Free Skill Matrix Template for Excel & Google Sheets | HR Gap Analysis Tool
Free Skill Matrix Template for Excel & Google Sheets | HR Gap Analysis Tool
An AI skills matrix template gives HR and managers a shared view of who can do what with AI. Before you roll out large training programmes, it makes the baseline and target profiles per role visible. Because this matrix is tool‑agnostic, it works across ChatGPT, Microsoft Copilot and embedded AI features in Office, HR systems or collaboration tools. The result: clearer expectations, fairer promotion decisions and focused learning paths instead of generic “AI awareness” sessions.
| Skill area | Aware | Beginner | Practitioner | Power User | Champion |
|---|---|---|---|---|---|
| AI fundamentals & concepts | Recognises terms like ChatGPT, generative AI and machine learning in meetings. Can describe in simple words why colleagues use AI for drafts, research or translations, but rarely uses it alone. | Uses AI with simple prompts to answer questions or summarise texts. Asks for help before using AI with internal data and follows team rules when someone reminds them. | Explains strengths and limits of AI to others (hallucinations, training data, bias). Uses AI weekly for concrete tasks such as drafting HR emails, meeting notes or concept outlines and checks output critically. | Identifies new use cases in their area (e.g. automating HR FAQs, manager dashboards). Compares AI vs non‑AI options and chooses the right approach, documenting impact such as time saved or errors reduced. | Sets AI vision for their function and connects it to strategy and KPIs. Sponsors AI initiatives, secures budget, aligns with Datenschutz and Betriebsrat, and communicates a realistic view of benefits and limits. |
| Prompting & workflow design | Understands that clear instructions change AI output quality. Uses predefined example prompts from colleagues or training slides without major adaptation. | Writes short prompts with basic context (role, task, language). Iterates once or twice to improve results when asked, but rarely documents what worked. | Designs multi‑step prompt workflows (e.g. “draft → critique → improve”) for recurring tasks. Saves and reuses prompts for HR, manager or IC workflows and shares them in a team space. | Builds end‑to‑end AI‑assisted processes such as candidate communication flows or monthly report generation. Tests variations, measures quality and standardises best prompts for others. | Defines organisation‑wide prompt patterns, templates and naming standards. Coaches teams on designing robust workflows and evaluates new AI features or tools for fit with existing processes. |
| Data literacy & privacy (incl. GDPR) | Knows that AI relies on data and that GDPR and company policies apply. Avoids sharing obviously sensitive information such as health data or salary lists with public tools when reminded. | Consistently avoids personal data in public AI tools and asks before using internal documents. Uses simple checks (spot‑checks, second pair of eyes) to see if AI output looks plausible. | Understands concepts like data minimisation, anonymisation and purpose limitation. Prepares clean, anonymised inputs for AI, documents sources and flags suspicious or biased outputs to the team. | Designs data flows and documentation for AI use cases in their area. Works with Datenschutz and IT to define retention, access rights and audit trails and trains colleagues on safe patterns. | Leads AI data governance across units, involving the Datenschutzbeauftragte and Betriebsrat. Reviews high‑risk use cases, signs off on guidelines and ensures audits and DPIAs are in place. |
| Tool proficiency (ChatGPT, Copilot, Office, HR tools) | Is aware that tools like ChatGPT, Copilot or AI features in HRIS exist. Has watched a demo or joined a basic training, but uses them only when someone sets it up. | Uses AI features in daily tools with guidance: drafts mails in Outlook, writes formulas with Excel AI, asks ChatGPT to rephrase texts. Needs support to troubleshoot errors or logins. | Works independently with approved AI tools across devices. Builds small automations (e.g. standard response templates, 1:1 note summaries) and helps colleagues fix simple issues during daily work. | Customises tools for team needs (prompt libraries in Teams, reusable HR templates in ChatGPT, Copilot views for managers). Creates short guides or videos and runs internal micro‑trainings. | Evaluates new AI tools with IT and HR, involving security and works council. Decides which tools to pilot, coordinates rollouts and ensures documentation, training and support structures exist. |
| Collaboration, communication & change | Is open‑minded about AI but unsure how it affects their job. Listens to others’ experiences and joins AI demos or brown‑bag sessions without leading them. | Shares simple AI tips with colleagues (e.g. “this prompt helped me summarise policies”). Asks for feedback on AI‑generated drafts and adapts when others raise concerns. | Leads small peer sessions to show concrete AI workflows for HR, leadership or IC tasks. Invites criticism, addresses fears and helps create psychological safety for experiments. | Drives cross‑team AI initiatives such as an enablement community or prompt library. Ensures diverse roles are represented, tracks adoption and removes blockers like unclear rules or tool access. | Integrates AI topics into company rituals: offsites, leadership meetings, works council dialogues. Connects AI use with culture, wellbeing and job design so people feel included, not replaced. |
| Governance, risk & ethics | Understands that AI can create legal, ethical and reputational risks. Knows that the company has AI rules and where to find them, but still needs reminders. | Follows do’s and don’ts (no confidential data in public tools, no automated hiring decisions). Escalates unclear cases to manager, HR or compliance instead of guessing on their own. | Identifies and documents potential risks in AI outputs (e.g. biased wording in job ads, unfair recommendation lists). Suggests mitigations such as human review steps or alternative prompts. | Writes or updates team‑level AI guidelines and checklists with HR, Legal and IT. Trains others on safe usage, keeps logs of important AI‑supported decisions and reviews edge cases. | Owns the AI governance framework for the organisation. Aligns with emerging regulation (e.g. EU AI Act), coordinates Betriebsrat consultations and ensures risks and incidents are monitored and reported. |
Key takeaways
- Use this matrix to map current AI skills and define realistic target profiles per role.
- Link gaps in each domain directly to concrete AI trainings and learning paths.
- Structure performance reviews and promotion cases around observable behaviours, not gut feeling.
- Run calibration rounds so managers apply the ai skills matrix template consistently.
- Update the matrix yearly with Datenschutz and Betriebsrat to stay compliant and relevant.
What this AI skills framework is
This AI skills matrix template is a vendor‑agnostic framework that describes AI‑related behaviours from basic awareness to organisational leadership. HR, managers and employees use it to align career paths, promotion criteria, performance reviews, development plans and peer feedback. By turning “AI skills” into clear, observable actions, it enables fairer decisions and targeted, role‑based AI training.
AI skill levels & scope
Each level in the matrix expands responsibility, autonomy and impact. Aware and Beginner employees execute defined AI tasks under guidance, while Practitioners run their own workflows. Power Users and Champions design new use cases, influence policy and enable others across teams.
Example: In an HR team, a Beginner uses ChatGPT to draft job ads from a template. A Practitioner designs a complete, GDPR‑safe sourcing workflow with anonymised profiles. A Champion shapes the company’s AI hiring guidelines and gets buy‑in from Betriebsrat and Legal.
- Define per level which scope applies: own tasks, projects, cross‑team programmes or strategy.
- Describe decision authority: who can approve AI use cases, tools or data flows at each level.
- Connect levels to role titles and bands so promotion paths are transparent for everyone.
- Use the same levels across functions (HR, Finance, Product) to keep language consistent.
- Ask managers to prepare 2–3 concrete examples per level for calibration sessions.
Example role profiles & target levels
You can assign target levels per domain for typical roles instead of expecting everyone to become a Champion. This keeps training realistic and focused on business needs.
| Role | AI fundamentals | Prompting & workflows | Data & privacy | Tool proficiency | Collaboration & change | Governance & risk |
|---|---|---|---|---|---|---|
| HR generalist | Practitioner | Practitioner | Practitioner | Practitioner | Beginner | Beginner |
| People manager (any function) | Practitioner | Practitioner | Beginner | Practitioner | Power User | Practitioner |
| Knowledge worker IC (e.g. Marketing, Finance) | Practitioner | Practitioner | Beginner | Power User | Beginner | Beginner |
| Leadership team / C‑level | Champion | Power User | Champion | Practitioner | Champion | Champion |
Use these as starting points and adjust to your context and risk appetite. For example, a data‑heavy role in Finance may need higher levels in “Data & privacy” than a comparable role in Internal Communications.
AI skill domains in your ai skills matrix template
The six domains cover what knowledge workers and managers need to use AI safely and productively. They range from basic understanding of AI concepts to governance, risk and change leadership.
According to a LinkedIn‑based analysis, AI job posts grew by roughly 450% year‑over‑year. Without clear domains and levels, this demand quickly turns into vague expectations and frustration.
- AI fundamentals & concepts: from “can explain ChatGPT in simple words” to “sets AI vision and priorities”.
- Prompting & workflow design: from one‑off prompts to robust, documented multi‑step workflows.
- Data literacy & privacy: from “knows GDPR exists” to “owns AI data governance with audits”.
- Tool proficiency: from joining demos to piloting and standardising tools with IT and HR.
- Collaboration & change: from sharing tips to orchestrating AI enablement and psychological safety.
- Governance & risk: from following basic rules to defining company‑wide AI policies.
When you define these domains, involve IT, Legal/Datenschutz, Betriebsrat and business leaders. This mirrors good practice from structured skill management frameworks and avoids building a pure “tech toy” matrix.
Rating scale & evidence
A clear rating scale turns subjective impressions into shared language. In this matrix, Aware to Champion describe how independently someone works with AI, how complex their tasks are and how strong their multiplier effect is.
Example: Two recruiters both use AI for job ads. One copies a prompt from a slide and asks a colleague to check every draft (Beginner). The other designs their own workflow, documents templates, trains peers and measures better response rates (Power User).
| Level | Short definition |
|---|---|
| Aware | Understands basic concepts, observes demos, uses AI only with close guidance and predefined prompts. |
| Beginner | Executes simple AI tasks with support, follows rules, starts forming own prompts for daily work. |
| Practitioner | Uses AI independently for core tasks, designs basic workflows and improves them through feedback. |
| Power User | Builds robust AI workflows, enables others, measures impact and influences tool and process choices. |
| Champion | Shapes vision, governance and culture, connects AI use to strategy, compliance and organisation‑wide change. |
- Ask for specific evidence: documents, screenshots, prompts, metrics, feedback or recordings of AI workflows.
- Connect ratings to existing processes in performance management and development plans.
- Use a simple grid where employees self‑rate and managers rate, then compare and discuss gaps.
- Document “Case A vs Case B” examples in a shared space so managers see how levels differ.
- Store ratings and examples in a system (e.g. Sprad Growth or another talent platform) instead of local files.
Growth signals & warning signs
Clear signals help you see who is ready for the next level and where risk sits. Promotions and bigger AI responsibilities should follow visible patterns of behaviour over time, not one impressive demo.
Hypothetical example: Maria, an HR business partner, automates her monthly people‑analytics report with AI, shares the template, supports two other BPs in setting it up and documents GDPR‑safe data flows. Over two quarters this pattern shows Power User behaviour.
- Growth signals: repeated delivery of AI‑supported outcomes with fewer errors and less supervision.
- Multiplier effect: colleagues use their prompts, guides or templates without needing them every time.
- Scope expansion: they move from team‑only use cases to cross‑team or cross‑country workflows.
- Compliance maturity: they proactively involve Datenschutz/Betriebsrat rather than asking last minute.
- Warning signs: inconsistent results, ignoring policies, hiding experiments or refusing feedback.
Team check‑ins & review sessions
Regular check‑ins keep the ai skills matrix template alive instead of a one‑off Excel file. They also reduce rating differences between managers and functions.
Example: Once per quarter, all People Managers in one business unit join a 60‑minute session. Each brings two short AI use‑case examples from their team, presents the evidence and proposes levels. The group discusses and aligns on ratings, while HR notes patterns and training needs.
- Run light, recurring calibration sessions by function (e.g. HR, Sales, Operations) using real examples.
- Use behaviour anchors from the matrix to guide the discussion instead of “senior vs junior” labels.
- Do basic bias checks: compare ratings across gender, age, location and manager to spot patterns.
- Capture final levels and key evidence in your performance or talent management system.
- Follow up with targeted AI trainings where many employees show the same gaps.
Interview questions by AI skill domain
You can also use the matrix for hiring or internal moves. Behaviour‑based questions reveal if candidates have real experience or only watched demos.
Example: For a People Manager role, you might ask, “Tell me about a time you helped your team adopt a new AI‑based way of working. What did you do, and what changed?” Strong answers mention specific workflows, doubts, metrics and lessons.
- AI fundamentals: “Describe when you learned a new AI tool or concept to solve a work problem.”
- Prompting & workflows: “Give an example where you iterated prompts to reach a reliable output. How?”
- Data & privacy: “Tell me about an AI use case where GDPR or Datenschutz questions came up. What did you do?”
- Tool proficiency: “How do you use tools like ChatGPT or Copilot in your weekly routine? What changed?”
- Collaboration & change: “Describe how you involved colleagues in testing or improving an AI workflow.”
- Governance & risk: “Share a situation where you stopped or changed an AI idea because of risk concerns.”
Probe for outcomes (“What was the result?”), scale (“How often do you do this?”) and learning (“What would you do differently now?”). Higher‑level candidates talk about patterns, stakeholders and metrics, not only one‑off tasks.
Implementation & updates for DACH HR
Rolling out an AI skills matrix in DACH needs more than a fancy template. You balance speed with co‑determination, GDPR and psychological safety, especially when AI skills may affect future promotions.
Start small: one pilot department, a simple self‑assessment and a workshop to align expectations. Then connect the results to your AI training programmes and individual development plans. Resources like AI training for employees and AI training for HR teams can provide ready‑to-use curricula that map directly to the six domains.
- Clarify early with Betriebsrat whether the matrix is used for development only or also for formal evaluation.
- Involve Datenschutz to define what evidence can be stored, where and for how long.
- Use anonymous, aggregated reports for training design; keep individual ratings within performance processes.
- Assign a clear owner (e.g. L&D or HRBP) and schedule an annual review to update behaviours and tools.
- Integrate the matrix with broader talent development and skill management efforts.
Benchmarks/Trends (2024)
Many companies still run one‑off keynotes instead of structured, role‑based AI enablement. Experience from skill frameworks and internal marketplaces shows that organisations linking clear skill profiles to learning offers and performance processes see higher internal mobility and lower attrition over time. The same logic applies to AI skills: clarity first, then targeted development.
Conclusion
An AI skills matrix gives your organisation a shared language for AI expectations. Instead of “be more digital” or “use AI more”, employees see concrete behaviours by level and domain, and managers have a fairer basis for feedback, promotions and training decisions. This clarity builds trust, especially when you explain how the matrix links to performance and career paths.
The framework also supports fairness. Behaviour‑based anchors reduce bias in performance reviews and talent reviews, because managers argue from evidence rather than from vague impressions. When you combine the ai skills matrix template with structured calibration sessions and transparent documentation, you raise the quality and defensibility of your talent decisions.
Finally, the matrix makes AI development manageable. You do not need everyone to be a Champion; you define realistic target levels per role and plan training accordingly. Over the next three months, you can pick one pilot team, run self‑ and manager‑assessments, align in a calibration workshop and design a focused AI learning path. Within six to twelve months, an HR owner can extend the matrix to more units, link it into performance reviews and internal mobility processes, and run the first annual update round with input from Datenschutz and Betriebsrat.
FAQ
How do we start using this AI skills matrix without overcomplicating things?
Start with one pilot team and the six domains only. Ask everyone for a quick self‑rating and 1–2 concrete examples per domain. Managers add their view and discuss differences in 1:1s. Then run a short calibration session to align levels and collect training needs. Keep documentation light at first and only add detail once people understand the language and see value.
Should AI skill ratings influence promotions and salary decisions?
Not at the very beginning. Use the matrix first as a development and training tool, so employees feel safe to be honest. Once behaviours and evidence standards are well understood, you can gradually link certain domains and levels to promotion criteria for relevant roles. Be explicit in your policies and involve Betriebsrat. Always combine AI skills with broader performance and impact, not as a standalone score.
How can we avoid bias when assessing AI skills?
Bias shrinks when decisions rely on observable behaviours and shared rubrics. Use the matrix as a checklist: “Which examples prove this level?” Collect evidence from multiple sources where possible, not only manager opinions. Run calibration sessions across teams to align expectations. Finally, monitor rating patterns by gender, age, location and manager. If you see clusters, review examples and adjust training or communication.
What is the best way to combine this matrix with AI training programmes?
Use the matrix as the “front door” for all AI enablement. After self‑ and manager‑assessments, group employees by target levels and domains. Map each cluster to a learning path: foundations, prompt labs, tool‑specific sessions or governance deep dives. Track before/after ratings and confidence to measure progress. This turns training from generic offers into targeted journeys tied to role needs and future career steps.
How often should we reassess AI skills and update the matrix?
Reassess key roles at least once per year, ideally linked to your regular review cycle. For high‑change teams (e.g. data, digital, HR), a light mid‑year check can help. Review the matrix itself yearly with stakeholders: adjust behaviours, add new tools and reflect regulatory changes such as the EU AI Act. Treat the document as living: stable enough for fairness, flexible enough to adapt to new AI reality.
Jürgen Ulbrich
Jürgen Ulbrich has more than a decade of experience in developing and leading high-performing teams and companies. As an expert in employee referral programs as well as feedback and performance processes, Jürgen has helped over 100 organizations optimize their talent acquisition and development strategies.
