccurate or ineffective AI outputs due to dirty input data

An AI readiness assessment helps you determine where you’re at today (and what you need to do to launch AI effectively). 

How do you assess AI readiness?

You’ll need a framework to assess AI readiness. Without guidelines and structure, it’s too easy to miss something important. 

There are several leading AI readiness assessment frameworks in use today: 

• Deloitte’s AI Readiness & Management Framework
• Microsoft’s framework for assessing AI readiness
• Cisco’s AI Readiness Assessment

Another option is to use our self-service, interactive AI Readiness Assessment at the top of this page. 

What’s covered in an AI readiness assessment?

A true AI readiness assessment requires far more than a checklist or interactive tool. Here’s what we cover when we conduct an in-depth assessment for a client: 

1. Strategy and Planning

• Conduct an AI strategy session with leaders from IT and the business side.
• Define key AI adoption goals and metrics of success (examples: time savings, data hygiene, reduced manual work).
• Identify high-impact use cases and recommended pilot groups.
• Establish guiding principles around data governance, AI ethics, and user enablement.
• Develop phased rollout plan for AI tool(s).

2. Environment Preparation

• Review user permissions structure in internal systems to be accessed by AI.
• Detect anonymous and/or external links.
• Identify instances of broken permissions inheritance or unmanaged sharing.
• Department-level risk prioritization (examples: HR, finance, legal)
• Confirm whether DLP (data loss prevention) is enabled and labels are in use.
• In Microsoft environments, assess readiness of auto-labeling features under current license.
• Review security and compliance policies.
• Review Secure Score, audit log settings, sharing defaults, and retention policies.
• Identify risks around prompt visibility, tagging gaps, and sensitive data access.
• Evaluate cloud environment structure and readiness for AI integration.
• Recommend AI governance tiers for Client, Project, and Internal content.

3. Environment Remediation

• Remove anonymous links and excessive guest access.
• Enable and/or confirm audit logs and appropriate compliance settings.
• Update or enforce Secure Score-recommended configurations.
• Assign basic site owner responsibilities and provide access audit templates.
• Document any settings that were modified, skipped, or recommended for further escalation.

4. AI Implementation

• Implement AI tool(s) if not already running in environment.
• Execute integrations to key systems with full testing and refinement.
• Establish cadence for periodic review of system integrity and performance.

5. AI Enablement and Training

• Provide your team with an overview of AI capabilities, limitations, and data privacy.
• In Microsoft environments, explain the different contexts of “Work” and “Web” when using Copilot.
• Train your team on effective vs. ineffective prompting in business workflows.
• Offer practical prompt-building exercises for all integrated business applications.
• Cover every high-impact use case in depth for relevant users. Break out sessions by job function and use cases so hands-on training is relevant to each team member.
• Tie every prompt and use case back to real operational challenges at your organization.