How CEE Employers Can Build the AI Skills They Actually Need
A practical framework for CEE employers to map AI skills, decide when to hire, develop or partner, and measure whether capability building improves workforce outcomes.
Maja Sušnik, Marketing Specialist
AI skills are no longer confined to data science teams. Finance analysts are checking generated forecasts, customer teams are using AI-assisted knowledge tools, and HR professionals are learning where automation helps and where human judgement still matters. For employers in Central and Eastern Europe, that changes the workforce question from “How many AI specialists can we hire?” to “Which AI capabilities do our people actually need?”
Why Is AI Skills Planning Urgent for CEE Employers?
AI capability is growing quickly, but specialist supply remains limited. The European Commission reports that EU AI talent more than doubled between 2016 and 2023, yet accounted for only 0.41% of the workforce. At the same time, AI is changing ordinary roles, so competing for a small pool of specialists cannot be the whole plan.
The training gap makes that challenge more immediate. According to the Commission’s DigComp 3.0 update, 30% of EU workers use AI systems, but only 15% have received AI training. Plenty of people are already learning by trial, error and whatever a colleague happened to show them before lunch.
AI talent… has more than doubled in the EU between 2016 and 2023, now representing 0.41% of the EU workforce.
European Commission, Shaping Europe’s Digital Future
Which AI Skills Does the Business Really Need?
Start with work, not fashionable job titles. A useful skills map separates people into three broad groups according to the tasks they perform and the decisions they make.
- AI builders design, develop, integrate or govern AI systems. They need deep technical capability alongside security, data and regulatory knowledge.
- AI-enabled specialists apply AI within finance, marketing, HR, customer operations or another domain. They need to evaluate outputs, redesign workflows and recognise when the tool is wrong.
- AI-literate users use approved tools responsibly in everyday work. They need basic prompting, verification, privacy and escalation skills.
This distinction prevents two expensive mistakes: asking every employee to become an AI expert, or assuming one technical hire can make the whole organisation AI-ready. Neither is realistic.
How Can HR Build an AI Skills Heat Map?
An AI skills heat map shows where capability is needed, what already exists and where the largest gaps sit. Keep the first version deliberately small. Choose critical roles or workflows, then record the required proficiency, current supply, location and business risk.
- List the business outcomes that AI is expected to improve.
- Identify the tasks that will change, rather than rewriting every job description.
- Define observable skills, such as checking sources, testing outputs or applying a model within a regulated process.
- Assess current capability using work evidence, manager input and employee self-assessment.
- Mark each gap for hiring, development, redeployment or external support.
Repeat this by country and location. CEE offers valuable technical and multilingual talent, but local supply, pay and retention pressures differ. A regional average can hide the exact shortage a hiring manager needs HR to solve.
Should You Hire, Develop or Partner?
Hire when capability is strategically important, needed now and difficult to grow internally. Develop people when adjacent skills already exist and the organisation can provide real projects, coaching and protected learning time. Use partners for temporary expertise, specialist implementation or knowledge transfer, with clear ownership so capability does not walk out when the contract ends.
Internal development deserves particular attention. The European Commission’s 2026 employment analysis says the gains from AI are likely to be uneven, with younger and lower-skilled workers more exposed without targeted support. An AI academy that serves only confident early adopters risks widening the very gap it is meant to close.
How Do You Test Applied AI Capability?
Certificates and tool names date quickly. Ask candidates and employees to show how they would handle a realistic task: check an AI-generated answer, explain its limitations, protect sensitive information and decide when a human should take over. The exercise should match the role and remain short enough to respect people’s time.
For AI-enabled specialists, domain judgement often matters more than polished prompting. Someone who understands a payroll exception, production constraint or employee-relations risk can use AI safely because they know what a plausible mistake looks like.
What Should HR Measure?
Track whether the strategy changes workforce outcomes, not just course completions. Useful measures include time to fill, salary premiums, internal fill rates, training-to-application rates and retention in scarce roles. For redesigned work, add a quality or productivity measure tied to the original business outcome.
Start with one heat map covering a handful of critical roles. It will expose where hiring is unavoidable, where nearby talent can be developed and where vague demands for “AI experience” need a much sharper definition. That gives HR something solid to plan around while the parade of new tools carries on.
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