Overcoming the AI talent gap isn’t as hard as you think

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Businesses in every industry are scrambling to launch Artificial Intelligence (AI) and Deep Learning initiatives. Yet, overall success of these plans is highly dependent on their workforce, and the biggest challenge for businesses is a real scarcity of people who have production-level experience with AI. 

Plenty of reports – from Accenture, Deloitte and others – show the primary obstacle to successful adoption of deep learning is a severe shortage of talent. The talent landscape is affected by several trends, but is even more compounded as digital giants like Microsoft, Google and Apple tussle for the lion’s share of expertise. For startups and even established companies, it’s clear attracting the right AI talent is difficult at best.

What can midmarket and enterprise businesses do to address the AI talent gap?

There are four main strategies most midmarket and enterprise businesses leverage to infuse the right AI talent into their organizations:

  • Compete Against the Industry Giants in Recruiting Talent 
  • Acquire Startups or Smaller Companies
  • Work with Industry Partners or Academia
  • Preparing and Training Your Current Workforce

While embracing just one of these approaches may lead to short-term gains, it will not deliver on the deep learning strategy if done in isolation. Instead, businesses need to embrace a holistic talent strategy that incorporates a blend of these elements. 

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Image Credit: Pixabay

(Image credit: Image Credit: Free-Photos / Pixabay)

Compete against industry giants by communicating your vision

The West Coast digital giants are magnets for both new grads and experienced practitioners in the field. Beyond being able to pay top dollar for scarce talent, they offer access to massive computing resources, the chance to work with the top minds in AI, and incredible amounts of data to train deep learning algorithms. 

However, an inspiring vision can help you stay competitive by providing a strategic advantage by focusing on outcomes that are more self-actualizing, e.g., reducing carbon emissions in the energy sector, life-changing medical breakthroughs, or even impacting the trillion-dollar-plus global financial crime problem. 

An older quote about the big data revolution from Jeffery Hammerbacher – archetypical data scientist, early Facebook employee and co-founder of Cloudera – seems even more apropos in the AI age, “The best minds of my generation are thinking about how to make people click ads. That sucks.”  

To acqui-hire or not to acqui-hire

AI startups are facing a competitive landscape where acquisition becomes the most viable exit path. While the acqui-hire may feel like a quick and easy way to add new employees, dismissing the target company’s current strategy can backfire.  

Without a clear vision for the future, acqui-hired employees won’t have the drive to stick around, making them prime candidates for your competitors or the industry behemoths. A recent study by an MIT Sloan doctoral candidate shows 33 percent of acquired workers leave in the first year of their startup’s purchase. This is staggering when compared to the 12 percent retention rate of regular hires with similar skills and work experiences.

To help minimize this risk, look for organizations that are a better fit with your own vision and mission. Ensure current projects have a defined road map to future endeavors. But make sure you’re inspiring and invigorating your new employees to take ownership and feel they are valued well beyond their industry knowledge.  

Explore external partnerships

Partnerships can be a quick way to gain external experience. Professional services providers with strong credentials in AI can help companies build an AI strategy, create internal awareness about practical AI applications while bringing talented practitioners to help build and run an AI platform. Working with an experienced partner with knowledge transfer in mind will yield the most value.  

For example, NVIDIA – a market leader in computational breakthroughs fueling the AI renaissance – has vetted a list of Service Delivery Partners that have passed their high standard of delivering high-impact outcomes from AI. Gaining momentum and demonstrating wins in the AI space right now will lay the foundation for attracting talent in two or three years, when it will be even more crucial.  

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Image Credit: Pexels

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Don't forget your current workforce

Though more people are retooling their skill set by acquiring deep learning knowledge through avenues like massive open online courses (MOOCs), it is rare to find people who can do it in practice. The classroom is certainly a step in the right direction, but doesn’t replace real-life experience. Ensure your current employees are getting the adequate training, support and hands-on experience to drive key aspects of building, training and deploying AI models. 

Typically, AI roles and skills are spread across the organization -- touching on policy, ethics, technology and data science -- but that will need to converge to build AI at scale. Rotate AI teams across various business units to enable experiential knowledge that can be brought together in a “Center of Excellence” carrying forward experiences from across the enterprise.

There is no single solution to address what every business will need when it comes to building AI capabilities. But keep in mind, whatever strategy you wish to employ, people are going to be what makes or breaks your program.  

Chad Meley, VP of Marketing at Teradata