Jeff Cann - A Rebel Story of Using AI as your Strategic Value Engineer

Speaker A: Foreign.

Speaker B: It's Rebels of SAS Almost holiday edition. So very excited to have Jeff can in the studio with us. I'm sure that you have seen some awesome AI uh, innovation and shares lately from Jeff Send. Including some fun meal planning for the family. Right Jeff?

Speaker A: That's right. Meal planning. Amongst other home solves for sure.

Speaker B: But Blake, yeah. So you're a top uh, 100Cs strategist, VP level, executive post sales, customer success, three SaaS exits, which is mind boggling. I mean I'm sure there's lots of skeletons editing magic. Jeff. Don't worry, there's probably lots of things that you can share around that process. Skeletons in the closet wise, so to speak. That's an exit is it brings its own challenges and unique opportunities. I am sure.

Speaker A: Yes. And my apologies but our connection isn't the strongest. So I missed a little bit of your uh, of your intro there of me. If you could give me the 22nd version. Their apologies Dan.

Speaker B: That's okay. The magic of editing is amazing, Jeff. So essentially I think that is super exciting. You've, you're on the top 100 CS strategist list. You've led teams through three exits, is that right?

Speaker A: Correct. Yeah.

Speaker B: So I'm sure there's lots of unique challenges and things that uh, you have picked up along the way.

Speaker A: Yeah. Hey Dana, I'm, I'm really sorry but you're, you're really blurry and I'm catching like every, every few words.

Speaker B: No, it's okay.

Speaker A: I don't know what if it's a bandwidth thing on your side or my side.

Speaker B: Hold on one second.

Speaker A: You're. You're clear. You're very clear now.

Speaker B: I am. Okay.

Speaker A: Yeah. Yeah.

Speaker B: Okay then we're just gonna ah, maybe we could take two or three. Yeah, you wanna do that? Okay.

Speaker A: Yeah.

Speaker B: So. Hey rebels, welcome back for an almost holiday edition. Super excited to have Jeff can top 100 CS strateg three exit Survivor with us. Welcome Jeff.

Speaker A: Thank you. Very excited to be here. Excited for the conversation and I appreciate you having me on.

Speaker B: Absolutely. So Jeff, one of the things that drew me to your content as a thought leader and as a leader in the space was some of your thoughts you had shared around AI it's um, kind of application, some of the fun things we can do with it, the usefulness inside of it, but sort of that beyond the hype. If you will talk to us a little bit about your thoughts there.

Speaker A: Yeah, yeah. Thanks. So I'm uh, seeing right now and I think a lot of us are a really Big gap between what boards are expecting, what CEOs are expecting in terms of how not just CS but all departments should be leveraging AI and where we're at. So uh, you know, whether it's speaking with my peers, being at conferences, there's a big gap between I think where we're expected to be and where our actual executional muscle and capability set is at the moment. And so that's been a bit of a soapbox for me lately. And one of the ways I think we can address it is by adopting AI at home. And there's so many wonderful ways that you can, you know, improve your day to day using AI. And in my, my thesis is that if you can get comfortable and proficient applying it at home, and there's some pretty advanced use cases that you can get into, you're going to be in much better shape to understand how it could be applied in the workplace. But to date, I mean, we're failing, I'd say we're failing uh, as a group right now. There, there's some stats that, you know, some of you may have seen. Less than 50% of CS teams currently are adopting Gen AI in, in, in their organizations. That's a uh, CS collective gainsight study. And that's not good enough. That's not preparing us. And the rate at which I see that improving is too slow. So yeah, I think if you can solve AI at home, start there where the stakes are a little different. It's a good, it's a good foundation to start to apply it in the workplace.

Speaker B: So I'm curious, give us an example of how one would go about doing that at home.

Speaker A: Yeah, I think, you know a few fun examples. One of which I built was a uh, meal planning, an automated meal planning agent. And so I'm in Canada. There's a 62 page Canadian food guide that spells out how and what and what constitutes a balanced diet. And that's a great example of uh, a resource or an artifact that you can upload into your favorite LLM. Um, and starts to give you an example, uh, a tutorial on grounding how to ground data. And so I've grounded a meal planner in a 62 page meal guide. It automatically creates recipes based on my family's taste profiles. So no asparagus on our dinner table. It automatically based on my geocode searches for what's on sale at uh, my favorite grocery stores and on a weekly basis it'll produce for me. Here's your seven day meal plan. Here are the sales you might want to take advantage of. And here's the shopping list organized by department in a grocery store. And so it sounds like magic. It kind of, uh, it does feel like magic. It is magic. But it's not too hard to set up something like that. And there's a lot of jumping off points. If you can do that, there's a lot you can solve because it takes, takes into account a lot of best practices that could be applied in a, in a corporate setting. So that's just one example. I got lots of, lots of interest in that. I've shared out the prompt to many friends and hopefully it's uh, making their lives a bit more efficient and effective as well.

Speaker B: That's awesome. So how did you, I mean, you get to a place where you're navigating all of these boardrooms, years of experience doing this. How did you find customer success? Where did you start? What does that journey look like for you along your way? How has it evolved?

Speaker A: Yeah, um, um, so I, I started in customer success way back when the word was invented in, in 2009 by Salesforce. So before that we were known as support, we were known as account management, many other things. And that's sort of ground zero, I'd say, for, for the beginning of the profession. I think it's, uh, you know, it's good to remind ourselves too why the profession exists and the reason why exists. It was born out of SaaS. The reason why it exists is because software by definition is always evolving. It's not set it and forget it, you do not buy it, take ownership of it. And it's the static code base that never changes. It's always changing. And so born was the need for someone to partner with a company to help them, um, continually extract value out of that investment. And in a world where it's constantly evolving and it's constantly changing and new use cases are opening up. So, yeah, born out of necessities. SaaS gave birth to that and it's changed considerably. I think we were the team that was really just, here's how you use the thing and let me get back to you as quickly as possible. We were basically a, uh, human, a human help center to start. Yes, very pretty much rooted in the how does it work and how do I enable the user? And, you know, happy to say that significantly evolved to the place where, you know, we, we know cs, which is really the why, why, why did you invest in this solution? What pain points are you looking to solve and how do we get you there? And how do we Demonstrate value. Um, I think a customer will continue to pay for a product or service so long as the perceived value is greater than the cost. We become those value engineers, ensuring that the perceived, not just the perceived value, but the actual value in that equation is always a positive one.

Speaker B: Yeah, it's interesting, right? We were born out of necessity and there's still a fundamental kind of need that. And you triangulated it, really triangulated it really well in my opinion there in terms of how we've met evolution, in terms of where we were and where we are. But it seems that that fundamentally making sense and goes back to that misalignment you said. And in the beginning of the conversation I feel how do we continue to show up in a way where there's survivability of what we do, um, for years to come with the advent of, you know, agentic AI and the evolutions of that. What I find so interesting about your perspective is that, you know, for you, I think you're in real time showing people how to get comfortable personally. Then, you know, getting that to a place where they can bring that into their team's workflows, into their personal workflows. Because, I mean, I think Harvard Business Review released some pretty awesome statistics. Maria Scobie Pilly actually was posting the founder of Women, um, of CS or Women in cs. But she was talking about like, according to Harvard Business Review, 5% of organizations, 95% see zero ROI on their AI investments. Uh, and there was a case study that they did based on a proxy group, 10,000 person company lost $9 million from misusing, poorly using and, or not implementing A.I. uh, and it's like, man, how do we, how do we first get to a place where we're not afraid to use it, but then also how, how we've got to really get to a place where we're orchestrating it and strategically integrating it into workflows too. Because right now it's like, a lot of, it's like, hey, we want to make the board happy. We're going to lay off 10% of our FTEs and we're going to go buy this tool. It's got AI in the name, but now we have enough data behind that on the backside of what's been this most recent AI, uh, bubble that it's like, oh, maybe we need to think about that differently. And that's what I love about your work and how you see the world, I think. And I want to dig in more here because you really, I think that a lot of what drives your leadership and your thought processes around this from a global level is how to use it strategically and in an innovative, useful way. And I think that starts at home not being afraid, figuring out how to make nutritional choices that make sense. And I saw your post on that, by the way, and I was like, I don't know him, but I need to know him. How do I not know him? And so anyway, that's how this conversation came to be. Just so all the rebels out there know.

Speaker A: Yeah, yeah, I think everyone's familiar with that study or should be by now. And you know, if 2025 was the year of A, I now see that AI works. 26 is the year that we have to get better at putting it in practice. And I think part of getting better at putting it into practice is understanding best use. And I think a lot at the enterprise level probably jumped in not being as educated or prepared for all that accompanies deploying agentic AI. And you know, you can get into grounding and hallucinations and, and privacy policies and security. There's, there's a lot, there's a lot there that you need to be, you need to go into it understanding, you know, what success looks like. But the other side of the equation is all of the low hanging fruit that is, you know, how do you effectively use an LLM? Um, which is, which is, you know, I think we're, we're seeing both at once. The most sophisticated applications are having some challenges and there's a learning curve, but then also there's a whole shadow economy and shadow usage of this, of these tools because people are perhaps afraid, afraid of sharing that, hey, I'm using this for work. Is there some admittance of I might not know what I'm doing? And so I'm maybe leaning in on, on AI. Is that a bad thing? And there's just a, a misunderstanding of how to get the most out of something as simple as a ChatGPT or a Gemini. And I see that gap. Um, and we could do a lot very quickly just by learning the basics, um, of how to use these tools more effectively. You mentioned some studies. There was one by Slack, 17,000 people. So significant sample size. The data showed that, uh, 61% of respondents have had less than five hours of training or learning on AI and then 30% had none.

Speaker B: That seems irresponsible almost. It does, it does the leadership, it's on the leaders.

Speaker A: That's right.

Speaker B: We have to prepare our people.

Speaker A: Yeah. So it's this unassuming technology that Looks simple, you just, you start talking to it. But being proficient and skilled and understanding how to, how to get the most out of it, it's significantly lacking. And so that's in part what, you know, I try to hone in on, on some of the content I post. And again, like safe environment, start playing with different, uh, automation tools. The Maker N8N Relay app, there's, there's lots of ways that you can start to stitch together systems, build things maybe a little bit more complicated, and the light bulbs will start going off immediately in terms of how you could start to apply that at work. But I think it's a big, I think it's a big miss and I think it's useful for leaders to think about when they hire a net new employee. What does the training path look like for AI knowledge and competency and best practices? If that's not a full day of training, that's a massive miss. And it's easy to implement. But I don't think we know what good looks like generally speaking.

Speaker B: That is so true. The whole concept of we don't know what good. And so it takes us back to another thing that you mentioned when you were talking about measuring success. What success looks like, what does that look like? What does success look like? And I think the one thing I was, all of this has been exciting to chat through. The thing that I was really excited to hear from you, quite frankly, Jeff, is what's next? You've seen the evolutions of where we've been. Uh, from a practice standpoint. Where does all this go next for customer success? Fundamentally, what does that look like five years from now?

Speaker A: Yeah, I think it's easiest to think about that question both from a internal perspective. How is AI shaping and making us more effective, more proficient? How do we scale ourselves? How do we improve the quality of our work? How do we improve the quality of our customers relationship with us? And then the other side of the coin is how is, are our customers consuming the product and how does that augment itself because of AI? When I think about the internal use case, everyone, what everyone's has a team now. Everyone in theory has uh, an assistant. And just like, you know, it's a cardinal sin, if you hire someone, you don't have much for them to do, or you don't train them and onboard them and you don't nurture their development. The same applies. We all have this assistant at our disposal, multiple assistants really, if you want to start to think about using it in, in, in ChatGPT or cloud terms, you, you've got the ability to build multiple agents that specialize in different tasks and our, our inability to take advantage of that is going to be problematic. And so when I think a year, two, three, four years from now, there is an assistant that we all have that has all of the context that we have from phone calls, from emails, from conversations, that is your partner that's able to take work off of your plate that could perhaps if we get into the future a little bit autonomously join an internal meeting for you and have the ability to share your perspective or have the ability to understand what all of your action items are and come back and, and share that with you, really, that's not that far off. But we need to start thinking of our relationship with AI in terms of, you know, we now have an assistant, we now have a partner. You need to train, um, these folks and you need to ensure that they have the right context and you had, ensure that you give them the right guardrails in terms of how to behave and how to best engage. So that's a big internal opportunity and I think a lot of people are starting to head that way. Again, kind of goes back to how do we get the most out of this technology? Which again, at uh, its Surface Chat interface looks really simple, but there's a bit more there to become proficient at once you start to unpack that.

Speaker B: What's some of the, in your opinion, what are some of the best ways to educate? Where are resources that you would personally use to train or share with your team potentially?

Speaker A: Yeah, prompt engineering, which you hear a lot of, um, and you know, it's hard to keep up with your LinkedIn feed and mine's filled with the world's best prompt to achieve X or Y or Z.

Speaker B: Yes.

Speaker A: So it could be a little overwhelming, but yes, once you understand the mode in which you can engage with an LLM to get the best quality feedback, you don't, you don't need to go hunting and gathering for prompts in every corner of the earth. You, you have a, you develop the language and you develop the appreciation of how to get the best out of the model. So I think, I think spending time really being a student of the prompt and how to engage an LLM properly is really important. Personalization is really important. ChatGPT just did a release believe this week, continuing to enhance the ability that you can personalize the characteristics of your own personal LLM. Uh, um, and so by that, if anyone goes into settings, this is how you hard code your instance in terms of how you want it to think and behave. And so you'd be surprised at how many people don't get under the hood and know that exists or don't take the opportunity to do that. And from a CS perspective, from a leveraging it in a work context perspective, it's always great to tell, uh, your agent to critique, perhaps critique your thinking, to push your thinking and stretch maybe what your assumptions are to help you sort of question and ask why and maybe get to the root of why it is that you're engaging in a certain project or doing research. You can personalize that to not just execute on what you're asking, but to partner with you and challenge you. And you can personalize it as well in terms of mine, for example, I knows that I engage, for example with the C suite regularly. And so please have that lens and pressure my assumptions and engage with me from the lens of content that's being shared with that audience. So there's so much you can unpack with just the personalization, um, and then you can create mini agents and many personalization roles within, in these systems as projects. So that's another, I think misunderstood, misused element to, to, to any LLM is, is the proper use of projects. And I think there's just so much fruit to be born out of getting more proficient there.

Speaker B: Wow. Very apropro, uh, if you will, for, you know, rebellion and positive innovation there. You know, you said so much there. But I'll just say that being able to think outside of the box, innovate all that, you know, if somebody asked me the question, what's next for cs? What does the next five years look like? I think it's exactly that. Uh, and I think getting back to basics, much like what you empower people, coach people to do, it's so fundamentally important because you're right, we hire people, we don't train them or empower them. I mean, honestly, that's why retention rates in SaaS are horrendous, especially in customer success. I think when I was at Pulse in San Francisco, I believe that the statistic that was shared, and this has been several years ago, as, uh, you know, we, as CS executives, we last 14 months on average, not including outliers. That was skewed central tendency, of course, but I would imagine now that's even unfortunately lesser. Right, because of this, uh, fundamental gap or disconnect, I think, between how we're not getting back to basics, really taking time to learn, to empower others, to be able to help stretch and innovate what they're doing internally, implement properly. We've talked about a couple of just absolute, absolutely crazy statistics that are mind blowing, that are showing that, you know, we're not doing that work inside of organizations. And it's uh, clearly problematic. And much as you mentioned, and I can't agree more, as we go towards 20, 26, we've got to get this right because. And I think it's a combination of that and I think it's also teaching our teams how to get back to being human too, because there's a certain segment of customers that, you know, we can probably really focus our FTEs on to be able to, you know, do all those great things that we want to talk about in boardrooms, you know, from a CLTV standpoint, nrr, from a lagging indicator seat, all those fun things. But the reality of it is right now there's a little bit of a myth, I think, and a little bit of lack of understanding in terms of what AI can actually do. But uh, but I think ultimately we can, it can do so much more just by getting back to those basics, really learning what it does, embracing an attitude of I'm gonna go figure it out instead of what's scary. Um, it's kind of like the caveman fire concept and it's just one of those things. And it's like everything else, there's fear mongering with it. Right. And the job market's not great at this moment. And so there's a lot of internal pressures as well, unfortunately, I think for survivability in terms of some of the organizations. You know, I'm just going to be raw with it because I think, ah, it's an important topic what we're chatting through. But how do we find more about you, Jeff? I don't even understand how. But we've come to the end of the chat. I've been having so much fun.

Speaker A: Well, I have, um, I guess in, in the great AI practitioner fashion, created an AI version of myself. Uh, so jeffcan, uh, CA is my AI doppelganger.

Speaker B: And so I thought you were gonna tell me that you are the doppelganger. You've been. I've been talking to the doppelganger. Okay.

Speaker A: That's right. It's having a meeting on behalf of me. Yeah. Two, two years from now maybe. No, that's a great way to continue this conversation, unpack my thinking. You know, not just on, on AI, but, but all things CS and, and retention and growth and customer management. Uh, that would be the best. I think the best way as well as LinkedIn. But yeah, appreciate you having me. And you know, given everyone's got New Year's resolutions perhaps coming up, you know, I suggest to everyone, actually ask, ask your favorite LLM, you know, based on our conversations so far, how could I be leveraging you better in the New Year and, and what could I be doing to get. To get more out of you? I think people actually forget to ask AI AI itself, um, the questions versus, you know, giving it, giving it answers or getting it to validate. So that's a great way to, to help expand your relationship with it. So I, I suggest everyone give that a try.

Speaker B: That is so cool. Well, thank you so much and we wish you all the best in the New year for sure. Thank you for coming on and spending some time with us today.

Speaker A: Thank you, Dana. Great to, uh, spend some time and yeah, happy holidays to you.

Speaker B: Thank you. And happy holidays to all the rebels out there. And until next time, um, do it big and do it different. Bye for now.

Speaker A: Thank you.