The forward-thinking CIOs and CTOs have already acknowledged that Line of Business (LoB) leaders will continue to be the primary decision-makers of new or emerging business technology selection and adoption.
That said, those who refuse to let go of the obsolete notion that 'Shadow IT' is a threat to their organization are likely to fail. There is no place for myopic thinking in a progressive enterprise's digital transformation agenda.
Digital Talent Leadership Market Development
For the past four years, the strongest demand for digital talent with artificial intelligence (AI) skills has not come from the traditional information technology (IT) department, but rather, from other business units in the organization, according to the latest worldwide market study by Gartner.
Gartner 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).
Note: The top countries are derived from the IMF 2019 ranking of countries by total GDP, excluding Italy, Spain and South Korea due to limited time-series data.
"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 a recent related Gartner market study, survey respondents ranked 'skills of staff' as the number one challenge or barrier to the adoption of AI and machine learning (ML). That's why digital talent strategy and leadership is so important to successful business outcomes.
Where is the most demand coming from within these organizations? Departments recruiting AI talent in high volumes include marketing, sales, customer service, finance, plus research and development (R&D).
What are the primary use cases that are driving demand? 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.
Outlook for AI Talent Development and Growth
"Given the complexity, novelty, multidisciplinary nature and the potentially profound impact of AI, CIOs are well-placed to help HR in the hiring of AI talent in all business units," said Mr. Krensky.
According to the Gartner assessment, 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. And, they must willingly partner with LoB leaders that they support.
That said, those who refuse to let go of the obsolete notion that 'Shadow IT' is a threat to their organization are likely to fail. There is no place for myopic thinking in a progressive enterprise's digital transformation agenda.
Digital Talent Leadership Market Development
For the past four years, the strongest demand for digital talent with artificial intelligence (AI) skills has not come from the traditional information technology (IT) department, but rather, from other business units in the organization, according to the latest worldwide market study by Gartner.
Gartner 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).
Note: The top countries are derived from the IMF 2019 ranking of countries by total GDP, excluding Italy, Spain and South Korea due to limited time-series data.
"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 a recent related Gartner market study, survey respondents ranked 'skills of staff' as the number one challenge or barrier to the adoption of AI and machine learning (ML). That's why digital talent strategy and leadership is so important to successful business outcomes.
Where is the most demand coming from within these organizations? Departments recruiting AI talent in high volumes include marketing, sales, customer service, finance, plus research and development (R&D).
What are the primary use cases that are driving demand? 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.
Outlook for AI Talent Development and Growth
"Given the complexity, novelty, multidisciplinary nature and the potentially profound impact of AI, CIOs are well-placed to help HR in the hiring of AI talent in all business units," said Mr. Krensky.
According to the Gartner assessment, 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. And, they must willingly partner with LoB leaders that they support.