Given the technologies such as machine learning and artificial intelligence are the next frontier of corporate innovation, the essential strategic question is how can legacy enterprises compete for AI talent.

The  “war for talent” has been a pressing challenge for organizations since 1997, when Steven Hankin of McKinsey & Company coined this term. Competition for workers with the right skills has only increased since then, with Indeed reporting in 2018 that 42 percent of employers are concerned they won’t be able to find the talent they need. The skills shortage is particularly severe in the field of artificial intelligence (AI), where demand is far exceeding the available talent pool.

While organizations of all experience levels typically have an AI skills gap, the size of that gap varies according to the specific skills. The roles most in demand include AI data scientists, developers, engineers, and researchers, with little difference in demand between legacy organizations and those already experienced in AI. Other AI roles such as business leaders, domain experts, and project managers have a lower priority.

Companies in all industries are vying for the top talent in this area, including high-tech giants like Facebook and Google. This shortage has even continued through the COVID-19 pandemic, which has caused a general disruption in the economy. If anything, the current economy has intensified the search for talent since AI can play a major role in reducing costs by increasing automation. Legacy enterprises can compete with leading organizations for AI talent by adopting strategies that include employee diversification, hiring citizen AI workers, and a strong AI purpose statement.

How Can Legacy Enterprises Compete for AI Talent

Diversity

Organizations with low maturity levels in AI can often close their skills gap in this area by diversifying their workforce. While many areas can benefit from this strategy the lack of diversity is particularly acute when it comes to AI. A 2019 study from Discriminating Systems shows that only 15 percent of the AI research staff at Facebook were women, while only 10 percent of these staff members at Google were women. This study also indicates that black employees at Facebook, Google, and Microsoft account for only 4 percent, 2.5 percent, and 4 percent of the staff, respectively.

Employers looking to fill their AI requirements often reduce their priority for diversity or overlook it entirely. However, organizations that prioritize diversity from the beginning of their search will ultimately win the talent war over AI. The small pool of talent in this area also means that companies need to plan more strategically in their quest to narrow their skills gap. For example, they should publish their compensation levels to create the transparency needed to ensure equal opportunities in their hiring practices. Additional measures that can help achieve this goal include publishing discrimination and harassment policies.

AI Purpose Statement

A 2018 Gallup poll shows that morale in the workplace has plummeted over the last few years, with only 34 percent of U.S. workers feeling engaged in their jobs. Research also consistently indicates shows that employees’ level of engagement has a high correlation with a sense of purpose. For example, employees are three times more likely to thrive when they work in an organization with a strong sense of purpose, according to a 2018 Mercer study.

Candidates for an AI position don’t want to spend their entire time on a project they don’t care about, no matter how technically challenging it may be. A purpose statement that’s specific to AI is, therefore, an important part of a successful recruiting strategy, especially for legacy enterprises. This statement should provide details on the type of data your organization is collecting, especially data that few other AI specialists can access. It should also explain the unique opportunities that working with this data will provide for the candidate.

AI Labs

An AI lab is another tactic that organizations are starting to use in an attempt to compete for the shrinking pool of AI talent. The New York Post reports that companies like Amazon, Facebook, Google, and Microsoft have already opened up their own labs in countries where the demand for AI talent isn’t as high as it is in the US, including Canada, China, Eastern Europe, and India.

Building teams in remote locations also provides legacy organizations with a means of increasing their global reach. Many AI startups in Silicon Valley also began expanding their teams remotely by 2020. One alternative to creating technology centers overseas is to build them in low-cost areas in the US that have large numbers of university graduates, including Michigan and Texas. This tactic is particularly appealing to companies that favor a hands-off approach to team management. Canada’s relaxed immigration policy also makes it a favorable location for attracting international AI talent.

Citizen AI Workers

A citizen AI worker performs AI-related tasks, but whose primary job is outside of AI. True AI specialists focus on designing and developing AI systems, while citizen AI workers are more interested in using those systems to derive meaningful insights from their underlying data and communicating those insights to other members of their organization. Citizen AI workers may lack concrete AI skills, but they can still provide a connection between AI specialists and other members of their organization.

The number of citizen AI workers has risen rapidly during the last few years, creating a potential hiring pool that traditional hiring practices often overlook. This pool will likely increase in the future as more workers begin performing less demanding duties of AI roles. A proactive approach to hiring and training citizen AI workers can help legacy organizations get ahead of their more experienced competition.

Training

Early adopters of AI have a greater awareness of their skill shortages than legacy enterprises. However, they also have a sharper focus on internal training that can cause them to underestimate their desirability to AI workers. About two-thirds of organizations experienced in AI are currently training their workforce to develop and deploy new AI solutions, according to Deloitte’s 2020 State of AI in the Enterprise Survey. In comparison, the corresponding figures for organizations with medium AI experience and those with little to no experience are 55 and 43 percent respectively. The portion of experienced organizations that are training workers to use AI for their regular duties is 67 percent, compared to 53 percent for organizations with medium experience and 48 percent for those with low experience.

Talent Source

Organizations generally need to acquire AI professionals from external sources, regardless of their current experience with AI. The fact that they’re all after the same small group means that recruiters need to explore other alternatives such as hiring recent graduates. Retraining existing employees is also a tactic that’s particularly effective for legacy enterprises, as early adopters are more likely to have employees who already have AI skills. In addition, organizations with a low level of experience in AI have a greater tendency to rely on partners with AI expertise.

Summary

Prioritizing the hiring of top AI talent should be an obvious decision for legacy enterprises, but it isn’t always clear how big an advantage it provides. A 2017 book by McKinsey reports that the top performers in complex occupations like AI are 800 percent more productive than the average workers in those roles. Organizations can close their skills gap in AI by diversifying their workplace and hiring workers to train into their positions. It’s also important to provide candidates with a purpose statement that explains the value of an organization’s AI positions.

As an enterprise technology executive, how is your legacy enterprise competing for AI talent?