July 27, 2025
Discover how organisations can use AI to unlock hidden talent, personalise development, and build agile, future-ready teams – ethically and effectively
Organisations have long recognised that building a future-ready workforce depends on acquiring and developing the right skills. Yet many still struggle – open due to the lack of clarity around the skills and competencies they need today and in the years ahead, as well as limited access to data-driven insights to guide their efforts. That’s where AI in skills management comes in.
Many successful companies have taken to integrating AI into their strategic workforce planning and skills management processes. From matching skills with tasks and roles and identifying current and future skills gaps to improving engagement through targeted employee development, AI-powered workforce planning solutions bring clarity to decision-making and allow business leaders to harness the full potential of their skills intelligence.
This article explains the ways in which AI in skills management is transforming strategic workforce planning. It also touches on the challenges of using AI-powered workforce planning tools, and key considerations when taking an AI-driven approach to skills management.
The use of AI in skills management has transformed error-prone manual processes into streamlined, data-driven systems. It provides clear, evidence-based insights, and helps reduce human bias across several HR and talent functions. From talent acquisition and role fit, to skills gap analysis and learning and development, AI is refining how organisations manage and leverage skills. Here are five areas in which AI is making the biggest impact:
Traditionally, skills mapping was a manual and time-consuming process, involving spreadsheets, static surveys, or annual HR audits, which created static skills matrices that quickly became outdated as they failed to reflect employees’ current capabilities. AI-powered skills mapping tools automate and simplify this process. They extract data from diverse sources – resumes, performance reviews, job descriptions, and existing skills profiles, etc. These tools also identify relevant skills needed for specific roles, uncovers hidden skill gaps (e.g. missing data analysis skills in a marketing team), and categorises data using predefined or dynamic skills taxonomies. Many platforms keep skills maps continuously updated by prompting employees to regularly update their skill profiles or managers to validate them.
A shared language around skills (skills taxonomy) is the cornerstone of effective skills management. A skills taxonomy defines, organises and categorises skills by type, proficiency level, similarity, and relevance to roles. At the same time, job architecture provides structure by standardising job titles, career paths, and compensation bands, ensuring clarity, consistency and fairness across the organisation. With AI, organisations can now create and manage dynamic, scalable skills taxonomies and job architectures that evolve with changing business needs. As the business grows or transforms, the taxonomy can also expand seamlessly to reflect new roles and required capabilities.
An AI-enabled skills gap analysis tool can go through vast amounts of data to point out where skill shortages lie in real time. A powerful enough tool can even pinpoint critical skills missing from an employee’s skills profile that are essential to their performance and growth within the organisation. Furthermore, such tools not only identify existing knowledge gaps but are also capable of predicting future skill needs based on insights and trends so that organisations can start planning ahead to acquire those skills.
Many business leaders struggle to identify the best-fit candidates for open roles, leading to mismatches and costly hiring mistakes. AI helps improve talent decisions by analysing large volumes of data – such as skills profiles, past performance, learning history, and job requirements – to deliver precise and actionable recommendations.
AI-powered talent mobility platforms go further by matching internal talent to roles and projects not only based on skills, but also on interests and career goals. This promotes internal mobility, giving employees more visibility into growth opportunities, and helping organisations reduce their reliance on external hiring. As a result, companies can build a resilient internal talent marketplace that addresses both current and emerging skill needs.
AI tools do more than just analyse current skills – they also identify individuals and teams with potential to grow, and recommend personalised learning pathways aligned with specific goals and future-fit roles. These recommendations are based on a combination of workforce data, job requirements, and employee aspirations.
It’s no surprise then that continuous learning is central to workforce strategy – with a 2022 MIT CISR survey suggesting that 38% of employees will require retraining within three years to address skills gaps.
AI can guide this process by helping employers select the most effective learning methods – whether that’s upskilling, reskilling, mentorship, on-the-job training, or digital courses.
By using AI-assisted personalised learning, organisations can help employees overcome individual skills gaps and prepare for future roles – while reducing reliance on expensive and less effective external hires.
Traditionally, skills management meant pouring over static skills matrices, manually tracking workforce capabilities, and relying on instinct and assumptions to predict future skills needs. But with the rise of skills intelligence software and AI-powered workforce planning tools, organisations can now automatically gather, analyse, and transform skills data into actionable insights. As a result, old-school methods are not only inefficient but also no longer necessary. Here are four key benefits of using AI-powered skills intelligence:
The power of skills intelligence software fueled by AI is such that it transforms fragmented organisational data – such as performance metrics, skills inventories, and development goals – into clear, actionable insights. This allows workforce planning professionals to make informed, data-driven decisions based on key indicators such as task completion rate, employee engagement, and goal attainment. The software also supports more accurate forecasting and scenario planning by identifying skills gaps, workforce trends, and potential capacity constraints – enabling organisations to proactively prepare for future disruptions and changing talent needs.
Most companies rely on resumes and certificates to identify talent, but these documents offer a limited view of an individual’s true capabilities. In reality, much of an organisation’s talent lies buried in overlooked data sources like project contributions, peer feedback, performance records, and internal mobility history.
AI-powered skills intelligence software can analyse this data to uncover hidden skill sets and high-potential employees, enabling organisations to tap into underutilised talent and develop it to meet current and future skills requirements.
Many organisations now use AI-powered solutions to deliver personalised learning and career development pathways to their employees. These tools analyse each employee’s skills, goals, and preferences to recommend targeted training programmes, mentorship, and growth opportunities. By offering personalised development, organisations not only improve the overall employee experience but also demonstrate a commitment to their workforce – leading to higher engagement and job satisfaction. This matters because organisations that prioritise their employees’ development are not only seeing higher retention rates than their rivals but also leading in AI adoption, according to LinkedIn’s Workplace Learning Report 2025.
Leveraging AI-powered skills intelligence is one of the most effective ways to help employees overcome skill gaps and rapidly acquire new competencies at scale. Skills intelligence tools serve numerous functions: For example, an AI-enhanced skills taxonomy can ensure workforce capabilities, learning initiatives, and employee experiences remain aligned with evolving business goals. Real-time analytics can support succession planning by identifying skills gaps and strengthening talent pipelines. Together, these capabilities help organisations build a more resilient, adaptable, agile, and future-ready workforce.
Artificial intelligence is not the perfect solution. It comes with its fair share of challenges. Here are some of the most significant, along with tips on the right ways to address it:
An AI system is only as good as the data it is trained on. If a company’s historical data contains biases – such as gender, racial or educational bias – the AI tool might learn and perpetuate these biases. This can lead to unfair outcomes, such as recruitment tools that unintentionally exclude certain groups or automated decisions that reinforce outdated trends.
To mitigate AI bias, human oversight is critical. Organisations must audit and curate training data carefully, ensuring it is appropriate from ethical, legal, and business perspectives. Regular data monitoring and testing and updates are also necessary to catch and correct unintended biases as they emerge.
Explainability and transparency are two major risks associated with AI adoption. For an AI tool to be trustworthy, its decisions must be understandable to humans. This means that users should be able to clearly see how and why an algorithm arrived at a specific prediction or recommendation. Explainability matters not only for building trust but also for meeting legal, ethical, and operational standards – especially in high-stakes industries like healthcare and financial services, where AI may influence diagnoses or lending decisions.
When there is explainability, there is naturally transparency. This depends on the quality of the data AI relies on. If the data is outdated, incomplete or inaccurate, the tool may produce misleading outputs – sometimes referred to as AI hallucinations. This makes it all the more important to ensure data integrity and validate AI outcomes through human oversight.
AI adoption is accelerating rapidly, rising from 55% in 2024 to 78% in 2025, according to the Stanford HAI 2025 AI Index. As more organisations use AI to transform workflows, it is important to remember that AI is not a replacement for human judgment, but a tool that augments it.
Successful organisations are those that integrate AI thoughtfully, ensuring human oversight at every stage – from data collection and model design to deployment, monitoring, and continuous improvement.
One of AI’s core limitations is its lack of human traits such as empathy, creativity, ethical reasoning, and critical thinking. These are essential for making complex decisions, particularly in people-centric roles. Its real value lies in combining the speed and scale of AI with human insight – a partnership that results in smarter, more responsible, and more effective outcomes.
Let’s not forget about the ethical implications of AI. Using AI ethically is not optional but essential. Responsible AI use involves eliminating bias, ensuring transparency, and complying with all relevant legal and regulatory requirements. In the context of skills management, AI systems often process large volumes of personal employee data. Safeguarding this data and upholding employee trust must be a top organisational priority.
Using AI ethically in HR requires understanding and adhering to data privacy laws such as the General Data Protection Regulation (GDPR) in Europe and the California Privacy Rights Act (CPRA) in the US, which regulate how personal data is collected, stored, and used. Ethical use also includes compliance with labour rights, contract terms, and workplace safety standards.
Research has shown that while many employees are willing to let their employers collect personal and work data, they remain deeply concerned about whether this data will be used responsibly. Ethical AI means committing to transparency, fairness, and accountability in every stage of deployment.
Skills management platform MuchSkills recently unveiled its newly enhanced analytical tools with AI capabilities that are designed to help build skilled, future-ready workforces. Here’s what is on offer:
Over the next three years, 92% of businesses expect to increase their investment in AI, according to McKinsey. This reflects the growing confidence in AI’s ability to transform workflows and build agile, future-fit workforces.
While adopting an AI-driven approach to strategic workforce planning comes with challenges, luckily, skills management and talent mobility platforms such as MuchSkills are here to help you on your journey. With our support, you’ll be able to harness the full potential of AI – making impactful, ethical decisions informed by data and guided by human oversight.
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