A Talent Strategy for Artificial Intelligence

Companies should consider a multi-faceted approach to recruited artificial intelligence (AI) and machine language (ML) talent.

The race to hire artificial intelligence and machine language talent is more competitive than ever. Enterprise executives are demanding greater automation in all sectors of business and that means hiring and retraining.

Is there a sweeping answer to the AI talent challenge? No, but there seem to be plenty of narrow fixes. Together they can help organizations acquire or develop people who will develop and run AI and ML initiatives.

Fix Number One: Don’t try to copy the enterprise-wide AI strategies used by tech giants such as Google and Facebook. Those companies were built on big data, vast collections of diverse data types that formed the core of the businesses.

Rather, the average company looking at AI and ML is likely to focus on a well-defined need. Think of applications tied to use cases such as consumer fraud detection and product quality assurance. Recent thought leadership examples highlight how use of AI and ML in that sort of narrow application might be manageable for many organizations.

Andrew Ng, writing in the Harvard Business Review, outlined a focused view for AI and ML. “Sure, it has transformed consumer internet companies such as Google, Baidu, and Amazon — all massive and data-rich with hundreds of millions of users. But for projections that AI will create$13 trillion of value a year to come true, industries such as manufacturing, agriculture, and healthcare still need to find ways to make this technology work for them. Here’s the problem: The playbook that these consumer internet companies use to build their AI systems — where a single one-size-fits-all AI system can serve massive numbers of users — won’t work for these other industries,” he wrote.

“Instead, these legacy industries will need a large number of bespoke solutions that are adapted to their many diverse use cases.”

A company like Facebook may utilize many thousands, even millions, of data points to train an AI or ML solution. However, a manufacturer looking to use ML and AI to detect flaws in a product might have only 50 data points, according to Ng. Yet, what Ng and other experts note is that the manufacturer might have dozens of experienced line workers who already know what flaws to look for in a product inspection. Those staff members can help to train the ML app by sharing their knowledge.

In fact, many niche AI and ML initiatives can benefit from such knowledge sharing. That and other techniques that add a business perspective to the project.

The manufacturer can’t compete with the $500,000 salary that Google might pay a job candidate with a PhD and deep knowledge of AI. However, it does need to hire someone with AI and ML tech skills to lead the AI team. In addition, software engineers are needed to build out an application. Those engineers may be hired from the IT team or outside sources. However, that analytics talent business leaders who understand how the company operates can supplement that tech talent.

Those business unit leaders may need to learn how AI can or can’t be used, which is an issue being addressed by companies such as Levi Strauss & Co. At Levi, some business leaders are going through a crash course in advanced analytics and data-based applications.