Playbook for stockpiling AI talent: Buy, borrow, build

Hiring, upskilling, and strategic partnerships are the cornerstones for assembling talent in data science and AI. CIOs would do well to employ all three approaches.

The playbook for stockpiling AI talent: Buy, borrow and build
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Many IT leaders will tell you hiring tech talent is right up there with culture change as a chief hurdle to business transformation. Finding enough software engineers, Scrum masters, DevOps leaders and other potential change agents remains a burden. But experts agree the top challenge is hiring experts in data science, including those with machine learning (ML) and artificial intelligence skills.

From healthcare to financial services, every sector is embracing some form of AI as a core business strategy. Eight-four percent of 500 business leaders surveyed online by consultancy EY in 2019 said that AI is critical in facilitating efficiencies and reducing costs, gaining a better understanding of customers and generating new revenues.

But the road to success depends heavily on the talent pool, as 31 percent of those same leaders said that a lack of skilled staff is the No. 1 barrier to AI adoption.

Here experts share their experiences with mining AI talent and provide tips for how CIOs can lure the right mix of data scientists, ML engineers and AI experts.

Casting a wide net

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