Gartner data shows strongest demand for AI Talent comes from non-IT Departments

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Mita Srinivasan
10X People
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Gartner data shows strongest demand for AI Talent comes from non-IT Departments

Gartner Talent Neuron data shows that although the IT department’s need for AI talent has tripled between 2015 and 2019

For the past four years, the strongest demand for talent with artificial intelligence (AI) skills has not come from the IT department, but rather, from other business units in the organization, according to Gartner, Inc.

Gartner Talent Neuron data shows that although the IT department’s need for AI talent has tripled between 2015 and 2019, the number of AI jobs posted by IT is still less than half of that stemming from other business units (see Figure 1).

Total AI Jobs Posted in Top 12 Countries by GDP, July 2015 Through March 2019
Total AI Jobs Posted in Top 12 Countries by GDP, July 2015 Through March 2019
Gartner Talent Neuron (March 2020)

Departments recruiting AI talent in high volumes include marketing, sales, customer service, finance, and research and development. These business units are using AI talent for customer churn modeling, customer profitability analysis, customer segmentation, cross-sell and upsell recommendations, demand planning, and risk management.

“High demand and tight labor markets have made candidates with AI skills highly competitive, but hiring techniques and strategies have not kept up,” said Peter Krensky, research director at Gartner. “In the recent Gartner AI and Machine Learning Development Strategies Study, respondents ranked “skills of staff” as the No. 1 challenge or barrier to the adoption of AI and machine learning (ML).”

According to Krensky, together, CIOs and HR leaders should rethink what skills are truly necessary for an AI-focused employee to have on Day 1 and explore candidate criteria adjacent to hiring specifications. CIOs should also think creatively about IT’s role in governing and supporting diverse AI initiatives and the evolving teams driving this activity.

A significant portion of AI use cases are reported from asset-centric industries supporting projects such as predictive maintenance, workflow and production optimization, quality control and supply chain optimization. AI talent is often hired directly into these departments with clear use cases in mind so that data scientists and others can learn the intricacies of the specific business area and remain close to the deployment and consumption of their work.