Ian Cook, VP, Research and Strategy at Visier, builds business success by linking people data to business outcomes.
You might think that the last person to be impacted by technology disruption would be the chief human resources officer (CHRO). And yet the recent and rapid advances in generative AI, leading to technologies that can be trained to perform tasks that are both interactive and creative, mean that the CHRO needs to be fully engaged in bringing this tech into their business. Why? Simply because the opportunities and repercussions are all about people.
While we’re still in the early stages of learning how significant the impacts of AI will be on the world of work, one piece of recent research published by NBER put the productivity gains from the use of generative AI in a call center environment at an average of 14%. This scale of performance improvement makes integrating GPT models into work an important focus—no business can afford to ignore the opportunity to reduce its labor costs by 14% or more. In addition, the research found reductions in the number of calls that were escalated, saving costs, and an overall reduction in employee turnover, delivering further cost savings.
Make no mistake: Talent retention is a key priority for organizations like call centers with high-volume, hourly employees; research shows that attrition rates are as high as 38%. But for those struggling to find qualified employees, augmenting existing employees with generative AI in order to enable each person to produce more can be one of the ways to cope with this enduring talent shortage.
AI is a people problem, not an IT problem.
So far, so good,” you may say. We should hand the opportunity to the operations leaders and IT folks to work out how to augment work, right? But in reality, the approach required to access the benefits of generative AI and integrate it effectively into the working approach of a company is not so simple. Rather than being about replacement—such as replacing a human worker with a robot—the process is about the augmentation and improvement of existing work processes, integrating generated content into human workflows and creating more speed, quality and impact. This means we need to understand the people and the work to be successful.
The research published in NBER went a level deeper to identify how productivity improvements were created and what this may mean for ongoing people management approaches. For example, the biggest benefits from the application of generative AI were experienced by newer employees, who were 35% more productive and got to this level of effectiveness faster. Clearly, having access to “expert” guidance from an AI can help people learn the right practices quickly.
However, at the same time, there was no improvement in the performance of the best employees. In fact, the AI nudges and prompts were considered a distraction by longer-tenured, more seasoned experts. But it was the work of the “experts,” the top-performing employees, that was being used to train the generative AI. The practices derived from this training process were then delivered to the rest of the employees. In this instance, the technology is being used to “learn” what a high-performing agent does, recognize the inputs or prompts that indicate what a customer needs and then return the approach taken by a successful agent for the regular agent to follow. In simple terms, it spreads the capabilities of the best to the rest, elevating performance overall.
What does “good” look like?
The details of how AI works highlights the fundamentally people-centric nature of these developments and raises many questions. For example, should the company reward the best employees for access to the data behind their performance, which is used to train the AI? If the company chooses not to recognize these individuals and they then leave, how will the AI continue to be trained and the performance level maintained as products and services change? If employees have been rewarded for performance, and now the AI is reducing the performance differential by effectively enabling the rest to copy the best, what should be the basis for rewarding employee performance? If there is no opportunity to gain higher-level rewards, it is likely that the best performers will move on to opportunities where differentiation exists.
In addition, if an employee follows the AI prompts and yet the customer outcome is negative, should that count against them in a performance review? And if the agent’s work is being so closely guided by an AI, then what happens to the role of the supervisor, who traditionally would have listened to and coached agents on their performance?
All of the above are people issues and challenges that require a careful and effective response from the CHRO and team. These are questions that won’t be solved by IT nor have one standard set of solutions across industries or businesses. I believe it will take a real human perspective into individual organizations to come up with the right plan of action.
Embrace your generative AI opportunities.
The hype around generative AI is warranted. The first research into these tools deployed in the workplace has provided evidence of significant business gains that are sorely needed in a tough economic climate.
However, the research also makes it clear that introducing generative AI is not an IT project. The primary benefits of this technology are about augmenting existing work, thus improving the speed, quality and impact of the work being done. That is why I believe success can only come when all of the people factors of these changes are identified, mitigated and properly communicated as part of the change. To do this, the CHRO and team need to be firmly integrated into any initiative to bring generative AI into your business.
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