How Generative AI Will Transform HR

Generative AI has done what no other technology trend has: accelerate HR’s engagement with artificial intelligence.

Adoption of AI tools has been slow to gain widespread momentum in the human resources space, but GenAI technologies—which can create content from disparate sources and quickly summarize multiple data sets—offer the HR discipline several powerfully compelling capabilities. Teams are already using chatbots and experimenting with AI in recruiting use cases, and as deployments bring efficiency and new insights to the people function, HR’s engagement with GenAI is set to escalate dramatically.

At the same time, executive teams are looking to HR to be a deeper, more insightful partner throughout the business. GenAI makes this future real. With HR continuing to shift its focus from administrative work to helping lead the company-wide strategic transformation that GenAI brings, the technology becomes a vital resource to upskill team members and unlock value as the function expands its influence across the organization. HR’s adoption of GenAI will also be an example to the rest of the business of how best to engage this critical, still-evolving technology in forward-thinking ways.

How GenAI Impacts HR

GenAI transforms HR into a more strategic function with the following impacts.

How GenAI Impacts HR

GenAI transforms HR into a more strategic function with the following impacts.

Dramatically Increased Self-Service. Employees have had mixed reactions to HR self-service in the past. But GenAI offers more conversational workflows and tailored information—just the sort of delivery that could boost adoption as more employees prefer GenAI’s ease of use in addressing their needs.

Productivity and Experience Enhancements. Leaders often use deep consumer insights to offer personalized, tech-enabled customer experiences. These same trends are now emerging for employees. With stronger automation and data insights, current GenAI use cases show three times faster content creation and visualization, automation of greater than 50% of tasks in an onboarding journey, and recruiting engagement rates that are twice as high as when personalized messages were written with GenAI.

In the moments that matter most, of course, employees want to connect with people. GenAI frees HR professionals to engage with the employees they serve and be present in the interactions that deliver higher satisfaction.

Truly Personalized, “Always on” Delivery of HR Services. GenAI-based HR “copilots” will guide employee and manager careers in real time. The technology helps HR know employees better: the rhythm of their work, the learning and development they require, when they may need a vacation, and if they would benefit from reminders of annual goals or other strategic programs. Managers can also customize onboarding plans, inspire high performers, and receive alerts to reconnect with a disengaged teammate.

A Comprehensive, Data-Driven Talent Ecosystem. Many companies have invested to better understand employee skills and drive talent upskilling and career planning. The question now is how to use this skills data to drive meaningful talent decisions across the business, not just in specific areas.

GenAI’s ability to join less structured data sources will enable more interconnected use cases, including talent assessment, developing career pathways, talent sourcing, and learning and development, as seen in the slide below. All this leads to a skills-based talent ecosystem linked to the company’s workforce strategy.

Increased Productivity with Ethical AI

GenAI’s capabilities unlock a new level of productivity while transforming the service model. With the right mix of maturity, clearly defined goals, and time, a balanced human and AI strategy could boost HR productivity up to 30% in the not-so-distant future. One early adopter in AI for HR has been able to reap financial benefits, cutting its annual budget by 10% year over year for the past three years.

To visualize the possibilities offered by a GenAI deployment in HR, consider how a global industrial goods company’s HR business partners spend time. Two scenarios then describe how a GenAI-enhanced HRBP supports the firm.

As shown in the slides below, one scenario unlocks 25% to 30% of HRBP time through GenAI deployments, including chatbots and automation solutions. This model would lean heavily on the HR copilot and manager to upskill staff through tech-based nudges. The result: HRBPs who are more productive, better able to support more employees given the reduction in administrative work.

The second scenario is to boost overall productivity and then reinvest the time freed up by GenAI efficiency. This scenario creates a version of the role—HR business talent strategists—to put time into deeper employee engagement, change management, and more strategic talent planning.

In each scenario, the HR organization and broader executive leadership team will see productivity gains, but they must decide to push for near-term cost savings or focus on driving greater talent effectiveness. Whichever choice the leaders make, GenAI deployments could create convincing results. Ultimately, HR leaders need to have a clear vision and North Star for the function to guide such decisions.

Of course, GenAI doesn’t just optimize the productivity of existing processes and activities. The technology lets HR reimagine how it serves talent, in turn changing the overall HR delivery model. Yet in the face of such change, the people function should always be cautious of GenAI’s many risks—especially when handling sensitive personnel information. For years, HR has been working to influence employee sentiment or decrease bias in real-time decision making. AI has the potential to further reduce the bias that exists in today’s processes—when done well. To get it right, HR will need to work closely with legal and business leaders to ensure that responsible AI is being implemented and that any bias apparent in GenAI systems is identified and addressed.

Leaders should anticipate a dynamic technology and regulatory environment, where new solutions and regulations are closely monitored. Humans remain critical to these advances. GenAI will be able to identify insights and summarize data, but HR will need to ensure that humans make business decisions that are sound, just, and well documented. This future is dependent on so many factors—only one of them being HR’s responsible use of GenAI.

Leading the Company’s GenAI Transformation by Example

Even as the early days of GenAI unfold, it is becoming clear that the entire workforce will transform as the technology brings new capabilities and objectives within reach. HR leaders need to help drive this broader change, just as their own function evolves.

Leading the change means getting started on several fronts, as shown in the slides here.

Engaging GenAI now will set up any HR team to deliver services in ways that the function is only starting to explore. Strive to use this evolving technology to guide the entire business forward—and keep employees satisfied and committed.

The authors thank Amber Conley, Jen Potvin, Rajiv Shenoy, Sebastian Ullrich, and the BCG U team for their contributions to this article.

The second risk is drawn from a sample of our interviews with participants. Roughly 70% believe that extensive use of GPT-4 may stifle their creative abilities over time. (See Exhibit 8.) As one participant explained, “Like any technology, people can rely on it too much. GPS helped navigation immensely when it was first released, but today people can’t even drive without a GPS. As people rely on a technology too much, they lose abilities they once had.” Another participant noted, “This [phenomenon] is definitely a concern for me. If I become too reliant on GPT, it will weaken my creativity muscles. This already happened to me during the experiment.” Businesses will need to be mindful of their employees’ perceptions of and attitudes about generative AI, and how those might affect their ability to drive innovation and add value.

We don’t yet have data to confirm our participants’ perceptions; this is a topic for further study. But if employees’ concerns bear out, it could compound the group-level risk. Specifically, the loss of collective diversity of ideas may be exacerbated if employees experience some atrophy of individual creativity.

The Generative AI Change Imperative

Inspired by the findings from our research, we envision a series of questions, challenges, and options that can help business leaders make generative AI adoption a source of differentiation—and, as such, an enabler of sustained competitive advantage.

Data Strategy. Any company that incorporates GenAI can realize significant efficiency gains in areas where the technology is competent. But if multiple firms apply the technology across similar sets of tasks, it can produce a leveling effect among organizations analogous to the pattern observed among participants in our experiment. As a result, one of the keys to differentiation will be the ability to fine-tune generative AI models with large volumes of high-quality, firm-specific data.

This is easier said than done. In our experience, not all companies have the advanced data infrastructure capabilities needed to process their proprietary data. Developing these capabilities has been a key focus of AI transformations, but with the arrival of generative AI, it becomes all the more important: As we have argued elsewhere, the power of GenAI often lies in the identification of unexpected—even counterintuitive—patterns and correlations. To reap these benefits, companies need a comprehensive data pipeline, combined with a renewed focus on developing internal data engineering capabilities.

Roles and Workflows. For tasks that generative AI systems have mastered—which, of course, is an ever-expanding list—people need to radically revise their mindset and their approach to work. Instead of the default assumption that technology creates a helpful first draft that requires revision, people should regard the output as a plausible final draft that they should check against firm-established guardrails but otherwise largely leave as is.

The value at stake lies not only in the promise of greater efficiency but also in the possibility for people to redirect time, energy, and effort away from tasks that generative AI will take over. Employees will be able to double down on the tasks that remain beyond the frontier of this technology, reaching higher levels of proficiency.

The value at stake lies not only in the promise of greater efficiency but also in the possibility for people to redirect time, energy, and effort away from tasks that generative AI will take over.

Turning the lens on ourselves, we can already envision our employees spending less time manually summarizing research or polishing slides and instead investing even more effort in driving complex change management initiatives. The impact of generative AI’s disruption will of course vary dramatically across job categories. But at least some workers—including the majority of our participants—are confronting this prospect with optimism.

Strategic Workforce Planning. To get the AI–human dynamics right in complex organizations, leaders must grapple with four questions that have no easy answers:

  • Which capabilities will you need? As with any other technology, it will take people to define what and how generative AI will be used. But it isn’t obvious which human capabilities are best suited to maximizing the tool’s value or how often these capabilities will change. We’re seeing this uncertainty play out in real time with respect to LLMs: The role of “prompt engineer” didn’t exist a year ago, but demand for this role during Q2 2023 was nearly seven times higher than it was in Q1.2 (GPT-4 was launched toward the end of Q1, on March 14, 2023.) And yet, prompt engineers may no longer be needed once generative AI itself has mastered the task of breaking down complex problems into optimal prompts (as it appears it soon will with autonomous agents). Even the selection of optimal LLMs for specific business applications, which is largely done by humans at present, may in the future be outsourced to AI systems themselves.
  • What is your hiring strategy? Because generative AI is a great leveler of proficiency on certain tasks, raw talent may not be a good predictor of high performance in a world of widespread GenAI use. For example, some people may have lower baseline proficiency for a type of task while being quite capable of partnering with generative AI to outperform peers. Finding these individuals will be an important goal for future talent strategies, but the underlying traits are not yet clearly identified.
  • How will you train people effectively? As our findings indicate, straightforward training won’t be sufficient. Effective training will likely need to explicitly address any cognitive biases that may lead people to over-rely on generative AI in situations where the technology has not yet reached the right level of competence.We also see a potentially deeper issue: Even as certain tasks are fully handed over to GenAI, some degree of human oversight will be necessary. How can employees effectively manage the technology for tasks that they themselves have not learned how to do on their own?
  • How will you cultivate diversity of thought? Our results suggest that GenAI detracts from collective creativity by limiting the range of perspectives that individuals bring to the table. This loss in diversity of thought may have ripple effects beyond what we can currently envision. One plausible risk is that it could shrink the long-term innovation capacity of organizations—for example, by making ideation more homogenous. It’s a slippery slope, as a decline in innovation capabilities means less differentiation from competitors, which could impede growth potential. The good news is that the ideas that humans generate on their own and the ideas that they generate when assisted by generative AI are vastly different. Setting aside the degree of diversity in each group, when we compared the output of the control and experimental groups, the overlap (semantic similarity) was less than 10%. The key for leaders will be to use both approaches to ideation—which ultimately will create an even wider circle of ideas.

Experimentation and Testing. Generative AI systems continue to develop at a stunning rate: In just the few months between the releases of OpenAI’s GPT-3.5 and GPT-4, the model made huge performance leaps across a wide range of tasks. Tasks for which generative AI is ill-suited today will likely fall within its frontier of competence soon—perhaps in the very near future. This is likely to happen as LLMs become multi-modal (going beyond text to include other formats of data), or as models grow larger, both of which increase the likelihood of unpredictable capabilities.

Given this lack of predictability, the only way to understand how generative AI will impact your business is to develop experimentation capabilities—to establish a “generative AI lab” of sorts that will enable you to keep pace with an expanding frontier. And as the technology changes, the collaboration model between humans and generative AI will have to change as well. Experimentation may yield some counterintuitive or even uncomfortable findings about your business, but it will also enable you to gain invaluable insights about how the technology can and should be used. We put our feet to the fire with this experiment—and we believe all business leaders should do the same.

Generative AI will likely change much of what we do and how we do it, and it will do so in ways that no one can anticipate. Success in the age of AI will largely depend on an organization’s ability to learn and change faster than it ever has before.

In addition to the collaborators from the academic team listed above, the authors would like to thank Clément Dumas, Gaurav Jha, Leonid Zhukov, Max Männig, and Maxime Courtaux for their helpful comments and suggestions. The authors would also like to thank Lebo Nthoiwa, Patrick Healy, Saud Almutairi, and Steven Randazzo for their efforts interviewing the experiment participants. The authors also thank all their BCG colleagues who volunteered to participate in this experiment.

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