Ep. 589 | Leading through the AI shift: Why generative AI demands new thinking, not just new tools

Speaker A: This is OnBase, your one stop shop for solving your most pressing B2B go to market challenges. Each week, hosts Chris Moody and Paul Gibson talk to sales and marketing leaders to get you in depth insights and creative strategies to set your revenue teams up for success.

Speaker B: Well, hello everyone and um, welcome back to the latest in the award winning Onbase podcast series. I'm your host, Paul Gibson and as you know we always look for hot topics to discuss and this is one that's the hottest of them all. And with that I'm excited that today I'm talking with Jim Stern. He's author, keynote speaker and I think probably well known to a, uh, lot of people on this call. Um, and we're going to discuss leading through the AI shift, which I think is a really important topic. Right now. Everyone's talking AI. So really excited. Jim, pleased to get you on the show and thanks for coming.

Speaker C: Thank you very much. It's an honor to be here and I'm very excited about this topic in general and I'm really looking forward to your questions.

Speaker B: So before we get into the topic, uh, today's podcast, I always like to start with a very simple question. Jim, do you mind sharing your journey to where you are today and uh, what particular has led you to focus this session on leading through the AI shift?

Speaker C: So I've always been at the forefront of helping companies use technology. I sold business computers to companies that had never owned one before. I sold software development tools to enterprise and government for COBOL and Fortran. Yes, I am that old. In 1995, wrote my first book about online marketing because the Internet was something nobody understood. I have written books on social media. I've written the, uh, last two books on artificial intelligence. I got there through web analytics. I had been advising lots of clients about their websites and how ugly they were. Said, wait a minute, we can measure how bad your website is and we can measure how much better it is when you make changes. We can do testing. So analytics led to statistics, that led to machine learning. That was my book eight years ago. And then machine learning. Now we've got generative AI. So my book from 2025 is the New Science of Customer Relationships. So being what a professional explainer has always been my realm. And helping companies understand how the technology can help them with communicating with their customers.

Speaker B: Yeah, makes a lot of sense. Thank you. And it's interesting that that website analytics is a, uh, is obviously a part of what our business demand base does and it's amazing how many people ignore that. They'll throw lots of money at advertising trying to get people to their website. They don't know who's getting to their website and if they do, they don't know what they're seeing, they don't know if it's the right quality. So yeah, I mean it sounds like a very basic thing these days, but there's so many people that don't do that at all. So yeah, very interesting. Start as a starter then for this particular topic today. How do you think generative AI is redefining that relationship between technology people and indeed the decision making inside a big enterprise?

Speaker C: The most important thing to understand first is that this is not computing as you know it. The computers that we are used to come all the way, go all the way back to the abacus. They add, subtract, multiply, divide. They are databases, they're search engines, they are deterministic. Ask a question, get an answer. The computer said so, therefore it's right. Now we know that statistics allow you to create any reality you want. So that makes it difficult when we move from deterministic to probabilistic, which is machine learning. So machine learning says, well, generally you've got five segments of customers and these customers act this way and those customers act that way. Incredibly useful predictive analytics, incredibly useful. But it is only useful when you have a tolerance for uncertainty. When you're running payroll, no uncertainty. You're doing self driving cars, no uncertainty. When you're doing lead scoring or putting the right message in front of the right person at the right time, there's no actual answer, there's just better or worse. So it's great for that. Now we're moving off of those completely into this generative AI. Uh, we've gone from deterministic to probabilistic to artistic. It generates, that's its job is to invent and it doesn't always tell the truth. And the technical term for that is hallucination. So we are having to relearn that the computer is about as reliable as the next human over who will make a decision and then rationalize it. Maybe based on data, maybe not. It's up to you to confirm and verify. That's really hard for us to get accustomed to.

Speaker B: That's really interesting. And we have lots of conversations and slightly off topic but similar really around, you know, an AI will tell a salesperson or a sales development rep or whatever you like to call them, go after that because we say so without anything behind it. I call it AI without the why. And I think sort of what you're touching on here is there is an element of trust going on here. But if you can get insight behind why the AI is telling you to do that, as a salesperson, you certainly get a lot more confidence than just someone saying, go after that, because I say so. Which I think is termed, um, the black box scenario sort of thing.

Speaker C: And that gets into some interesting nuance because you can absolutely ask a large language model why it decided to tell you what it told you, and it will invent a reason, just like humans. So I need to buy a red sports car. Why? Well, because I earned more money this year and I deserve it. And I was really good with my physical health, and so I deserve a reward. It's like, no, I just want a fast car. Stop making things up.

Speaker B: Yeah, yeah, absolutely. Yeah. It's an interesting. And definitely the world is changing in that regard, for sure. So a lot of people that will be listening to this, their organizations will be looking at, okay, how do we properly adopt this stuff and how do we start using it? So where do you see the biggest organizational barriers? Uh, when it comes from embedding AI into existing workflows and the cultures as well?

Speaker C: Uh, yes, culture is the operative word here. It is difficulty at the top. The people who are using these tools are at the coal face. They're doing the work, and they're finding these tools to be very helpful doing the work. But they're extremely overwhelmed by mixed messages from upstairs. Where two years ago, oh, no, can't use it. It'll steal our data. Which was true for about six months. But that's when everybody's policy got solidified. That's what everybody was told, no, no, you can't. We don't understand it well enough. Everybody used it anyway. The people using the tools understand the tools. The people at the very top, your average CEO, if you tell them, absolutely, this product AI is not going to do your job well, of course it can't do my job, we all say. But it can do tasks that can help you optimize your work. Well, a, uh, CEO doesn't have tasks. They have people who do tasks for them. I need a plane flight. I need to set a meeting. Set me up a phone call, get so and so on the phone. I don't. I have people. So the upper echelon is not using the tool. So they don't understand the tool. Now, let's go back to 1995 when I'm in a boardroom and they say, we don't understand why you insist that we invest in this website thing, we don't understand what it's good for. And I turn to the Chief Financial Officer and say, what do you like to do in your spare time? Like what? What are you talking. Just humor me. Well, I, I like sailing. It's like, okay, look, sailing dot com. Boom. And suddenly I've got his attention. Now I see how it relates to something I care about. The CEO. Their job is to absorb lots of information and make decisions. They don't understand yet how AI can be a thought partner, a brainstorming tool, a infinitely patient therapist, and that until they start doing and using AI, they're not able to lead by example. If they're not leading by example, the rest of the company says, are we supposed to use it? Will we get in trouble? Will I get fired? Will I train this thing to take my job? And there's just a complete disconnect between the levels in the organization.

Speaker B: Yeah, yeah, some great points there. And I think it's an interesting scenario because you often hear the CEO CFO will say, what's the ROI on that? And it's not. In many cases, what we're delivering here isn't necessarily revenue generating, but the productivity savings are, uh, vast in this. And I think it, sometimes it's overlooked what's my financial return on this? Rather than what are the product savings benefits? And then obviously down the line, that does have benefits to you as well.

Speaker C: And we have two big problems with the roi. First of all, we're going through an enormous learning curve. Anybody who goes all in on AI is spending a lot more time working, trying to understand this tool. It's not like here, it's a spreadsheet. Let me give you the manual. This is not like that. We have to learn how to use it individually. There's a million YouTube videos, there's a thousand podcasts, there's thousands of blog posts. And I have spent an enormous amount of time on those in order to understand it well enough to teach others. But that's extraordinarily inefficient. The rest of the world is banging away. They ask a Google question, they get a bad answer, they go, eh, ah, it doesn't work. It's like, no, no, no, you're using it wrong. It works. So part one is, if you hand this tool to somebody, their productivity goes down because they have to learn to use the tool. Once they do, and they start automating and optimizing and transforming their work. That's great, but it's going to take A year before we see the numbers show up in productivity, before we see the numbers show up in additional sales and lower costs. So we're asking the financial people, the ROI people, hey, invest a lot in this thing, but it won't pay off for six to eight to 12 months. It's like, oh, no, I have to turn in a quarterly report to the board and to Wall Street. You can't ask me to invest that much. It's like, yes, we can.

Speaker B: Yeah, absolutely. And you touched on a really good point there. You know, adopting these things is not easy. And in a transformation where the pace of technology seems to be outstripping human adaption to it. Ah. Or adaptation to it. Should I say, what do you think the effective change management approach should be in this situation?

Speaker C: It's several pieces. It starts with bringing together an AI council. So it's. It is not a center of excellence. They're not going to be the rules makers. They're going to be the people who are the most interested, the most enthused, the best informed because they are listening to the podcasts every night. They do care. They are the nerds who want to know more. But we also need to bring it because there's security issues and access issues, data issues. We need to bring in legal, obviously, painfully need to bring them in. We also need to bring in the folks who are responsible for getting the work done because they know what the jobs really are. They know what's actually happening on the factory floor, so to speak. You bring them together and their first responsibility is policy. Now they need to educate senior management so that they senior management will sign off on policy. But I have to have policy to clearly tell my people what is expected and what is allowed and what is not allowed. Then I can create rules. Thou shalt not. And then I can create guidelines. This works better than that today, check back tomorrow, it'll change. So with rules, policies and guidelines, my people now have authority to go do things on their own. And that means I have a rubric around which I can create education, getting as many people on board as possible as soon as possible. The common problem has been, oh, let's go buy. Let's do a test. We'll buy a hundred licenses and hand it out and see what happens. Productivity goes down. 75% of the people do a Google search and say, nah, uh, I don't care. Another 10% say, I'm too busy to learn this thing. I'm so frantic running down the street, I don't have time to jump in the Car. And then you've got a handful, a small handful of people who are doing amazing work because of uneven education. So policy education and then the celebration of quick wins and proof of concept gets culturally activated. All the while senior management has to be on board and talking it up and encouraging rather than demanding. The carrot works much better than the stick. I said last week was a two, uh, day long closed conversation and there was one Fortune 500 company that said, first of all we require people to use it. Somehow they have to tell their boss once a week some use case or we will hold it against them. But on the carrot side, we award them points and the points are useful at the company store to buy tchotchkes and swimwear and who knows. So we award them points for a unique use case, a shared story. And you get extra points if you describe a uh, failure and what you learned because that, you know the old saying of uh, you learn this much in theory and this much in practice and this much by screwing up and if you share that, you save everybody else the pain.

Speaker B: Yeah, I like that carrot and stick approach. Just going back a little bit to what you used when you started this little segment. We talked around getting legal involved and things like that. We find that uh, actually getting a yes from sales and marketing from what we're offering is normally pretty quick. The impact is obvious, the benefit is obvious. But the legal side of things can take weeks, months longer than it always used to. A lot of that seems to be around data privacy and data transfer, impact assessments and all of these weird things. Do you see that more or less from an AI perspective? And you may or may not know this, I'm sure you will. Is there a difference, like from the US as opposed to maybe Europe where I'm based?

Speaker C: Well, how much time have you got? So first of all the legal team has to be involved because if you ignore them, you do so at your peril. The now we, we go immediate to corporate culture. The legal department is there to tell you no, that is their job. They are, are risk averse. That is their responsibility. And if they don't understand it, well then that's just a flat no. There's no, there's no recovering for that. We have them there to say what are the bright lines? And then where are the fuzzy areas? And the fuzzy areas, well that's up to management. Yes, we're willing to accept that risk. That's a corporate culture question. An industrial question. Healthcare, finance, not so risk friendly. Research and development, Sales and marketing. Yeah, we'll we'll try stuff. So they need to be in the room, but they are not. They don't. Let's see. They're not going to lay down the law, so to speak, because there is no law yet. We're still inventing this stuff. There we have. We're going to wait years for lawsuits to. To reveal what precedents look like. In the meantime, everybody just says no. So that's part one. What's the difference between us and Europe? The United States has assumed. The culture here assumes a lack of privacy. It's. We've just been trained to click on. Okay, okay, okay. No matter what, Europe has had a bad experience with a database getting into the wrong hands. Something that I fear Americans are going to discover shortly. With major corporations having access to government data, with government buying private information. The US Government is not allowed to spy on its own citizens. Doesn't need to. It just buys the data. So culturally, as a young country, we just gave up privacy for convenience. Sure. I want you to make recommendations. Yes. I want my $2 off at the till, and then there. There is a fundamental legal difference going way back. Uh, the shortest way to describe it is the United States is a law by rule and Europe is law by principle. So in principle, do no harm. But the law didn't say I couldn't do that, therefore I did not break the law. Yes, it was rude, it was crude, it was immoral, but I didn't break the law. Is the United States approach which is a, uh, moral failing?

Speaker B: Yeah. Okay. It sounds like your thinking is that US, uh, will end up probably more like Europe than the other way around.

Speaker C: Well, that's the hope. In the meantime, we have a government administration that's going to make every use of all the data they can get to forward their agenda rather than serve the public. Personal opinion. Yes.

Speaker B: Yeah, absolutely.

Speaker C: All right.

Speaker B: That was really interesting. But getting back, I suppose, to the topic. If we look at businesses adopting the AI side of things, how can leaders ensure that when they adopt the AI approach, they're strengthening, not sidelining the skills, the creativity, judgment of their existing teams?

Speaker C: Ooh, judgment. Excellent word. So technically, when model, a large language model invents something. The technical term is hallucination, what the human brings. The technical term that has been adopted by the industry is taste. It's judgment. It's discernment. It's gut feel. It's emotional. And that is what we need to hang on to desperately. So the proper approach is three points. Education, optimization, transformation. We have to teach everybody this is New, it's different. Here's how it can help you, here's how you can use it. Here are the fun things you can do, here are the dangerous things. And then optimization. What tasks are on your desk that you don't really need to do? Do you really need to read this report? Well, if your job is to pull the data out of the report and put it in a spreadsheet, no, you don't. That's just, that's just mechanical. You can automate that. If your job is to understand the import of that report and apply it in a judgmental way to make decisions, then yeah, you better read the whole thing. That's the difference. So optimization is workflow. Let's make things faster. Let's connect up all of our customer data from all of those silos in ways that we've had it try to do forever with pipelines. Now we've got a tool that has a little bit of flexibility in it to monitor and go, oh, the data coming in from this data stream looks fine. This one had a dropout. So we have to accommodate that. And those are human activities that can be automated because they have an absolute outcome. Writing code is a great example. The code runs or it doesn't, that's great. Then finally we get to transformation, and that is automation. Computers, the Internet, AI, they help us understand, automate, they help us speed up the things that we do. But generative AI helps us do different things. Do we really need that report? Do we need that standard operating procedure? That was a procedure that was put eight put together in 1872 for this particular. To solve this problem that doesn't exist anymore. That is a very large ask. It's a huge challenge. But the winners will run away with the prize. People who can. I mean, think of the huge transformations of the Internet. The Airbnbs and the Ubers and then the Netflixes, they all found a different way to do not just do things differently, but to do different things. And we're with generative AI at this point. We have discovered the surface. We found the surface and just realized we have nails and we might be able to scratch. We haven't even started yet.

Speaker B: Yeah, yeah, I think it's very exciting. And uh, it's funny, you see all these films from years ago, the sci fi films, and it's all starting to very slowly come together. The only thing I haven't seen is the flying skateboard yet from Back to the Future. But the rest of it seems to be.

Speaker C: There's my flying car.

Speaker B: Exactly, exactly Although I did see there's, uh, there's. I think it's Uber are looking at flying taxis. So that's, uh, maybe not a million miles away. So what role do you think transparency and communication play in maintaining trust during AI driven change? I mean, I mentioned that AI without the why and sending salespeople off on wild goose chases, which kills all the trust between sales and marketing and things like that. But what are your thoughts around that?

Speaker C: Well, I'm in the camp of transparency in all things. Without information, people make things up. Um, so let me take that to the extreme. I believe that a company's finances should be transparent to employees. Because if I'm in customer service and somebody in sales comes in and says, we just sold this giant account, then, well, he's making this huge commission. All the people upstairs are going to buy mansions and boats, and I can't even get a 2% raise if you make it transparent that, yeah, we just sold this giant contract. But here are the costs we're going to. We're actually only going to earn this much off the top. And are you familiar with how much it costs to keep the lights on? It's like, no, nobody is, because finances are what we can't tell anybody. I'm a believer in transparency and I think with AI, because we are all scared of might do something we don't want it to do. It might take my job, it might make human work irrelevant. What is senior management thinking right now? Not just what are they telling the financial world, it's like, what is the plan? Are you going to, as we've seen, fire thousands of people because you think AI can do their job? Or are you going to upskill us and train us to do our jobs better? Have that conversation open. That's the management side. On the technology side, it's much more complex. The people making these tools don't know why they are exhibiting these new capabilities. They just know if they throw more compute, if they use more data, and if they use more creative, intelligent algorithms, these things get smarter, more capable. They exhibit traits that we didn't know were possible. And suddenly really it does. That now is even happening in the laboratories. So that transparency of what is to come is really important and nobody knows.

Speaker B: Yeah, it's exciting and scary in equal measure. And I think you hit on a really good point. Now we have lots of conversations where if this stuff, if we get it right, it actually frees up marketers to be marketers rather than database owners and things like that. So I think, yeah, you're definitely right in what you're saying there. So just trying to think of a practical example as you look back over the last few years. Have you got something you could share around where generative AI maybe created some measurable business value while still enhancing that employee engagement or capability or something like that?

Speaker C: Let me share a story of a small company. There's 25 people in this company is construction, restoration. So when there's a problem with a building, they go in and fix it. It's not renovation, adding a new room or anything. It is, there was a fire, there was water damage, they go in and fix it. So a year and a half ago, this woman bought this company and immediately downloaded her own large language model behind the firewall to create a co CEO, help me run this company. And worked feverishly to make it help so much. Um, prime example, use case she goes into a building wearing meta glasses with video and says, I see there's some water damage up there. It looks like that electrical outlet had a problem and the floor is buckling over there. And by the time she gets back to her car, it has created a statement of work of what it will take to fix it with a bid. That's how many hours have been saved by how many humans that would have to do that work. And then she sits down at her desk and uses the meta glasses to watch her work on her computer and at the end of the day says, how can I be more efficient? That creative use of computing is what excites me. And uh, how do you measure roi? She did the work of seven people. Does she fire seven people? No, no, no. She upscales them, she makes them more, more. There is no limit to white collar work. There's a limited number of widgets you can create in a factory because of the factory. But working at your desk, working with ideas, working with people, there's an infinite amount of work. So if I can do twice the work in half the time, does that mean I take half a day off? No, I do the other stuff that I could never have gotten to. So how do you measure ROI out of that? It's kind of the wrong question. Is this a valuable tool?

Speaker B: Yes. Yeah, absolutely. That's a great story. I really like that. So if we look now, uh, scaling some of these AI initiatives, are, uh, there particular leadership qualities you think will need to be there to able to drive both that innovation but also keep aligned with the human element?

Speaker C: Well, the philosophy that came out of Silicon Valley that got out of hand is move fast and break Things I think that should be replaced with celebrate failure. And that's, that's not something that business is used to. You know, you missed your numbers 3/4 in a row. You're fired. What did you learn from that is the question. And if you didn't learn anything, then you're fired. You tried something and it didn't work. Tell us so that the rest of us can avoid that. That's a leadership quality. That is cultural, cultural. Corporate culture comes from personal psychology. So the people at the top who are fear driven, scarcity driven are going to have tough time with this. People who are all about abundance and growth, they're going to do well.

Speaker B: Yeah, great point. And I'm a big believer in, you know, making mistakes is all part of learning. If you keep making the same mistakes over, uh, and over again, that's a different story. But yeah, definitely agree with that. Slightly more generic, but it's a trending topic. And so we're getting, getting this a lot. So uh, worth asking. What's the toughest feedback you've ever received and how did it shape you?

Speaker C: Well, we're going back to the 1980s. I was selling software development tools. We had built tools for the DEC Vax and we just ported them over to IBM mainframe. Very exciting. And I flew from California to, to Pennsylvania to give a presentation to a large railroad company. And I got about five slides and this is overhead foils into my presentation. And the guy at the head of the table said stop, just stop. You're speaking us, uh, speaking to us in terms of mini computers where you're using files and folders. This is an IBM mainframe. We have directories and subdirectories. You're not speaking our language. I want you to go back to the airport, I want you to fly home and I want you to tell your boss that I said that he did not train you well enough and it's his fault. I was as angry as I've ever been. I spent, I mean uh, it's a six hour flight to get home without television on the back of the screen in front of you, without a phone in your hand. I just sat there and stewed and determined that I would never give another presentation if I didn't know what I was talking about.

Speaker B: Yeah, great learning. And those things stick with you, right? It's the making of you is when you get into a scenario like that and you actually learn from it. So I think that's a great one. You sound like someone and I'm sure you are who consumes lots of content. Is there a particular book, a blog, a uh, newsletter podcast you'd recommend to ah, our listeners?

Speaker C: Let's start with LinkedIn. There is a professor from Wharton School named Ethan Molic who posts a couple of times a day. Very short, very snackable, but he is very focused on what's right over the horizon, what's coming, how are these tools, what's new, what's different, how do you use them in higher education, how do you use them in business, and how to use them for some silly things. One of the things he likes to do is when it was text to image first happened, he said, okay, here's make me a picture of an otter on an airplane using wifi. And it was a little cartoon that was humorous. Well, fast forward to today and that command brings out a full video with audio and a script and characters interacting. Just otter on an airplane using wi fi and it generates it. So I follow him. I listen to the AI podcast. I wish it had a better name. It's from the SmarterX AI is the company that does it, their consultancy. It is kind of the news of the week. Also in the news of the week, I listened to Hard Fork, which is a gentleman from Platformer and a gentleman from the New York Times talking about the news, philosophy, psychology. They interview the leaders of the tech world. So there's a LinkedIn. Oh, for those in marketing, I'm going to strongly recommend LinkedIn and his blog, a gentleman named Andy Crestadina from Orbit Studio who recommends how to use generative AI in incredible detail. Not from the technical perspective, but the how do I use it for marketing? Here is a 17 page long blog post on how to create a ideal customer profile Persona. Here is how to measure the success of your blog post. Using AI to be your thought leader. Really solid stuff. And about 20 other people that I follow.

Speaker B: Yeah, they're all names that are new to me, but I suppose that's my weakness being across the pond. But they're definitely ones I'll be looking into after this for sure. That's always a big thing for me. You've been an awesome guest, Jim, have no doubt there's an argument for a follow up because we haven't even scratched the surface of the surface yet. And so I think there's an opportunity to do some more going forward. But right now this is a cheeky ask, I know, but could you share the names of maybe two or three inspirational people in the B2B space that you recommend? We should also bring on this Show.

Speaker C: Oh, well, I'm, um, you know, I don't know if you can get Seth Godin, but he's always inspirational. Yeah. Over on the creative side, storytelling, which is becoming even more important now that we have AI, there's a gentleman named, uh, Mark Sylvester who has been running a local Santa Barbara TEDx events for 15 years and has the last year and a half. He's a podcaster and he said, oh, I'm going to create a couple of agents to help me with my podcast and my writing. And he now has something like 43 individual assistants who are responsible for some portion to the point where he can be in his car and say, hey, Walter, I have an idea about, blah, blah, blah, interview me about that. And he has a half an hour conversation. Walter then hands it off to a whole series. It's a whole workflow. And the result out the other end is here's a podcast script, here's a Facebook post, here's a LinkedIn post, here is a medium, um, post, and this is for your substack and creates it. Now, he is not a technologist, but he had, he dove in and would be fascinating to talk to. I would also recommend, well, the guy who runs the podcast, the AI podcast in SmartRx AI is Paul Reutezer. R O E T Z E R. He runs a marketing AI conference every year I get to be a speaker at. He is right on top of things and paying attention. Who else would I recommend right off the top of my head? If your audience is more, uh, interested on the data side, the co author of my last book is Tom Davenport, who got acclaim for writing the article that data scientists will be the sexiest job of the 21st century. So he's been in analytics as long as there's been analytics and is extraordinarily insightful.

Speaker B: Awesome. Again, great names. I know. I'm pretty confident we have hadn't had any of them on, uh, our podcast yet. So I'll be working hard to get them on. So I really appreciate it.

Speaker C: One of them, you tell them I recommended them.

Speaker B: Absolutely. We always would do that for sure. Now I'm pretty confident you've opened up lots of potential conversations going forward. So how would someone listening to this show, if you're happy for them to get in touch with you after this podcast?

Speaker C: I am on LinkedIn. I'm very happy to connect on LinkedIn. I have had the same email address for 35 years, so it's extremely easy to find me that way. Between, you know, there's there's Slack and Reddit and Twitter and. And there's just so many channels. Email, LinkedIn. That's me.

Speaker B: Awesome. Brilliant. Wow. What a great session. Jim. Thanks so much for your time today. I hope you enjoyed it, and I hope everyone listening enjoyed the episode. And don't Forget, there's nearly 600 other episodes in the series, so make sure you check some of those out as well. In the meantime, thanks again Jim. Thanks everyone for listening. Until next time, Cheerio.

Speaker A: Thanks for joining us for On Base. If you enjoyed this episode, please be sure to share, rate, review and subscribe on SP, Spotify, Apple, YouTube, or wherever you get your podcasts. This show is brought to you by Demand Base, the only account based experience platform built for sales and marketing alignment.