Upskilling, hiring, and strategic partnerships form the cornerstones for assembling talent in AI and data science. CIOs should focus on employing all three approaches.
IT leaders will agree that hiring tech talent continues to be the chief hurdle to business transformation. The top challenge is hiring data science experts, finding those with artificial intelligence and machine learning (ML) skills. From financial services to healthcare, every sector embraces some form of AI as its core business strategy. A survey conducted by consulting firm EY says 84% of 500 business leaders surveyed online agreed that AI is critical in facilitating efficiencies. It reduces costs, gaining an improved understanding of customers for generating new revenues. However, the road to success depends on the availability of talent pool, as 31% of them confirmed that the lack of skilled staff is the biggest roadblock to AI adoption.
Below are tricks for mining AI talent to provide tips for how CIOs can lure to hire the right mix of data scientists, AI experts, and ML engineers.
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Casting a wide net
Recruiting talent is hugely challenging, as there is more demand than supply. One of the reasons behind this is that candidates for data science are likely to possess sharp problem-solving skills, often hailing from all manner of backgrounds and disciplines.
For zeroing in on data science talent, decision-makers should –
Widen their aperture: A good data scientist combines interpersonal skills with an analytical mind.
Identify problem solvers: Has a candidate solved real-world problems? Has a prospective hire built a software product or published an open-source software library others use to solve problems? These are critical markers of whether someone may be a good fit for the team or not.
No data scientist stone left unturned
Identifying engineering talent who can build AI capabilities presents a more narrow challenge, often requiring coding skills, and excellent statistical skills with experience in developing algorithms.
The Gartner talent acquisition survey published in early 2019 recommends broadening talent outreach beyond fertile grounds such as San Francisco Bay Area, New York City, and Seattle to Austin, Philadelphia, and Denver. Looking at these alternative locations will assist organizations to capture the market early easing pressure on salary costs, Gartner suggests. Companies should conduct significant cross-training, reskilling the staff across several disciplines, including physics, topology, cryptography, and astrophysics.
Boiling it down to the main principle of – buy, build and borrow
Recruiting talent requires a combination of approaches: buy, borrow, and build; followed by the execution of projects that create a flywheel of continuous innovation. The path selected depends on the culture of the corporate.
Buy: For hiring AI staffs, firms should be prepared to pay a premium amount, especially if the firm is based in specific geographies. E.g., a CIO based in Milwaukee, Wisconsin, or Columbus, Ohio, is going to have a harder time filling positions than a peer in San Francisco, New York or even Austin, Texas.
Borrow: Tap talent from consultancies or universities to work on projects or build products can help firms to fill short-term gaps in less critical areas.
Build: Firms can also groom talent in-house. This takes time and patience. To achieve this, firms can start slowly with the hope of letting talent bloom. PwC up skilled a small portion of its staff, including those of an entrepreneurial persuasion, to work on robotic process automation (RPA), and is making a similar push in AI and ML.
Once companies have the talent, they can identify champions to help construct AI solutions. Then it will be a matter of execution of the projects, monitoring progress, soliciting feedback, and correcting course. Every business problem will require companies to tweak the defined strategies. There is not a one-size-fits-all approach in this scenario.