AI Maturity Self-Assessment

An Innovation Vista Analytics & AI Maturity Survey instrument

Mid-Market AI Maturity Index

A 5-minute diagnostic for mid-market leaders. Answer about 20 questions about how AI actually operates at your company — not how it's supposed to — and get a maturity score from 0 to 100, a dimension-by-dimension breakdown, and a comparison against peers in your industry and revenue band, drawn from our 2026 Analytics & AI Maturity Survey.

⏱ Takes about 5 minutes

The six dimensions we measure

  • Leadership & Accountability 22% of score Named AI ownership, board engagement, strategy above the pilot level, and budget commitment as a share of revenue.
  • Value Capture 20% of score Whether AI has moved from pilots to production to measured P&L impact, and whether ROI is measured at all.
  • Workforce Readiness 16% of score Employee access, training, and whether workflows are redesigned around AI rather than tools bolted onto old processes.
  • Governance & Risk Posture 16% of score AI-use policy, data protection for prompts and outputs, agentic action controls, and whether caution is proportional to the sector's cost of being wrong.
  • Data & Platform Foundation 14% of score Data quality, integration, and analytics maturity on the Survey-to-Optimize-to-Monetize ladder.
  • Agentic Readiness 12% of score Awareness and controlled deployment of agentic workflows and human-in-the-loop design.

What the assessment asks about

  • Pick the sentence closest to the truth about AI at your company right now.
  • When an AI decision needs making — a tool purchase, a policy call, a budget ask — where does it actually land today?
  • When two of your systems report the same number — revenue, headcount, inventory — do they agree?
  • An employee wants to use AI for their job today. What actually happens?
  • A new employee asks what the rules are for using AI here. What do they get?
  • Is any AI at your company doing work — taking actions, not just answering questions — today?
  • Your CFO asks what last year's AI spend returned. What can you actually show them?
  • Think about your last three board or ownership meetings. How did AI show up?
  • What stops sensitive data — client records, financials, contracts — from ending up in an AI tool it shouldn't?
  • How did the last new hire in a key role learn to use AI in their job?
  • How does information move between your key systems?
  • How would your executive team describe agentic AI — systems that plan and act, not just answer?
  • If we asked three of your executives to describe your AI strategy, what would we hear?
  • Think of the last AI win in one of your teams. What happened to it?
  • Where AI can act on its own — send, buy, change records — how is that governed?
  • Compare one of your core processes today to the same process two years ago. What's different because of AI?
  • Looking at last year's actuals, what did you spend on AI — software, people, and services — as a share of revenue?
  • What do you actually do with your data?
  • Think of your riskiest AI use case and your safest. Are they governed differently?
  • Before an AI agent does something consequential, what happens?

How scoring works

Each answer earns points on a 0-4 scale. Answers roll up into six weighted dimensions, then into a single composite score from 0 to 100. Your composite is compared against peers in the same industry and revenue band to produce a percentile. Where fewer than 30 peer responses exist for a segment, a modeled baseline benchmark is used and is clearly labeled as such, then replaced by live peer data as responses accrue. In sectors where the cost of an AI mistake is high, governance is weighted more heavily and judged more strictly, so identical adoption can score differently across industries.