In this episode of the Me, Myself, and AI podcast, Sam Ransbotham is joined by Jacqui Canney, chief people and AI enablement officer at ServiceNow. Jacqui outlines how the software company has embedded AI agents into processes like employee onboarding to automate tasks, personalize experiences, and free up people’s time to focus on higher-value work. She emphasizes that successful adoption of artificial intelligence requires strong change management, workforce training, and a focus on talent — not just technology — including companywide AI skill assessments and personalized learning paths. Tune in to learn why Jacqui sees AI as a human capital opportunity.
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Transcript
Allison Ryder: We hear a lot about using agents for workflows. One company has 80,000 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.
Jacqui Canney: I’m Jacqui Canney from ServiceNow, and you’re listening to Me, Myself, and AI.
Sam Ransbotham: 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 MIT SMR since 2014, with research articles, annual industry reports, case studies, and now 12 seasons of podcast episodes. In each episode, corporate leaders, cutting-edge researchers, and AI policy makers 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.
Jacqui Canney: Thank you. Thank you, Sam, for having me. I’m really excited for this conversation.
Sam Ransbotham: [It’s] going to be fun. Let’s start with ServiceNow. It’s huge, [an] S&P 100 [company], but some listeners might not be familiar with all that the company does. Can you give us a bit of background?
Jacqui Canney: Sure. I’ll start with what our purpose is, which is to put AI to work for people. At that core, we are the AI platform for business transformation. If you think about automated workflows, you think about the ability to drive your business results, [and] it comes down to how you direct work. 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 companies are seeking to do with their organizations. I was a customer of ServiceNow, so that brought me to be really excited about working here, too.
Sam Ransbotham: You really led with AI right there. How did that happen? We’re just [a] relatively few years into this whole AI world. How do you have 80 billion [workflows]? I thought for a second, that seemed like a huge number. How do you have that many workflows using AI already?
Jacqui Canney: We have a very innovative company. It’s 22 years old, I want to 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 of getting work done and Excel spreadsheets, etc. So at our core, innovation has been something that we’ve always tackled. You’ve seen the movement — analog to digital, [on-premises] 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, even before people talked about ChatGPT.
Sam Ransbotham: Maybe give us an example. What is one of these 80 billion [workflows], and how is artificial intelligence involved in that?
Jacqui Canney: I’ll take one in my area that I see a lot. When somebody gets hired to work here, there [are] lots of steps to onboard people. That can be a lot of conversations. It can be different managers, different departments. But with our onboarding platform, you say, “Hey, this is the person [who’s] starting. This is the kind of computer that they want. This is the kind of cellphone that they need. This is the training they need to have happen, the proof of identity so that they can be paid, that they get paid, that they show up, and they’re feeling productive” before they even start on that day. And then [you include] what happens post that onboarding because there [are] follow-ups, [such as] reminding a manager, “Hey, so-and-so started 10 days ago. Why don’t you check in?” Or, “So-and-so got their first kudos, a recognition, [so] why don’t you check in and see how they’re doing?” It’s an automated workflow that takes [out] the guessing and makes the manager and the employee really feel a relationship right at the gate, that’s personalized.
Sam Ransbotham: In that process, then, where was artificial intelligence, or how does that fit into all those steps?
Jacqui Canney: You can have an agent [that] 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. Agents [are] working in the background while people are able to focus on what they need to, which is welcoming this great new employee.
Sam Ransbotham: That seems like a good separation of tasks, the classic getting rid of the dirty, dull, and dangerous parts [in favor of] the things that humans are better at. Tell me a little bit more about how you would organize a process like that. I think I would be tempted to get whatever computer or phone I wanted without oversight perhaps. How do you integrate that?
Jacqui Canney: It’s a really great question because it does bring it down to [the] practical, like, how do you get this work done? There’s governance built into the platform. You’re creating that governance as a leader when you implement the technology. Price points, options, whatever it is that your company is governing, get 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 is trying to get done. Here’s what HR is trying to get done.” But [by] 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 — at its core … the right governance [is] around it.
Then, if something goes wrong, because that can happen too, what’s the feedback loop if the wrong computer came, or it didn’t come in time? Or [how can we] get the signal so that we can continue to improve our process, and certainly find where a process flow might break down so that [we] can correct that in the tech?
Sam Ransbotham: That makes sense. Let’s go back to your new-hire example. How much do people know that artificial intelligence is involved in this process? Or where is it obvious, and where is it not obvious?
Jacqui Canney: It’s becoming less obvious, is what I would say. We’ve acquired a company called Moveworks, which is in and 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 [knowledge base] articles, things that were all easier [but] not perfectly seamless. Now this conversational layer, which we’ve implemented for all our people, is like going to search. You go to it and say, “Hey, I’m meeting with Sam. What was the last meeting that we had?” 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.
Sam Ransbotham: Actually, one of the things I think about — and maybe this is just my own personal weirdness — [is] I feel like I interact with people differently than I do with machines. For example, if I was talking to you about getting a computer, I might say, “Oh hi, Jacqui, how are you doing? It sure is snowy here. It’s really cold. 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, “Buy machine now.” Maybe the robot overlords will come back and get me for that. But it seems like there could be some efficiency in being transparent: Hey, you’re talking to a machine; you can drop the conversation about the weather, perhaps, or the social glue.
Jacqui Canney: It’s funny. You can sort of have social conversations with the machines, too. It can recognize if you’re stressed or in a hurry [by] the tempo of our voices, and it directs to responding in that way. You also can find a way out, to talk to a person. You can click through to get to a person. That way, you can get out of whatever chain of conversation that you’re in.
One thing you bring up, though, that I do worry a little bit about us as humans: If we are abrupt with the machine, are we going to forget and be abrupt with each other [when] we’re talking to [another] human? I think that’s at the core of what I’ve been spending a lot of my time on; there’s a lot of technology talk. There [are] 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 … that’s what I’ve been thinking about.
Sam Ransbotham: I haven’t thought about the spillover the other way, but that’s a good point, that maybe I’m becoming brusquer to my humans. Well, now I’ve got a new thing to worry about.
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?
Jacqui Canney: We’ve invested quite a bit in this space. Every person who works here — we’re 30,000 people now — has had AI training, and we’ve been doing this for a couple of years. One, because the products we build, no matter what part of the company you’re in, understanding what AI is, [having] a common vocabulary about that, that was really important to our CEO and our leadership team for the company.
We’ve invested [in] 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 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. I think by being transparent, by offering opportunities, by giving people learning experiences, even for myself, I’ve been seeing more confidence grow. We ask our people all the time how they are feeling. They feel pretty strongly that they’re getting the tools that they need. So we’re going to keep at it.
Sam Ransbotham: There [are] 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: How much do people need to know? Vocabulary, I think, was one of the things you mentioned, which makes sense. 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?
Jacqui Canney: Prompt engineering is something we all have been talking about. It is not something we talked about that long ago, right? 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 come to double the productivity of what my team could do. It was 1-to-400 to 1-to-900 that we were serving because of the tech. Now, I didn’t want people to be displaced because of that. But then they became better at a couple of 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 continues to be better answers. Over 90% of our inquiries that go to our Now Assist, which is our own tech, get answered by the tech.
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. [These are] adjacent skills that I’ve seen the team lean into. 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 an 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 [or] a CIO somewhere out there using our tech, and they know they have this problem they want to 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. Work around it, and work it into your solution.” It’s more in service of the human.
Sam Ransbotham: Those are some interesting numbers, like the 1-to-400 to 1-to-900, and your first reaction would be “OK, yeah, that’s going to lead to reduction.” But as you point out, there [are] just a bunch of new tasks that are coming up and new roles that are coming up as quickly as maybe whack-a-mole. You’re trying to eliminate some work, and new work is getting created.
What’s your sense of the net? If we’re moving from reducing things that people are needing to do, by the two-to-one-ish type of number that you mentioned, but you mentioned new roles, too. 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. That’s big. Which direction is it right now?
Jacqui Canney: A crystal ball would be really good on that one right now. I think every company is 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 [it] has [been historically], is 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 even in my old job at Walmart, where you could see where you implement this powerful tech, but it does create expansion. 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’ve got to design that future. And if you don’t design it, you’ll lose the capacity.
Sam Ransbotham: I think I was too sort of crude to say, “Is it net plus or minus?” I’m sure in many areas it’s plus and [in] many areas it’s minus. And then we’re looking at the net of the net across a big aggregate — the crystal ball is not quite polished enough for that.
I think this training program you mentioned is part of the ServiceNow University. I like the idea that 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? The details on this seem very difficult with 30,000 people in a rapidly changing world.
Jacqui Canney: Well, we have jumped on this with all of our selves. The board, our CEO, the leadership team, everybody is fully supportive of the changes that we’re making and that we’re driving inside our own company. This assessment of the 30,000 people was important. I felt like we needed an X-ray of the company to know where we were, to be able to go forward. We didn’t use it as anything scary or a negative. It was really meant to be like we’re all going to get smarter about what we know we have as skills and what we know we’re going to need.
Then if you take what we’re going to need, you’re able to say — and this is with the help of Pearson; they’ve been a good partner to us — “Here [are] the jobs, here [are] the skills, here [is] the new work that you’re planning, and then here [are] 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.
I have other HR leaders [who] I really love working with, 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?
Sam Ransbotham: Actually, do you let outsiders take this? I’m ready to sign up because … I screw up a lot of stuff, and [it] can be so nice to know ahead of time. … I always think about this in one incremental hour. If I had one extra hour, what would I do with that hour? 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 these technologies could help us learn about these things.
Jacqui Canney: ServiceNow University [has] a lot of free courses out there. You can go check it out. I’d love your feedback about it.
Sam Ransbotham: Great. So you gave me homework. That’s no fun.
Jacqui Canney: There you go.
Sam Ransbotham: One of the things you’ve talked about is soft skills. … [For] the idea of a soft skill versus hard skill, first, what are your thoughts on the relative importance of those two types of skills going forward?
Jacqui Canney: I have always believed that critical thinking, the ability to pattern recognize, those things that you learn, whether it’s through your work, your university, 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 an easy thing. Not everybody has those skills. But people can be nurtured, I think, to better learn how to create those skills.
One of the things that I’ve been really thinking about is we talk 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 [who] have the wisdom is really important. 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. 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 pattern recognize, have the wisdom to say, “These are the use cases I want to go after,” as opposed to, “These are just the use cases that everybody’s bringing to me”? [Those are the] … really important, nontechnical capabilities we all should be focused on growing.
Sam Ransbotham: It was interesting. We had Taylor Stockton, who’s a former student of mine, on a previous episode. He works at the [U.S.] Department of Labor, and we were asking [about] hard skills, soft skills. He talked for a bit about soft skills and the importance of that, but then at the same time, he said [that] we also need those technical skills. 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 don’t pick on me. [Let’s say] one of my students wanders in here. What’s the one hour? Where do we spend it?
Jacqui Canney: I might say 30 minutes on what they are curious about with the tech. Is it protocols? I think protocols [are] going to be the next thing [we’ll be] talking about. How do you govern the agents inside a company? That’s really important. Understanding the nature of how you build and create protocols is not something you need to be a computer science person to do.
And then the second is, I 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 an idea or a point of view? I think the world of my university, and that was a lot of how we were taught, not just to be great accountants or great finance people, but also to be great thinkers. Having that be part of what you’re thinking about if you have one hour, I think, is worth it.
Sam Ransbotham: I have a ton of students who are about to graduate, and they’re talking about difficult job markets. 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?
Jacqui Canney: I think two things are really important. 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 going to teach you a lot. They’re going to teach you how to work. They’re going to 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 you are a great speaker. Whatever it is, but really know what that skill is and how you’re going to sell that to an employer that you’re going to 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 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 going to be the ones with the most adaptive, agile workforces. So I look for those kinds of qualities, especially the early-in-career talent that I get to meet.
Sam Ransbotham: I like that. It’s hopeful. I think your point about how well prepared students are — I love job descriptions that have something like, “needs 30 years of experience with large language models” — it’s just not possible. So the students graduating now are just as, or maybe probably more, familiar with this technology than many of us are. … I was thinking about blind spots. You’ve [now worked] at Walmart, WPP, [and] ServiceNow. What are people getting wrong? What are leadership blind spots here when people are thinking about artificial intelligence?
Jacqui Canney: Well, I think focusing on the tool and not the talent is one of the top things. People really get wrapped up around [questions] like, “What’s my AI strategy?” [but] it’s really your business strategy. Then, how does the business use technology, but certainly, how does it bring its people along with it? That gets missed a lot. … 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? It’s like you don’t have this all figured out. Like you said, 30 years of LLM experience — where’s that going to come from? It doesn’t exist yet.
Sam Ransbotham: I feel seen with that one.
Jacqui Canney: 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.
Sam Ransbotham: I think there’s a lot of ways to screw this up, too. I mean, there [are] a lot more ways to get things wrong than there are to get them right. Your idea of not having a perfect plan to start with feels wrong. I was reading something that … you had AI write a poem for [a] family trip. I was thinking about that. It struck me as funny because we actually, just for a cringe moment, I had my classroom write a theme song for our ML (machine learning) class. What would generative AI say is a good theme song for our class? We did not all recite the class anthem afterward, but you said that surprised you as 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 tools?
Jacqui Canney: I think it’s the ability to be better prepared for X meeting. … We have seen in our sales organization where they have access, obviously, to all the data about our customers, about the work that they’ve been doing. Now, how to prepare for those meetings in minutes and not days has been, I think, really exciting and eye-opening. People are loving that because it’s easier to get to answers quicker.
The other thing that I saw that people were super excited about, especially in our sales organization, [is] it went from like four or five days to find out what your commission is going to be to eight seconds. So if you have a workforce that’s motivated to know that, making that easier has been a great, well-received use of what the technology has been able to do in the day-to-day. I probably could think of a bunch more, but those two come closest to me right now.
Sam Ransbotham: Actually, I like the quick feedback part because … earlier you were talking about assessing people’s skills, and I was thinking about 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. This is 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. HR is historically driven by the idea of the annual performance review — 364 days ago, what did I do right or wrong? I don’t learn very well from that. You were mentioning commission, but that’s the example of quicker feedback. Both of those — I’m going to push back a little bit — feel like productivity enhancing, but we said earlier that there’s a bit of a trap of getting too sucked into productivity. 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 want to add to that to make it a non-productivity?
Jacqui Canney: I think it would mean the sale got better, bigger. If I would have had all the things I maybe before wouldn’t have known, like what did they say on LinkedIn, what’s the stock price doing? 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 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 have had the capacity to do. We have really stepped up [on] what does it mean to be a leader here? And [we have] invested in that [more] than I’ve ever seen because we know that that’s really the unlock for the organization. I think because of AI maybe creating the capacity, even for my own team, to be able to dream a little bigger about … the future of leadership and 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 going to build? Are we going to build a new business? Are we going to have totally different companies that are created? That’s what I think we’re on the cusp of figuring out.
Sam Ransbotham: You’ve touched on this. 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, [what] is this relationship between these formerly quite separate parts of organizations going to be, as you’re using more of these tools?
Jacqui Canney: I think AI is disintegrating the org chart, and not just between HR and IT. It’s sort of coming across a bunch of places because it just doesn’t see [it] that way. It doesn’t see silos, right? It sees across. 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.
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. That was because [CEO] Bill [McDermott] 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 sort of [keep] score of how we’re doing with that. And I think that says a lot about what he sees as a guy who’s seen across technology for decades of where change really [goes].
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 impacts the experience, we’re kind of like the filter on it, and we work really closely together. We have a 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 kind of keeps us all square because we can see very openly what’s happening. But yeah, HR roles are totally evolving. If you’re a [chief human resources officer] who’s really focused on process and policy and annual cycles, the CIO is going to come for you.
Sam Ransbotham: We have a little segment where we ask quick questions. Just answer [what comes to] the top of your mind. What about artificial intelligence is moving faster or slower than you expected?
Jacqui Canney: Moving faster in headlines, moving slower in, I’ll say, scalability.
Sam Ransbotham: Getting something across an organization, I’m sure you think about that a lot.
Jacqui Canney: Yeah.
Sam Ransbotham: How are people using AI poorly?
Jacqui Canney: I think they’re writing poems like I did.
Sam Ransbotham: All right. There you go.
What do you wish that AI could do better?
Jacqui Canney: I wish it could … I think it’s getting there, but [in] context and memory [it’s] being better. But I think that’s maybe more even how humans are using it. [But how can] I truly make AI be a digital twin of me? I haven’t figured that out yet.
Sam Ransbotham: Are you finding because of AI you’re spending more time with technology or less time with technology?
Jacqui Canney: I think it’s just in the flow of work now for me. I’m not really discerning [whether] I am in the tech or not.
Sam Ransbotham: Well, this has been fascinating. I think one thing we’ll come back [to] is this idea that the use of artificial intelligence is eroding these org charts. I think that’s a really interesting high-level thought to come away from this. Thanks for taking the time to talk with us.
Jacqui Canney: Thank you, Sam. This was great.
Sam Ransbotham: Thanks for joining us today. On our next episode, I’ll talk with Peter Koerte, chief technology officer at Siemens, and we’ll talk about industrial AI. Please join us.
Allison Ryder: Thanks for listening to Me, Myself, and AI. Our show is able to continue, in large part, due to listener support. Your streams and downloads make a big difference. If you have a moment, please consider leaving us an Apple Podcasts review or a rating on Spotify. And share our show with others you think might find it interesting and helpful.