The webinar discussed the concept of the “Superworker Era”, emphasizing AI’s transformative impact on workforce productivity. Josh Bersin highlighted that while AI can reduce job demand, it also offers opportunities for re-engineering workflows and enhancing productivity. He noted that 95% of AI product users are unsure of their ROI and 45% of AI queries return incorrect answers. Kathi Enderes identified Pacesetters in AI transformation, such as Moderna and DBs, which focus on AI for growth rather than cost control, dynamic work design, and talent density. These strategies involve continuous innovation, redesigning work, and systemic HR practices to adapt to rapidly changing skills and business needs.

Introduction

• HRE Moderator welcomes attendees and introduces the webinar titled “The definitive guide to AI transformation: Building the Superworker organization.”
• HRE Moderator thanks sponsor Eightfold and explains the AI Interviewer agent’s capabilities, including 24/7 operation across time zones and languages.
• HRE Moderator provides housekeeping notes, including instructions for using the Q&A module, resizing modules, and receiving the recording post-event.
• HRE Moderator introduces Josh Bersin, CEO and global analyst at Josh Bersin Company, to discuss the Superworker Era and AI transformation.

Overview of the “Superworker Era” and AI’s impact on the job market

• Josh Bersin introduces the concept of the “Superworker Era”, emphasizing AI’s role in enhancing productivity and job performance.
• Josh Bersin discusses the current job market in the United States, noting the lack of job creation and increasing layoffs due to technological advancements.
• Josh Bersin highlights the need for companies to redesign their processes to leverage AI effectively, reducing the demand for human labor.
• Josh Bersin mentions the over-optimism about AI, citing a study showing 95% of AI product users are unsure of their ROI and another study indicating 45% of AI queries return incorrect answers.

Challenges and opportunities in AI implementation

• Josh Bersin discusses the challenges employees face in adapting to AI, including the rapid pace of technological change and the confusion in the vendor market.
• Josh Bersin emphasizes the importance of re-engineering business processes to integrate AI effectively.
• Josh Bersin highlights the need for guardrails, training, and systems to ensure AI is used responsibly and productively.
• Josh Bersin explains the concept of the Super Worker, likening it to Superman needing to know what to do and what not to do with his powers.

Steps in AI-driven workflow re-engineering

• Josh Bersin outlines the steps in AI-driven workflow re-engineering: using AI to automate tasks, developing AI automation tools, and redesigning entire workflows.
• Josh Bersin provides examples of how AI can streamline processes in various industries, such as recruiting and learning and development.
• Josh Bersin discusses the potential for AI agents to communicate and collaborate, enhancing productivity and efficiency.
• Josh Bersin emphasizes the importance of re-engineering processes to achieve breakthrough productivity improvements.

Case studies and examples of AI in action

• Josh Bersin shares examples of companies using AI to improve productivity and efficiency, such as his own company using AI for research and training.
• Josh Bersin discusses the potential for AI to create new revenue streams and improve customer experiences.
• Josh Bersin highlights the importance of integrating AI with existing systems and processes to achieve comprehensive transformation.
• Josh Bersin emphasizes the need for continuous re-engineering as AI technology evolves.

Introduction to Kathi Enderes and Pacesetters in AI transformation

• Kathi Enderes introduces herself and discusses the concept of Pacesetters in the AI era, highlighting companies leading the way in AI transformation.
• Kathi Enderes explains the Global Workforce Intelligence Project, based on data from Eightfold, which identifies industry leaders in AI transformation.
• Kathi Enderes discusses the accelerating pace of skills change and the challenges companies face in keeping up with new skill demands.
• Kathi Enderes highlights the importance of skills velocity in addressing business problems and driving innovation.

Strategies of Pacesetters in AI transformation

• Kathi Enderes outlines six strategies of Pacesetters in AI transformation: Using AI for growth, innovating at the core, dynamic work design, talent density, change agility, and systemic HR.
• Kathi Enderes provides examples of companies using these strategies, such as Moderna investing in digital technologies and DBs using an internal AI talent marketplace.
• Kathi Enderes discusses the importance of continuous innovation and redesigning work to leverage AI effectively.
• Kathi Enderes emphasizes the need for change agility and systemic HR to support employees and drive transformation.

Case studies of AI in HR and talent management

• Kathi Enderes shares examples of companies using AI in HR and talent management, such as Mercy Health using AI to uberize nursing jobs and L’Oreal using AI for digital skin tools.
• Kathi Enderes discusses the importance of continuous innovation and redesigning work to leverage AI effectively.
• Kathi Enderes highlights the need for change agility and systemic HR to support employees and drive transformation.
• Kathi Enderes emphasizes the importance of skills velocity and talent density in addressing business problems and driving innovation.

Conclusion and Q&A

• Kathi Enderes concludes by emphasizing the importance of skills velocity, talent density, and systemic HR in AI transformation.
• Kathi Enderes encourages attendees to incorporate the Pacesetters’ strategies into their AI transformation efforts.
• Kathi Enderes opens the floor for Q&A, addressing questions about redesigning work, skills assessment, and internal mobility.
• HRE Moderator thanks attendees and concludes the webinar.

HRE Moderator 0:04 Hello and welcome to today’s webinar: “The definitive guide to AI transformation: Building the Superworker organization.” Thank you to our sponsor of today’s event, Eightfold AI.

The Eightfold AI Interviewer agent is transforming how organizations hire. Powered by the Eightfold Talent Intelligence foundation, AI Interviewer is an autonomous, assistant, and agentic interviewer that thinks, adapts, and evaluates like your best recruiter, operating 24/7 across time zones and languages, while assessing every candidate fairly and consistently. It works across the entire interview process, from initial screening to final assessment, enabling teams to make faster hiring decisions, deliver a seamless candidate experience, and complete interviews in under a business week so you can find your best-fit talent.

And now I will pass the stage to Josh Bersin, CEO and global analyst at The Josh Bersin Company.

Josh Bersin 1:44 Thank you, and welcome, everybody. Kathi and I are going to talk about what we call the “Superworker Era” — of course, all about AI, which is the number one topic on everybody’s mind. I’m going to give you a little overview, and then Kathi is going to share with you some comprehensive research we’ve done on the Pacesetters: the companies that are driving the most success in these early days of AI transformation across their companies.

The theory and the concept of the Superworker is that AI is making everybody more successful, everybody more powerful, everybody more productive. If you look at the job market in the United States, it’s questionable whether we’ve created any jobs at all during the entire year, and the layoffs are coming. I mean, I just saw the Amazon announcement today, and then there was another one this morning. That’s not because companies are seeing an economic slowdown, because the economy is actually doing reasonably well. It’s really because they’re forecasting and projecting the technology to reduce the demand for people over time. That, of course, happens only when you as an organization redesign the company to use the AI in a way that you can produce more output with the same number of people.

Now, this is not as simple as giving everybody a copy of Galileo or the Copilot and telling them to “go hither” and do your thing in a more productive way. Because what happens with these AI systems, as I’ll show you, is they start to revolutionize the business processes as a whole. The problem, of course, in all of that, is that the employees in many companies are not ready for this. The transformational efforts are new, the technologies are changing almost every day, and the vendor market is very confusing. So this re-engineering process is about a year in but not in any way complete, and employees are worried. Let’s just talk a little about that before we give you these best practices.

Interestingly enough, two things I want to mention to you about the sort of over-optimism about AI. This is a study that was done earlier this year that essentially shows that 95% of the people that have purchased AI products don’t know if they’re generating an ROI yet. The conclusion that the MIT people made, which is the same conclusion that we’re coming to as well, is that it’s not a matter of the technology not doing what it’s advertised, but it’s also the organizational gap of people learning how to use it and deciding on the use cases and the re-engineering processes that need to take place.

The second thing I want to mention, which is not on this slide, is a study that came out over the weekend from the BBC, which I wrote about on our website, which shows that 45% of the queries that OpenAI, Gemini, Perplexity, Microsoft Copilot, and Anthropic came back with incorrect answers when querying for news. So these are not perfect systems. They are probabilistic systems. Any small amount of error in the data could result in a large amount of incorrect answers because the data is all integrated through these large vectors. This stuff is not exactly simple to use. And if you read all these articles about super-intelligence and about AI being 100 times more intelligent than humans, I would question that where we are today, because we’re going to have to learn how to use it for what it’s good at and be careful not to use it for things that it could cause risk.

There’s a lot of opportunities for AI all over companies. I’m just highlighting a few statistics that have been out there from many different consultants, including us. The right side is mostly stuff from us; the left side is from other places. The challenge that you have as an organization is: where do you focus? As Kathi is going to share with you, we can give you some advice on that. Cost-cutting is one approach, but there’s a better place to focus — usually to focus on customer experience and revenue growth.

I won’t bother belaboring the job market data, because more data will come out every couple of weeks. But if you read the articles that have come out just the last week or two, there’s more and more reflection of this: that companies have decided that because they’re spending a lot of money on these new tools, they’re not going to hire people. That, in a sense, forces re-engineering simply from fewer people available to hire. There’s also this potential issue where CEOs can, and sometimes do, use this as an excuse to lay off people when they are probably over-hired in the first place. I think Salesforce is an example of a company that’s gone through this many times; it’s a very well-run company, but they buy a lot of companies, and when they do, they end up with too many people.

I’m not seeing a lot of companies coming up with breakthrough productivity improvements yet, but there’s a lot of good stuff going on, and it is coming in many places. That’s the idea of the Superworker.

The other idea of a Superworker is that in order for Superman to do good things for society, if you read the Superman comics, he needs to know what to do and what not to do. When he was a young boy and he first landed on Earth, he broke a lot of things, too, because of his powers. This is exactly the same situation here, where these AI tools have the potential to do great things but also the potential to do dangerous things. Our jobs as HR people or business people are to put the guardrails and the training and the systems in place so that employees can use these systems for effective transformation and not hire the wrong people, make bad decisions, incorrectly assess data, or possibly make incorrect decisions on other more critical things, like safety or other tools.

There’s a lot of re-engineering going on. We have this model that’s worked very well for people to sort of think through this re-engineering process. I’ll just touch on it, and then let Kathi walk you through some examples.

Generally, the way this works is the first thing that happens is you use Copilot or ChatGPT or Galileo or whatever you have, and you find that there are things about your job that are easier to do — writing, reading, assessing data. Like, for example, I like to put spreadsheets into Galileo and let Galileo do the pivot tables and stuff for me instead of doing it by hand. It’s just faster.

Josh Bersin 9:13 But that doesn’t change my job; it just makes my job a little bit quicker. I’m doing the same work.

Then what happens is, at step two, you reach the point where you’ve done enough of this that you have your own little tricks or automation tools. If you’re a software engineer, for example, and you know how to use this thing on a regular basis, you might get a 30% automation improvement. You might save a day a week if you’re in a very routine job with a lot of manual labor, but that only takes you so far.

The next thing that happens is you say, “Look, if I’m automating the stuff I’m doing — maybe I’m a recruiter, and the person right after me is doing the interview scheduling, then the person after them is doing the interviewing, and the person after them is creating the offer letters — what if we did this as one big process?”

At level three, where the big improvements happen, is where you re-engineer the workflow. I think a lot of you are going to be doing much more of that over time, as the agents become more sophisticated and more packaged. Then what happens is the agents become smarter, and they can use data to optimize the process.

Many, many examples of this are in recruiting. Those of you on the talent acquisition side of your company, or you’ve done a lot of recruiting, or you’re in HR, know there are dozens of steps to recruiting. Even the simple step of opening up a req [requisition], where you talk to a hiring manager to get a sense of what the job is — AI can do that. AI can interview the whole team and come back with a set of requirements for this job, not just based on the manager’s opinion, but based on the opinions of the whole team. There are things that can be done all the way from the very beginning, all the way through the process to selecting, hiring, and the candidate experience.

The reason that we like to show recruiting is this is where the HR market is the most mature. Products like Paradox, mpathic AI, and Eightfold can do all of this. We have examples in HR where we can replace an entire process now with a series of AI agents.

Ultimately, the way this is going to work, and I think you’re beginning to see this in the press now, is that your company, whatever it may be, is going to find its own productivity improvement curve relative to your peers or competitors. To some degree, if you lag behind or delay your implementation of AI, you might find a competitor doing something faster and better than you can without you even knowing that it happened.

I’ll give you an example. In our company, we do a lot of research, publish things, write articles, do podcasts, give speeches, and share information. Up until two years ago, we had a very traditional publishing model. We had a publishing group that did a lot of that editing and publishing. We now take all of our IP and put it into our AI called Galileo, which means that all of our customers — more than 5,000 people now using Galileo — can get access to the research we do instantaneously. The day it’s produced, it goes into Galileo; the next day, everybody has it. Not only can they read it, they can ask it questions, and they can analyze their own situation relative to what research we’ve discovered. That, for us, gave us maybe an order of magnitude improvement in speed. If we were still doing it the old way, it would be taking weeks and months. The same thing happens with our training.

You can do that in your company. Learning and development, for example: it’s possible to generate content directly from documents in days, in multiple languages, as opposed to months. And these things are going to talk to each other. The future of the agents is not just buying one agent and trying to get it to do everything, but one agent talking to another agent. There’s a technology called Model Context Protocol (MCP), which is an agent-to-agent communication protocol that more and more software vendors are starting to use. So if you’re using the ADP agent for payroll, using Galileo for some job analysis, and using the Eightfold agent for interviewing, these things, within a year, I guarantee you they’ll talk to each other. They’ll be sending each other messages, and you’ll be able to bring these agents together as a collection of activities.

There’s a lot of re-engineering potential here. The final thing on this, just at the high level: I think it also crosses between business functions. If you think about the way companies work, every company basically has a very similar multi-process function. There’s some process of building a product or a service in R&D or in research, it comes to market, you launch it, you market it, you sell it, you support it, you collect money, you go back and try to get the customer to renew. The cycle goes on and on. What we’ve done in our companies is we’ve set up different functional areas in each one of those parts of the process: a head of research, a head of product, a head of engineering, a head of customer support, sales, marketing, etc. Those are all separate groups, and we optimize each one of them as if they’re independent, but they’re not independent at all. They actually chain together. What you’d like to know is, if a customer didn’t renew, you’d like to go all the way through the chain and figure out what happened, because any one of those things could be involved.

I think 2, 3, 4, 5 years from now, there’s a massive change in the way we think about our businesses with AI that you have the opportunity to be involved in. The human side of this, though, as Kathi will explain, is much more complex than the tech. You could design this on a whiteboard, but the actual reality of doing it involves people, employees, changing roles and jobs. They’re worried about their jobs, and you need to make sure they’re taken care of and skilled so that they’re comfortable taking on these more agent-powered responsibilities.

The role of leaders (which we just recently did a big research report about) is that if the leaders don’t facilitate this change and transformation — and Kathi is going to talk more about this — you can’t get people to do it on their own, because they need help and support. The leaders, of course, are worried about their jobs, because if the organization gets smaller, maybe they will lose their position.

Then there’s the issue at the company level: Where do we focus this? What are the areas of the business we should put the most attention? Should we stop hiring everywhere, or only in the areas where we have the highest cost or the best customer service?

We’ve got sort of three decision points in this. The way I think about it visually is if you think about the rate of change of technology, it’s literally going vertical. Every day there’s a press release from one of these vendors about something that seems spectacularly better than ever before. I think OpenAI has tripled the capacity of just their AI systems in one year. That never happens in technology. Yet we as individual leaders and managers cannot adapt that fast. We can’t even keep track of what this stuff is, forget about figuring out how to use it and how to re-engineer our processes.

What we really see as the key here, this gap, is the role of managers. You may think that all of these great AI re-engineering things are going to get done by some corporate Transformation Office, but I would be surprised if maybe 20% or 25% of the big AI re-engineering will come from corporate IT. A lot of it’s going to come from line leaders coming up with good ideas, trying things, experimenting, and building things on their own. That leads to this issue of management and the role that leaders and managers play in facilitating this re-engineering process. This is not a re-engineering process like the old days where you bought a piece of software, implemented it, and then all of a sudden, poof, you were done. This is going to go on step-by-step, day-by-day, week-by-week, over the coming years as AI evolves, because we’re really re-engineering our companies to use a very different type of technology than we ever had before.

Okay, so that’s the overview. Kathi, let me turn it over to you, and Kathi will give you the secrets that we’ve learned on how to do this.

Kathi Enderes 18:14 Well, thank you, Josh. It’s always so fascinating to hear how you position all of these trends happening in the market.

Let me talk a little bit about what we are seeing, who these Pacesetters are, who these winners are in the AI era and the Superworker era. We’ve actually spent all year doing a lot of research. Each of these pictures that you have here is a research report on what it means to be a Superworker, how to be a Super Manager (as you just said, Josh), how you get more impact with AI in HR specifically, and how AI is also transforming all of our people and talent and HR processes overall. What does it mean? How does it actually work? How does AI shape our processes?

As we’re looking at all of this, we discovered that there are winners and, I don’t want to call them losers, but maybe non-winners in the AI transformation journey. It’s understandable that not everybody is leading the pack, but there are certainly some companies that are. We wanted to see: What are they doing, and how are they driving that success?

This is part of our Global Workforce Intelligence project that we’ve been doing now for the last four years, based on data from Eightfold, where we’ve been looking industry by industry at the roles, the skills, and the career pathways that are rising and declining in order to support and address the biggest business problems that each of these industries have. Over the last four years, we’ve studied literally millions of data points based on Eightfold’s talent intelligence platform. The best companies, the most successful companies, we call them Pacesetters.

While we didn’t start this Global Workforce Intelligence project to understand AI transformation, now, of course, we wanted to know: What are these companies that are leading that industry doing differently than everybody else?

We’re seeing a couple of things. First, as you look at skills — and this comes from a study from Lightcast — they are seeing that skills, as we’re studying them in each of these industries, are changing faster than ever. That’s not a surprise, of course, because as you see AI disrupting every job, every role, and all the work that people do, skills are changing really, really fast. You see all these statistics here, but basically, this accelerating pace of skills change is really straining a lot of companies, and you probably feel this in your company, too. How can we keep up with these new skill demands all the time? And how can we help our employees keep up with these changing skills over time?

At the same time, every industry is converging with another industry. Here you can see all the industries that we’ve studied in the Global Workforce Intelligence project: consumer packaged goods, pharmaceutical, healthcare, delivery, banking and financial services, the insurance industry, and the automotive industry. The way this chart goes is basically every industry is transforming, overlapping, and changing. We see this not just in our own research, but the CEO studies — for example, PwC’s CEO study — showed that too. All the CEOs are saying, “I don’t really know what industry we’re in anymore.” We used to be a retail company, and now we have to go into financial services, and now we have to go into healthcare. Or we are a healthcare company, but of course, we also need to be a tech company. Or we’re a banking company, but really, we need to be more into insurance and cryptocurrencies and product management, on and on and on.

From a skills and talent model perspective, it’s very challenging for us in HR, talent, and leadership, because we used to know exactly what roles we had and what skills we needed, and now that’s not clear anymore. From a people and talent perspective, we’re now competing with every other industry, not just our own.

We see that these Pacesetters, these most successful organizations, are the top 5% to 10% in each industry. They are industry leaders that don’t just lead in financial performance, but they’re also a talent leader. They’re a great place to work, they have really high Glassdoor ratings, and they are recognized as a really good place to work. They’re also leading their industry in products, offerings, or innovation for their customers, and they’re leading in their HR practices overall. As we look at our own systemic HR practices, we also evaluate them there. This is very scientific. We looked at all of these six industries, and we saw that there’s a number of companies — these are just some examples — around the world, like the companies here, that are leading the market.

This is all nice and good, but what we really wanted to know is: What are they doing differently? It’s great to know that MetLife, for example, is one of these Pacesetters. But how did they get there? Or L’Oreal? How are they doing things differently? That’s what we wanted to discover here, and we have a big report that we wrote on that. We’ll share with you how they approach AI transformation.

As we looked at each of these industries, we looked at what their biggest business problem is that they have to address, and how these Pacesetters address the problem. Each of these problems was unique and different. We wrote a big report on each of them. You see the pictures here.

For example, in automotive manufacturing, not surprisingly, they need to transition to electric vehicles and have to have much more integrated, holistic offerings—much more customer-first. From a roles and skills perspective, for example, they had to focus much less on just old-fashioned manufacturing roles and much more on robotics and software engineering.

Similar story in consumer banking, where they had to address their biggest problem, which was having a different offering on digital products, not just traditional banking. They had to deploy talent intelligence to hire the right people and to find the right people, because not a lot of people with leading tech skills wanted to work in an old-fashioned consumer bank.

CPG had a different problem. They saw that their consumer preferences were changing and new market entrants were much more agile. They had to focus much more on R&D, for example, and go directly to consumers, not just partner with retail.

Healthcare, of course, has a huge talent shortage. Every time you look at the open positions…

Kathi Enderes 26:19 …healthcare or nursing is one of the most open positions. What they had to do is redesign the work itself and think about how they bring in AI.

Insurance had all of these natural disasters and financial pressures, so they had to get much better at predicting risks and having much more data analytics skills.

Pharmaceuticals, of course, always need to bring their treatments or drugs to the market faster, but also manage compliance.

Different problems to address, but in the middle of all of these problems and how they address them, was what we call skills velocity. Going back to this point where skills are changing so quickly, skills velocity is this ability to really quickly identify what skills are needed, what skills are trending, and predict what skills you need for the future. Then, use your internal talent processes to build, retain, and recruit the right skills at scale and with speed, but also redesign the work (to the point that Josh made) with the AI transformation to leverage these new skills.

I brought up these new skills: the EV skills in automotive; the tech and AI skills in consumer banking; CPG’s customer experience and design skills; healthcare having much more AI-enabled care and telehealth; insurance with predictive data analytics; pharmaceuticals with biotech skills. Whatever the skills are that are increasing in demand in your industry, really focusing on that velocity — not just what skills you have, but how quickly as an organization you can build them up. That’s what really differentiates the Pacesetters.

So how do they approach AI transformation? We identified six secrets of these Pacesetter organizations. You can download this report from our website if you want to read more. Basically, we saw they are doing six things fundamentally differently:

1. AI for Growth, Not Cost Control: As Josh said, you can either think about AI as a cost-control mechanism or you can think about how you can create better products, new offerings, and faster go-to-market. I was just at the SAP conference a couple of weeks ago, and their head of product said, “We have 35,000 software developers, and we used to operate like a company with 35,000 software developers. Well, now with AI, we operate like a company with 120,000 software developers.” They see AI for growth, not just cost control.
2. Innovating at the Core: Having every employee innovate, not just an innovation team or an R&D team.
3. Dynamic Work Design: They are really thinking about the work that’s happening. How do we need to rethink, re-engineer, and reimagine the work that people do?
4. Talent Density: This is a new way of thinking about talent management. Not just adding more people when you grow, but thinking about complementary skills and team skills.
5. Change Agility: Really, really important. They support the entire change process. AI is a huge amount of change, and employees are stressed about it. These Pacesetters support their employees really well.
6. Systemic HR: They rethink their HR service delivery model, their operating model, their roles, and their skills, all around AI.

Let me give you some examples. First, these Pacesetters use AI transformation for growth. We saw that these Pacesetters have a lot more of these future-forward technology skills. You see one of these examples here on the automotive companies, but in every industry, we see the Pacesetter companies have many more of these future-forward tech skills.

First, these Pacesetter companies focus their investment on AI and technology skills. You see the example here of Moderna. They’ve been in the news a lot for their AI-first HR processes. They also invested over $100 million in digital technologies to create this AI-first enterprise to speed up their drug development. Their mindset is “obsessed over learning and digitize everywhere possible.” This mindset shift to invest in these AI and tech skills for every employee, not just for your technology employees, is really important.

Second is transforming the work with AI. DBS, a very forward-thinking bank in Singapore, is completely agile. They have an internal AI-powered talent marketplace that they developed themselves, called iGrow. They use AI to integrate workforce planning, L&D, and career mobility. They have a 40% internal fill rate for any of their positions because they’re using this talent marketplace.

Another one is creating new revenue streams with AI. Tesla, for example, is using AI to rewire their entire supply chain and operational intelligence. They’re using AI across the board, not just for vehicle automation, but for machine learning in manufacturing, dynamic pricing models, and predictive maintenance of cars.

Zurich Insurance is also using AI to streamline back-end operations, but then also…

Kathi Enderes 36:07 …investing in branding, retention, and talent analytics to create a better employee experience—using AI not just for efficiency, but for people-focused and better work environments as well.

We also see that these Pacesetters use AI in HR for more than just efficiency. This is a model we came up with in our research on how you maximize the impact of AI in HR. Not just for efficiency, but also, how can we create a better employee experience? How can we create better outcomes—better quality of hires, for example, or better learning outcomes?

Some great examples here: Mercy Health, a really big healthcare organization, uses an AI-based system they call “Mercy Works on Demand” to “uberize” their nursing jobs. They give their nurses the option to just work five hours a week, not 40. They thought first maybe everybody was going to scale down their work and they wouldn’t have enough nurses, but it was actually the opposite. Just having this opportunity for a flexible approach was enough for them to attract more people into these nursing roles and retain them. Their service levels went up, and the cost went down because they didn’t have to staff up with outside nurses.

L’Oreal is another great example, using AI for their digital skin tools. You can basically try out makeup or skincare products online in virtual reality. They’re using AI to personalize the customer experience.

Another one is Nestlé, in consumer packaged goods. They are using an AI-based chatbot for candidate questions. Many companies are using that, but they’re having candidates ask the AI chatbot any questions during the recruiting process to create a better candidate experience, not just to save money for HR.

Second secret: continuous innovation at the core. Pacesetters are always innovating everywhere, including frontline innovation, not just their R&D or tech teams. As Josh said, the best ideas in AI transformation are going to come from your frontline. They’re going to come from an employee having a great idea and saying, “Hey, how can we use these AI tools to have a better customer experience?”

Bayer is a very innovative company. They have this “Leaps Initiative” where they are supporting innovation everywhere, and they have competitions on AI use and innovations. Toyota is one of the most forward-thinking companies on that front. They are even expanding that, not just for their own payroll employees, but to the 40% or 50% of their workforce that’s contractors, having really fast prototyping and lean manufacturing applied across the board. They are also investing in innovation capabilities and skills. AdventHealth is a great example of using AI to reimagine “whole-person care” in the healthcare setting.

Okay, the third one has to do with how you redesign the work, jobs, and roles of every person around the company. If work and roles change so quickly, how do we have this bottom-up approach, but also a top-down approach where we are looking at where the biggest opportunity is to use AI and then support that innovation? These Pacesetter organizations really redesign all of their jobs to make work more meaningful, to have people be these Superworkers, and really incorporate AI into all the workflows.

Really good examples here from healthcare organization Providence. They are using AI to redesign all of their clinical roles and are focusing on what they call “top of license” patient care. This means having every nurse, for example, do only the work that they are uniquely qualified for, so patient outcomes are better. AI can take some of the more mundane work, like taking patient notes, and also direct the clinical people to the cases where they have the highest need.

ING, another really forward-thinking bank, has been doing agile work teams for a number of years, breaking down all of these boundaries. As Josh mentioned, AI can bring together your sales, marketing, product, and tech teams in these agile squads and use technology to do all of that.

Redesigning work is one of the number one approaches. We saw this in our healthcare study, where we saw that redesigning the work with AI is the biggest area where healthcare organizations can actually close the big nursing gap they have. At the same time, of course, you need to still recruit the right nurses, retain the people they have, and reskill people from declining professions into these future-forward roles. But the biggest opportunity comes from this work redesign.

Okay, fourth one has to do with talent density. This is a new approach, thinking about complementary skills and skills velocity, not just jobs. The job model is an industrial model, where we think every person needs to fit into these jobs. Your job architecture is this complicated construct. When somebody leaves, you try to match another person to that job. But in the meantime, because skills and work are changing so quickly…

Kathi Enderes 44:27 …the job architecture is already obsolete. What the Pacesetters are really thinking about is from a people perspective, from a skills perspective. They say the job model is almost obsolete. They are using talent intelligence to see the skills that they have, the skills that are in the outside market, and then match people to the right skills.

(Addressing a question from the audience) I see a great question from Shelly on the previous topic about redesigning work: “How does one get started redesigning the work?” Excellent question. Where do you start? We always say to our clients and the market: start either with the pain or start with the gain. The pain is where you have a big business problem. Maybe your salespeople are not selling enough, or your software engineers are not developing enough product. That would be starting with the pain. Or you start with the gain: where’s the biggest upside? Where do you see jobs contributing the most to your financial performance or customer satisfaction? That is not one-size-fits-all. Depending on your organization, that’s where you could prioritize. Always start thinking about what problem you can solve, rather than seeing this as a big infrastructure project.

That also applies to this skills conversation. Think about how you can hire for the right skills, as Scotiabank, for example, did when they used skills assessment for campus recruiting rather than resumes. It totally makes sense, because when you think about campus recruiting, most college students don’t really have a lot on their resumes. Think about what skills they have, what transferable skills they have.

Think about internal mobility. Bon Secours Mercy Health is using AI to think about career pathways and internal mobility programs to get people into nursing jobs—where they have the biggest talent shortage — from, maybe, receptionist roles or environmental services, building these career pathways and upskilling people internally.

Think about adjacent skills. BNY Mellon found that they needed next-generation technology skills, and they had people with adjacent skills. Maybe they didn’t have the exact right technology skills, but maybe they knew some of the software products. Once they saw what skills people had, they could move people around, get them projects, get them mentors, and move them into these tech roles that they couldn’t hire for.

Also think about dynamic skilling and this skills velocity—building the skills you need in the future quickly. Coca-Cola Europacific Partners is a great example of using talent intelligence. They call this a “career copilot.” Every employee can see, “What are my skills? How can my skills map into future career opportunities?” And then they get career suggestions. At the same time, Coca-Cola themselves can fill the skills they have.

Always focus on upskilling. L’Oreal is dedicating over 100 million euros every year on upskilling, and they have capability academies, clustering the learning together for people and helping them build these next-generation skills quickly. Bayer is using a talent marketplace as well to move people around dynamically based on the skills they have and that are adjacent to other skills.

Okay. Next, the fifth secret that these Pacesetters do differently. Change is huge in the AI transformation, and we know employees are stressed about it. What the Pacesetter organizations do is think about change agility. They’re not just thinking about how to manage change, like when you had to implement a new ERP system. That was all about change management: you had a due date, you communicated, you trained people, you managed it as a project, and then you were through it. That doesn’t work anymore because, as Josh said, AI changes every day.

Rather than doing this legacy, top-down change management, focus on this bottom-up, employee-driven change agility approach. Maybe you have focus groups…

Kathi Enderes 52:08 …communities of practice, discussion groups, hackathons, or sharing of best practices on how you’re using AI tools. Leverage the Super Manager as well. Leadership is really important there, too. ING has this approach of agile leadership, always thinking about how they can collaborate across the organization for customer outcomes.

Analytics are really important here. To change agility, you need to know what the trends are. You need to always think about how you can connect business data with workforce data to create better transformation outcomes. Toyota is a great example of using data across their entire workforce to drive innovation. Providence is integrating data on employees—survey data, compensation data, turnover data—with patient care data to see what processes they can strengthen to create better patient outcomes.

The last area is rethinking your entire HR operating approach. These Pacesetters really think about HR as a problem-solving discipline, not a service-delivery discipline. It’s a complete mindset shift that we call Systemic HR. How can you focus on “falling in love with the problem”? How can you focus on predicting what workforce needs you’ll have, as Bon Secours Mercy is doing with workforce planning and org design, all based on data? Breaking down HR silos. Rather than thinking about your recruiting team, your learning team, and your compensation team, think about this in a more systemic way, as Unilever is doing — all with a focus on a better employee experience, not just HR service delivery.

Mahindra & Mahindra, an automotive company in India, is using AI agents to create better employee services and a better employee experience, not just to save money for HR, but to focus on manager effectiveness.

All of this boils down to what we call the Four R framework, where you’re bringing together, around this skills velocity, all the different practices of HR. You are recruiting for the skills you need, but at the same time thinking about how to retain the people you have. At the same time, thinking about how you can reskill, create career pathways, and skills programs. And at the same time, redesign the job, work, and employment models with AI.

The last area we wanted to call out for these Pacesetters: They also rethink the role of the CHRO. Josh, our team, and I were just at the Unleashed conference last week in Paris, and we saw a lot of discussions on CHRO roles changing. Companies are redesigning and expanding the CHRO role. Great examples we have here: Standard Chartered Bank, where their CHRO, Tanuj, leads HR, but also strategy, transformation, and all the corporate functions. Or Moderna, where Tracy, their CHRO, leads the digital team and the HR function. Lots of examples of this CHRO role really expanding.

So how do you get started with this? Here are some things to do:

• First, think about this skills velocity, not just skills depth.
• Then think about how you are doing on these six strategies that the Pacesetters are doing, and where you want to get started.
• Incorporate these Pacesetter secrets into your AI transformation.
• Always rethink your organizational and talent strategies, because AI is changing all the time.

The bottom line is: your role in HR, talent acquisition, L&D, or as an HR business partner is more important than ever. This is our time in the sun, as Nicole Lamoureux, the CHRO of IBM, called it. How do you think about your role? How do you step up in this AI transformation and really take this leadership role? Because if work, roles, and skills are changing, this is our time to make a big impact.

With that, let’s see if we have any other questions. I was monitoring the chat, but if you have any other questions, please put them in the chat. I think we have maybe another minute or so.

Kathi Enderes 59:26 We’ll hand it over back to you, moderator, or if you have one more thing to think about, and otherwise, we’ll hand it back over to you to wrap us up.

HRE Moderator 59:39 Thank you all so much for attending today’s webinar. You may disconnect and have a wonderful rest of your day.