Businesses across the globe are fascinated with the idea of AI and automation because this advanced technology promises operational efficiency, enhanced processes, and substantial cost savings. However, AI and its allied technologies have also created uncertainties, confusion, and doubts about the human capability for adopting, deploying, and executing these magical systems in actual business situations -- simply because the business leaders and operators are still all humans. Today, it is widely acknowledged that automation and AI technologies will gradually transform the global workplace, with intelligent machines performing human tasks in some cases and aiding the human in other cases. The presence of robotic machines in the workplace will ultimately increase efficiency and reduce costs. As a result, many human occupations will disappear, while others will adapt to technology-enabled roles.

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). "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)." 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.

New data from Gartner Inc. suggests that the recruiting, management, and retention of artificial intelligence talent (AI) will be a strategic challenge globally for the foreseeable future. For the past four years, Gartner found, the strongest demand for talent with AI skills has come from non-IT departments. Departments recruiting AI talent in high volumes include marketing, sales, customer service, finance, and research and development, Gartner said in a press release. "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." Gartner TalentNeuron data released Wednesday shows the total AI jobs posted by non-IT departments in Top 12 countries by GDP, grew 74%, to 156,294 through March 2019, from 89,895 in July 2015.

"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)." 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.

Machine learning and artificial intelligence are coming close to the peak inflated expectations stage in a hype cycle. In a webinar, Gartner director analyst, Peter Krensky, outlined the current state of machine learning and artificial intelligence, the next five years, and some of the challenges likely to impact adoption, development, and deployment. According to Krensky, ML and AI are coming close to the peak inflated expectations stage in a hype cycle. Augmented and virtual reality have already hit the'trough of disillusionment', which follows the peak, and autonomous vehicles and drones were past the peak, but have yet to hit the bottom of the cycle. That said, there is still a large amount of untapped industries for AI and ML.

Core data scientists, or data scientists who have been trained or educated specifically in the field, are hard to come by, Krensky said. There's a talent shortage, he said, and increasing demand for machine learning tools to automate what might have been done by traditional data scientists isn't helping. While Krensky noted that more colleges and universities are beginning to offer degrees in the fields of data or business analytics, and academia is one of the top sources of data science and machine learning talent, hiring a core data scientist can be costly. Internships, however, can serve as a "win-win" for both businesses and students, he said. By working with schools and welcoming student internships, a business can "get an injection of the latest and greatest tools and techniques that are being used" and taught, and students can earn valuable experience, Krensky said.