Measuring skills more precisely and efficiently is now possible thanks to advances in artificial intelligence in the organizational sphere. According to  McKinsey, organizations are increasingly exploring AI tools to improve their selection and training processes. 40% of respondents state that their organizations plan to invest more in AI. 

These technologies allow for the evaluation of both technical and soft skills, adapting to the specific needs of each position.  For example, organizations like IBM have implemented AI in their HR platforms, achieving a 36% increase in talent retention thanks to more personalized assessments. These figures highlight the positive impact of AI on how organizations identify and develop talent.

In this article, we explore how artificial intelligence is redefining the evaluation of technical skills, its benefits, and the challenges it poses in a constantly evolving job market.

What Are Technical Skills?

The Technical assessments are processes designed to measure an individual’s specific skills and competencies in areas related to their professional performance. 

These assessments can include practical tests, simulations, technical interviews, project analysis, and aim to ensure that candidates possess the necessary capabilities to meet the requirements of a role or project.

In the corporate world, technical evaluations are essential for identifying talent, planning professional development, and ensuring quality in task execution. 

However, traditional evaluation methods are often limited in terms of accuracy, objectivity, and adaptability, leading to the integration of advanced technologies such as AI.

How Does AI Enhance Skills Measurement?

AI has revolutionized how we assess technical skills by introducing tools that automate and personalize the evaluation process. 

Through advanced algorithms and machine learning models, AI enhances skill measurement by analyzing large volumes of data, identifying patterns, and delivering more precise results.

Can AI-Powered Platforms Run Real-Time Personalized Simulations?

For example, AI-driven platforms can run real-time technical simulations, assess candidate performance, and provide instant feedback. These technologies also adapt tests to the specific needs of each role, ensuring more relevant and effective evaluations.

What Is the Role of AI in Technical Evaluations?

AI plays a crucial role in transforming technical evaluations. 

Its ability to automate repetitive tasks—such as grading tests and analyzing data—frees up time and resources so evaluators can focus on strategic aspects. Moreover, AI minimizes human bias, ensuring a fairer and more equitable evaluation.

Another important aspect is personalization. AI can design assessments tailored to each individual’s skills and competencies, improving the test-taker’s experience and increasing result accuracy. It also facilitates continuous progress tracking,helping organizations identify areas for improvement and plan professional development strategies.

Key Benefits of Using AI to Measure Skills

1. Greater accuracy:AI algorithms analyze data more precisely, ensuring reliable results.
2. Objectivity: AI removes human bias, guaranteeing fair assessments for all candidates.
3. Efficiency: Automates repetitive tasks, speeding up the evaluation process and optimizing resources.
4. Customization: Designs tests tailored to the specific needs of each role or individual.
5. Immediate feedback: Delivers detailed real-time analysis, enabling continuous learning and improvement.

Using AI in Code-Based Technical Assessments

AI is transforming code-based technical assessments by introducing advanced tools that automate, optimize, and personalize the evaluation process. 

These technologies can analyze large volumes of code, identify errors, suggest improvements, and assess code quality more efficiently.

What Tools Are Used to Measure Coding Skills with AI?

Tools like GitHub Copilot and DeepCodese machine learning models to offer real-time code suggestions, detect vulnerabilities, and ensure that code meets quality standards. 

In addition, platforms like SonarQube conduct both static and dynamic code analysis to identify security issues and improve maintainability. 

AI not only enhances code quality but also boosts developer confidence in evaluations,creating a more inclusive and collaborative environment.

AI is not just changing how talent is assessed—it’s also changing who can assess it, when, and how deeply. It automates, personalizes, and professionalizes technical assessment processes, becoming a strategic ally for HR leaders, CTOs, and development teams.