Skill gap analysis with AI: How to precisely identify training needs

The Skill Gap Challenge in the AI Era

You know the feeling: your project managers are operating at maximum capacity, while new technologies like ChatGPT and other generative AI tools promise significant efficiency gains. But there’s often a crucial gap between vision and reality—the skills gap among your employees.

These skill gaps aren’t just an HR issue. They slow growth, waste time, and may allow your competitors to pull ahead. This challenge is particularly strategic in mid-sized companies, where every team member counts.

The good news? Artificial intelligence doesn’t just help close skill gaps—it’s also revolutionising how we identify them. Rather than months of surveys and Excel spreadsheets, you now get accurate, data-driven insights in just weeks.

But how does it actually work? And which methods will deliver measurable results without overwhelming your teams?

What Is a Skill Gap Analysis?

A skill gap analysis systematically identifies the difference between the skills your organisation currently has and those it needs. It shows where your employees stand today and what capabilities they’ll need for future challenges.

Traditionally, this process runs through three phases:

1. Current State Analysis: Assessing existing skills through self-evaluations, manager reviews, or tests
2. Target Definition: Deducing future skills requirements based on business strategy and market trends
3. Gap Identification: Comparing current and target states to pinpoint concrete development needs

The process is resource-intensive. Anna, the Head of HR at a SaaS provider, shares her experience: “Our last manual skill analysis took four months and parts of it were outdated by the time we finished.”

This is exactly where AI-based approaches come into play. They don’t just speed up the process—they also make it more objective and continuously updatable.

Why AI Tools Are Essential for Skills Analysis

Artificial intelligence delivers four key advantages to skill gap analysis:

Objective Data Evaluation

AI systems analyse work patterns, email communication or project documentation without personal bias. They detect patterns that humans might overlook.

Thomas, CEO of a mechanical engineering company, reports: “We thought our CAD team was ready for digitisation. The AI analysis then revealed that 60 percent had never worked with cloud-based tools.”

Continuous Monitoring

Instead of periodic surveys, AI tools enable ongoing monitoring. New skill gaps are identified immediately—not just at the next annual review.

Personalised Development Paths

Machine learning algorithms create individual learning recommendations tailored to learning style, time available and company goals.

Scalable Efficiency

What once took weeks is now done in days. Even for Markus, an IT Director with 220 employees, the analysis has become financially viable.

Methodological Approaches to AI-Powered Analysis

Successful AI-based skill gap management combines different data sources and analytical methods:

Behavioural Analytics

This method analyses actual work behaviour. AI systems track which software employees use, how much time is spent on tasks and where inefficiencies arise.

Example: A salesperson still creates quotes in Word instead of using the CRM system. The AI detects this workflow break and flags upskilling needs for CRM usage.

Natural Language Processing (NLP)

NLP algorithms analyse emails, project documentation or meeting minutes. They pick up on technical terminology used (or missing) and identify knowledge gaps.

In practice, it works like this: The AI scans internal communication for terms like “machine learning” or “API integration.” Employees who never use these terms may need to upskill in these areas.

Predictive Skill Modeling

Especially valuable for strategic planning: AI models predict which skills will be needed in 12–24 months. They analyse job postings, technology trends and your company’s broader strategy.

This lets you act proactively instead of reacting to gaps after they emerge.

Assessment Integration

Modern systems combine traditional skills assessments with AI evaluation. Adaptive tests match the knowledge level and cut test duration by up to 70 percent.

What’s crucial: The AI not only evaluates right or wrong answers, but also looks at response times, uncertainty patterns and learning progress.

Specific Tools and Technologies

The market for AI-powered skill gap analysis is evolving fast. Here are the most important categories:

Enterprise Learning Platforms with AI

Platforms like Cornerstone OnDemand or Degreed integrate AI-based skills analysis into their learning environments. They automatically track progress and highlight knowledge gaps.

Benefit: Seamless integration of analysis and learning within a single system.

HR Analytics Tools

Specialised solutions like Workday Skills Cloud or SAP SuccessFactors use machine learning for competency analysis, linking HR data with business metrics.

Workplace Analytics

Microsoft Viva Insights and similar tools analyse Office 365 usage and identify skill gaps based on software and communication patterns.

Custom AI Solutions

For specific requirements, companies develop their own AI solutions. These are more effort to build, but allow maximum customisation for unique processes.

At Brixon, we’ve had great success with hybrid approaches—using standard tools for the basics, complemented by custom AI modules for industry-specific challenges.

A Step-by-Step Guide to Successful Implementation

Successful implementation requires a systematic approach. Here’s our proven method:

Phase 1: Goal Setting and Scoping (Weeks 1–2)

Clearly define which skill gaps you want to identify. Are you focused on technical expertise, soft skills, or industry-specific knowledge?

Establish:

• Which departments/roles will be analysed
• Which data sources are available
• Which compliance requirements apply
• How the results will be used

Pro tip: Start with a pilot department. This reduces complexity and delivers early wins.

Phase 2: Data Collection and Tool Setup (Weeks 3–6)

Gather relevant data from HR systems, learning platforms, anonymised email metadata and project documentation.

Important: Be transparent with employees about the purpose and data privacy. Trust is the foundation of any successful skills analysis.

Phase 3: AI Model Training (Weeks 7–10)

The AI learns your specific requirements, training on your data to distinguish relevant from irrelevant skill gaps.

In this phase, work closely with the tool provider. For customised solutions, this stage may take longer.

Phase 4: Initial Analysis and Validation (Weeks 11–12)

The AI delivers initial results. Validate them with domain experts and management. Do the identified gaps match your experience?

Adjust parameters if needed. Good AI tools learn and improve through feedback.

Phase 5: Rollout and Action Planning (from Week 13)

Expand the analysis across further areas. Develop targeted upskilling programs based on the results.

Establish regular reviews—ideally quarterly. Skill gaps shift along with the business.

Typical Pitfalls and Solutions

Even with the best planning, challenges can arise. Here are the most common—and how to overcome them:

Data Privacy and Employee Acceptance

Many employees worry about surveillance or negative consequences. Offer transparency about purpose, methods and data privacy.

Best practice: Use anonymised, team-level analyses instead of individual ratings. The AI should highlight opportunities for growth—not judge people.

Data Quality and Availability

AI is only as good as its data. Relevant info is often scattered across systems or incomplete.

Solution: Start with what’s available and gradually add more. Perfection is the enemy of progress.

Interpreting the Results

AI provides correlations—not always causation. An employee rarely uses Excel—does that mean they can’t, or simply don’t need to?

The key is combining AI analysis with human expertise. Technology spots patterns, but people interpret them in context.

Change Management

New analytical approaches transform established HR processes. Managers need to learn how to make use of data-driven insights.

Invest in training for HR and management. A tool is only as valuable as its users are skilled.

Monitoring and Measuring Success

Measure the success of your AI-driven skill gap analysis by tracking specific KPIs:

Quantitative Metrics

• Time-to-Insight: How quickly do you spot new skill gaps?
• Accuracy: How many identified gaps are confirmed in practice?
• Coverage: What percentage of staff is included?
• Cost Efficiency: Cost per analysed employee vs. manual analysis

Qualitative Indicators

Hold regular conversations with managers and employees. How do they rate the quality of the development recommendations?

A good sign: Do your teams use the recommendations voluntarily, or do they need to be pushed?

Business Impact

The most important gauge: Are measurable business metrics improving? Are projects completed faster? Are error rates going down? Is employee satisfaction rising?

Document these links systematically. They’re your best argument for further investment in AI-powered talent development.

Future Trends and Strategic Considerations

The field is evolving rapidly. These trends are ones to watch:

Real-Time Skill Tracking

AI will increasingly monitor skills in real time—via work patterns, project contributions, or even biometrics. What’s measured quarterly today will soon be continuous.

Predictive Skill Planning

Algorithms are becoming more precise at forecasting tomorrow’s skill requirements by analysing market shifts, technology trends and business strategies.

Personalised Learning Paths

AI will create individual development programmes tailored to learning styles, time available, and career goals. One-size-fits-all upskilling is a thing of the past.

Integration with Performance Management

Skill gap analysis will become seamlessly integrated with appraisals and career planning, giving you a holistic view of employee growth.

For mid-sized companies, this means: those who start now gain a vital competitive edge. The technology is getting more accessible, but the learning curve remains steep.

At Brixon, we support you in navigating these developments—from the first strategy session to practical AI deployment.

Frequently Asked Questions