In this interview from Appian World 2026, Susan Charnaux, chief people officer of Appian, joins theCUBE's Dave Vellante and co-host Alison Kosik to discuss how AI is fundamentally redefining the workforce — from what belongs in a job description to how organizations are restructuring around human-agent collaboration. Charnaux argues that job descriptions must shift from task lists to vision-setting frameworks, enabling employees to offload repetitive work to agents and focus on creative, high-judgment contributions. Rather than framing AI as a threat, she presents the moment as an opportunity for individuals to pursue more meaningful work — and urges employers to ask not how many roles can be eliminated, but how much more can be accomplished together.
The conversation also explores the debate over whether recent tech layoffs are truly AI-driven or a correction from COVID-era overhiring, with Charnaux noting that workforce constraints can serve as a forcing function for faster AI adoption. She underscores the critical importance of redesigning workflows before embedding AI, explaining that simply inserting AI into existing operational processes rarely succeeds. On hiring, Charnaux details a growing demand for creativity, big-picture thinking and strong interpersonal skills — traits that complement AI supervision rather than compete with it. The interview also addresses a reported gender gap in generative AI tool adoption and examines Marc Benioff's observation that today's managers are the last generation to oversee humans exclusively. From rethinking organizational flatness in an era of agent pools to reimagining career development through apprenticeship-style models, Charnaux provides a human-centered roadmap for navigating the AI-powered workplace.
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Susan Charnaux, Appian
Dave Vellante and Alison Kosik sit down with Susan Charnaux, Chief People Officer, Appian, at Appian World 2026 at the JW Marriott Orlando, Grande Lakes in Orlando, FL.
In this interview from Appian World 2026, Susan Charnaux, chief people officer of Appian, joins theCUBE's Dave Vellante and co-host Alison Kosik to discuss how AI is fundamentally redefining the workforce — from what belongs in a job description to how organizations are restructuring around human-agent collaboration. Charnaux argues that job descriptions must shift from task lists to vision-setting frameworks, enabling employees to offload repetitive work to agents and focus on creative, high-judgment contributions. Rather than framing AI as a threat, she pre...Read more
>> Welcome back to Appian World 2026. We are streaming live here in Orlando. I'm Alison Kosik alongside Dave Vellante. And we are going to be talking about how AI is not just shaping how we work, but who we are when we work.
Dave Vellante
>> Yeah, we are. It seems like all we talk about these days is agents, but let's talk about people.
Alison Kosik
>> Absolutely. They're a part of this. They're part of this equation. And to talk more about this, I want to bring in Susan Charnaux. She's the chief people officer at Appian. Welcome to theCUBE.
Susan Charnaux
>> Thank you for having me.
Alison Kosik
>> Glad that you're here. So we hear a lot about AI changing software and productivity, but let's go ahead and talk about people. How is AI changing the very definition of a job description?
Susan Charnaux
>> Yeah. I mean, it's really fundamental. You have to think about it. When we create new jobs, typically you write a job description and you say, "Here's all the things that you should do." But we're actually now right in the midst of moving out of that from being a list of tasks. Typically would list out, here's what we expect you to do in your job day to day, all the normal things. And instead, this job description really has to paint the vision. We have to ask someone who can really design the future as that job. Because instead of all the manual tasks they would have done, they can now have an agent do that. So it starts really fundamentally with having a completely different picture of what a job entails.
Dave Vellante
>> And tech companies are probably less concerned. I shouldn't say that. There've been a lot of layoffs in tech lately, but generally speaking, tech employees, they're leaning into technology. They're the first to use AI. I think they believe the line that it's going to be somebody who uses AI that takes your job, not AI itself. But outside of the tech industry, there seems to be a lot of consternation. As somebody who's been a people person for all her career, working in a number of organizations that are people intensive, how do you communicate to folks? What's the message to them to
Susan Charnaux
>> Yeah. Oh, I think it's challenging right now. A lot of people are afraid of their jobs and what this will mean for the nature of the work they do and also whether or not they're going to have future employment. But I don't think we have to think about it that way. So I do believe that there's an opportunity for strong positivity in this moment, which is figuring out how AI can help each individual actually do more meaningful work. This should be a moment of great excitement. To instead say, wow, all those things that were frustrating in my job before or that I felt like, why am I here doing this? It's all the things about being in a modern workplace that is frustrating. Oh, I'm on the screen all the time and this is so just repetitive and all the things that are hard about that. Now, instead, you might actually think, "What can I create? What is the good in the world or the good in the interactions that I have with other people in my workforce?" So it's pretty radical actually. Yes.
Alison Kosik
>> So it's freeing up more time for humans to do more creative work and to redirect that energy. You hope that employers recognize that just because they're bringing on automated systems that are doing the easy work. And so how does an employee prove that, especially if maybe employers are thinking it's one for one, we're getting automated and we're going to get rid of 10 people because of this.
Susan Charnaux
>> Well, maybe answer that question in two ways. So there's one part about the employer, what the employer can and should be doing right now to think about that. But also you asked about the individual. So I think for an individual. If you're thinking, okay, AI might come and take my job, what can I do? First of all, use and master the AI tools that are available. So spending the time, maybe even spending time outside of work, figuring it out, learning, experimenting on your own with the things that are available to you. And obviously there's so many things that are available. Even if you don't have the perfect solution for your own work at work, there's lots of things you can do on the personal side which actually show you how to do it. And so that's really compelling. And then when you can start to apply it in your own work, I see this all the time with our incredible workforce at Appian, people are trying things. It frees up their own time because they're just more productive. So they're trying things with AI and then they can go raise their hand and say, "Hey, I can help with some other project or let me help a colleague with something." And obviously that's going to make you stand out longer term and of course make you valuable in the workforce. On the employer side though, I do think employers should be thinking about the bigger picture. If it's going to displace 10 people because people are so much more productive, instead, why not say, "What is the 10 times more work we could do together?" It doesn't necessarily need to displace people, but instead you could go further and just get more done or do it so much faster.
Dave Vellante
>> Well, in our lives, we've all done research. We used to have to go to the library. We'd go through microfiche to find the old, in my case, the old Boston Globes to see what happened in the 1960s. And it was terrible. And then the internet comes along and you're like, "Wow, this is incredible. I could do so much more." And now we're moving from a world where we're talking to AI instead of saying, "What is, how does it work?" We're saying, "Build me this." And that's profound. So my question to you is, how does that change the way you think about you, Appian, your profession of hiring folks? What kind of skill sets are you looking for now?
Susan Charnaux
>> Yes. It really is starting to change. I would say we're at the beginning phases of it still, but increasingly what I'm hearing from our teams and our hiring managers who are looking to hire and Appian is still hiring, we want people who are really creative. Who are good at thinking ahead about what they need, who are going to be, of course, still those great, hardworking, reliable employees, but also have a sense of the bigger picture. And the last thing really importantly is to have strong interpersonal skills. So someone who can help lead a change. Who can help talk to someone else and bring them along in that change. Whether you're working with a customer or you're working internally, that's going to be something really different. So you might need the technical skills still to be able to supervise the AI and look at the process, but then to be able to talk to people about it. That's going to be more important than ever.
Dave Vellante
>> So you said Appian's still hiring. I'm glad you threw that in because there are companies that aren't, you're seeing some layoffs. I wonder if you could give us a perspective on this from your point of view. I feel like a lot of these layoffs that organizations or companies attribute to AI is probably not really AI. It's probably they overhired during COVID.
Alison Kosik
>> They're using AI-
Dave Vellante
>> They got too much capacity. However, I think there is potentially a nuance where they're saying, "You know what? Yes, we overhired. Let's now reduce the headcount and force people to figure it out and close the gap with AI." Do you think that's a valid premise? And it may be a little bit risky, but it's a necessity is the mother of invention, as they say.
Susan Charnaux
>> Yeah. I'm with you. I do think some of this is a little overblown, so pegging it all on AI and therefore we need to make this many layoffs. I think a lot of leaders are under immense pressure to show that benefit really quickly when it might not fully be there yet. And it may be a forcing function either to get people to use AI or just to work harder, spend more hours. So it's a little hard to tell, to read between the lines on that. However, some of those same companies are also still hiring. So I think that tells you a little bit about the fact that maybe they're just reallocating and needing different things than they needed before or in different places. This is why I think, I mean, from our perspective, thinking ahead about how you could retrain people, help direct people to have the new skills that they'll need to be successful in this environment using AI.
Dave Vellante
>> So that forcing function maybe is a viable technique. I mean, Elon's at the other end of the spectrum, delete, delete, delete, and we'll fill it in later, seems to have had results irrespective of what you think about as politics, but so maybe it's a viable strategy. It's like, "Hey guys, yeah, we're overworked. Let's figure out how we can apply AI to solve this problem."
Susan Charnaux
>> It could. So I think when you have a constraint, it does force different choice making and you have to think about how can I use AI and get creative and figure it out. At the same time, companies have always... I think exiting 10% of Google or whatever is not... It was not even that. It was 10,000 employees. It was far less. So I think many companies just in a normal performance cycle might do something like that. And so it's hard to say.
Dave Vellante
>> Yeah. GE used to every year purposely fire 10% of their employees.
Susan Charnaux
>> Yeah, exactly.
Dave Vellante
>> That's just a matter of practice.
Susan Charnaux
>> Exactly. But it does raise the question of how can you help encourage leaders of teams to figure out AI faster. I think that's what everyone is trying to do is to say, "Okay, we know these tools are available. How can you now put into workflows?" And I mean, that's really what we're partly talking about here at Appian World, is that it's not necessarily an intuitive when you just have this one block of AI, now go do it in your workflow. Yes, for software developers who are creating code, it's easy. They can code a lot faster, but if you're running a big business with a lot of handoffs and many operational processes, it's not necessarily easy to just stick AI in the middle of that. You have to rethink the workflow and redesign the work first, so that's hard.
Alison Kosik
>> Or incorporate training or upskilling.
Susan Charnaux
>> Yes. Yes.
Alison Kosik
>> I mean, at what point do employers have to take some responsibility for that?
Susan Charnaux
>> Oh, they must, yeah, to figure out. I mean, but I think this is where it comes, redesign the process, think about the process you really need, have AI in that and then figure out what is the work that the humans are going to be supervising and doing and arranging around it that will lead to then lots of training that has to happen. It's an interesting question though, even how training is changing with AI now.
Dave Vellante
>> I was going to ask you about that. I think it's sports analogies all the time. And I think in the early parts of whatever sport you're playing, you don't necessarily get into the specific plays or the technique. You basically just get in shape, run backwards, do karaokes because that's the motion that you need. And I wonder if it's just a matter of exposure to these different things because things are changing so fast. If you say to someone, "Okay, this is how we're going to do it with AI." And then all of a sudden something like DeepSeek or OpenClaw comes out, it's like, "Whoa, now we move over here." So it's like getting some kind of general muscular readiness-
Susan Charnaux
>> That's right....
Dave Vellante
>> training, exposure, what's possible, and then maybe best practice comes out of that. Thoughts on how you've changed training?
Susan Charnaux
>> Yes, definitely. Well, there's a lot of just grassroots experimentation happening. And I think as much as possible, seeing our own employees come forward with their ideas and solutions based on what they're experimenting with and just try... I think it's one thing to talk a lot about this. And try and say to people, "Go learn AI, learn AI." No, just do it. As soon as you put it into practice, then new possibilities emerge and a new idea comes up that you can say, "Oh my goodness, well, we can actually completely rethink this whole piece of work now that we've done it." In terms of training, we went through a big era of e-learning, everyone doing e-learning courses and courses online and remote learning. Probably many of us live through that. This is going to radically change that too. So we put a lot of effort into training all of our Appian developers and our broader ecosystem of developers. This is going to change it as we think about how people will learn differently.
Alison Kosik
>> So there have been these studies, these gender studies showing that men are more involved in using AI tools, generative AI tools more than women. And there's one that shows that 20 to 25, that women are using generative AI tools roughly 20 to 25% less than men, partly because they say they feel like they're cheating or it's overwhelming, they don't know where to start. I mean, what do you think about that with workforces in general where as these stats come out more and more, does this mean just in general, we're going to see fewer women in the workforce? I mean, I know that's dramatic, but the statistics are a little jarring.
Susan Charnaux
>> Yeah. I haven't seen the statistics. That's really interesting to learn more about that. I mean, I'd say anecdotally, my own team, it's women who are coming forward with the ideas, which is really great and experimenting themselves. So I think it may be too early to tell on that. And I would have a lot of faith, especially in the current generation of women taking on a lot.
Dave Vellante
>> It's interesting that-
Susan Charnaux
>> AI, not being afraid of it.
Dave Vellante
>> It's interesting, Alison, you come from that angle because you wrote a book on women's finance because you had to take control of your own finances where you didn't have great experience and said, "I can do this." And so maybe something similar happens-
Alison Kosik
>> With AI, yeah. There is similarities. Yeah.
Susan Charnaux
>> Yeah. It could actually be a great advantage for women in the workforce, I would think, as we think about the nature of the work and what people are spending time on.
Dave Vellante
>> Marc Benioff wrote an article in the Wall Street Journal saying, "We're the last generation of managers that will be managing humans only." So it was about a year ago, maybe a little less. What do you make of that and what does that mean for organizations and managers?
Susan Charnaux
>> Yeah. Well, maybe reflecting on that, I think it might not be the first time in some ways because we had an industrial evolution and humans were managing machines also. So I think that was probably a similar leap forward where people had to fundamentally rethink work. And there was a whole practice of the philosophy of management and the structure of corporations and industrialization that resulted. I mean, fundamentally changed society, actually, pretty amazing. So we are probably just on the cusp of that and we are going to learn a lot more about what it means, but it didn't mean that humans went away entirely in the work either. We had to instead learn to relate to people differently and structure our work. I think we're going to have a lot of difference in how we structure teams now and how someone grows their career. So probably much more like an apprenticeship model or something else where you might have one expert and one person learning from them beneath, which will be very different than the big flat teams that we had in the past.
Dave Vellante
>> Interesting. You mentioned flat and teams. I wanted to get your perspective on flat versus hierarchical. I grew up in an organization at IDG. IDG was the publishing arm of Computerworld, InfoWorld, all those magazines, very flat organization, exceedingly hyper decentralized. That was the corporate philosophy. Others, actually Ziff Davis, which is a big competitor, had a very hierarchical structure, both worked. You're starting to see, I think, more flat organizations, but you suggested maybe the opposite. I wonder if you could expand on that.
Susan Charnaux
>> Yeah, yeah. So I do think it's a dilemma. I think flatness, yes, and Appian too has become a flat organization as much as possible. We really believe in this that you shouldn't have a lot of layers between you and the top. But what does this mean when we insert agents into that workforce? So I think a manager can certainly supervise a large group of agents and that will be your flat. So much work happening, one off, managing different processes and figuring that out. But the expert who's designing the process and figuring out the workflow or supervising it and looking across to figure out how to edit, how many other humans will do that with them. I think it means that we can take on a lot more processes and cover so much more territory.
Dave Vellante
>> Well, that's interesting because the whole concept of the flat organization is push decision making as close to the customer as possible, but if it's the agents making those decisions, I'm not so sure we're ready for that.
Susan Charnaux
>> No, I don't think we are. I think agents with human supervision, with the guardrails and controls, then yes. Yes, but it has to be set up in the right way.
Dave Vellante
>> So the organization, the human aspect of the organization could get much flatter because instead of... The max number of reports that a human can handle is maybe eight, maybe 10. Sometimes you see 12. I know Jensen's got 50 or something, but that seems remarkable. But generally speaking from experience, I mean, eight to 10 gets really hard, just doing performance reviews on 10 people.
Susan Charnaux
>> Yes.
Dave Vellante
>> But if you have an army of agents, you can maybe flatten that organization out even further.
Susan Charnaux
>> It might look different. Yeah, it might look different. Or each of your direct reports will have their own pool of agents. Yes.
Alison Kosik
>> All right. Well, such a great conversation, so timely. I mean, it's going to continue to be timely as the workplace evolves. Thanks so much for your time.
Dave Vellante
>> Thanks, Susan.
Alison Kosik
>> Thanks for stopping in on theCUBE.
Susan Charnaux
>> My pleasure. Nice to talk to you.
Alison Kosik
>> Nice to talk to you. And you've been watching theCUBE, the leader in high-tech enterprise analysis and live coverage. We'll be right back.
Alison Kosik
Dave Vellante