AI leaders are in high demand, but come from a wide variety of backgrounds and experiences. We look at common career paths and profiles, based on our recent analysis of more than 100 AI leaders worldwide.
AI Talent Landscape: Defining and Finding the Leaders Your Company Needs
AI has steadily evolved from a near-mythical technology to a pressing reality for organizations in every industry. Hailed as the next Industrial Revolution, AI is projected to increase labor productivity by up to 40 percent, and profoundly change the nature of work.1 Current estimates show AI creating 2.3 million jobs, eliminating 1.8 million jobs and producing insights that will assist one in five workers.2
As AI technology matures, two major talent challenges confront organizations aiming to tap its rich potential. One is the growing talent shortage. Globally, millions of AI professionals will be required to bring the technology to fruition; recent research puts the current worldwide population of AI engineers and scientists somewhere between 22,000 and 300,000.3 The second relates to leadership profiles: Many AI professionals have been groomed as technical or academic experts, but not as business-minded technology executives who can bridge the gap between science and application. Combined, these two challenges mean that many organizations will have to weigh the feasibility and benefit of hiring an AI expert to lead internal efforts against partnering with an external organization that can provide the expertise.
Whether hiring or partnering, it is essential to understand the range of possibilities and capabilities in the current world of AI talent. To support CEOs and senior executives as they embark on the various phases of this journey, we’ve compiled a series of resources:
Russell Reynolds Associates’ analysis of the career paths of more than 100 AI executives around the world.
Five common leadership profiles among today’s AI executives – and how to know which one is right for your company
Three emerging options for sourcing AI talent, both from outside and within the company.
Our research indicates the majority of senior AI expertise is concentrated in six regions, namely the US (42 percent), China (25 percent), UK (12 percent), Asia Pacific (12 percent), Canada (5 percent) and Continental Europe (4 percent).
Changing macro-economic and political conditions have recently led to increased – and o en unequal – cross-regional movements. Most notably, 31 percent of AI leaders moved from the US to China for their most recent role, compared with just 5 percent moving in the opposite direction. The UK and Canada have also seen an influx of AI leaders, with 50 percent of those in the UK and 67 percent of those in Canada coming from outside the country.
THREE FACTORS CONTRIBUTE TO THIS INCREASE IN MIGRATION
Flourishing opportunities in China: The Chinese government has designated AI as a key economic driver with aims to foster it as a $1 trillion industry by 2030. As an example, the government has built a $2.1 billion technology park tailored to AI development. Private equity investors have put in over $4.5 billion into 200 AI companies in the past five years.6 In addition, the wealth of available data and comparatively flexible data privacy protocols in China support AI development needs.
Uncertainty around US research funding and political climate: The US appetite for science investment has been a constant source of anxiety in recent years, with no clear direction on future funding levels. At the same time, the US State Department has also recently toughened visa restrictions for Chinese graduate students planning to study aviation, robotics and advanced manufacturing.7
Increasing prestige and global expansion for non-US AI companies: While US so ware companies have led the field in the past, non-US companies are rapidly becoming global players. China’s Alibaba is leveraging sophisticated AI to enhance sales, pushing Singles Day sales to $31 billion – a 27 percent increase over the previous year.8
While we see these concentrations of AI investment and talent, we also see an increasing trend for technology companies to invest in localized talent pools. Google is opening an AI research lab in Being, while Alibaba is opening five AI labs overseas in the US, Russia, Israel and Singapore. Ultimately, this could lead to more regional migration rather than international movement.
Methodology: We identified 103 executives around the world with backgrounds in AI-related areas such as machine learning, neural networks, and natural language processing who are currently in technical roles leading AI development within their organizations. These executives represent a total of 69 organizations across 14 countries. Forty-two percent hold VP-level or above titles, including 15 percent who are founders.
Creative Solutions for Sourcing AI Talent
Given the shortage of AI talent, leading companies are increasingly looking for creative solutions to solve talent needs. Beyond outsourcing AI development entirely, there are a variety of ways to tap the talent needed for transformation.
1. CONSIDER JOINT APPOINTMENTS
Academia is a leading source of AI expertise, yet many academic researchers are reluctant to leave their university posts entirely. One solution for executives at companies aiming to bolster AI capabilities is to create flexible working arrangements with academic researchers, allowing them to hold joint positions in academia and industry. As an example, academics at Facebook usually spend 80 percent of their time at the company and 20 percent conducting research.
2. RE-TRAIN MID-CAREER EMPLOYEES
Many current AI leaders began their careers in a related field such as computer science, data science, statistics or electrical engineering. This comes through in our data: AI leaders have spent 9.1 years working directly with AI, on
average, but have total career experience of 19.9 years, excluding time spent in PhD programs.
Looking ahead, companies in search of AI talent should consider proactively recruiting and training experts in adjacent areas, including existing employees. Some companies are already pursuing this path on a large scale. One model is the approach AT&T has adopted: The telecom giant has invested $1 billion in upgrading the skills of about 40 percent of its workforce in areas like data science and computer science through on-demand platforms.9
3. ENCOURAGE A START-UP MINDSET WITHIN AN ESTABLISHED ORGANIZATION
AI experts who have current or past start-up experience are more likely to switch companies than others. Those with start-up experience have joined a mean of 4.7 companies throughout their career, compared to 3.6 companies for those without. While this is a positive for those looking for AI talent to help build a new team and structure from the ground up, hiring such entrepreneurs can create frustration in the long term.
To help retain such leaders long enough to see an AI organization to maturity, executives at established companies may want to consider cultural changes that allow for experimentation, innovation and risk-taking in some of the ways that a new venture does.
As CEOs and other top executives look to hire their next or first AI leader – or partner with others to develop AI strategy and applications -- the first step is to clearly define the specific needs at hand. Is the task to begin an AI initiative, or to sharpen an existing effort? Is it to stake and industry leadership claim, or to defend against being left behind? Tactically, what applications will be most relevant and how should they be prioritized?
The answers to these questions will help clarify which types of AI leadership profiles to consider, and also which trade-offs are acceptable. Is an executive’s AI technology expertise or business acumen more important? How about his or her propensity for disruptive innovation compared with the likelihood of a long tenure? In all cases, however, AI executives must have the capability to adeptly translate business requirements into technical specifications and AI outputs into insights.
AI is a far-reaching and far-sighted solution space which requires patience, determination and resources to succeed. The leaders who are chosen today will likely have an outsized impact on the future. Is your organization ready?
Fawad Bajwa is a member of Russell Reynolds Associates’ global Technology Officers practice and leads the Artificial Intelligence work globally. He is based in Toronto and New York.
Nick Chia is a member of RRA’s Technology sector as well as its Industrial & Natural Resources sector. He is based in Singapore.
David Finke leads RRA’s global Technology sector as well as its global Hardware and IOT practices. He is based in Palo Alto.
Zheng Wei Lim is a member of the knowledge team for RRA’s Technology sector. He is based in Singapore.
1 Accenture Institute for High Performance and Frontier Economics, “Why Artificial Intelligence is the Future of Growth,” September 28, 2016.
2Gartner Inc. “Predicts 2018: AI and the Future of Work,” December 13, 2017.
3Vincent, J. (5 Dec, 2017). “Tencent says there are only 300,000 AI engineers worldwide, but millions are needed.” The Verge.https://www.theverge.com/2017/12/5/16737224/global-ai-talent-shortfall-tencent-report and Gagné, J-F. (2018). “Global AI Talent Report 2018”. JFGagné.ai. http://www.jfgagne.ai/talent/
6 Churchill, O. Nature. “China’s AI dreams.” https://www.nature.com/articles/d41586-018-00539-y
7 Mervis, J. Science. “More restrictive U.S. policy on Chinese graduate student visas raises alarm.” https://www.sciencemag.org/news/2018/06/morerestrictive-us-policy-chinese-graduate-student-visas-raises-alarm
8 Liao, R. TechCrunch. “Alibaba sets new Singles’ Day record with $31B in sales, but growth is slowing.” https://techcrunch.com/2018/11/11/alibaba-singlesday-2018-31b/
9 Susan Caminiti, “AT&T’s $1 Billion Gamble,” CNN.com, March 13, 2018. https://www.cnbc.com/2018/03/13/atts-1-billion-gambit-retraining-nearly-half-its-workforce.html