The demand for artificial intelligence (AI) skills is skyrocketing, particularly in fields that are becoming more dependent on tech-heavy specialties. A new study by PwC uncovers a shocking development. AI-driven skills requirements in sectors exposed to AI are shifting as much as 66% faster relative to other occupations. This urgency has prompted the UK Government to announce a partnership with major technology companies aimed at training 7.5 million UK workers in essential AI skills. At a time of unprecedented breakthroughs in AI, our workforce will be hard pressed to pivot fast enough to address these constantly changing demands.
Glynn Townsend, senior director of education services at SAS, adds just how important it is to focus on outcomes. He’d like to make the case for the benefits that AI can provide to employees. The challenge is immense. As a recent study by MIT’s Networked Agents and Decentralised AI project found, 95% of generative AI pilot programs don’t pay for themselves. Consequently, there has been minimal or zero impact on bottom line profit and loss. Even more concerning is what this statistic implies about the efficacy of current AI training and implementation strategies.
The UK’s determined efforts to position itself as a global AI powerhouse were evident in its AI Opportunities Action Plan, launched in January. This push makes addressing the lack of AI skills more pressing than ever. More than three-quarters of companies report facing a significant gap in AI skills, according to US IT services company UST.
The Importance of AI Literacy
Defining AI literacy remains a complex task. Glynn Townsend notes that “AI literacy from a consumer is very different from AI literacy from somebody who is going to be building models relative to what they are doing on a daily basis.” This seemingly semantic distinction is extremely important. Most importantly, it assists in designing training programs to better fit an individual’s position within a firm’s larger AI adoption plans.
Townsend is wary of how AI can bar decisions from being challenged if they were made using algorithms without an understanding of model bias and where data comes from. He emphasizes that it’s important to know the biases of your models, and to explore the data they’ve been trained on for potential harm. This crucial understanding provides the basis for an AI-integrating workforce, as these are the very workers who will be using AI technologies in their jobs.
It’s a belief that’s echoed by Rob Woodstock, managing director at technology consultancy Slalom. For him, the goal of an AI training curriculum should be very outcome-oriented. The biggest success I’ve ever seen is when we begin with a business impact and work backwards,” he said. By prioritizing outcomes over outputs, organizations can create a culture that promotes ongoing learning and flexibility.
Ongoing Learning and Adaptation
The AI landscape is changing by the second. Glynn Townsend is clear that mastering these skills needs to be a sustained practice, not just one-off program. This isn’t an occasional training seminar. Given how quickly AI technology is advancing, we should all pledge to learn, not just once a year but year after year and all year round, that’s how he justifies it.
This cultural shift towards lifelong learning will be key in adapting to this new landscape. And so I think the big cultural shift has got to be around continual lifelong learning,” Townsend explains. To get the most out of AI companies need to create a space where employees can experiment with AI technologies risk-free companies.
Once again, trust is a key variable in this equation too. The public’s trust—or lack thereof—when it comes to AI systems can heavily influence people’s desire to interact with these technologies. Beyond the lessons learned, overcoming these challenges will require a renewed commitment to trust and confidence. Let’s give innovators the legal certainty to tinker, to automate and augment their everyday work without worry or intimidation.
The Future Workforce and Job Market Dynamics
As AI technologies become more widely adopted, fundamental changes to the labor market are on the horizon. As Mark Graham reminds us, the younger workers are losing precious entry-level “stepping stone” tasks that will lay the building blocks to their careers. The more companies automate to make processes faster and easier, the fewer opportunities there will be for those entry-level roles.
Fabien Braeseman reads the current skills gap as a temporary phenomenon. He believes that people being prepared for jobs exposed to AI are seeing a shift in tech needs/jobs and tech requirements as well. “I am thinking this is a short-term observation, that people who are still training to go into these [AI-exposed] jobs are now seeing a transition of technological demand and requirements,” he notes.
This new reality not only fosters new challenges that cities must adapt to, but presents new opportunities. Organizations must recognize that the definition of a skilled workforce will likely evolve, incorporating the ability to leverage AI effectively. Understanding this transition will better enable employers to develop strong training programs that meet the demands of the workforce of tomorrow.