3 ways to address skills shortages and win the AI talent war

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From taking over menial tasks to automating security, artificial intelligence (AI) has vast potential to transform the world of work

Far from being a tool for a select few, AI is today working on complex challenges in 18 core industries, from consumer packaged goods and retail to the life sciences and healthcare, where AI has been deployed to model viruses, among other things.

"Leaders are looking to reduce costs through automation and efficiency, and AI has a real role to play in that effort," writes a senior Deloitte executive. They added that "companies across all industries have been scrambling to secure top AI talent from a pool that's not growing fast enough."

It's no wonder 3,500 business leaders have revealed that hiring is the most significant difficulty they're facing when it comes to the success of AI integration. The demand for AI talent is so dire that major tech player Tencent has stated that, while there are millions of vacancies, there are only about 300,000 AI talents globally.

However, with the right strategy and talent partner in place, companies will build the teams they'll need to accelerate from proof of concept to deployment.

A strategy to overcome AI talent scarcity

Companies can make, regenerate and take talent to address AI talent shortages. Here's how.

1. Make the right kind of talent.

As opposed to early innovators who only had a few books and their wits to contend with, the tech industry is spoiled for choice with resources to train AI talent from the ground up.

It can be as rudimentary as imparting the basics of AI to anyone and everyone in the company or ensuring lifelong learning through a collection of workshops, guides and tutorials from one of the world's leading AI giants, Google. Not to mention the countless other 'minidegrees' and courses from a variety of course providers.

AI leaders invest 1.5 to 2 times more into talent than laggards, according to this McKinsey report.

With that said, Massive Open Online Courses (MOOCs) are beneficial for companies as they:

• Only require companies to spend on what's relevant (many courses are free).
• Enable staff to set their own pace.
• Are modular and can quickly impart competency in a specific area.
• Provide training from and exposure to the top experts in universities and multinationals across the world.

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Companies can then encourage IT executives and managers to run internal projects to allow talent to iterate AI proofs of concepts and team-build.

2. Regenerate high performing talent from other verticals.

Talent from other verticals has core skills that translate well into artificial intelligence. Instead of hiring externally, take a look at your current talent pool and ask yourself if a person:

• Has a background in economics, mathematics, computer science or statistics.
• Already works with, and is passionate about, large data sets.
• Knows how to foster strong collaboration, is good at problem-solving and can think strategically.

In fact, companies stand to improve their talent retention goals by providing a clear career path for current employees to transition to a new team that can deliver business success.

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A regenerated career pathway can reignite a talent's passion for tech

One of the biggest challenges in retention is when the interesting projects they've been working on, come to an end.

It's at this point where most ML Engineers and Data Scientists will start to consider new opportunities.

If you're an employer, and you can't find any challenging work for them, don't try and "keep them busy". You will lose their focus and attention.

A great way to keep your employees engaged between projects is to offer an educational programme that will help develop their skills.

In turn, this creates a more experienced, knowledgeable and valued employee.

Andy Mcloughlin, Senior Manager - Airswift

What's more, hiring from a more diverse background stands to deepen the sophistication of their AI research

According to McKinsey, when one company hired "a deeper bench of architects, engineers, and scientists to build on the company's data assets, refine their AI processes, and introduce more sophisticated initiatives … [it realised] more value from its investments — growing AI-related ROI by 50% over three years."

3. Take from the industry.

The third strategy is to create a strong AI talent acquisition plan by engaging a recruitment partner. Tech companies cannot afford to constrain their hiring options in the global war for talent and rely on long and complex hiring processes.

Companies need an expert talent partner that can turn knowledge of the talent market into an acquisition 'force multiplier' if they hope to compete with FAANG companies for the same small pool of AI talent

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Such a partner knows where the best opportunities lie, as the right AI talent might not be in your zip code. They can source in-demand talent locally and globally and deliver the right mobility solution between sectors and nations if they have a solid network.

They can also help you to market your brand to the talent you want to attract. A well-developed employee value proposition gives candidates an insight into your company and creates inbound demand.

Meet your AI talent acquisition partner

Here's where Airswift can help companies ensure a healthy pipeline of enterprise-wide AI talent to deploy AI projects successfully. With over 70 strategic offices worldwide, we have a big-picture understanding of companies' challenges when hiring data scientists and machine learning specialists.

The right AI talent pipeline enables you to create a project team that can balance technical skills, mastery of complex business systems, and stakeholder and project management to accelerate the deployment of machine learning systems.

This expansive network helps us make data-driven decisions regarding talent acquisition. We can help you draw upon a much wider talent net even if they are hard-to-find, passive candidates.

Ready to get the human capital edge?

Get in touch with our workforce consultants today, even if you're starting with talent mapping and workforce planning, so that you can accelerate your AI projects from proof of concept to deployment.