AI Skills Assessment for Any Role
The same rubric works everywhere AI touches work
AI proficiency is not limited to technical roles. Marketing teams use AI to generate campaigns and analyze sentiment. Operations teams use it to optimize processes and automate reporting. Finance teams use it for forecasting and anomaly detection. Legal teams use it for contract review and research. In every case, the same core skills matter: can this person communicate effectively with AI, evaluate its output critically, and integrate it into a reliable workflow? AISA's five-dimension framework applies universally — the conversation adapts to the domain, but the measurement stays consistent.
What We Assess
Specific AI competencies we probe through natural conversation, tailored for others.
Domain-Specific AI Application
How does the candidate apply AI within their professional domain? We look for evidence of AI usage that reflects understanding of domain-specific constraints, standards, and quality expectations. A marketer should know that AI-generated copy needs brand voice review. An analyst should know that AI forecasts need assumption validation. The assessment surfaces whether this domain awareness exists.
Output Evaluation in Context
Can the candidate evaluate AI outputs against the standards that matter in their role? For marketing, that means brand consistency and factual accuracy. For finance, that means numerical precision and regulatory compliance. For HR, that means bias awareness and legal appropriateness. We assess whether the candidate applies the right evaluation framework for their domain.
Communication with AI Systems
Does the candidate communicate effectively with AI tools? This means providing sufficient context, specifying constraints, and iterating on outputs. We see significant variance here — many professionals in non-technical roles use AI the same way they would use a search engine: short queries, hope for the best. The assessment differentiates between casual and structured AI communication.
Risk Awareness
Does the candidate understand what can go wrong? AI-generated content can contain hallucinated facts. AI-assisted analysis can reinforce existing biases. AI-drafted communications can misrepresent the organization. We assess whether the candidate has a working mental model of AI failure modes relevant to their role and whether they have safeguards in place.
Dimension Focus
AISA scores across five dimensions. Here is how they weight for others.
Critical Thinking
22%Across non-technical roles, Critical Thinking is consistently the most consequential dimension. When a marketer accepts AI-generated copy without checking for factual accuracy, when an HR professional uses AI-drafted policy language without legal review, when a finance analyst trusts an AI forecast without validating assumptions — the risk is the same. We assess whether candidates have a structured approach to evaluating AI outputs in their specific domain.
Workflow & Application
25%The gap between "I use ChatGPT sometimes" and "AI is integrated into my daily workflow with quality gates" is where professional value lives. We assess whether the candidate has built repeatable, domain-specific AI workflows — not just ad hoc usage. A recruiter who has a structured process for AI-assisted candidate sourcing, screening, and outreach demonstrates fundamentally different capability than one who occasionally asks ChatGPT to write a job description.
Safety & Responsibility
10%Non-technical roles often handle sensitive data — employee records, financial data, customer information, legal documents — and AI usage in these contexts carries specific risks. We assess whether candidates understand data privacy implications of AI tool usage, recognize when AI-generated content might create legal or compliance exposure, and know when human judgment must override AI suggestions.
Roles That Benefit from AISA
The assessment conversation adapts to any professional context. Here are some of the roles teams commonly assess beyond the core four.
Marketing & Content
AI-generated campaigns, SEO content, sentiment analysis, audience segmentation
Operations & Strategy
Process automation, report generation, forecasting, decision support
Finance & Accounting
Financial modeling, anomaly detection, audit preparation, compliance analysis
Legal & Compliance
Contract review, regulatory research, risk assessment, policy drafting
HR & Talent
Candidate sourcing, job description writing, policy development, employee analytics
Sales & Customer Success
Prospecting, proposal generation, call analysis, churn prediction
Why One Rubric Works for Every Role
The five dimensions AISA measures — Prompting & Communication, Critical Thinking, Technical Understanding, Workflow & Application, and Safety & Responsibility — are universal AI skills. A marketer and a developer both need to communicate clearly with AI systems. They both need to evaluate outputs critically. They both need to integrate AI into reliable workflows.
What changes is the conversation topic, not the measurement framework. When AISA assesses a marketing professional, the conversation explores AI-assisted content creation, campaign optimization, and audience analysis. When it assesses a finance professional, the conversation explores forecasting, data analysis, and compliance-aware AI usage. The scoring criteria remain the same — the evidence is domain-specific.
This consistency is what makes cross-team benchmarking possible. When an organization assesses multiple teams using AISA, they can compare dimensional profiles across departments and identify which teams need which types of AI skills development. Read more about how the scoring framework works in the AISA rubric documentation.
Getting Started
To assess a team member in a non-standard role, select "Other" when creating the assessment and specify the role context. AISA's conversation will adapt to explore AI skills through the lens of that role's daily work.
For large-scale team benchmarking across multiple departments, including guidance on framing the assessment and interpreting cross-team results, see our AI skills gap implementation guide.
Why It Matters
Every team in the organization is adopting AI tools. The question is whether they are adopting them well. A marketing team with strong AI skills produces more content, faster, without sacrificing brand standards. An operations team with strong AI skills automates routine analysis while maintaining accuracy. A legal team with strong AI skills accelerates research without introducing risk. AISA gives functional leaders the same evidence-based measurement that engineering leaders use — a dimensional profile showing exactly where each team member's AI skills are strong and where they need development. The report includes direct quotes from the assessment conversation so you can see the reasoning, not just the score.
Further Reading
Understand the methodology and context behind our AI skills assessment.