Johnson & Johnson’s adoption of skills inference using AI technology to identify and address skills gaps among technologists showcases a strategic approach to workforce retraining in response to evolving technologies. The process involved skills taxonomy definition, data analysis for skills assessment, and personalised development initiatives resulting in increased employee engagement and improved proficiency.
The Role of Skills Inference in Workforce Retraining at Johnson & Johnson
In response to emerging technologies such as artificial intelligence, businesses are emphasizing the need for employees to acquire new skills. A survey conducted in 2022 revealed that executives believe 38% of their workforce will require fundamental retraining or replacement within three years to bridge skills gaps. This challenge was highlighted at the MIT Digital Technology and Strategy Conference, where Nick van der Meulen of MIT Center for Information Systems Research discussed the importance of training for performance, retention, customer experience, and innovation.
Johnson & Johnson initiated a project in early 2020 to bolster digital expertise among their 4,000 technologists, later extending the initiative to other units. The program employs a process called “skills inference,” which uses AI to analyze employee data and assess skills proficiency.
The skills inference process at Johnson & Johnson unfolds in three steps:
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Skills Taxonomy: This involves defining essential future-ready skills, such as master data management and robotic process automation. A total of 41 specific skills grouped into 11 capabilities were identified.
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Skills Evidence: Selecting appropriate data sources for skills inference is crucial. Johnson & Johnson utilized HR information systems, recruiting databases, learning management systems, and project management platforms. The data was anonymized, and employees could opt out, ensuring it was used only for strategic workforce planning.
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Skills Assessment: A large language model evaluated each technologist’s proficiency on a 0-5 scale. Employees also conducted self-assessments. Scores with less than a one-point deviation were considered valid.
The results revealed individual skills gaps and strengths, encouraging employees to use the company’s professional development ecosystem, which saw a 20% increase in engagement after the first skills inference round. By March 2024, 90% of technologists had accessed the learning platform.
Executives utilized the insights for strategic workforce planning, using heat-map data to identify regional proficiency strengths and weaknesses.
Nick van der Meulen emphasized the need for a “career lattice” rather than a “career ladder,” accommodating various career aspirations and enabling lateral movements, skill-specific emerging titles, or personal career adjustments. Effective training combines online modules with mentorship and hands-on experience.
In overcoming resistance to change, illustrating the benefits of upskilling through specific examples can demonstrate the importance of continuously evolving skills.
This structured approach to understanding and addressing skills gaps underscores the dynamic nature of skills in today’s workforce landscape.
