The pressure is on for CIOs to deliver value from AI, but pressing ahead with AI implementations without the necessary workforce training in place is a recipe for falling short of their goals.
At the organizational level, CIOs are being called on to help ensure employees are up to speed on the skills necessary to make good on the promise of AI. For many IT leaders, being central to organization-wide training initiatives may be new territory.
“At the CIO level, we’re in charge of the vision and how that vision is met,” says Gary Flowers, CIO of transformation and technology services at nonprofit workforce development organization Year Up.
And many CIOs are stepping up. Of organizations making use of generative AI tools today, 47% have already implemented some sort of workforce training, with another 38% planning to implement it soon, according to a recent Gartner report.
But, as with AI itself, workforce training requires a set understanding of objectives to succeed. As Flowers says, a lot of CIOs chase AI for AI’s sake simply because the CEO or board compels them to. Without a targeted use case, however, CIOs may be trying to use AI to solve a problem it’s not right for, or one that’s different than it seems at first glance.
Take Year Up’s predictive matching, for instance. As part of its career development program, the nonprofit wants to match a young adult to the right work-based experience to help them establish a life-sustaining career.
“We have 25 years’ worth of data on opportunity and very deep connections with our corporate partners. Well, what’s missing? Let’s put together a model that sits in the middle using AI and machine learning and use all of this data to better match our young adults to the right opportunities,” Flowers says. “When you position it that way, it doesn’t sound like AI. It sounds like you’re solving a business problem.”
The same is true for workforce training, he says, suggesting CIOs ask, “What can AI help us with and how do I prepare my workforce internally to support that?”
A top priority
For CIOs setting out on a workforce AI training journey, Whit Andrews, AI analyst at Gartner, says deciding what business problem you want to solve with AI — and how you want to train for that to be achieved — requires employee input from the start.
Too often, users are considered after the fact, Andrews says, adding, “We say to the worker, ‘I know what you want, and I built an application to do it. Are you happy now?’”
Leaders should instead gather sentiment through surveys and observe tasks to better understand where frustrations and inefficiencies lie, Andrews advises.
Of course, people unfamiliar with gen AI may not yet understand the technology’s capabilities, let alone how it can help resolve their problems. That, Andrews says, is where tinkering comes in. “The more we can encourage people to approach something with childlike wonder, the better off we are,” he says.
Marc Kermisch, chief digital and information officer of agriculture machinery and tech company CNH Industrial, believes in this type of hands-on approach to gaining familiarity with new tools. But he also recognizes the importance of having goals for AI. For CNH Industrial’s IT organization, that includes an internal challenge to save 10,000 human hours using Microsoft’s Copilot this year.
“Certainly, in the down cycle in our industry today, cost savings are important, so there’s a true benchmark to work towards,” he says.
CNH Industrial has assigned an internal team to drive Microsoft Copilot training across the organization. “This team has worked hand-in-hand with Microsoft to leverage training they have created for their customers and have added virtual round tables to answer questions and expand the knowledge of our employees on what Microsoft Copilot can do,” Kermisch says.
Of businesses that have adopted formal AI training for their workforce, external training courses are the most popular, with 82% choosing this method, the Gartner report cites. Most used a hybrid of in-house and external solutions, but the rapid advancement of AI makes vendor relationships — like the one CNH Industrial has with Microsoft — vital to upskilling the workforce.
The team’s engineers are also playing with GitHub Copilot through a trial-and-error method to drive productivity gains, though this is not a structured training protocol.
Regardless of strategy, Kermisch says it’s important to approach new technology with the right mindset.
“You’ve got to go in assuming probably 90% of your tests are going to fail as you’re going after a particular use case,” he says. “You’re going to have to learn by skinning your knees.” Because of this Kermisch advises starting small to avoid financially overextending the business.
This is especially crucial given the notion that CIOs often apply AI to improve workforce efficiency and create a healthier bottom line, an endeavor Kermisch can relate to. “We’re working off of limited budgets this year,” he said. “Of course, that means limited hiring, so it’s a way for us to hopefully relieve some pressure from our existing staff.”
A place to innovate
Year Up’s Flowers points out that skipping out on training personnel on AI does not mean they won’t use the technology.
“If you don’t train them, they may not know the way that you want to use it at your organization,” he says, noting they’ll most likely still use it for work, but in ways that might be more suitable for personal use and aren’t conducive to privacy, data protection, ethics, and other AI concerns.
With that in mind, Gartner’s Andrews says giving employees a place to innovate with AI is crucial. “The most important thing to do is give people something to do with information that is relevant to their work in a safe environment, where they are not being forced to defend their compensation,” he says. This includes hackathons, promptathons, and other innovation workshops of the same vein, which Andrews says any type of organization can adopt.
AI puts a lot of pressure on three things, Flowers says: curiosity, critical thinking, and collaboration, which includes collaboration with others and with technology itself. Because of this, the risk of disruption is great, making it vital for organizations to leapfrog themselves and avoid the so-called Blockbusterlabel by the Netflix-es of their industry.
Ultimately, CIOs should view AI as a solution to a specific problem rather than merely a tool that can be applied anywhere, says Cai GoGwilt, co-founder and chief architect at AI-enabled digital contract management software Ironclad. “A lot of people say, ‘I have this hammer called AI,’ and when you have a hammer every problem looks like a nail.”
But even that ideology has a fine line to it, because being too cautious has its own risks, he says. “We’re in this transformative moment with generative AI where there’s so much hype,” GoGwilt adds. “I think we all have to get used to that hype, because there’s a ton of substance there as well.”
For CIOs, reading between the lines is nothing new, and taking a mindful approach to both implementing AI and preparing their organizations’ workforces for making the most of it may prove to be the best practice that emboldens all the others.
