AI is quickly becoming an essential part of daily work. It’s already being used to help improve operational processes, strengthen customer service, measure employee experience, and bolster cybersecurity efforts, among other applications. And with AI deepening its presence in daily life, as more people turn to AI bot services, such as ChatGPT, to answer questions and get help with tasks, its presence in the workplace will only accelerate.
Much of the discussion around AI in the workplace has been about the jobs it could replace. It’s also sparked conversations around ethics, compliance, and governance issues, with many companies taking a cautious approach to adopting AI technologies and IT leaders debating the best path forward.
While the full promise of AI is still uncertain, it’s early impact on the workplace can’t be ignored. It’s clear that AI will make its mark on every industry in the coming years, and it’s already creating a shift in demand for skills employers are looking for. AI has also sparked renewed interest in long-held IT skills, while creating entirely new roles and skills companies will need to adopt to successfully embrace AI.
Emerging AI jobs and skills
The rise of AI in the workplace has created demand for new and emerging roles in IT and beyond. Chief among these are roles such as prompt engineers, AI compliance specialists, and AI product managers, according to Jim Chilton, CTO of Cengage Group.
Other emerging roles include AI data annotators, legal professionals specializing in AI regulation, AI ethics advisors, and content moderators to track potential disinformation around AI, says Robert Kim, CTO at Presidio.
Organizations are also seeking more established IT skills such as predictive analytics, natural language processing, deep learning, and machine learning, says Mike Hendrickson, VP of tech and dev products at Skillsoft. In addition to these skills, he says he’s also seen an uptick in demand for skills around large language models, ChatGPT, and similar generative AI bots.
AI has also created a demand for new C-suite roles “focused purely on leveraging generative AI throughout all aspects of business—from internal ways of working to external AI-powered product solutions for customers,” says Chilton.
“Those who embrace the technology and understand how to use it to accelerate and improve their work will be rewarded, while those that don’t will be left behind,” says Chilton. “Ultimately, the profitability barrier between those who embrace AI and those who don’t will determine the longevity of those businesses, or even those industries.”
Agile skills will put you a step ahead
Agile might not be the first skill you think of when it comes to AI, but companies that have already embraced agile workflows and mindsets will be in the best position to integrate AI tools and solutions. These organizations will be better prepared to accommodate the rapid change associated with AI, making it easier to adopt new technology as it emerges.
Organizations with an agile and DevOps mentality, where you’re continually deploying, redeploying, and testing, are already well accustomed to the process of releasing new processes, services, or products, and then getting feedback and continuously improving on it, says Hendrickson. This mentality will make it easier for those companies to quickly embrace and deploy AI tools and solutions compared to companies with slower processes, legacy technology, or roadblocks to deployment.
There’s also a growing need for domain and organizational knowledge associated with AI, as it’s vital to have a deep understanding of organizational needs in order to determine which AI technologies will be best suited to a given application. “Those that have agile in their organization are going to be able to harness that domain expertise and domain knowledge much better,” Hendrickson says.
A stronger focus on security
AI opens new doors for security threats and compliance issues as well that organizations must be prepared to address.
“On the technical side, I see security as hugely important,” says Hendrickson. “A lot of companies say, ‘We’re not letting people touch ChatGPT yet, we’re just not allowing it—it’s blocked.’” But end-users’ propensity for finding ways to improve their work processes will no doubt lead to greater levels of shadow IT around such emerging technologies, and thus, security implications will eventually need to be tackled beyond simply trying to hold back the tide.
Moreover, Hendrickson points to the fact that just a few years ago, discussions around machine learning centered around its ability to break encryption, and with quantum machine learning on the horizon, that concern has only increased. As companies navigate AI in the workplace, they’re going to need skilled professionals who can identify potential risks and pinpoint possible solutions.
There are also increased complexities around “managing the infrastructure and platforms that provide resources to power applications, and to store and access data,” says Kim. Organizations will need people capable of employing automation to help with securing, provisioning, and orchestrating these modern distributed platforms.
Soft skills persist
Technical skills are changing faster than ever—to the point where it’s likely that what students learn in their first year of a CS degree could be obsolete soon after they graduate. AI will only accelerate the pace of technology, and even automate some of the hard skills IT professionals have to offer, which means soft skills will only become more important.
“The half-life for hard skills or technical skills is getting shorter as technology rapidly changes,” says Chilton. “Just a few years ago there was a big push to have everyone learn to code. While we still need people who can code, the growth of low-code or no-code platforms now reduces the need for coding skills. Skills that are more enduring tend be those such as the ability to think critically, problem-solve, communicate effectively, and collaborate with others.”
With AI, there’s also the opportunity for organizations to decrease mundane, tedious, and administrative tasks, says Kim. This will free up workers to focus on projects that require more brainpower and require a stronger emphasis on time management, team collaboration, and leadership to ensure success.
Demand for workers invested in continuous learning and development will also continue. Going into tech, workers make an “implicit commitment to themselves that they’ll continuously learn and improve because tech changes so quickly,” says Hendrickson. Companies will be even more motivated to hire tech workers who demonstrate a passionate commitment to learning new skills and maintaining a finger on the pulse on emerging technologies.
An eye on upskilling
As with most things IT, demand for AI skills will outpace the talent market, so companies will need to turn inward and identify opportunities for training.
To address this, Hendrickson says Skillsoft has created teams around individuals with AI backgrounds, tasking them with upskilling others in the organization. Such approaches to building talent from within provide a huge benefit, he argues, as they emphasize the importance of domain and organizational knowledge.
“You want to upskill the people in your organization because they already have the knowledge of the potential products or any benefits,” he says. Rather than hiring from competitors or outside the organization, “take the talent you have and upskill them into the right roles,” he adds. You’ll not only gain the skills you need to advance with AI adoption, but you’ll retain that expertise, and domain and organizational knowledge that’s so vital to digital transformation.
Another area that will benefit from upskilling is AI ethics. Having employees with strong domain expertise and organizational knowledge who can keep an eye on ethical questions that arise surrounding AI will be crucial. Hendrickson calls these folks the “humans in the loop,” as they provide the human checks and balances to monitor the veracity and value of generative AI.
Hendrickson gives the example of using Bard and ChatGPT to write code to scrape a website, and using one AI to check the other AI’s work. The final programs didn’t work, yet both AI bots claimed the programming was correct. In this case, a human eye was necessary to identify the mistakes made by both bots. Ultimately, results from generative AI are not solid enough to be relied on without having humans involved to fact check.
“Even if we’re programming with a bot, the human is the one who’s going to hopefully make the final choice,” says Hendrickson.
Add AI sanity checks to your list of future must-have skills.
