The ability to analyze complex data-sets makes generative AI a powerful tool for scientific research. In the field of drug discovery, it can be used to design new molecules with specific properties, potentially accelerating the search for new life-saving medications.

Viewed from a distance, human progress looks like a story of steady gain and acquisition. From the wheel to the steam engine to electricity to the computer, every new technology has added to humanity’s supply of productivity-enhancing tools that let people do more with their time and their talents. New tools have consistently offered new ways to be productive, leading to the emergence of new types of work, careers, organizations and, often, entire industries.

Look more closely, though, and it becomes clear many of these miracles of addition began with an act of subtraction. Technology ultimately introduces new work and new value, but it also takes away work that existed before The age of gen AI  

The latest addition to humanity’s toolkit is generative artificial intelligence. As with previous technologies, it will be a story of not just simple subtraction but also addition and even multiplication. While certain job roles will be fully automated by the technology, there are far more opportunities for job roles to be enhanced, augmented and transformed by generative AI.

This pattern was evident in our most recent research study New work, new world, in which we partnered with Oxford Economics to model the economic impact of generative AI on the world of work. Our study forecasts generative AI will inject $1 trillion in annual productivity growth by 2032 into the US economy. But along with those productivity gains, the vast majority of jobs (90%) will be impacted in some way in that timeframe, and over half (52%) greatly so The age of gen AI  

The question is what the impact of gen AI will be and how businesses can plan for it:

  • What kinds of jobs and job tasks will gen AI take over, and which will remain in human hands?

  • Which human skills will increase in value, and what new skills will become necessary?

  • How can businesses foresee which jobs will be enhanced, augmented, transformed or fully automated?
  • How will this transformation affect talent models and skilling needs, both now and in the future?

Our study provides a solid basis to answer questions like these. Using the 18,000 tasks and job roles defined in the O*NET database (the primary source of occupation data in the US), we analyzed 1,000 professions to calculate an “exposure score” for each. The score represents the percent of job tasks for each that could be automated or assisted by generative AI by 2032, weighted by the relative value, or importance, of those tasks The age of gen AI  

Based on the insights gained through this methodology, it’s clear there will be vast changes to workforce structures and how work gets done. To fine-tune the transformation, business leaders will need to understand generative AI impacts at a task, skill and job level, and then create a talent management strategy that allows people to deliver value to the business in a new way The age of gen AI  

Already leaders are plotting their course. We followed up on our “New work, new world” research with a global study, in which we asked 2,200 business leaders in 23 countries and 15 industries about their generative AI strategies. When we asked respondents about their plans for workers displaced by generative AI, only 2% said they planned to lay off employees. Instead, many plan to find existing roles within the organization or retrain people for new roles created by generative AI (60%). Other plans are to offer displaced workers generative AI training to increase their productivity (32%) or create mentorship programs or other support initiatives (23%).

All of this will require significant attention to talent management strategies. In this report, we’ll provide a framework for leaders to assess jobs across their workforce to understand which tasks will be most impacted by generative AI, the importance of those tasks and how automation will  change the value of the job role and the talent pyramid. Using this assessment, they can categorize the workforce into job groups to design best-fit skilling strategies and talent models for the generative AI age.

Find existing roles with the organization

Offer training and tools to improve productivity with gen AI

Create mentorship programs or other support initiatives

Retrain to move into new jobs created by gen AI

To understand the impact of generative AI on the workforce, we first need to look at two critical and deeply interconnected dynamics: the changing value of specific job roles and the impact on the talent pyramid.

When it comes to AI automation, it’s not just about the number of tasks The age of gen AI   that can be automated in a particular job role but also how central those tasks are to the role’s core purpose. If lots of low-value tasks are automated, for example, the job role will change to some degree, but its value will be relatively untouched. But if AI can perform enough high-value tasks, the job role and its value will see radical change. These dynamics will inform the talent strategy that’s most relevant for the people in each job role. 

Additionally, these value shifts will also influence the shape of the talent pyramid. The talent pyramid represents the distribution of roles across different levels of seniority—typically with a broad base of junior roles that narrows as you move up to more senior or more specialized positions.

Pyramid image

Generative AI could reshape this pyramid in a variety of ways. In some cases, it could narrow the base of the pyramid by diminishing the number of people needed in highly automatable roles. In others, it could also allow people in these roles to acquire skills more quickly and rapidly ascend to higher value roles, effectively widening the middle of the pyramid.  

Over time, as more junior-level employees enter the workforce with generative AI skills honed at educational institutions, it may also shift what entry-level work looks like and where in the talent pyramid it’s performed. 

Conversely, as the value of certain senior roles becomes more contingent on tasks that AI cannot easily replicate—such as strategic decision-making, complex problem-solving and vetting and validating AI outputs—as well as big-picture responsibilities like judgment, taste and accountability, the demand for these highly experienced professionals may increase. This will enlarge the traditionally narrower top levels of the pyramid.

In essence, the changing value of job roles due to AI-driven automation won’t just alter individual roles; it will also transform how talent is distributed within an organization. 

Leaders need to anticipate these shifts, ensuring that their talent pyramid remains aligned with the organization’s evolving needs—balancing between the automation of tasks and the human expertise required to perform work in these newly transformed ways.