Executives in a recent IBM survey estimate that 40% of their workforces will need to reskill due to implementing AI and automation over the next three years. “That translates to 1.4 billion of the 3.4 billion people in the global workforce, according to World Bank statistics,” the study’s authors add.
Some 40 years ago, developing a skill had a “half-life” of at least 10 years until it was time for a refresher. Today, that half-life has shrunk to about four years — and keeps shrinking, says Kian Katanforoosh, lecturer at Stanford University, and CEO/founder of Workera, speaking on CXOTalk with host Michael Krigsman.
With digital technology and artificial intelligence skills in demand, that half life may be about two years at most until a refresh is needed, he adds.
AI skills development is not a luxury — it’s now a must for organizations seeking to compete in today’s economy. “We have to understand that everyone will be an AI person, just at different levels of proficiencies,” Katanforoosh says. There are two main groups in organizations — AI builders and AI users — that need ongoing skills refreshes. “Builders of AI, producing language models and developing AI systems. are about five percent of the organization. Most of us are users of AI, interfacing with AI, calling APIs and language models to solve problems.”
It’s incumbent on business leaders and managers to keep refreshed on AI skills as well, he believes. “As a business leader you’re probably facing many use cases, and not everything will be solvable per AI. But the reason AI will matter is by understanding how it works, you will understand how to develop a competitive advantage. You will be better positioned to determine what strategic investments to make.”
In addition, “understanding the capabilities of AI the learning process will allow you as a business leader to determine what type of data you need to collect, and whether that data can translate into a competitive mode for your organization.”
It’s critical that people across the organization gain a measure of AI fluency, he continues. “Imagine an HR professional trying to support a data organization, but doesn’t understand what Python is, or what TensorFlow is, or what technologies are needed or is not able to recruit the right folks. Imagine a marketing professional trying to market a new AI feature without understanding how to talk about AI. Imagine a sales professional or an account executive trying to pitch a data infrastructure product without understanding this the concerns around data privacy, compliance.”
Managers and their workforces will be augmented by AI for these specific tasks as well, through AI agents, Katanforoosh predicts. “Maybe one agent that is expert at skills measurements. another agent that is expert at recruiting. an agent that is expert at learning science. By surrounding yourself with those systems you will become a better talent manager you will align learning with your business objectives, and then you will have your employees accelerate their transformation.”
To prepare workforces for the AI era, Katanforoosh advises employers to break down jobs into sets of tasks, some of which may be highly automatable, while others aren’t. He sees this configured in a T-shaped formation, with essential and “durable” skills that are needed for the long run on the horizonal axis, and “perishable” skills on the vertical axis. Many organizations don’t map their skillsets this way, and wither underestimate or overestimate the skills needed for the tasks at hand, he states.