How A Telecoms Giant Is Using AI to Predict Its Future Workforce Needs


Diletta D’Onforio, Head of digital transformation at SnapLogic shares how a U.S. telecoms giant is using machine learning to predict its workforce hiring needs years from now, providing a glimpse into how AI will shape hiring efforts in future

Do you know the skills your organization will need five to 10 years from now to meet its evolving business goals? Some HR leaders may claim they do, but do they really have the capabilities to ensure an adequate supply of top talent when they need it?

The traditional recruitment model is a real-time or near-time process, driven reactively by the workforce needs of today. In most cases, a company recruits a headhunter to find a candidate with the right skills to fill a gap that exists currently or is about to open up. These recruiters use an an array of state-of-the art tools to support expeditious hiring — but they still lack the means to address long-term talent acquisition needs.

This a big problem. To execute on a long-term recruitment plan, HR leaders must not only be able to predict their future hiring needs but also fulfill demand when it arises. Today’s HR tools don’t support this long-term objective.

Now imagine your forecasts are vastly more accurate, and that locating elusive technical skills is as easy as hitting the return button on a keyboard. Imagine a tool that can rapidly locate wide-ranging skill sets around the world, wherever you need them, for essential roles that will open up at your business in the future. Years before these skills are actually required, an organization can cultivate relationships with individuals so that they’re ready to hire when the time comes.

This is no longer a pipe dream. My company is part of a team of technology suppliers that is working with a multibillion-dollar telecommunications provider in the U.S. to build just such an application. It uses the power of artificial intelligence — specifically, machine learning — to predict future hiring needs and source talent based on insights from hundreds of data sources. We hear a lot about AI these days, but this is a real-world case where it’s being put to work, illustrating what strategic hiring will look like in the years ahead.

The data sources employed include university databases, social and professional networking sites, applicant tracking systems, and specialized engineering, finance and HR blogs. We’re using these sources to build a “data lake” that holds a wealth of information about labor, education and employment trends worldwide.

We then apply machine learning to this data to extract insights that allow us to construct a long-term workforce planning model. On the supply side, for example, we can uncover patterns in the type and volume of qualifications being awarded by universities in specific regions around the world. If more students in Brazil are graduating with advanced degrees in data science, for example, we can uncover that data — and predict based on the current trends how those graduation patterns will evolve in the coming years.

The system also takes into account the evolving geopolitical and regulatory climate, to determine how they are likely to impact traditional outsourcing markets. This will allow our telecoms client to predict how its access to overseas labor will evolve, and determine how its strategy may need to adapt geographically.

On the demand side, the model looks at the company’s strategic business goals for the coming years to predict what type of skills it’s likely to need, and in what numbers. If the company wants to capitalize on the development of 5G wireless networks, for example, the model will look at hiring patterns in that field today — including those of competitors already working on 5G — to predict what its own needs are likely to be.

Armed with this data, the HR department can start building a pipeline now to fulfill its future talent needs. This can involve partnering with universities to fund student projects, for example, and nurturing relationships with the brightest students that may be beneficial in the years ahead.

At present, no HR organization I know of has such a comprehensive hiring system, but it illustrates how technologies like machine learning are being applied at the most forward-thinking businesses.

The benefits of such a system need not be limited to the corporate world, either. There will be nothing to prevent governments from adopting a similar technology to forecast their national labor needs, including the skills that will be required to support certain industries. By partnering with schools and universities, governments can ensure the right skills are being taught, in the right quantities and in the right locations. We still have a long way to go, but suddenly full national employment starts to look like a real possibility.


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