Disintegrating the Org Chart: ServiceNow’s Jacqui Canney
Me, Myself, and AI · 7 Apr 2026
Source: https://sloanreview.mit.edu/audio/disintegrating-the-org-chart-servicenows-jacqui-canney
Transcript: https://podcasts.opennash.com/shows/me-myself-and-ai/disintegrating-the-org-chart-servicenows-jacqui-canney-99f258/ (via OpenNash Podcasts, machine-generated)

We hear a lot about using agents for workflows. One company has eighty, zero active workflows and believes it's making innovation, employee experience and other aspects of its business better with AI. Learn more on today's episode.

I'm Jacqui Canney from ServiceNow, and you're listening to me, myself and AI.

Welcome to Me, myself and AI, a podcast from MIT Sloan Management Review, exploring the future of artificial intelligence. I'm Sam Ransbotham, professor of analytics at Boston College. I've been researching data analytics and AI at MITSMR since 2014, with research articles, annual industry reports, case studies, and now thirteen seasons of podcast episodes. In each episode, corporate leaders, cutting edge researchers and AI policymakers join us to break down what separates AI hype from AI success. Hi listeners, thanks again for joining us.

Today I'm talking with Jacqui Canney. She's the Chief People and AI Enablement Officer at ServiceNow. She leads all talent strategies for the company's rapidly growing global workforce. We've known each other for a few years and I'm glad the timing finally worked out for us to talk with our microphones on. Jacqui, thanks for joining us.

Thank you, thank you, Sam, for having me. I'm really excited for this conversation.

Could be fun. Well, let's start with ServiceNow. It's huge, S P one hundred, but some listeners might not be familiar with all that the company does. Can you give us a bit of background?

Sure, I'll start with what our purpose is, which is to put AI to work for people. And at that core, we are the AI platform for business transformation. And if you think about automated workflows, you think about the ability to drive your business results. It comes down to how you direct work. And our platform is literally built on AI so that we can help companies in. I think it's now 80 billion workflows that we manage that produce either better service, more analytics, all the things that you know companies are seeking to do with their organizations.

I was a customer of ServiceNow, so that brought me to really be excited about working here, too.

You really led with AI right there. How did that happen? We're just relatively few years into this whole AI world. How do you have 80 billion? I thought for a second that just seemed like a huge number. How do you have that many workflows using AI already?

We have a very innovative company, it's twenty two years old, I wanna say, and was built on how to help people experience work better. Fred Luddy, our founder, built the first workflow for a colleague who was struggling with the swivel chair, you know, getting work done in Excel spreadsheets, et cetera. So at our core, innovation has been something that we've always tackled. And you've seen the movement, like analog to digital, prem to cloud, cloud to mobile, now this conversation to AI.

And ServiceNow has had these amazing engineers and product leaders who've been thinking about this for a long time, a long time before even people talked about, like, chatgpt.

Maybe give us an example, like, what is one of these 80 billion? And how does artificial intelligence involved in that?

Yeah, so I'll take one in my area that I see a lot. When somebody gets hired to work here, there's lots of steps to onboard people, and that can be a lot of conversations. It can be different managers, different departments. But with our onboarding platform, you, you know, say, Hey, this is the person that's starting. This is the kind of computer that they want, this is the kind of cell phone that they need. This is the training they need to have happen, the proof of identity so that they could be paid, that they get paid, that they show up and they're feeling productive before they even start on that day.

And then what happens post that onboarding, cause there's follow-ups, you know, reminding a manager, Hey, so and so started ten days ago, why don't you check in? Or so? And so, you know, got their first kudos, you know, a recognition, like, why don't you check in and see how they're doing? So it's an automated workflow that takes the guessing and makes the manager and the employee really feel a relationship right at the gate that's personalized.

So I guess in that process, then, where was artificial intelligence? How does that fit into all those steps?

So you can have an agent, you know, if I say I want a MacBook, it makes the order, the agents get the order done, the agents get the order shipped to your house. It's agents working in the background while people are able to focus on what they need to, which is welcoming this great new employee.

Yeah, that seems like a good separation of task of the classic getting rid of from the dirty, dull and dangerous parts to the things that humans are better at. So tell me a little bit more about how would you organize a process like that? Because, you know, I think I would be tempted to get the whatever computer or phone I wanted without oversight, perhaps. How do you integrate that?

It's a really great question, cause it does bring it down to practical, like, how do you get this work done? And there's governance, you know, that is built into the platform. You're creating that governance as a leader when you implement the technology. So, you know, like price points, options, you know, whatever it is that your company is governing, gets embedded into the choices. But also there's design, which is something that maybe not everybody thinks about when you talk about platform technology.

But designing the experience is equally as important. So that it's not just about here's what the CIO is trying to get done, here's what procurement's trying to get done, here's what HR is trying to get done. But putting the person at the center, the manager and the employee, and designing a process that's really great for them. And we also have it so you could do it on your phone is at its core with the right governance around it.

And then if something goes wrong, cause that can happen, too, what's the feedback loop? If the wrong computer came or it didn't come in time, or you know that we can get the signal so that we can continue to improve our process and certainly find where a process flow might break down so that you can correct that in the tech.

Yeah, that makes sense. So how much do people then know? Let's go back to your new hire example. How much do they know that artificial intelligence is involved in this process, or where is it obvious, where is it not obvious?

It's becoming less obvious is what I would say. We've acquired a company called MoveWorks, which is in it of itself, a front door conversational experience. Earlier versions of our platform would feel potentially more like I'm interacting with technology. I'm searching, I'm getting directed to KB articles, things that were all easier, not perfectly seamless. Now, this conversational layer, which we've implemented for all our people, is like going to search. You know, you go to it and say, Hey, I'm meeting with Sam.

What was the last meeting that we had? And it's literally having this conversation. So I think it's becoming less clear if you're talking to a person or you're talking to tech, which is making it really easy to get to the answers that you want.

Actually, one of the things I think about, maybe this is just my own personal weirdness, but I feel like I interact with people differently than I do with machines. Like, for example, if I was talking to you about getting a computer, I might say, Oh, hi Jacqui, how are you doing? You know, it's sure, a snowy here, it's it's really cold. Uh, I was thinking about getting a computer. On the other hand, if I was talking to a machine, I might be a little bit more brusque and say, you know, buy a machine now, you know, uh?

And maybe the robot overlords will come back and get me for that. But it seems like there could be some efficiency in being transparent about, Hey, you're talking to a machine. You can drop the conversation about the weather, perhaps, or the social glue.

and it's funny. You can have sort of social conversations with the machines, too. You know, it can recognize if you're stressed or in a hurry, you know the tempo of our voices, and it it directs into, you know, sort of responding in that way. You also can find a way out to talk to a person, like you can click through to get to a person. So that way you can, you know, get out of the whatever chain of conversation that you're in.

It's one thing you bring up, though, that I do worry a little bit about us as humans. In if we are abrupt with the machine, are we gonna forget and be abrupt with each other that we're talking to the human? And I think that's at the core of what I've been spending a lot of my time on. Is that there's a lot of technology talk, there's 80 billion workflows just with us. But without getting the change management of the users right, whether they're your employees or your customers, or the end users of your technology, I think is where that's where the concern for me.

That's what I've been thinking about.

I hadn't thought about the spillover the other way, but that's a good point that, uh, maybe I'm becoming brusk or to my human.

You're humans.

Yes, uh, okay, well, now I got a new thing to worry about. So how much do these employees need to know about artificial intelligence? What's your thinking on how much awareness people need to have of these technologies in order to be successful?

We've invested quite a bit in this space. Every person who works here has had AI training, and we've been doing this for a couple years. Which one? Because the products we build, no matter what part of the company you're in, understanding what AI is, have a common vocabulary about that was really important to our CEO and our leadership team for the company. We've invested, having from speakers to AI day to different kinds of training, and we've evolved quite a bit now, where we've assessed the whole company on AI skills.

And it's not like one size fits all. Different roles have different expectations and different experiences, so we've customized the assessments and built personalized learning journeys so that people can grow their skills. And we've seen the our organization really lean in and be excited about that. We also celebrate people who use AI tools really frequently because they're learning from each other. I want to eliminate as much fear in the workforce about what AI is and what we're using it for and how we can use it in the future.

And I think by being transparent, by offering opportunities, by giving people learning experiences, even for myself, I've been seeing more confidence grow. And we ask our people all the time, how are they feeling? And they feel pretty strongly that they're getting the tools that they need. So we're gonna keep at it.

Actually, there's like four or five things that I wanted to follow up on there. You mentioned lots of good topics. Maybe the first one I'll start with is like, just how much do people need to know, like, what kinds of things? You mentioned vocabulary, I think, was one of the things you mentioned, which makes sense. I mean, we need to be able to talk about technology in ways that make sense to communicate with each other. But what are these skills that people are trying to pick up on?

Yeah, so, like, prompt engineering is something we all have been talking about, and it is not something we talked about that long ago, right? And you have a team, like in my organization, which is a human resource people team. And we have implemented obviously our own tech, and we were able to double the productivity of what my team could do. It was one to 400 to one to 900 that we're serving because of the tech.

Now, I didn't want people to be displaced because of that, but then they became better at either a couple things. One is prompt engineering, so that they could help create better questions that they're asking so that we can get better answers. And then train AI so that it's continues to be better answers. Cause over 90 of our inquiries that go to our now assist, which is our own tech, get answered by the tech. So the more we can make that smarter and better, the more people will be happier to use that.

And then we also created new roles. It's just adjacent skills that I've seen the team lean into. You know, we have product engineers and product designers inside HR. We didn't have that before. We've built a new role called forward deployed engineer, which is somebody who is quite technical, but has a interest and a desire and is really great at talking about business problems and business transformation and marrying those conversations together. So you can imagine talking to an HR lead, a CIO somewhere out there that is using our tech and they know they have this problem they wanna solve or this opportunity to fix.

Now we've built a workforce that can go meet with that team, talk about their problem and then say, here's how we suggest the technology can solve the problem versus saying here's the technology. Like, work it around it and work it into your solution. It's more in service of the human.

Yeah, those are some interesting numbers, like the one to 400 to one to 900. And your first reaction would be okay, yeah, that's gonna lead to reduction. But as you point out, there's just a bunch of new tasks that are coming up and new roles that are coming up as quickly as we maybe whack-a-mole a little bit, you know, you're trying to eliminate some work and new work's getting created. What's your sense of the net?

Like, if we're moving from reducing things that people are needing to do by, let's, sort of the two to one-ish type of number that you mentioned, but you mentioned new roles too. Like, it seems like a big deal. If that is a one to one swap, a one to a half swap or a one to two swap, like that's big. Which direction is it right now?

A crystal ball would be really good on that one right now. I think every company's tackling it in their own way. I think that at its core, some companies have gone after this with a cost cutting lens. And I don't think that's the way I would start. If someone asked me, I really think the opportunity, as has in history, technology provides capacity and creativity, hopefully, or new adjacent business lines, the things that can grow. I've seen it not just here at ServiceNow, but in even in my old job at Walmart, where you could see where you implement this powerful tech.

But it does create expansion and the hard work is the work redesign that has to happen. And that's where leaders, CEOs, chief people officers really should be spending their time. Because I think whether it's a one to one or you're flat or you're growing, you gotta design that future. And if you don't design it, you'll lose the capacity, is what I think.

And I think I was too sort of crude to say, is it, you know, net plus or minus? I'm sure in many areas it's plus, and many areas it's minus. And then we're looking at the net of the net across a big aggregate. Uh, yeah, the crystal ball is not not quite, uh, polished enough for that. So I think this training program you mentioned is part of the ServiceNow University. And I like the idea that you're, you mentioned the skill assessment as part of that.

But at the same time, you also mentioned just a second ago that prompt engineering wasn't something you were paying attention to a couple of years ago. So we have the changing skills of people and the changing needs of people. How often are you measuring these things? How are you measuring these things? What kinds of? I mean, just the details on this seem very difficult in a rapidly changing world.

Yeah, well, we have jumped on this with all of ourselves, the board, our CEO, the leadership team. Everybody's fully supportive of the changes that we're making and that we're driving inside our own company. And this assessment was important. I felt like we needed like an X-ray of the company to know where we were to be able to go forward. And we didn't use it as anything scary or a negative. It was really meant to be like, we're all gonna get smarter about what we know we have as skills and what we know we're gonna need.

And then if you take what we're gonna need, you're able to see. And this is with the help of Pearson. They've been a good partner to us to say, like, here's the jobs, here's the skills, here's the new work that you're planning, and then here's the gaps you need to close. So it's very personalized, but it's also how we're moving our change management through as a company. And I have other HR leaders that I, uh, really, you know, sort of love working with, and and we all talk all the time about how they're tackling it.

And I think commonly that's what I'm hearing my peers talk about, how we're sort of going after it. It's like your X-ray, your gaps, what can you build, what's adjacent, who can you train, who can you grow, who do you have to hire, you know?

Actually, I'm ready to do. You let outsiders take this? I'm ready to, I'm ready, I'm ready to sign up. Cause, you know, one of the things like, I screw up a lot of stuff. And I. It would be so nice to know ahead of time of the areas that I'm, you know, I always think about this in the, you know, one incremental hour. If I had one extra hour, what would I do with that hour? And I lots of times, I just don't know what the right thing to learn is, or the new thing that would help the most.

And I'm fascinated by the promise that this technologies could help us learn about these things.

yeah, well, ServiceNow University's got a lot of free courses out there. You can go check it out. I'd love for your feedback about it.

Great, so you gave me homework, that's no Yeah, there fun.

you go.

One of the things you've talked about is soft skills, and so, you know, the idea of a soft skill versus hard skill. Well, so first, what are your thoughts on the relative importance of those two types of skills going forward?

I have always believed that critical thinking, the ability to, like, pattern, recognize those things that you learn, whether it's through your work, your university. You know, all the experiences that you have are never more important than they are now. And I know lots of people are talking about that, and it's not meant to be like an easy thing. Not everybody has those skills, but people can be nurtured, I think, to learn better how to create those skills.

And one of the things that I've been really kind of thinking about is we talk also a lot about leadership, and we've all talked about leadership for a very long time. But now, more than ever, the ability to find the people with that have the wisdom is really important. So if you're leading a company or you're leading a team, it's never been harder. Everything's really complex, people are on the road, people are hybrid. We still have some Covid stuff that we're dealing with, you know, and now you have this really important technology that's kind of hit everybody's desk.

But at the same time, the world is moving faster than ever. So how do you have the confidence to, literally, like, pattern, recognize, have the wisdom to say? These are the use cases I wanna go after, as opposed to these are just the use cases that everybody's bringing to me. Because that's where I think these soft, they're not so soft, but they're really important non technical capabilities that we all should be focused on growing.

That's interesting. Yeah, we had Taylor Stockton, who was a former student of mine on a previous episode. He's worked at the Department of Labor. And we were asking them, you know, hard skills, soft skills, and so he, he talked for a bit about soft skills and the importance of that. But then at the same time, he said, Well, you know, but people also need those technical skills. So so what's your take? If I have one hour this afternoon, should I spend it on developing a soft skill or a hard skill?

Or listen, not me, you know, don't pick on me. One of my students who wanders in here, what's the one hour? Where do we spend it?

Oh, I think the one hour, I might say 30 min on. What are they curious about with the tech? So is it protocols? I think protocols is gonna be the next thing we're all talking about, like, how do you govern the agents inside a company? That's really important, so understanding the nature of how do you build and create protocols is not something you need to be the computer science person to do. And then the second is, I do really think the ability to drive this critical thinking.

I'm absorbing problems, I'm absorbing information. How am I able to take that and process that into a an idea or a point of view? And, you know, I think the world of my university, and that was a lot of how we were taught was not just to be great accountants or great finance people, but also to be great thinkers. And having that be part of what you're thinking about, if you have one hour, I think it's worth it.

Have a ton of students who are about to graduate, they're talking about difficult job markets, and I know you get asked this probably every time someone talks to you given your role. But what should students who are close to graduation be doing? What should they be thinking about as they enter this job market?

Yeah, I think two things are really important, and one is what are the skills that they're taking out of their university experience. When you go to work at a company, they're gonna teach you a lot, they're gonna teach you how to work, they're gonna teach you a lot about that company, about how they work. But if you can come out of school with one great skill that you're super proud of, it could be you're a great writer, it could be you're a great coder, it could be that you are a great speaker, like whatever it is.

But really know what that skill is and how you're gonna sell that into an employer that you're gonna work at. You're probably more AI native than anybody else in the company because of the nature of how you're growing up and like the world that you're in already, so that's also on your side. But the second thing is growth mindset, demonstrate your ability to learn and change and be agile. Because I've also said, and I don't have this written down because somebody told me.

But the companies with the best language models are not gonna be the ones with the most adaptive agile workforces. So I look for those kinds of qualities, especially in the early and career talent that I get to meet.

I like that, it's hopeful, and, you know, and that I think your point about how well prepared students are. I love job descriptions that have something like, you know, needs thirty years of experience with large language models, you know, like it's just not possible. And so the students graduating now are just as, or maybe probably more familiar with this technology than than many of us are. So that kind of leads to some thinking about. I was thinking about blind spots.

You've been working at Walmart, WPP, I think, uh, ServiceNow. What are people getting wrong? What you know, what are leadership blind spots here when people are thinking about artificial intelligence?

Well, I think focusing on the tool and not the talent is one of the top things people really get wrapped up around. Like, what's my AI strategy? where it's really your business strategy, and then how does the business use technology? But certainly, how does it bring its people along with it? And that gets missed a lot. I would say I talked about the cost cutting exercise. I think people get that wrong when they lead with that.

Waiting for a perfect plan is another one. I think people get stuck in. I know, sometimes even I do, right? You know, it's it's like you don't have this all figured out. Like you said, thirty years of LLM experience, where's that gonna come from? That doesn't exist yet.

Ooh, I feel seen with that one. Yeah, right. And Yeah,

I think people skip the hard parts, they skip the culture, they skip the trust, they skip the people part. I feel like that's the stuff that I've seen go wrong.

hm, hm, hm. I think there's a lot of ways to screw this up, too. I mean, there's just a lot more ways to get things wrong than there are to get them right. You know, your idea of not having a perfect plan to start with feels wrong. Well, I was reading something that you that you'd written about a, I think it was a family trip where you had AI write a poem for the family trip. Okay, and I, I was thinking about that.

It it struck me as funny, cause we actually just for a cringe moment, I had my classroom write a theme song for our ML, you know, machine learning class. You know, what would generative AI say is a good theme song for our class? And we did not all recite the class anthem afterwards. But you said that surprised you is something that the tool could do. What's surprising people about what these tools are capable of? What are the things that people are learning?

Aha, from these

yeah, I mean, I think it's the ability to be better prepared for X meeting opportunity. We have seen in our sales organization where they have access to all the data about our customers, about the work that they've been doing. And now how to prepare for those meetings in minutes and not days has been, I think, really exciting and eye-opening. And people are loving that because it's just easier to get to answers quicker. The other thing that I s I saw that people were super excited about, especially in our sales organization.

It went from like four or five days to find out what your commission's gonna be to eight seconds. And so if you have a workforce that's motivated to know that, making that easier has been like a great, well-received, you know, use of what the technology's been able to do in the day-to-day. And I probably could think of a bunch more, but those two come closest to me right now.

actually, I like the quick feedback part, cause, as as you were thinking about earlier, you were talking about assessing people's skills, and I was thinking about testing, you know, in how in the education world, we do a fair amount of testing. And one of the things I was thinking, as you were saying, that is that students actually don't dislike tests. Now I'm sure people are freaking out right now as I'm saying that. But people like to get feedback about what they know and what they don't know, people like quick feedback.

And so the same thing with your commission example there, if you do something and you get feedback quickly, then that helps us reinforce, it helps us know what to do better. I mean, HR is historically driven by the idea of the annual performance review, which, you know, 364 days ago, what did I do right or wrong? Is a, you know, I don't learn very well from that. And that's a the example, I mean, you were mentioning commission, but the example of quicker feedback.

Both of those, though, I mean, I'm gonna push back a little bit. Both of those feel like productivity enhancing, but we said earlier that there's a bit of a trap of getting too sucked into productivity. But faster meeting preparation, faster readiness is good, faster feedback is good, but both of those feel like productivity. What would be the missing thing that we would wanna add to that to make it a non productivity?

I think it would mean like the sale got better, bigger, you know, if I would had all the things I maybe before wouldn't have known about. Like, what did they say on LinkedIn? What's the stock price doing? What's the biz? You know? Like, there's an opportunity to not be incremental, but to be more impactful. And maybe the sales commission one is a little bit about productivity, but I think it's also highly motivating. That might, you know, get the salesperson to say, if I could just sell this much more, look at what my commission could be, and then lean into being better prepared for that.

I think, too, that I've seen us think about leadership in a different way that I'm not sure. Without AI, we would've had the capacity to do like we have really stepped up. What does it mean to be a leader here? And invested in that bigger than I've ever seen, because we know that that's really the unlock for the organization. And I think because of AI, maybe creating the capacity, even for my own team, to be able to dream a little bigger about what the future of leadership.

And I, you know, this concept of wisdom, I see that opening too. And I would say this lane of opportunity is what we still haven't figured out yet. What are we gonna build? Are we gonna build a new business? Are we gonna have totally different companies that are created? Like, that's what I think we're on the cusp of figuring out.

Hm, well, I don't know about figuring out. So you've touched on this. I mean, you're obviously from a human resources background, but you're talking about a lot of stuff that feels like you're stepping on some IT toes here. So how is this relationship between these formerly quite separate parts of organizations? What's the relationship gonna be as you're using more of these tools? As they're, yeah,

I think AI is disintegrating the org chart, and, you know, not just between HR and IT. It's sort of coming across a bunch of places, cause it just doesn't see that way, it doesn't see silos, right? It sees across and leaders are having to get comfortable with that. It doesn't mean that the roles aren't important, it's just that they're changing. And here at ServiceNow, I was promoted to AI enablement officer along with the chief people officer role just a little bit over a year ago.

And that was because Bill felt like this is truly a human capital moment. It doesn't make me in charge of it all. I'm the team captain. I'm not alone, but I have to have sort of, like, keeping score of how we're doing with that. And I think that's says a lot about what he sees as a guy who's seen across technology for decades of where change really gets. Now, our CIO, our product team, we work really closely, and we have agreed that the employee experience sits primarily with me and my team.

So how technology, how processes, how policies, how all that impact the experience, we're kinda like the filter on it. We work really closely together, we have very transparent look at what use cases are in productivity across the company, who's driving ROI, who's not. We have a control tower for that. I think that that, you know, kinda keeps us all square because we can see very openly what's happening. But yeah, HR roles are totally evolving. If you're an a CHRO who's really focused on process and policy and annual cycles, the CIO's gonna come for you.

Yeah,

we have a little segment where we ask quick questions, just answer the top of your mind. What about artificial intelligence? Is moving faster or slower than you expected?

moving faster in headlines, moving slower in, I'll say, scalability.

Hm, yeah, getting something across an organization, I'm sure you think about that a lot. How are people using AI poorly?

I think they're writing poems like I did, Say, Okay, like,

all right, I'm, there you go. What do you wish that AI could do better?

Hm, I wish it could. I I think it's getting there, but it's like the context and memory of being better. But I think that's maybe more even how humans are using it. It's like how I truly can make AI be a digital twin of me. I haven't figured that out yet.

Hm, hm, hm. Are you finding because of AI, you're spending more time with technology or less time with technology?

I think it's just in the flow of work now for me. I'm not really discerning, am I in the tech or or not?

Hm, hm, hm. But this has been fascinating. I think one thing we'll come back on is this idea that the use of artificial intelligence is eroding these org charts. I think that's a really interesting, high-level thought, uh, to come away from this. Thanks for taking the time to talk with this.

Thank you, Sam, this was great.

Thanks for joining us today. On our next episode, I'll speak with Peter Corte, chief technology officer at Siemens, and we'll talk about industrial AI. Please join us.

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