Generative AI will have little impact on manual work, especially when it’s highly specialized. It’s mainly lower-value and peripheral tasks, like writing patient notes in the case of a surgeon, that could be augmented by generative AI. 

The technology will, however, change the work that happens around the job role. For a surgeon, this could include medical diagnostics or more personalized patient communications and follow-up care. The quality of the work performed would also increase as the systems the surgeon uses evolve to provide more information on the patient, the best treatment approach and the latest clinical research and procedures—in effect, surfacing the highest value and most relevant insights from the vast quantities of data available.

Specialized training in generative AI is not a top priority for this cohort. However, understanding the basics of generative AI, ethical considerations and how to collaborate with AI tools can help these professionals integrate technology into their workflows, boosting efficiency without changing their core job functions.

There may be a very slight reduction in the number of workers needed as generative AI enables tasks to be completed more quickly. But overall, the traditional staffing pyramid remains intact, as workers in this cohort will continue to need to develop seniority over time as they accrue hands-on experience and knowledge.

When generative AI first emerged, a key differentiator was its ability to produce creative outputs like marketing copy, poetry and visual designs. As a result, roles like art directors, creative managers, copywriters and designers seemed under threat of being fully automated.

Now, that threat seems to have abated, and not only because image-generating AI models still produce outputs that can be fantastical and unrealistic vs. authentic and natural.  It’s also because these powerful tools can’t produce anything at all until somebody tells them what to produce. That work, most companies have discovered, is best performed by someone with the ability to conceive of and express a clear set of instructions for an excellent piece of design: in other words, a designer or creative director. 

The same goes for other augmented roles. For teachers, AI can accelerate tasks like lesson planning and homework assignments. For lawyers, it can help analyze precedents and prepare briefs and file appeals. Business executives, meanwhile, can lean on generative AI for tasks such as preparing and approving budgets and reports, and analyzing business operations.

But while the skills required to perform automatable tasks will diminish in value, those associated with non-automated tasks will grow in value as these professionals will have the bandwidth to refine these uniquely human capabilities.

Teachers will become more focused on engaging students and personalizing learning experiences. Lawyers will shift their emphasis to presenting cases, negotiating settlements and advising clients, where complex problem-solving and interpersonal skills are crucial. Similarly, business leaders will apply more empathy and decision-making to drive strategic initiatives. 

Generative AI tools can significantly boost both the productivity and quality of the work done by these professionals. This is especially true at the lower levels of the talent pyramid, where less experienced people can more quickly reach higher skill levels using generative AI tools than it would take to develop these skills on their own.    

The most pressing skilling concern is to augment workers with the tools and training they need, such as prompt writing, to deliver more value from an existing range of tasks. 

Because gen AI can automate so many low-level tasks done in these job roles, there could be a diminished need for the number of people performing routine tasks. However, over time, as junior workers acquire generative AI skills on the job—or enter the workforce with these skills—it could change the nature of what bottom-of-the-pyramid work looks like, pushing it up toward the middle of the pyramid. 

These dynamics could also increase the need for senior professionals who handle more complex responsibilities that involve judgment, verification or quality assessments, collaboration or strategy. 

While generative AI can perform many of the key tasks of these job roles today, the new types of work that emerge will be extremely high-value—if people are reskilled to do it.

For example, many of the highest value tasks performed by computer programmers are very likely to be automated by generative AI, including writing code. But the need for an analytical skill set and understanding of system design won’t go away.  

The role won’t disappear—it will be transformed, increasingly centering on high-value human skills such as communication, collaboration and problem-solving. An example is computer programmers becoming more focused on iterative, fast-paced development. They’ll need to sit down with business users, co-designing solutions on the fly and making changes based on the real-time results they see.

They’ll also bridge gaps between programming, engineering and technical teams to foster a more integrated and cohesive development process. 

The impact on talent pyramids is significant. If 80% of a job is automated, for instance, companies may not need as many of those employees. But at the same time, new jobs will emerge—often at the middle layers of the pyramid. 

For example, companies might create a pool of hyperspecialized workers who perform the 10% to 20% of tasks that could not be automated across multiple job roles. One example is a new job role created by combining the nonautomatable tasks that remain for both software developers and software testers. 

Or they may choose to create entirely new job roles. As financial analysts spend less time doing highly automatable tasks like analyzing data and compiling research notes, for instance, they could focus more on working with investment bankers to attract new clients, presenting reports and acting as the source of truth for asset pricing. But they could also move into new roles such as becoming an expert in articulating the value of the company’s assets and products.

In still other cases, jobs will become hyperspecialized on high-value tasks or tasks that emerge through the use of AI. Consider that if 50% of a job can be automated, workers can focus 100% of their energy on the remaining tasks, spiking their productivity. This could come in the form of programmers using AI toolsets to produce better software than ever or financial analysts translating information into recommendations far more efficiently and then broadening their responsibilities to assist sales teams in helping clients understand investment strategies and product suggestions.

This cohort includes many traditionally “low-skilled” occupations whose job tasks—from compiling reports to processing paychecks—can be done more quickly, efficiently and accurately with generative AI. But while these job roles could be fully automated by generative AI, doing so could result in significant challenges for sustaining the talent pyramid since many of these roles have traditionally formed the talent pipeline businesses rely on for more mid-and senior-level roles.

Further, the critical thinking, decision-making and problem-solving skills required for many of these roles will always have value. So, while reducing headcount is one solution, reskilling employees for higher-value roles is a viable, often favorable, alternative.  

Customer service agents are a good example of this opportunity. Over time, service reps develop deep institutional knowledge of the company’s systems and processes, as well as the products and services they offer. This knowledge is highly valuable in positions like business analysts, solution designers or product architects.

As generative AI automates their routine interactions, service reps could focus on the more complex problem-solving aspects of the job, ultimately helping them do their job in a new way or transition to a new role more efficiently.

An example of reassigning customer service roles is IKEA, which has reskilled 8,500 call center representatives since 2021 to transition into roles as interior design advisors. So far, the company has no plans for workforce reductions even as its chatbot has handled 47% of the customer queries routed to call centers over the past two years.

Business leaders must develop a clear strategic position: Are the professionals in highly automatable job roles rediverted to other areas of the business, or are they simply let go? Differing corporate strategies will have different answers. But the reality is, businesses must prepare for a world in which large segments of their workforce can still deliver value but just very differently from how they do so today.