Identify capability gaps and prioritize training with evidence.

An AI skills gap analysis compares current proficiency against target capability to identify where training is needed, which skills to prioritize, and how to allocate development resources. Evidence from assessments reveals gaps that self-reported proficiency and course completion rates cannot.

What assessment data reveals

Emeri assessments provide individual grades (E1 to E4), percentiles, and six-competency profiles. For individuals, this shows personal strengths and gaps. An organization may ask participants to share those results voluntarily, but Emeri does not currently aggregate or analyze team results.

A practical gap analysis process

Step 1: Establish baseline proficiency. Assess current capability through individual assessments. The free check provides private results; certification provides verified credentials. Organizations interested in a future team program can contact support@emeri.org to discuss a pilot. Aggregate reporting is not currently available.

Step 2: Define target proficiency. Decide what level of AI skill each role requires. Not every job needs E4 (Exceptional) proficiency — E2 (Proficient) may be the right target for roles where AI is occasionally useful but not central. Be honest about what each role actually needs.

Step 3: Identify gaps by competency and role.Compare baseline results to target proficiency. Look for patterns: if most people score low on “Checking the work,” that signals a verification training need. If one department is significantly weaker than another, that may suggest where to focus enablement first — but only if the organization has collected comparable results with participants’ knowledge and consent.

Step 4: Prioritize based on impact and feasibility.Not all gaps deserve equal attention. Focus on skills that are load-bearing for business goals (e.g., if customer support uses AI daily, “Delivering” and “Safety” may be critical). Balance high impact with achievability — a foundational gap may be easier to close than an advanced one.

Step 5: Develop and deploy targeted training.Use the six-competency framework to design training that addresses specific gaps. Emeri’s free study guides cover every competency measured by the assessment. Custom training should focus on behaviors, not trivia — practice writing better prompts, checking sources, and recognizing when AI adds risk.

Step 6: Reassess and track improvement. Measure progress by reassessing after training. Improvement in specific competencies validates that training worked; persistent gaps signal the need for different approaches or more practice. Organizations can make before-and-after comparisons only from results participants choose to share; Emeri does not currently provide a team progress dashboard.

Limits of gap analysis

Assessment data shows proficiency at a point in time, not readiness for every future task. A person with an E3 grade may still struggle with an unfamiliar AI tool or a novel use case. Gap analysis identifies where capability is weak NOW; it does not predict every scenario where someone might need help later.

Also, proficiency is only one dimension of AI readiness. Skill gaps matter, but so do tool availability, policy clarity, and organizational support. See the Workforce AI Readiness page for the distinction between people capability, infrastructure, and governance.

Team vs individual gap analysis

Individual gap analysis works with the free check or certification. Results show six competency scores, making it clear which skills to study. Anyone can do this independently today.

Team gap analysis requires aggregate data: grade distributions, average scores by competency, and segmentation by role or department. Emeri does not currently provide those features. Organizations would need to compile consented individual results independently, or contact support@emeri.org to discuss a possible future pilot. See the Teams page for what is available now versus what requires a pilot.

Related resources

The AI Skills Assessment Framework defines the six competencies and behavioral markers for each band. The AI Skills Matrix maps competencies to observable behaviors at foundational, developing, and stronger levels.