A 5-minute self-assessment for business leaders
Rate each statement on a scale of 1 to 5 based on how accurately it describes your organization today. Write your score in the box next to each question.
Our key business data is digitized, centralized, and accessible (not trapped in spreadsheets or silos)
We have clean, labeled datasets that could be used to train or fine-tune AI models
We have data governance policies (privacy, security, retention) documented and enforced
We have team members who understand AI/ML concepts and can evaluate vendor claims
Our technical staff could integrate an AI API or tool into existing workflows
We have a plan (or budget) for AI-specific training and upskilling
We have documented, repeatable processes for our core business operations
We can identify at least 3 workflows where AI could save time or reduce errors
We measure process KPIs and would know if an AI tool improved performance
Executive leadership has explicitly endorsed exploring AI for the business
There is a named person or team responsible for AI initiatives
Leadership understands that AI projects need 3-6 months to show measurable ROI
We have allocated (or can allocate) budget specifically for AI tools and implementation
We are prepared to invest in both technology and the change management required
We have a realistic expectation of costs — not expecting enterprise AI for free
Add up all 15 scores. Find your tier below.
Focus on digitizing your data and documenting your processes before investing in AI tools.
You have a foundation — now prioritize quick wins to build momentum and executive confidence.
You're well-positioned to implement AI — the key is choosing the right first project and moving fast.
Data Infrastructure: Scores below 9 mean your data isn't AI-ready. Start here — everything else depends on it.
Team Skills: Low scores here mean AI projects will stall. Budget for training before buying tools.
Process Maturity: If you can't measure a process today, you can't improve it with AI tomorrow.
Leadership Buy-in: Without executive sponsorship, even great AI projects die in pilot. Align leadership first.
Budget & Resources: AI isn't free, but it doesn't have to be expensive. Realistic expectations prevent disappointment.
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