AI maturity assessment model: compare teams fairly

Give your AI maturity assessment model boundaries

Your board wants to know why sales looks ahead of finance. Before your AI maturity assessment model becomes a department ranking, you need to answer: "Are we comparing similar opportunities to use AI, and whose answers are we seeing?"

An overall score gives you a starting point. We think treating it as a league table asks it to do too much; use the comparison to choose support, not declare a winning department.

The checks below are editorial guidance, not a mandatory standard or validated fairness test. Complete this first check when the comparison has a defined scope and a decision attached.

• Name the departments, eligible employees and assessment rounds.
• Choose between a department snapshot and change within a department.
• Specify the decision: allocate support, investigate a barrier or review progress.

Put tasks and access beside scores

Imagine sales uses an approved assistant for draft emails while finance handles data that isn't permitted in that tool. Before interpreting their scores, ask: "What work could each team reasonably do with the access it actually has?"

We'd investigate working conditions before assigning more training. Your AI maturity assessment model comparison is ready for interpretation when each department has a context note covering the checks below; don't invent a formula to compensate for differences.

• Describe main tasks, plausible AI uses and unsuitable or restricted work.
• Record approved tools, actual access and permitted data.
• Record policy restrictions and tool rollout dates.
• Assign an owner to investigate each access difference.

Check who each result represents

AAPOR's December 2025 guidance explains that response rate alone cannot establish the extent of nonresponse error. Ask "Which roles are missing?" alongside "How many people answered?"

Pew Research Center's 2024 survey-design guidance distinguishes repeated snapshots of a population from panels following the same people; panel membership can also change. That distinction matters when you describe improvement: different respondents don't establish that particular employees improved.

Complete this check when the represented population and missing groups are visible. Use these suggested records to decide whether to narrow the comparison or report groups separately.

• Record eligible employees, invitations and completed responses.
• Compare respondent roles and tenure across departments and rounds.
• Label repeat department snapshots or follow-up of the same employees.
• Document missing groups and material changes in respondent composition.

Sources: Standard Definitions - AAPOR, Writing Survey Questions | Pew Research Center

Related reading: How to improve employee survey response rates without skew

Keep the measure and timing comparable

AAPOR's 2024 survey guidance recommends keeping the measure consistent when assessing change.

Ask "What changed between rounds besides the answers?" Complete this check when you can identify stable elements, document changes and flag comparisons needing qualification or a fresh baseline.

• Compare questions, response options and scoring rules.
• Check administration methods and the period employees were asked to consider.
• Record collection windows relative to training, launches and policy changes.
• Explain which changes affect interpretation before presenting a trend.

Sources: Best Practices for Survey Research - AAPOR

Turn the comparison into an owned action

AI Adoption Score's 360° AI Maturity Assessment reports levels from L1 Observer to L5 Native across Usage, Skills, Impact, Culture and Vision. Its results reflect self-reported practice, including perceived impact; they don't verify skill or establish causal productivity gains, and response checks catch only some overstatement.

We'd judge an AI maturity assessment model comparison by the decision it enables, rather than how tidy its ranking looks. Complete the final check with a record that connects the result to an action you can review.

• Present dimension scores alongside the overall level, respondent counts and context notes.
• Describe changes in answers without turning them into department performance rankings.
• Choose an intervention to investigate: access support, clearer rules or skills development.
• Record the action, accountable owner and next assessment window.

Allow time for the chosen action to reach employees.

Related reading: AI maturity roadmap: sequence fixes, not workstreams

Frequently asked questions

Who should own the comparison?

Assign one AI program owner to coordinate it. As editorial guidance, ask department leads to confirm task context, IT to confirm access conditions and a survey analyst to review respondent composition. Give the person responsible for the chosen intervention a separate, explicit action so ownership doesn't disappear into the report.

Should we delay reporting until every department responds?

You can publish a scoped interim report. Our editorial recommendation is to identify the departments and groups included, mark missing coverage clearly and limit decisions to that scope. Keep a later review point for broader coverage rather than presenting an incomplete organization-wide comparison as settled.

How much detail should the board receive?

Give the board enough detail to assess the proposed decision. As an editorial reporting approach, use a short decision note containing the comparison scope, the main uncertainty, the requested intervention and its owner. Keep supporting department records available for scrutiny instead of filling the main presentation with every survey item.