Talent, skills shortages may define next decade, tech pros say

Dive Brief:

Most workers (59%) are optimistic about how technological advancements will change their careers in the next decade, according to an ISACA survey of 5,000 business technology professionals.
However, survey respondents expressed concern about talent shortfalls in cybersecurity; only 18% said they believed the cybersecurity skills gap will be mostly or entirely filled during the upcoming decade. And 81% of respondents said companies aren't investing enough in the people skills needed to navigate technological change.
Survey respondents also said they expect emerging technology to phase out many everyday activities, such as using cash, keys, IDs and physical office locations.

Dive Insight:

Other sources affirm ISACA survey respondents' conclusion that investing in talent is key. A study released by MIT Sloan Management Review and Boston Consulting Group's BCG GAMMA and BCG Henderson Institute resulted in similar findings. According to the study, AI talent strategies work best when organizations are selective about importing AI talent for leadership roles and upskill their current workforce in AI.

AI- and automation-related cost savings may incentivize companies to invest more in tech talent. A Hackett Group report released in September concluded that "HR organizations can reduce costs by 17% and operate with 26% fewer staff hours" by integrating automation into their practices. In fact, the report found that "world-class" HR organizations' operating costs are 20% lower than non-digitally savvy organizations, and that they leverage 31% fewer staff members to provide the same services.

Despite the economic advantages of AI, HR leaders can't dismiss the ethical dilemmas technology often raises in their operations. In fact, the Wharton School and ESSEC Business School identified four problems with AI in HR management to monitor: 1) nuances of HR outcomes (for example, weighing such things as performance is hard, with sometimes unreliable results); 2) the generation of data that's too small and infrequent to measure, such as employee dismissals; 3) ethical or legal limitations, which generally require decisions to be verified; and 4) employee reactions, which algorithms have trouble handling.