Utilizing AI is an effective method for analyzing extensive HR data stored within organizations. This technology enables companies to pinpoint talent and skill gaps among their workforce. The present piece is a component of the series “Exploring the Impact of AI on Talent,” which delves into how AI is transforming recruitment, training, and staff retention practices. As technological advancements progress and businesses strive to stay competitive in meeting market demands, the skills required by employees are also evolving. A growing number of companies are investigating how to address these skill and workforce shortages through the use of artificial intelligence.

HR departments can employ AI to uncover “patterns and gaps” and evaluate “current workforce competencies against developing business requirements or industry trends,” as stated by Lauren Winans, CEO and primary HR consultant at Next Level Benefits. According to Will Howard, head of HR trends and AI at McLean & Company, what AI brings to this area isn’t groundbreaking; however, it streamlines the process by automating tasks that HR teams have historically carried out manually.

In this context, HR professionals share four key considerations when utilizing AI to identify skill gaps within the workforce:

1. Data Organization
Organizations possess vast amounts of HR data, such as job postings, performance evaluations, employee work histories, and training records that can be examined to identify skill gaps, as mentioned by Sanmay Das from Virginia Tech. However, Winans notes that this data often lacks accuracy and completeness. Before integrating AI solutions, organizations must prioritize “data hygiene” by verifying that the data intended for analysis is precise, up-to-date, and consistent, according to George Denlinger from Robert Half’s US technology talent solutions division. Without these measures in place, the insights provided by AI will be limited or inaccurate.

Howard emphasized the necessity for companies to establish a clear and standardized approach for collecting, maintaining, and updating workforce data. For example, he suggested standardizing job descriptions with detailed skills, knowledge areas, and responsibilities so that AI can make reliable comparisons.

2. Insights Analysis
Large language models like ChatGPT and Microsoft Copilot can summarize data effectively according to Das. Nonetheless, specialized AI tools tailored for HR functions such as workforce planning and analytics are often required for in-depth analysis purposes as highlighted by Howard. Examples of such tools include Workday and Disco. These AI tools can leverage existing data to reveal strengths and weaknesses within the workforce.

For instance, based on data concerning employee performance on specific projects alongside sales projections, AI could recommend necessary skills or positions essential for meeting future organizational demands suggested Howard. By examining an employee’s work history and training records using AI technology could assess their potential for acquiring new skills through upskilling or reskilling as outlined by Winans.

IBM is a prime example of a company utilizing AI systems to analyze employees’ internal digital activities in order to identify their skills levels and predict proficiency levels accordingly. This analysis is then used to provide personalized educational opportunities and career guidance to employees enabling them to explore new career paths and job prospects.

In 2024 IBM reported a 20% increase in employee engagement following the implementation of this approach.

3. Recognizing Limitations of AI
Despite its ability to analyze data effectively according to Das, AI may miss subtle nuances and human elements crucial for determining role success such as unlisted minor tasks in job descriptions or soft skills exhibited by employees. Winans emphasized the importance of focusing on aspects like data privacy, trustworthiness as well as ensuring employee buy-in.

Employees may harbor concerns regarding how their data is utilized impacting factors like role changes or additional responsibilities she noted. To address these concerns transparent communication about data usage practices is essential suggested Winans.

Howard pointed out that ensuring HR teams possess the necessary data literacy skills is another challenge; they must be adept at interpreting AI results effectively he added. He stressed that despite advanced AI capabilities a human touch remains necessary to contextualize results within a business framework communicating insights across the organization taking appropriate actions based on these insights.

For instance deploying AI analyses on skill gaps should inform decisions regarding creating new roles within the company or determining training needs for existing staff members proposed Winans.

4. Strategy Refinement
Winans stressed that skill requirements evolve rapidly hence leveraging AI to identify skill gaps should be an ongoing process rather than a one-time review she added.

While tracking ongoing skill gaps using AI can be beneficial Denlinger noted this application of technology is still developing likely evolving further over time.

Howard cautioned that while useful AI isn’t a magic solution capable of instantly transforming operations from mediocre to best-in-class he remarked organizations should not view it as a shortcut rather they should ensure foundational elements like accurate data management alongside employee proficiency with technology are firmly established before incorporating AI into operations.

Accordingly once these prerequisites are met Howard suggested that AI acts as an enhancement elevating workforce planning and data analysis processes to greater heights within an organization’s operational framework.