In the War for Machine Learning Talent, Who’s Investing to Win?
Most business leaders would agree that extracting insight from corporate data is a strategic imperative. Machine learning promises to automate the process of interpreting large amounts of information, and using that analysis to drive positive business outcomes. However, machine learning technology is complex and requires significant expertise to use effectively. We think access to top-tier machine learning talent will define winners, losers, and opportunities for disruption in the data-driven world.
Our analysis of employment trends for machine learning experts shows that certain industries, like technology, banking, and industrials, are making a significant investment in machine learning, charting a course for long-term business innovation. However, other sectors like business services, insurance, and logistics are data rich but have made little investment in machine learning talent, and may be most exposed to potential disruption.
Tracking Top Talent Across the Fortune 500
We believe hiring and retaining top machine learning talent will be a strategic priority for companies hoping to unlock the full value of data. Using LinkedIn, we identified roughly 38,000 people fitting the profile of top-tier machine learning talent, and cross-referenced this list with Fortune 500 companies to understand where these experts work. Fortune 500 companies employ 23% of the top machine learning talent we identified, with 18 machine learning experts per company on average. However, the range was very wide, with technology leaders like Alphabet and Microsoft employing over 1,000 experts, while half of Fortune 500 companies didn’t have any employees on LinkedIn matching the profile of top-tier machine learning talent.
Data Stuffed and Ready to Digest
Machine learning is a top priority for technology companies both big and small, and it’s not surprising to see that the bulk of machine learning experts employed by Fortune 500 companies work for Internet and software giants like Microsoft, Alphabet, Amazon, and IBM. These companies are fully invested in using data to make their own products and services better, and building the next generation of machine learning tools to sell to other businesses. More interesting is how many machine learning experts don’t work for the biggest technology companies. The number of machine learning experts employed by small software and Internet companies with less than 500 employees is roughly equal to the number working for Fortune 500 technology giants. This suggests that technology giants don’t have a lock on innovation in the space, and the startup community is just as rich in talent with more great ideas to come.
Outside of the technology sector, companies in the Aerospace & Defense, IT Services, Commercial Banking, and Industrial verticals have invested heavily in top tier machine learning expertise. We believe this investment is linked to a focus on emerging technology areas like cybersecurity and Industrial Internet of Things (IIoT), which have the potential to unlock sizable new markets for leaders in the space. Within banking, vast stores of customer data offer the opportunity for tighter customer segmentation and better risk models, which can translate to competitive advantage and new revenue streams over time.
At Risk of Being Left Behind
On the other side of the coin, some industries have made little-to-no-effort to invest in data talent. For certain asset-heavy industries like Engineering and Construction, Utilities, or Energy, the risk of disruption from data-first newcomers is lower. In these markets, we think the most likely path to success for technology companies will be vertical solutions that can help these companies use data more effectively.
Those at greatest risk of disruption are asset-light companies that have made little to no investment in data talent despite being populated by data-rich businesses. The examples that jump out to us include the Insurance industry, Business Services (which includes Staffing and Marketing companies), and Healthcare Services — all industries where the core asset is people, significant data volumes exist, and the traditional leaders aren’t investing in top tier data experts that can turn information into a competitive advantage. We think this leaves the door open to disruption from outside vendors who can offer a superior competing service with a tech-first approach, similar to how AirBnB and Uber disrupted the lodging and transportation industries respectively.