Some 87% of companies either already have a skills gap, or will have one within a few years and some 85 million jobs are predicted to be displaced by 2025 while 97 million new jobs will emerge, making it more urgent than ever for corporates to move towards skill-based approaches at scale. That is where TechWolf, a World Economic Forum Technology Pioneer, comes in. The Ghent, Belgium-based startup automatically monitors workers’ skills based on HR information and the digital footprint employees create on the job to pinpoint what capabilities are already present, see which future skills are lacking and identify efficient paths to upskilling, reskilling, and hiring. Clients include pharmaceutical company GFK, Booking.com and KBC Bank, an integrated Belgium bank-insurance group.
TechWolf is capitalizing on a trend. Organizations are moving toward a whole new operating model for work and the workforce that places skills, more than jobs, at the center, consultancy Deloitte says in a report entitled The Skills-Based Organization: A New Operating Model For Work And The Workforce. That’s because “confining work to standardized tasks done in a functional job, and then making all decisions about workers based on their job in the organizational hierarchy, hinders some of today’s most critical organizational objectives: organizational agility, growth, and innovation; diversity, inclusion, and equity; and the ability to offer a positive workforce experience for people,” says the report.
But tracking skills is more difficult than tracking jobs, says TechWolf CEO and Co-founder Andreas De Neve. An individual may have 20 to 30 dynamic skills so many organizations struggle with data collection and keeping their system up to date. “Part of the problem is that the systems that need data on skills are different from the systems where people perform the work,” he says.
Rather than ask people to self-assess their skill sets, TechWolf uses algorithms to evaluate the digital footprints employees leave through their daily actions and their use of software systems at the organization and then make a skills assessment from that data.
TechWolf uses an application programming interface (API) to extract, interpret and compare skills and jobs from unstructured data .“We try to solve our clients’ data problem without introducing new tools,” says De Neve. “We deeply integrate with the systems they already have.”
Demand is coming from a wide range of sectors such as financial services, insurance, telecommunications, pharmaceuticals and semiconductors. The sectors most in need are industries undergoing radical transformation, such as the energy and automotive sectors, says De Neve.
For example, British Petroleum (BP), which has over 70,000 employees and has committed to being carbon neutral by 2050, is facing s a massive transformation in its business and work force. “The big bottleneck for its transformation is people,” says De Neve. While BP knows how to build offshore wind farms it needs 5000 people to construct them and no universities are currently training engineers with these skills. That means BP will need to build many of the skill sets by training and redeploying its current workforce. To do that BP needs to first understand what skills its current employees have and then determine the upskilling paths needed in offshore wind, hydrogen, and other energy transition- related jobs. “The better they understand the workforce the better they can facilitate the workforce’s transition towards the future,” says De Neve.
Using AI to track what employees are doing and draw conclusions is not without controversy. TechWolf says it addresses concerns by taking steps to ensure its AI is explainable and unbiased.
“We are very transparent about the use case for the data and what data sources we use for the inference,” he says and an “ethical and transparent design principle has been built into the algorithm.”
That said, an AI tool is only as good as the data sources it works with, says De Neve.. He recommends that before companies start training AI on skills data that they do an audit to ensure their data is as accurate and representative as possible. For example, TechWolf discovered that male employees tend to over-report their skills, whereas female employees tend to under-report them. “If you fail to understand such contextual factors when using skills data you’ll find that your system excludes important talent, undermining the entire point of the skill-based organization,” says De Neve in an article about using AI responsibility for The World Economic Forum’s annual meeting.
“Employees generate data throughout the workday, with every document written, project finished and course completed,” De Neve says in the article. “Ideally, the data generated and skills inferred from this will be portable across organizations and used by individuals to grow their career.” But to safeguard data privacy workers must own their own data and have the final say on whether skills are part of their profile or not and if AI can use it, he says.
In a survey quoted in the Deloitte report 79% of respondents said that they’d be okay with having their skills data collected by employers and a further 14% open to it, depending on the purpose.
“Employees are open to sharing their skills data with their employers, but many only wish to do so if the benefits to them are clear,” de Neve says in the Forum article. “ If they get fairer hiring, tailored work experiences and growth opportunities, they are happy to share data.””
TechWolf, which has raised a total of €12 million to date, focuses on Europe but has plans to expand to the U.S. next year.
Competitors include Reejig, Beamery and Retrain.ai. “Our differentiator is our narrow focus and API-first platform, laser focus on skill inference and data quality rather than broad functionality,” says De Neve.
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