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Generative AI Itâs powerful. Itâs accessible. And itâs poised to change the way we work. On this episode of the McKinsey Talks Talent podcast, talent leaders Bryan Hancock and Bill Schaninger talk with McKinsey Technology Council chair Lareina Yee and global editorial director Lucia Rahilly about the promise and pitfalls of using gen AI in HRâfrom recruiting to performance management to chatbot-enabled professional growth. An edited version of their discussion follows. Whatâs so differentâand so disruptive Lucia Rahilly There has been so much buzz in recent months about generative AI and tools like ChatGPT. Many people seem to be ricocheting between wonder at the potential of these tools and fear of their inherent risks. Lareina, whatâs different about generative AI, and whatâs behind its disruptive potential? Lareina Yee A couple of things stand out about generative AI. In November 2022, OpenAI released ChatGPT and within five days, there were a million users. So the speed of adoption was unlike anything weâve seen. For me, what was most profound about that moment was that anyoneâof any age, any education level, any countryâcould go onto GPT, query a question or two, and find something practical or fun, like a poem or an essay. There was an experience there that was accessible to everybody. Weâve seen a lot of advancement in the technology since then, and itâs only been a couple of months. A second super-interesting thing is you donât need to be a computer scientist to leverage the technologyâit can be used in all types of jobs. OpenAIâs research estimates that 80 percent of jobs can incorporate generative AI technology and capabilities into activities that happen today in work. That is a profound impact on talent and jobs, and itâs different than how weâve talked about it before. In some ways, the genie is out of the bottle. Itâs probably not the best strategy to try to put it back in. Lean forward and figure out how to use it in a way thatâs productive and safe. Lucia Rahilly The immediacy of the use cases feels so novel and so lightning fast. Explain what generative AI is, so weâre working from a common definition of that term. Lareina Yee Generative AI is a technology that prompts the next best answer. A lot of people have used ChatGPT to summarize information, to draft a response to something, by pulling together an enormous amount of public data. But thereâs also amazing imaging. I might want a song, audio, video, or code. Code is a huge example. Itâs amazing the range of things that generative AI can do in the world, and itâs just getting started. Bryan Hancock I asked ChatGPT about myself, and it accurately reported that I do a lot of work on talent. However, it inaccurately reported that I went to Cornell because it assumed that Cornell was the most appropriate answer based on my background instead of the University of Virginiaâwhere I did go. I thought it was very interesting that you donât necessarily get whatâs right but rather whatâs logical. Lareina Yee In some ways, that emulates how we think. Iâm not suggesting itâs thinking the way humans do, but in many ways, we use shortcuts and cues to make assumptions. That is kind of why people say, âGosh, it feels really clever.â But to your point, Bryan, itâs not 100 percent accurate. Thereâs a great term for that âhallucinating.â What gen AI means for recruiters . . . Lucia Rahilly Weâll talk more about some of the risks, but letâs turn to what these kinds of generative AI capabilities mean for talent in particular. Do you expect generative AI to reshape or alter the recruiting process in any meaningful way? Bryan Hancock I think itâll reshape recruiting in two meaningful ways. The first is helping managers write better job requirements. Generative technology can actually pull on the skills that are required to be successful in the job. Thatâs not to say managers donât need to check the end product. Theyâll need to be that human in the loop to make sure the job requirement is a good one. But gen AI can dramatically improve speed and quality. The other application in recruiting is candidate personalization. Right now, if youâre an organization with tens of thousands of applicants, you may or may not have super customized ways of reaching out to the people who have applied. With generative AI, you can include much more personalization about the candidate, the job, and what other jobs may be available if thereâs a reason the applicant isnât a fit. All those things are made immensely easier and faster through generative AI. Bill Schaninger The best application of gen AI is in large skill pools where youâre trying to fill a reasonably well-known job. We need a more productive and efficient way to navigate all the profiles coming through. Where it makes me a little anxious is anytime itâs a novel jobâa new roleâor even, in US law, a job thatâs changed more than 25 percent or 33 percent. In those cases, you have to go back and revalidate the criterion by which you would judge people in or out of the pool. The challenge with validation is you need a performance criterion to regress against and say, âWhatâs the difference?â In some cases, that means figuring out how to get that criterion out of a data lake without encroaching on other peopleâs proprietary performance data. If you say, âWell, weâre only going to use our data as the employer,â then you are only basing the criterion off people youâve already hired. And to validate, you have to look at the people you didnât hire. So it doesnât mean the technology canât be used. It just means thereâs probably a little bit more front-end work on applying it to novel jobs and a wide-open opportunity for the big skill pools. Lucia Rahilly We talk a lot about having over-indexed on credentials and under-indexed on skills in the recruiting process. Does generative AI have a role in accelerating that shift from credentials like college degrees to the skills that candidates are actually capable of contributing to the workplace? Lareina Yee Iâm optimistic it can. One thing this technology does extremely well is taggingâthe ability to tag unstructured data for words. There are a lot of businesses that are thinking about applying that to e-commerce, to different types of retail experiences. But you could also apply it to talent acquisition or looking for capabilities. Now you donât need to look for a credential or a degree. You could look for keywords in terms of capabilities and skills. Looking at social media, how do people talk about certain capabilities? You may find there are better words to associate with those who have those skills. Think of a world where you want to be able to find candidates who have amazing experience from learning on the job but donât have PhDs or college degrees. Iâm optimistic that this could open more doors for folks like that. Bill Schaninger This is an interesting trade-off in the business world, which likes proprietary data sets and grouping of profiles. The real power might be, âHow much can you get in the public domain until you start bumping up against paywalls?â Long ago, when LinkedIn was bought, the APIs got limited to job titlesânot necessarily all the spec that was underneath it. There is power in these poolsâin particular, in profiles of jobsâbecause then you can go look at tasks and skills. Iâd imagine thereâs going to be a race here toward figuring out how we can piece these together to form the ontological cloud, if you will, of âthese 17 things describe this skill.â Because it really is about skills and not credentials. . . . And what it means for professional growth Bryan Hancock You can also think about this as aiding a skill-based transition not just from the employerâs perspective but from the candidateâs or employeeâs perspective. In the current world, if youâre somebody who may have some skills but donât have a very clear view of what your career opportunities might be, you are highly dependent on a manager or somebody taking an interest in you and helping to navigate you to ânontraditionalâ paths. But in a world of generative AI, you could have a conversation with a very intelligent chatbot and say, âHey, here are my skills and experiences. What jobs could be open to me?â And it could come back and say, âWell, most people with your skill profile do these things, but some do A, B, C,â with âCâ being coding. And then, you could say, âTell me what these jobs in coding would be,â and it could pull a job description for a coder that is not just geared toward an IT person but translated into words you understand. Then you could say, âOK, this is great. Iâm interested. What learning experiences do I need?â And generative AI could tell you what those learning experiences are. So for somebody who has the innate ability but not the visibility, generative AI can illuminate a range of career paths and start helping people understand how to get there. Lareina Yee Imagine Iâm ten years into my career and Iâm feeling a little stuck. What if I had a professional development AI assistant that helped me think through questions like, âWhat type of job should I seek? What are the types of roles within my company? How do I think about them?â and âWhat classes would I take?â as opposed to waiting for someone to reskill meâwhich sounds awful. How do I take the initiative ten years into my career to build the skill sets and understand the range of jobs available for my capabilities? That would be so cool. Bill Schaninger Depending on the regulatory environment youâre in, youâre not allowed to make any selection decision without a human being involved. This is particularly true in the EU. Itâs a nice way of augmenting human work but not cutting out the decision making. On the employee side, it should provide much more transparency; you can actually see how close you are to a lot of things. I love it for the employee experience part. I get anxious about the selection part just because weâre still not sure about whatâs in the data lake and how good people are at prompting the AI. Lareina Yee Right. Itâs great to give you some options, but itâs not an answer or a recommendation engine. Your judgment matters. Bryan Hancock Another thing weâre seeing is that ChatGPTâand generative AI more broadlyâcan be particularly good at getting new workers more quickly up to speed. Thereâs interesting research that Erik Brynjolfsson at Stanford, along with others from MIT, have recently come out with, which looks at call-center workers. They found that generative AI functionality wasnât all that helpful for the most experienced representatives. It was incredibly helpful with new folks because they were able to get that institutional knowledge much more quickly. It was at their fingertips. They could ask a question and get the answer. So the productivity of new folks was dramatically higher. Generative AI really gets you 80â90 percent of the way to full proficiency. Lareina Yee Bryan, I love that, and I share the optimism. Whatâs new for the performance review Bryan Hancock One of my personal favorite uses for generative AI on the people front is actually for performance reviews. Hear me out I donât want generative AI actually generating somebodyâs performance review. That needs the human in the loop, needs human judgment, needs empathy. But let me use this example of what I do as a McKinsey evaluator I get written feedback from 15 to 20 individuals. They enter it into a digital system. Iâve got long-form feedback. I look at upward feedback scores that include written commentary as well as specific number-based scores. I look at how often people were actually deployed on engagements. I look at compliance-related measures. Did they turn in their stuff on time? A whole range of things. For me, as an evaluator, getting to a first draft is an incredibly arduous process. I take pride in the time and the thoughtfulness that goes into it. But what if I could hit a button and get a draft? When I have each of the conversations with the 15 people that best know the person Iâm evaluating, what if I had a draft I was already working from? Itâs not a replacement for going through everything, but that initial synthesis would help me get more quickly to what I really need to probe for that personâs development and growth. Iâm excited about that use case because it eliminates a lot of work. At first, many people would think, âIâd never want generative AI anywhere near performance reviews.â But itâs exciting if we think of this as a productivity aid or as something that helps us be even better. Lareina Yee Now letâs talk about the employee heâs evaluating. The employee gets the feedback, and Bryan probably wrote it clearly, and he delivered it with empathy, so the person is feeling, âOK, Iâve got some strengths, and Iâve got some development needs.â But what if I, as the employee, can query, âWho are five success models with my strengths and weaknesses, and what have they gone on to do? How can I visualize my career development? How can I continue to work on it?â I could also have an assistant that helps me map my professional development. In that way, when we check in a year later, Iâve really improved and increased my aspirations. What if Bill is someone I should model myself on? Instead of Bryan having to introduce me to Bill, generative AI helps me realize that Iâve got the makings of a Bill Schaninger. I can be inspired by that. I think thereâs a lot that enhances what weâve been trying to do so laboriously for years. Bill Schaninger We talk about putting the manager back in performance management. Every time you talk to somebody about something good or bad, log it away. That way, at the end of the year, itâs more of an aggregation and synthesis, and itâs not a surprise to anyone. But that requires regular entry. So while I love what youâre describing, itâs not the tech that does that; itâs the people committing to the common data capture and the common approaches that enable it. Bryan Hancock Your point is well-taken. Then, as an evaluator, I apply my human judgment. Bill Schaninger The normative data is nice. When we get our sponsorship and mentorship data at McKinsey, we see how we compare to other partners in a given region. If you donât have a reference point, though, how would you know what âgoodâ actually is? When you get the normative data, you can start getting some guidance. I like all that, and itâs all enabled by huge amounts of data. If this enables a more robust and wholesome view of actual performance, it makes it a whole lot easier to have a difficult performance conversation. We need to put the manager back in performance management. But can we make it easier on managers so they can spend the time managing instead of scribbling out a schedule or knitting together 15 data points? Bias and other risks Lucia Rahilly Letâs talk a bit more about some of the risks. Generative AI learns based on historical data, and historical patterns of data reflect historical biases. By relying on generative-AI-driven tools, whatâs the risk we are inadvertently propagating these inherited biases? Lareina Yee Certainly, today, generative AI can amplify bias. Letâs say Iâm recruiting, and I describe some different qualifications. Iâm looking at urban centers of talent, and I decide Iâd like to look for basketball captains; or perhaps, instead, I say that lacrosse captains are desirable. These are team sports with captains and leadership, so in some way that makes sense. But if you look at demographics, who plays basketball in cities is very different from who plays lacrosse. And so, by emphasizing lacrosse, you will typically get more young White male leaders, whereas if you chose basketball, you might find more African Americans or Latinos. What about softball, where we see women? What happens if, instead, we select a whole set of sports? Even then, just the selection of the sports as a filter could amplify bias in the questioning. I think the power of the question is on us as humans. Bryan Hancock Of course there are also intellectual property concerns. But I also think thereâs a risk of us all becoming less interesting. If you are somebody in a creative field and you leverage generative AI to get your output up from six articles a week to 12, youâre spending less time per article. You may need to do that to get to publication in time, but that also means youâre not spending as much time in the shower, on a run, or in the car thinking about the articles. Your productivity will go up, but you may not necessarily have as much time for creative thinking. We know that the most creative thoughts come from downtimeâwhen youâre doing something else and letting your mind wander. This risk of being less interesting is important, and one that we may not have fully thought through yet. Lareina Yee Precisely. There are a lot of risks. Letâs also think about leaders who are implementing this technology. Often people had a workflow where they would think about a technology and the business return on investment, and only at the end would ask, âAre there any risks we should worry about?â I would strongly recommend that you think about risk up front in the workflow design. The other thing is thereâs a real opportunity for what we typically call âchange management.â If you donât think through how the technology changes the job, workflow, or collaboration model, then youâre not necessarily directing that additional time toward something thatâs more value added. You need to think about how it affects the rest of the workday and workweek. Bill Schaninger In many cases, weâd like to blame the technology and not highlight the poor problem solving that happened just before implementing it. Getting a better, shinier tool thatâs faster and more expansive doesnât relieve you of the burden of thinking things through. Lareina Yee The bigger thing to call out here is that three of us have spent this time thinking about all the positive intentions and the ways we can use this for good. But there are probably people who are thinking about this technology and asking, âHow can I use this for harm?â Traditionally, this is why government regulation, policy, and international standards play a fundamental role in our society. I donât think you can completely leave it to the private sector to self-regulate. Preparing for the inevitable Lucia Rahilly A big concern for people is that these kinds of tools will eliminate their job orâpotentially even worseâbecome their bosses. What do you think people can do now to prepare for the changes that are coming with generative AI? Bill Schaninger I would try to make it easier for them to learn and play with it. This is better than continuing to try to resist it. I donât think we should become beholden to these fears. Lucia Rahilly And assuming HR and talent processes become increasingly automated, how can leaders ensure that generative AI doesnât get in the way of what Bryan called âthe human in the loop?â Lareina Yee Leaders have a huge role to play in two ways. One is to modernize and leapfrog their own talent capabilities within their functions. And second, if 80 percent of their workforce is shifting, they play a huge role in how that happens and how it affects employees at their companies. I think leaders have a huge voice at the table. Bryan Hancock Itâs a tremendous opportunity for HR to increase access to opportunities for huge swaths of their workforce. Itâs an opportunity to get managers more consistently up to the level of performance that HR leaders have always wanted them to achieve instead of working on administrative tasks. I hope that HR would view this as an opportunity to routinize and get rid of the work that they donât have to do. Then for the work that they do have to do, they can use this technology to find a way to get better answers more quickly.