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Artificial Intelligence in Business



AI-Powered Job Skills Inventory

Brian Ray

Brian Ray

28 minutes

Mar 31, 2024

AI-Powered Job Skills Inventory
Interview Highlights

Have you ever wondered how organizations manage to keep track of all the skills and capabilities of their workforce, especially when they've undergone multiple acquisitions? It turns out, it's no easy feat.

In fact, it's a complex challenge that requires a strategic approach and, increasingly, the assistance of artificial intelligence.

When companies merge or acquire other businesses, they inherit not only new talent but also different company cultures, structures, and visions. This diversity can make it incredibly difficult to create a cohesive understanding of the skills and capabilities within the organization. Without a clear taxonomy of skills, organizations face obstacles like workforce agility, decision-making bottlenecks, and reduced efficiency.

To address these challenges, organizations are turning to AI-driven solutions. In an insightful interview with Brian Ray, Global Head of Data Science and Artificial Intelligence at Eviden, we explore strategies for constructing an AI-driven skills taxonomy tailored to the intricacies of companies navigating multiple acquisitions.


Starting with Historical Data

Brian suggests that organizations start by leveraging their historical data, such as resumes, job descriptions, and other available information about their current workforce. By analyzing this data, they can identify existing skills and capabilities within the organization.


Defining Business Objectives

Next, it's crucial to align the skills taxonomy with the organization's business objectives. By understanding where the company wants to go, leaders can identify the skills needed to support future growth and innovation.


Analytical Exercise

An analytical exercise helps identify key phrases and skills that occur most frequently within the data. This process leads to the development of a taxonomy that categorizes the organization's current capabilities and highlights areas for future development.

The Importance of Clean Data

Brian emphasizes the importance of starting with clean data. While human data can be messy and ambiguous, conducting thorough analysis helps separate the signal from the noise, revealing valuable insights into employees' skills and expertise.


Diverse Range of Data Sources

In building an AI-driven skills taxonomy, organizations should consider a diverse range of data sources, including:

- Historical candidate data (resumes, CVs)

- ONET occupation codes

- Correspondence with potential hires

- Timelines of individuals' career progression


By leveraging these data sources, organizations can gain a comprehensive understanding of their workforce's skills and capabilities.


Human Expertise in the Loop

Despite advancements in AI, human expertise remains essential. Brian emphasizes the need for human input to validate AI-driven recommendations and ensure fairness and accuracy in skill assessments.


Adapting to Future Skill Trends

Organizations must continuously review and update their skills taxonomy to adapt to evolving skill trends and workforce needs. This process involves monitoring for concept drift, where external changes necessitate adjustments to the taxonomy.


Personalized Skill Development

An AI-driven skills taxonomy can provide personalized skill development recommendations for employees, aligning individual goals with organizational objectives. By identifying skill gaps and recommending relevant training opportunities, organizations can support professional growth and development.


Final Note:

As organizations strive to navigate the complexities of workforce management, AI-driven solutions offer a promising path forward. By constructing comprehensive skills taxonomies and leveraging AI technologies, companies can enhance agility, improve decision-making, and empower their workforce for success in an ever-changing business landscape.

Official Transcript

Organizations that have undergone multiple acquisitions face a particularly daunting challenge, the absence of a comprehensive skills taxonomy.

This deficiency not only hinders workforce agility, obstructs informed decision making and undermines overall efficiency, but it also compounds the complexities brought about the disparate company cultures and structures and vision possessing a dynamic and adaptive system capable of evaluating present skill levels and anticipating future requirements. That is the ultimate aspiration. The savings in terms of HR and leadership efficiency spanning from hours to potentially years would be beyond measure. 

In our upcoming interview with AI expert, Brian Ray, Global Head of Data Science and Artificial Intelligence at Eviden, we delve into strategies for constructing an AI driven skills taxonomy tailored to the intricacies of companies navigating multiple acquisitions are just for themselves offering insights that are invaluable in surmounting these multifaceted challenges. 

Brian, thank you so much for being with us today. Welcome. 

Brian Ray: You bet. Thanks for having me. 

If I were to embark on constructing an AI powered skills taxonomy within an organization, where would I start?

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