AI is reshaping our approach to skills data, yet organisations must navigate its challenges with caution

In recent years, several organisations have undergone a deliberate shift towards prioritising skills over the traditional reliance on job titles and work histories. That’s because putting skills front and centre has several benefits including addressing talent shortages, closing skills gaps, increasing employee engagement, and boosting business growth among other things.

A skills-based approach not only aligns with the demands of a fast-paced, globalised economy but also fosters inclusivity by focusing on an individual’s abilities rather than conventional markers of status such as the college/university one went to.

Today, advancements in AI are allowing us to deploy skills-based approaches more widely and effectively, and organisations are increasingly looking at AI-enabled solutions to help streamline their skills management processes.

AI-powered skills-management systems promise agility, scalability, and data-driven insights that can transform how organisations understand and harness the potential of their workforce. From talent acquisition to developing skills taxonomies; from matching job skills to people; from employee development to performance management, AI offers a wealth of opportunities to enhance how organisations identify, organise, cultivate, and utilise skills.

But as exciting as all this is, using AI in skills management comes with its challenges and complexities.

In this blog, we will examine both the opportunities and challenges of utilising AI-enabled skills management systems, aiming to provide a comprehensive understanding of how organisations can leverage the benefits of AI to unlock the full potential of their talent pool for optimal outcomes while also being aware of potential obstacles along the way.

AI and skills management

Gartner has defined AI-enabled skills management as:

“…[A] foundational capability within talent and day-to-day work contexts that applies natural-language processing, knowledge graphs and other AI techniques to build a dynamic representation of skills data. It is used to automate skills inference for people, content, work tasks, career paths and jobs.”

These are the various ways AI can be used in skills management.

1. Talent acquisition

Talent acquisition is among the earliest areas where AI has been extensively applied.

Over the past few years, numerous organisations have utilised AI to streamline the hiring process. This includes creating accurate job postings and efficiently screening applicants, resulting in reduced time-to-hire, enhanced quality of hires, and the elimination of human bias and subjectivity in candidate selection, thus increasing diversity in the talent pool. However, it's worth noting that while AI can mitigate human bias, it is not immune to bias itself, a point we will explore further in the ‘Challenges’ section below.

By 2022, nearly one in four organisations reported utilising automation or artificial intelligence (AI) to support HR-related activities, including recruitment and hiring, according to a report by SHRM (the Society for Human Resource Management). The report said that of those employing these tools, over two-thirds of HR professionals stated that the time it takes to fill open positions has improved, with 53% reporting somewhat better results and 16% reporting much better results due to the use of automation or AI. Furthermore, a subsequent SHRM study from January 2024 revealed that among organisations that have adopted AI for HR purposes, talent acquisition emerged as the primary area for its implementation, with 64% of organisations utilising AI in this domain. 

2. Identification and organisation of skills

AI-powered skills management has the potential to significantly transform how organisations identify and organise skills within the workplace. Through the utilisation of sophisticated algorithms and data analysis, AI systems can leverage skills taxonomies and ontologies to categorise and comprehend competencies within the organisation. (Taxonomies offer a structured framework for classifying skills, while ontologies elucidate the relationships between them, thereby enhancing our understanding of skill dynamics.) 

Furthermore, AI can facilitate the creation of dynamic skills matrices, which continuously map employees' proficiencies against organisational needs in real-time. This comprehensive approach ensures optimal workforce allocation and productivity. AI solutions can capture shifting skill dynamics by actively soliciting feedback and getting users to engage with evolving skills graphs, enabling organisations to stay abreast of skill developments and promptly respond to changing demands.

Automation plays a crucial role in this process, streamlining the building and updating of skill data to ensure consistency across various systems and talent areas.

3. Aligning skills with jobs and individuals

AI is transforming the process of aligning skills with jobs and individuals, empowering organisations to identify the essential skills for specific roles and the skills possessed by potential candidates. This facilitation of candidate-job pairing is made possible through matching algorithms that effectively connect talent with work, learning, and job opportunities. Leveraging dynamic skills data captured by AI systems, these algorithms ensure more accurate and efficient talent matching. 

Furthermore, AI plays a crucial role in strategic workforce planning by tracking emerging skills and comparing them with external market trends. This enables organisations to accurately predict potential talent gaps in the near future. By analysing employees' skills in comparison to competitors, AI ensures that organisations stay aligned with industry developments. 

Leveraging this information, AI also offers recommendations for internal talent mobility, suggesting adjustments to employee roles or positions to optimise workforce capabilities and address evolving skill demands effectively.

4. Learning and development

AI is widely employed for learning and development in organisations. As per a 2024 SHRM study, learning and development come second only to talent acquisition in AI usage, with 43% of organisations implementing AI in this domain. AI-driven skills management solutions enhance learning and development efforts within organisations by leveraging advanced algorithms to power learning management systems:

• They can provide personalised learning recommendations tailored to individual employees, ensuring access to relevant learning opportunities. This personalised approach not only fosters continuous development, upskilling and reskilling but also enhances employee motivation by catering to their specific learning needs. Learning experience platform Degreed, for instance, already uses AI to improve upskilling and reskilling experiences and is working on expanding the use of AI in skills management. It features an AI-driven recognition tool that offers personalised skill-based resource suggestions. By analysing employees' skills profiles and learning histories, the tool identifies areas for development and suggests relevant skill domains. It also recommends specific learning resources like online courses and books to facilitate skill acquisition, empowering employees to achieve their professional goals effectively.
• AI systems enable rapid content creation, allowing organisations to keep pace with the evolving learning landscape efficiently. Additionally, real-time tracking of learner progress ensures that employees receive timely feedback and support, further facilitating their growth and development.

6. Performance management

Efficient performance management is among the benefits of using AI in skills management. Organisations are transitioning from traditional annual performance reviews to continuous real-time feedback facilitated by AI analytics. This shift promotes skill growth, ensuring that employees and the organisation remain adaptable to new challenges. According to the 2024 SHRM report, of the organisations utilising AI for performance management, 57% utilise it to enhance the provision of feedback by managers, while 46% use it for facilitating employee goal setting.

To sum up, AI is transforming skills management across diverse organisational functions, including skills identification, talent acquisition, skill matching, performance management, and learning and development.

AI in HR: Challenges

While the integration of AI in HR processes holds promise, it is also crucial to acknowledge the risks associated with AI implementation in HR. Once organisations are aware of these risks, they can proactively manage them. Some of the reasons include:

1. Bias

One of the primary concerns when deploying AI in HR processes is bias. AI algorithms, like any technology, are susceptible to bias, which can manifest in various forms such as gender, racial, or socio-economic bias. These biases perpetuate existing inequalities, undermine diversity and inclusion efforts, and adversely affect decision-making processes.

In the 2022 SHRM study, only 2 in 5 employers sourcing these tools from vendors reported high transparency regarding steps taken to ensure tools protect against bias. Additionally, the report found that 19% of organisations using automation and AI in HR-related activities discovered instances where AI tools inadvertently overlooked or excluded qualified applicants or employees.

“Clearly, automation helps HR deliver value to organisations, especially when it comes to acquiring top talent,” the report quoted Its Chief of Staff and Head of Government Affairs Emily M. Dickens as saying. “But we need to be assured the tools we use do not lead to bias in the hiring process, performance management or other areas of HR.”

2. Employee privacy concerns

A significant risk associated with AI in skills management is the potential invasion of employee privacy and the discomfort stemming from being constantly tracked and monitored. Employees may feel uneasy knowing that their skills, performance, and even personal characteristics are under continuous scrutiny by AI systems. This tracking can lead to feelings of surveillance, distrust, and stress among employees, ultimately impacting morale and productivity within the organisation.

Gartner research has shown that employees whose organisations are transparent about the purpose and functioning of these tools are more likely to accept them. Transparency about why tracking is conducted can significantly increase acceptance levels, with employees being 50% more likely to accept the use of these tools when organisational leaders openly discuss the reasons behind their implementation. Additionally, academic research has also found that employees are generally more accepting of technology-operated behaviour tracking compared to human-operated tracking methods. Striking a balance between leveraging AI for skills management and transparently addressing employees' concerns is essential for fostering trust and maintaining a positive work environment.

3. Legal concerns regarding data privacy

Implementing AI for skills management involves navigating complex legal landscapes, particularly data privacy laws such as the General Data Protection Regulation (GDPR) in Europe and the California Privacy Rights Act (CPRA) in the United States. These laws impose strict requirements on collecting, processing, and storing personal data, including employee information used in skills management systems. Compliance is crucial to avoid legal consequences such as fines and reputational damage.

Improper handling of employee data can lead to violations of such data protection laws, posing significant legal risks. Sending sensitive employee data to external AI services heightens the risk of data breaches and unauthorised access.

Understanding current legal frameworks and establishing proactive risk management practices are essential for organisations utilising AI in skills management. Compliance with data privacy regulations necessitates ongoing monitoring and adaptation to changes in legislation and industry standards.

A report published on the American Bar Association website sums up the issue: “AI was touted by many companies as a hiring panacea… However, without proper vetting and analysis, these tools can actually introduce bias into the process and expose employers to liability under various federal, state, and local laws,” the report said.

Given these challenges and complexities, what is the best way forward? 

Recognize AI as a valuable tool, not a magic bullet

While AI offers numerous benefits, it should be viewed as a tool rather than a singular solution for effective skills management. Organisations can leverage AI to enhance HR processes while being mindful of potential drawbacks and the associated risks. By acknowledging these challenges and implementing strategies to address them, organisations can strike a balance between harnessing AI's benefits and upholding fairness, equity, and inclusivity in the workplace.

Key strategies for effective AI implementation include:

• Diverse and representative data: Incorporate more varied data into algorithms and matching engines to mitigate bias. Ensure that AI algorithms are trained on diverse datasets to provide an accurate reflection of real-world diversity and enhance talent matching.
• Regular monitoring and evaluation: Continuously monitor and evaluate AI algorithms for bias and fairness. Establish internal processes to systematically detect and address bias as it arises.
• Transparency and explainability: Prioritise transparency in AI algorithms by opting for white box solutions that offer full visibility into decision-making processes. This fosters trust and ensures accountability in AI-driven HR processes. White box solutions offer full visibility into the logic and parameters driving decisions, allowing HR teams to understand and modify variables considered by algorithms. In contrast, black box systems operate with opaque internal processes, lacking transparency, which can lead to potential bias.
• Human oversight and intervention: Maintain human oversight, especially in sensitive areas like recruitment and performance evaluation. Human judgement can help identify and rectify bias that AI systems may overlook.
• Understand it’s not a one-solution fits all: AI solutions may vary across different organisations. What works well for one organisation may not necessarily work the same way for another.
• Embrace a hybrid approach: Utilise a hybrid approach to skills management, combining structured AI-driven skills databases with open-ended sections for employee input. This encourages engagement and ensures a comprehensive understanding of skills.
• Optimise skill taxonomies: Strike a balance between depth and breadth in skill taxonomy creation. While AI enables comprehensive coverage of skills, an excessive number of skills can dilute the effectiveness of skill mapping. Prioritise relevant skills to maintain clarity and focus.
• Review AI assistance: Each organisation has its unique complexities. Stakeholders must review AI-generated outputs to ensure that they align effectively with the organisation’s specific goals and values.
• Prioritise ethical considerations: Follow key ethical principles in AI development and usage, such as amplifying human potential, positively impacting society, championing transparency and fairness, and commitment to data privacy and protection. 

By implementing these strategies, organisations can effectively harness the benefits of AI in skills management while mitigating potential risks and upholding ethical standards.