Enterprise AI Maturity Assessment
Answer 25 questions and get an Enterprise AI Intelligence Score out of 100, scored across AI visibility, adoption, spend intelligence, governance, and value & ROI.
Free, no account, no payment. It takes about five to seven minutes, and your answers stay in your browser.
What an AI maturity assessment measures
An AI maturity assessment evaluates how well an organization can see and manage its AI program. Maturity is not the same as deployment: an organization can have AI in daily use across a dozen departments and still be unable to say what is in use, who uses it, what it costs, who owns it, or what it returns.
A mature enterprise AI program requires visibility across several areas at once — the AI ecosystem in use, adoption of it, spend on it, governance around it, business impact from it, and consistent measurement of the value it produces. Strength in one area rarely compensates for absence in another, which is why this assessment scores each dimension separately rather than issuing a single verdict.
The result is a directional score based on your own answers. It is not an audit, a certification, or a comparison against other organizations.
The five dimensions
- AI Visibility
- Whether the organization knows which AI tools, platforms, APIs, and agents are in use, and where.
- AI Adoption
- Whether real usage is measured, by whom, and whether that measurement holds up over time.
- AI Spend Intelligence
- Whether total AI cost is understood, attributable, and reviewed alongside how much of it is used.
- AI Governance
- Whether AI systems have owners, a sanctioned status, and a record that governance decisions can rely on.
- AI Value & ROI
- Whether AI investment is connected to defined outcomes and reported to leadership consistently.
Five sections, roughly five questions each. Choose the option closest to how your organization operates today — there are no right answers, and nothing is asked about confidential systems, finances, or individual employees.
AI Visibility
Section 1 of 5 · 0 of 25 questions answered
Whether the organization knows which AI tools, platforms, APIs, and agents are in use, and where.
Answer every question in this section to continue.
The five stages of AI maturity
The overall score maps to one of five stages. The stages describe measurement maturity — how reliably the organization can answer questions about its AI program — rather than the sophistication of the AI itself.
- 0–20 — Fragmented
- AI is being used across the organization, but visibility and measurement are limited. Most answers about what exists, what it costs, and what it returns would need to be assembled by hand.
- 21–40 — Emerging
- The organization has started to establish AI processes and measurement. Coverage is partial and depends on individual effort rather than a repeatable cadence.
- 41–60 — Developing
- AI adoption is becoming structured, but visibility and value measurement remain inconsistent across tools and teams. Some questions can be answered quickly; others still cannot.
- 61–80 — Managed
- There is meaningful cross-functional visibility and increasingly consistent measurement. The main gaps tend to be currency of the data and depth in one or two dimensions.
- 81–100 — Intelligent
- AI is managed with strong visibility across adoption, spend, governance, and business value. This reflects measurement maturity — it is not a statement about risk, compliance, or guaranteed returns.
How organizations improve AI maturity
Progress is almost always sequential. Adoption cannot be measured for tools nobody has recorded; spend cannot be attributed to teams whose usage is invisible; ROI cannot be defended when the adoption behind it is estimated. Programs that try to start with value reporting usually end up rebuilding the foundation later.
Start with visibility. A single enterprise AI inventory covering tools, APIs, and agents — with an owner and an owning team for each — is the artifact everything else attaches to. This is also where shadow AI becomes visible, as the gap between the sanctioned list and observed usage.
Then measure adoption honestly. Licenses assigned is a procurement number, not an adoption number. The adoption metrics that hold up measure active use, depth, and trend, reported by team.
Attach spend. Once usage is visible per tool and team, AI spend intelligence turns cost into cost per active user, which is what makes renewals, resizing, and consolidation decidable.
Formalize governance on the same record. Enterprise AI governance works when ownership and sanctioned status live on the inventory leaders already use, rather than in a parallel policy document.
Close with value. Value realization depends on the four links before it. When adoption, spend, and ownership are current, an AI ROI figure can show its inputs rather than assert a conclusion.
Why AI visibility sits underneath every other dimension
Every question an executive asks about AI resolves to a visibility question first. "Are we getting value from Copilot?" requires knowing who has it and who uses it. "Are we overspending?" requires knowing what is paid for and how much of it is used. "Are we exposed?" requires knowing which systems are in use and who owns them.
This is why the assessment scores enterprise AI visibility as its own dimension and why a low visibility score tends to cap the others: the data required to answer adoption, spend, governance, and value questions is the same data, viewed differently. It is also the argument behind Enterprise AI Intelligence as a category — one current record of the AI estate, rather than five disconnected reporting efforts.
On reassessment cadence: once or twice a year suits most organizations, plus a fresh run after a significant rollout, consolidation, or governance change. Reusing the same questions is what makes the movement between scores meaningful.
Common questions
What is an AI maturity assessment?
An AI maturity assessment is a structured review of how well an organization can see and manage its AI program — not just whether AI has been deployed. It examines whether the organization knows which AI tools are in use, who uses them, what they cost, who owns them, and what they return.
Is this assessment really free?
Yes. There is no payment, no account, and no signup gate. You see your full score, dimension breakdown, and recommendations before anything is asked of you.
How is the Enterprise AI Intelligence Score calculated?
Each of the 25 questions is answered on a five-point scale worth 0, 25, 50, 75, or 100 points. Each dimension score is the average of its questions, and the overall score is the unweighted average of the five dimension scores. Equal weighting is used for transparency, not because it is derived from research.
Is my score compared against other companies?
No. The score reflects your own answers only. We do not present industry benchmarks or comparisons against Midgentic customer data, because publishing a benchmark we cannot substantiate would make the result less useful, not more.
How long does the assessment take?
Most people complete it in five to seven minutes. It is written for executives and does not require technical detail or data lookups.
What happens to my answers?
They stay in your browser. Nothing is submitted or stored, no result URL contains your responses, and analytics records only that an assessment was started or completed — never the answers themselves.
How often should we reassess AI maturity?
Once or twice a year is a reasonable cadence for most organizations, plus a reassessment after a major rollout, consolidation, or governance change. Using the same questions each time is what makes movement comparable.