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.
“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 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).”
Gartner: AI knowledge is hot commodity
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.
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.
“Given the complexity, novelty, multidisciplinary nature and potentially profound impact of AI, CIOs are well-placed to help HR in the hiring of AI talent in all business units,” said 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.”