Adapting to the Skills-First Economy: An Introduction
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This is the first in a series of articles.
Skills have been the currency since the dawn of man. Early human ancestors put premiums on the ability to hunt and gather, make fire, build shelters, and so on. Today, skills are the currency organizations use to find the right talent, effectively and efficiently fill gaps they have identified, and re-skill and up-skill. Even for performance reviews, conversations centered around skills have been found to be a useful way to guide a collaborative and constructive conversation.
The skills-based organization (SBO) is not merely a trend any longer, but a key element of the future of work. As more and more organizations turn to skills to acquire the right talent and to fill gaps, one could get the impression that skills are being mined like gold during the Rush. And just like during the Gold Rush, the people with the skills come from all backgrounds, all levels of society, and from all across the globe. And this growth trend is reflected in the numbers, too. The global Human Resources technology market size is projected to reach $76.5 billion by 2031, growing at a CAGR of 9.2%.
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Understanding Skills Taxonomies
Corporate giants that have the resources, as well as vendors in the HR and L&D tech space, have worked hard to develop their own sets of skills taxonomies. Given the investment required in time and resources, harboring them as one of the best-kept trade secrets. Even large enterprises purchasing such solutions on contracts worth millions of dollars annually have in recent years found providers unwilling to give insights into their taxonomies, leaving clients unable to elegantly link these solutions to other tools and to available data within the business.
Furthermore, skills taxonomies or skills frameworks are no end in themselves. Once the framework exists, the next step is to gather proficiency data, in a reliable, efficient, and ethically justifiable way. Successfully tackling this challenge opens up the opportunity of targeted performance support, learning and training intervention, and workshops as well as coaching and mentoring. The outcome of which, however, in turn again needs to be reflected in the proficiency data within the framework.
Vendor calls promising the ability to tackle these challenges have been around for many years, but have had varying levels of success. In addition, HR professionals must consider employee disengagement with technologies not living up to their promise, as well as time wasted on tedious self-reported skills and competencies, which is highly questionable and arguably unethical.
Change Is All Around Us
However, this landscape is now drastically changing. Take for example the cumbersome frameworks and taxonomies. With the advent of large language models (LLMs), these unworkable, rigid constructs can now be designed and built, updated, and even translated from one framework to the next. LLMs solve the semantics problem because we have the capability to understand language and nuance, and we can translate from one framework to the next, even if a different wording was chosen from the dictionary, for what is essentially the same skill.
This drastically changes the skills framework landscape, a process that has already begun. Such frameworks will no longer be secrets, and translation from one framework to the next will become straightforward. Updating such frameworks with new skills will also be a relatively straightforward task, great news as the future of work introduces change at an accelerating pace, and agility becomes vital.
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Furthermore, new technologies also promise some progress in regard to "measuring" proficiencies, and through different routes as well. One such route is performance itself, and performance data is more accessible than ever. Another route is performance support. Performance support mechanisms such as chatbots are not only offering performance support, as required, in the moment of need, but queries to such a system also give valuable data and insights into proficiencies. Lastly, learning and training innovations are becoming more adaptive, more personalized, measuring not only progress but also proficiency, pace of progress, and even confidence metrics. This again provides useful data to feed into the framework of skills and proficiencies. And data, especially up-to-date data, is key.
After all, what is the worth of decisions based on a skills framework, if the framework and the data are wobbly?
Recently, Microsoft has also made a big announcement on skills in its Viva product, utilizing data from its own suite of products, as well as from other big players in the market, while smaller players, such as Eightfold AI and Techwolf, for example, are disrupting this space as well.
Lastly, while the challenges remain first and foremost of a technical nature, such as measuring skills accurately, within context, the opportunities are vast, and all of what we have already discussed above will have a huge, positive impact, critical for the organization of the future: talent recruitment and development; diversity, equity, inclusion, and belonging (DEIB); addressing current skills gaps, planning for future skills requirements, and even strategizing for an organization’s resiliency.
What to Expect from Skills-Based Organizations
In a world where skills have always been a currency of value, the challenge has shifted from merely identifying them to understanding, mapping, and leveraging skills in dynamic and meaningful ways. The advent of LLMs and AI offers a promise—a promise of moving beyond rigid, opaque frameworks to a fluid, semantically-rich understanding of skills.
In this article series on skills and the skills-based organization, we’ll delve deeper into the current developments in AI, exploring its implications for the future of skills-based organizations. Throughout the series, we will continuously strive to explain and understand the role that AI can play, while also highlighting the present limitations and challenges that emerge.
As we look ahead into a new era, during which 94% of the workforce might lack the full suite of skills needed in just a few years, the urgency is rather clear. The future will not just be about identifying skills but understanding their nuances, interconnections, and real-world implications. Organizations that embrace this change and leverage the power of AI and LLMs, will not only find the right talent better, but will also be equipped to nurture, develop, and adapt to the ever-evolving landscape of skills - all of which should result in a positive impact on the bottom line.
In this series, we will explore the role of tools, such as talent marketplaces and LLMs, and linking skills to the wider organization, moving away from an HR-centric vantage point. Following this, we will dive into DEIB, finding and nurturing talent internally, as well as recruiting externally. Finally, we will shine a light on practical ways in which skills can be measured and, more importantly, how this data can be utilized in creative, new ways.
MORE RESOURCES:
Allied Market Research: HR Technology Market
The Economic Case for Reskilling in the UK: How Employers Can Thrive by Boosting Workers Skills
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