Your Next Big Thinker

When ChatGPT was introduced in 2022, it expanded access to artificial intelligence for employees in a broad range of white-collar industries.

Suddenly they had new ways to find and analyze data and create and develop initiatives beyond what had been possible before.

At the same time, employers in many industries have been faced with rising costs on everything from supplies to healthcare. The potential to save time and money with AI is appealing, and many employers are looking to eliminate some entry-level jobs by instead using AI and automation. But that risks destroying a key talent pipeline at a time companies may need it most.

Because in the background, the workforce itself is changing. Baby boomers are retiring. According to data from investment advisor Unbiased, 3.2 million have already retired. And more, if not all, will be clocking out for good in the coming years: the U.S. Census found in a 2018 analysis that by 2030 all baby boomers will be older than 65. This means that businesses are losing their institutional knowledge while at the same time eliminating the starter positions that help build that knowledge foundation.

AI Reshaping Jobs

Demographic changes may be slowly altering the workplace, but technology is accelerating changes to how work is done to warp speed. Many workers fear their jobs will eventually be replaced by AI. They’re not necessarily wrong—white-collar starter jobs could be at risk as AI becomes increasingly capable of taking on repetitive administrative tasks and junior-level responsibilities. Experts say that savvy employees will use the technology to increase productivity and make themselves even more valuable to their organization.

“The workforce won’t just be defined by humans versus AI,” says Renee Gorman, senior manager and executive of market analytics at Allegis Global Solutions, a workforce consulting firm based in Hanover, Maryland. “It will be defined by people who know how to work effectively alongside AI.”

According to a 2026 survey by Boston Consulting Group, 74% of front-line employees—non-managerial white-collar workers—use AI daily or several times a week. That is up 23 percentage points from 2025.

Another report from the MIT Sloan School of Management found that businesses that lean in heavily on AI generate about 6% more employment growth and nearly 10% additional sales growth over five years than those that lag. Employers that don’t use AI could be more likely to lay off workers because they aren’t as competitive and are more stagnant, according to the report.

Gorman’s company works with large employers across a range of industries; nearly all of them use some kind of AI, she says. Even smaller companies can use off-the-shelf products like Copilot or ChatGPT.

“It’s not concentrated in any one industry, we are truly seeing this across the board,” Gorman says. “It’s not just for tech roles. It could be everything from marketing, underwriting, analysis, and finance. Regardless of their generation, if you don’t remain agile and nimble in these times, you’re probably going to make yourself irrelevant. Anybody who is resistant to change is not going to do well in this new labor environment.”

The Boston Consulting Group survey found that 42% of workers who have adopted AI reported that it saves them eight hours a week. In some jobs even higher numbers report it being a time saver, including marketing, IT, and human resources. If AI can save a day’s worth of work for some employees, it’s inherently reshaping those jobs and the skills needed to perform them.

At this stage in its development, AI can expedite a wide range of administrative and customer service tasks. Employees don’t need to do as much research—they pose the questions and AI does much of the work. The technology can pull from notes and documents to draft written copy. Data analysis also isn’t as necessary— AI can rapidly analyze large amounts of information and summarize it in an outline or report. This could help a broker, for instance, quickly find the best plan for a client based on the company’s risk underwriting guidelines.

Still, artificial intelligence has limitations. This is where human skills become more valuable. “AI tools blow humans out of the water” at predicting a company’s earnings when an organization has a long history of detailed data, says Bryan Seegmiller, assistant professor of finance at Northwestern University and co-author of the MIT report. But if an organization has a unique business model or is under financial distress, institutional knowledge and discernment outperform AI.

This trend tracks to other industries as well, including insurance. Artificial intelligence could struggle to analyze risk on a new commercial line or when there isn’t enough historical data in a specialty line for assessment. When sufficient client records are not available or legacy software doesn’t integrate well with AI tools, humans must also analyze the data and look for errors or perform manual entry.

For brokers and consultants, artificial intelligence reduces the cost of data assembly and preliminary analysis. In these cases, the human must know the questions to prompt the AI, interpret the results, ensure there aren’t errors, and take the results to the client. For managers, AI does much of the data work as well, but it takes a person to analyze the output for errors even while handling the other aspects of the work—resolving disagreements among employees, making hiring decisions, and motivating people.

“If a job can be done more efficiently, then whatever complements that, whatever skills go along with it, all of a sudden becomes a lot more valuable,” says Seegmiller. AI, he says, is making human skills more marketable. Employers are looking for critical thinkers, good judgment, creativity, and the capacity to make decisions based on experience.

“When you are using AI, you have to come to it with an idea or a problem to be posed,” according to Seegmiller. “So, people who only have the technical know-how to execute a task that’s given or produce some analysis they are told to do are going to have a harder time relative to people who are trained more to understand the big picture, to work with other people, to communicate, and to be able to understand why you want to do something.”

Reskilling for AI

Generative AI is in its nascent stage. Many companies are still learning how to integrate it into their workflow. Organizations want more in-depth training for employees on these systems than on some other areas of technology, say cybersecurity, says Seth Robinson, vice president for industry research at CompTIA, a technology credentialing company based in Downers Grove, Illinois.

Businesses, for example, want employees trained to identify a suspicious email and then report the cybersecurity threat to IT. But they want all employees ready to use AI to manage, understand, and analyze data. The challenge is that many employees don’t have the general tech knowledge to manage data even before artificial intelligence enters the mix.

“That’s one thing that we’ve heard a lot from our clients,” Robinson says. “This desire to try to level up the AI playing field across all occupations, whether they’re technology occupations or non-technology occupations.”

The issue, then, is understanding the skills that are needed to use AI and how to apply them in the workplace, he says. During initial adoption in the corporate sphere, organizations have focused on employee comfort with the technology. Here, little restructuring occurs, Robinson says. Employees learn about AI and begin to adapt their skills based on the ways it helps them with their current role.

But this approach is limited, he believes. Businesses moving into deeper AI adoption, where the technology is integrated into technology stacks and workflows, will drive redefinition of job skills and expand the capabilities of artificial intelligence. Meeting the moment demands that employers understand how to redesign jobs and upskill so employees can use the technology more effectively. Employers must understand exactly where AI fits in and what is left over for employees to perform.

Businesses will need to analyze each position to understand how much work AI can handle and what new skills are needed for the work that remains. The International Monetary Fund predicted in 2024 that, worldwide, about 40% of jobs would face high exposure to AI; that rises to 60% when considering only advanced economies like the United States.

“You identify the skills you are looking for, and then you either go get them or train for them, but I think companies are finding even the first step of that process is difficult,” Robinson says. “A lot of companies don’t know what skills they need. They don’t have the resources to build out their own taxonomy of skills.”

Companies can do this skills analysis internally, but also through programs from vendors such as CompTIA.

“Managers still need to understand which skills are most important to their own business,” Robinson says. “But once that mapping is complete, these tools can help identify gaps and build a plan for skill development.”

A CompTIA report on 2026 technology learning trends found that only 34% of companies have formal, organization-wide programs for reskilling employees. The primary obstacles cited are the cost of training and training fatigue among employees. A 2026 survey of 35 enterprise leaders and 1,300 employees by The Conference Board found that more than half of the workers use generative AI at least weekly, but 28% said their employer provides no training on the technology. More than half of the surveyed employees indicated their company doesn’t give them sufficient time and other resources to build out their AI capabilities.

The Boston Consulting Group survey found that, of those who were saving a day’s worth of work by using AI, 66% haven’t received guidance from their employer on what to do with their extra time. Nearly three-quarters of the respondents said the skills they need to perform their jobs have shifted because of AI, but only about one-third said they have received upskill training.

There is clearly a gap between the skills employees think they will need because of AI and how their employers are helping upskill. Part of this is because breaking down jobs into miniscule roles to understand their requirements can be difficult. Historically, employers have been comfortable using broad definitions to define job roles—e.g., getting a degree as an engineer was a proxy for the skills needed for that job. But as workforce dynamics have changed and candidate supply has tightened, there has been a shift toward more specific skill-based talent requirements, Robinson says.

“AI is now exacerbating both sides of the equation, causing companies to accelerate the definition of their skill-based approaches while simultaneously considering how AI factors in,” he says.

Hiring the Next Generation

When it comes to hiring new employees, Gorman says employers must think beyond the next three to five years when selecting for entry-level positions, such as underwriting, that they suspect AI could handle.

“It’s [AI] actually replacing the roles where people have traditionally learned and then evolved into a more experienced leader,” she says. “For those organizations that are being negatively impacted by boomers and older Gen Xers leaving the workforce, it’s going to create a pretty significant gap.”

Seegmiller concurs on the value of a long-term view: “People in those very companies have gotten to where they are by virtue of having been there to learn the important, nuanced, specific details of the business. The next generation is going to be the decision-makers of the company. So, I encourage businesses to steer their young hires to understand the big picture and work on communication, judgment, and critical thinking.”

This approach can be difficult given the rapid pace of technological changes, Gorman acknowledges. One way to do this is to hire for those human skill sets rather than focusing on credentials, she says. Many companies still seek out employees with a college degree. While that’s not always a bad thing, it can prevent them from finding the best talent, particularly in a tight labor market, Gorman adds.

Robinson says some companies are moving away from requiring college degrees and toward specific skill sets or certifications. But he says his organization wants students to understand the value of a university education.

Third-party credentials like IT certifications do a good job of targeting specific technical proficiency or job-related skills, but they don’t necessarily speak to those human capabilities that an employer might think is baked in when they receive a college degree.

Understanding what is needed from a job, and the skills employees have, can also help when hiring internally. If a hiring manager is looking for a data analyst, they can do more than just sort through resumes.

“Let’s ask ourselves, ‘What does it require to analyze data?’” Gorman says. “Who else could potentially do this? We know that somebody with a finance background can do it. We know that marketing has exposure to this. So, there are other transferable skill sets that could also do exceptionally well in the data analysis role.”