Dr. Richard George

Q&A | May 19, 2022

The data behind the disruption

Learn how AI is upending the workforce in this Q&A with data scientist Dr. Richard George

The recent proliferation of AI-powered technologies will have a profound effect on the way the world works. When coupled with the growing pressure for enterprise-wide digital transformation, there’s no denying that the global workforce will need to adapt—sooner rather than later.

To plan effectively for the future, business leaders need access to data and insights that predict where the global labor market is going. This is where data scientist Dr. Richard George comes in.

Dr. George is a co-founder of Faethm AI, a workforce and AI predictive analytics company that now operates as the research wing of Pearson. As VP of workforce apps and analytics at Pearson, he uses predictive analytics to forecast the impact of technologies on workers, companies, industries, and even entire economies. We met with him to discuss the insights gathered from a recent research study and partnership with ServiceNow.

Faethm AI was built to be a platform that uses machine learning to help businesses prepare for the future of work.

We originally launched the company after witnessing so many businesses—and individuals—fall victim to the effects of a lack of planning. Time and time again, we’d see people losing their jobs unnecessarily, quite simply because companies couldn’t prepare for the unknown, be it the unexpected threat of automation to a job role, a business underperforming next to its competitors, or otherwise. We wanted to bring in a tool that could help organizations understand—and prepare for—whatever the future might throw at them.

Now, as a part of Pearson, we continue to help corporate companies, government organizations, and individuals understand the changing demands of the workforce.

It’s essentially about predicting the future using the resources available to us. It sounds like an impossible task, but we use machine learning and AI to mine huge amounts of data, from public job advertisements to published emerging technology reports. This not only gives us an accurate view of the working landscape as it is right now, but also allows us to predict how it will change in both the near and distant future.

By analyzing which keywords are becoming more prevalent in job adverts, we can predict what skills will be more in demand as we head into the future and which skills are becoming less important. For example, our research in collaboration with ServiceNow showed “data analyst” and “application development” to be in high demand, while keywords such as “administration” are on the decline.

Making use of this data allows organizations to plan accordingly by giving them an idea of what types of investments to prioritize, which employees may benefit from additional training or reskilling, and in what areas we might see significant skills shortages.

[Want to discover which skills will be most relevant in five years? Explore the data here.]

There are a lot of components working together to make what we do work, which is why you don’t really see any other product doing the same thing. We have about 30 different machine learning models working at the same time, including large language models applied to create our Pearson occupation framework of jobs, tasks, and skills.

In evaluating the demand for specific skills, for example, we use several AI methods to create our reports. We then use another model to adapt research and apply it to other countries, building out several data curves that account for different landscapes in terms of infrastructure, development, education, etc. It’s an incredibly complex process, but one that produces more accurate results.

We take an extremely scientific approach to our research. A lot of consulting companies predict future demands by hosting workshops with futurists, gathering insights from conversations with experts, or looking at previous reports. These can be helpful, but they rely largely on our assumptions about the future.

By working with AI models and undertaking large-scale data mining processes, we don’t need to make assumptions; we can make accurate predictions based on real data and scientific evidence.

Automation is happening, whether you like it or not.

Business leaders have two options: They can ignore the change that’s happening around them, or they can embrace it and plan for it accordingly. By making the very most of all the information available about the future of work, it’s possible to use it to boost efficiency, improve employee experience, and streamline processes across the whole organization. But this requires business leaders to change their mindset—working with automation rather than rallying against it.

The ongoing digital skills shortage has created an opportunity gap: The number of jobs that require specific technology skills is increasing, while the number of qualified candidates remains the same. This means organizations must work to recruit new, fresh, qualified talent.

Understanding exactly what skills you need to prioritize means you know exactly where to look. Similarly, employers can use the information they have to identify opportunities to reskill or hire candidates with transferable skills from nontech or nontraditional talent pools.

Businesses looking to navigate the future of work successfully should partner with organizations that can provide them with the necessary support, whether that involves supplying additional information about the business landscape or championing new and fresh talent initiatives such as RiseUp with ServiceNow.

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