Doug Hanna, Kustomer COO on Agentic AI and Modern Ops Teams

Michael Koenig: Hey, it's Michael. If you've been on a

Doug Hanna: Yeah, thanks for having me again. Happy to be here.

Michael Koenig: Last time we spoke, you were COO at Grafana Labs. That was a crazy rocket ship ride. Grafana is everywhere, of course, and now you've moved on to Kustomer. Tell us a little bit about Kustomer, what you're doing now, and why you're so excited about it.

Doug Hanna: Of course. So I joined Kustomer about 6 months ago as president and COO. So I lead all go-to-market, sales, marketing, post-sales, and when I was thinking about what I wanted to do next post-Grafana, I knew I wanted to be in the AI space. So timely and pertinent to this conversation. And I looked at things at the AI infrastructure level, the model level, kind of all the different levels of the AI stack. But I kept coming back to kind of the application layer because I felt that combining the AI kind of technologies and, but with the use cases, business processes, all those sorts of things was going to be where it was very clear to me that there could be real differentiation, kind of impact business results. So I was drawn to customer service, which I'd worked in before. I got connected to the team at Customer and we hit it off and it seemed like a great opportunity. So yeah, 6 months in, learning about the customer service space again, learning about AI, learning about all the things that, that are happening in this exciting time.

Michael Koenig: Yeah, and just as a refresher, Doug spent a long time at

Doug Hanna: Yeah, I think everybody did, at least in their early days.

Michael Koenig: Yeah, yeah, that's true. It was all hands on deck. Let's talk about the president role. It sounds very go-to-market oriented. Are there any G&A functions that you have?

Doug Hanna: Not reporting to me, but I have a pretty big, I think, influence and impact across the company. So as we're thinking about our product strategy or as we're thinking about different talent strategies or how we position the company broadly, involvement in fundraising, all those sorts of things, like I think as, as a pretty senior leader at the company, I'm, I'm having a bigger impact than go-to-market. But I do spend most of my day thinking about how we go to market, how do we work with our customers, how do we make them happy. How do we get new ones? So that's the functional focus. But I think broader kind of that one team sort of mentality of it's not just the go-to-market teams I'm trying to impact, but the broader company means that I'm working across a bunch of areas.

Michael Koenig: And Custer is pretty unique in that it was a private company, then acquired by Meta, and then spun out maybe 2 years ago or so. Wondering, what is that like now? It's almost third iteration of this company. How has it changed? What is the feel of being in a space like this?

Doug Hanna: We call the current iteration of Customer K2, which is kind of funny internally. Obviously, I wasn't there before the company was acquired by Meta or during the Meta time, but practically we're a fully independent company. Meta is not involved at all. Unfortunately, I can't go to their offices and get lunch there. They're not on our board., and we, we raised as part of the divestiture, we raised a round from same, same investors that had partnered with us before. And they're on our board. Like we just had our board meeting the other day, super supportive. So it was, I think it was an interesting time. A lot of the people at Customer Today, a good portion of them were around prior to divestiture, prior, they were at Meta, they were maybe there even prior to the acquisition. And which now is, I think, almost like 4+ years ago. We're, to your point, we're about 2 years out of Meta today. And yeah, it was, it was very interesting time for the company. I think the stat I tell most people is we're about 300 people going into Meta and about 90 coming out of it. So there was a lot of kind of team building, getting the company kind of built back up to fighting weight is what I call it, in the first year or so. And now I think we're well past that and it's really future looking into how we continue to grow, building the right— continuing to build the right team, making sure we're, we're pushing ahead in new product areas like AI. At the time that Customer was acquired, I don't think AI was really on the mainstream sort of radar for folks. Now obviously it is. So the space has changed a lot in a couple of years and so has Customer.

Michael Koenig: Fighting weight. Let's talk about that. 90 people and now you got to get back up to fighting weight. What is fighting weight for Customer and how do you determine that? Like, what is the process for going through it and going, this is what we need to be effective?

Doug Hanna: I think we knew it was definitely art and science. I think we knew 90 was not it. People were having to do a lot to— it was focused on keeping the lights on. So very rapidly building the team out following that, we scaled, I think, from about 90 to 140, 150 in a relatively short amount of time where we're right around 200 now. That definitely feels appropriate. And we're, we're kind of well past fighting weight, not to imply that we're overweight. We're now hiring kind of ahead from an investment perspective, thinking about what initiatives we really want to staff, thinking about new capabilities we want to add, as opposed to, okay, let's hire enough to make sure that like we can keep everybody happy and we can like make sure new sales inquiries are answered and kind of the basics of a company. We're now Okay, we're actually, we're gonna try this new initiative. We're gonna set this new thing up in marketing or customer success and really think about scaling, uh, more intentionally.

Michael Koenig: Okay. Let's talk about agentic AI. One of the reasons when I, when I reached out, I was like, let's have a conversation about this because it's such a fun time right now and there's so much rapid development. First, let's talk about what agentic AI actually is. Let's just set some baseline knowledge here because Uh, it's important. It often gets thrown around, and there's a, I think, a good question of whether or not we actually have agentic AI at this point. And so maybe can you just give a little bit of background here, and then also how do you view this?

Doug Hanna: Sure. So I do not claim to be an AI expert. I am, I am, uh, I follow the space because I think it's relevant to tech and I think it's relevant to us, uh, obviously. I'm much more steeped in what's going on with AI and customer service because that feels most relevant to me, uh, versus some of those model layers or infrastructure levels where it's less relevant. When, when we use the language of agents in Agenic AI, we really think of it as they're kind of different— AI agents is the word we use, but I think it's different kind of special specialty sort of programs effectively that our customers can set up and configure as opposed to kind of a one-size-fits-all. This is just your, your AI, your, your, your generic model. And what we found and what our customers have found is that allows them to execute specialized tasks better with higher quality, higher reliability, and more predictably and in a way that makes more sense for their business. So when we talk about Eigenic AI, when I think about it, I think of it as these like specialized sort of agents that you can task to do something. Like in our space, it would be like, for example, a company could start or use an agent to handle returns or to handle bookings of travel or to handle exchanges for a certain product line or whatever. Like there's a bunch of use cases, obviously, and you can then kind of set it up to, to do that task quite well as opposed to here's your generic sort of AI question and answer bot, which I think is a lot of the application and customer service today, uh, our, our approach has been like the, the agents have multiple capabilities.

Michael Koenig: So that's really interesting. The big thing about Customer Here and a lot of the other customer service products that are out there is that move from deflection, just having a bot that tries and sends you to an answer, to actually problem resolution. How did you guys go about this? Because it, it's a monumental challenge, and what I'm wondering here is like, How this is a, a product that can be applied across so many different industries. You have a demo video that is talking about hotel bookings and concierge and all of these things, and it's actually a phenomenal example of humans and agentic AIs, or let's just call it agents, actually working together. So how do you go about this? Like, it is huge. It's such a difficult problem.

Doug Hanna: So that's why we have encouraged our customers to think about it on a per-use-case kind of situation, as opposed to let's try to boil the ocean. To your point, there's a million things you can do. And like, while the technology is great, it's not perfect. And we've heard, like, I was chatting, I was at a customer dinner last night, and one of the people next to me said that he His company has been able to see like an 80% like kind of deflection resolution rate with AI, which is incredible. And that's not uncommon that I hear that. I think the average is more kind of in the 40% plus or minus range. And that's still incredible. Like if you think about you can buy a technology and deploy it and use it and it doesn't even take one FTE to sort of maintain it and you can deflect 40% of your contacts. Through customer resolution or knowledge or whatever the kind of outcomes are, it is really powerful. But what we've seen and encourage is like try to use specialized use cases. And if you're doing that, you're going to have better results and higher customer satisfaction and higher kind of stickiness of the resolution versus someone coming back 10 minutes later and being like, hey, I want to speak to a human to resolve this.

Michael Koenig: It's really interesting. I was reading, and this is obviously not customer, but I was reading this I don't know, newsletter, blog post, whatever we want to call it these days, about an AI agent in the talent acquisition space reaching out to someone and it just completely fumbled. First, it didn't identify itself as an agent. They figured it out when they asked, are you an agent? Next, it asked a question like a very detailed rapid-fire thing. They would start talking and then it would say, great, thanks so much for the answer and cut them off. It's a very poor experience that you can come across. And if it's done at scale, you don't necessarily know if this is happening. Like, how do you all guarantee or monitor to make sure that everything is actually working properly?

Doug Hanna: There's definitely a risk tolerance involved here. And sometimes I challenge customers and like customer service organizations for a long time have been, they do QA, they, they listen to calls or they read emails and they're like, okay, did this follow standards? Did they ask the right questions? Did they have the right knowledge? So this is that's been happening, and that's due to the pesky truth that humans are not always accurate or correct. Like, we've all had customer service experiences where we've called and someone said something was true, and then you show up or you go to do the thing and someone else tells you, well, that's not true, where did you hear that? Uh, we've all dealt with that. So I, I try to remind people of that fact when we're talking about AI. It's not going to be 100% perfect, but I do think that some people get overly ambitious, and, and as a result, maybe your customer satisfaction will suffer. Uh, and it, it then brings up the interesting question of how, like, you have your levers here, presumably, of like customer satisfaction and, and cost. And if you are a business leader, a customer service leader, and maybe your customer satisfaction goes down 5 points, but your cost goes down 50%, like, is that a trade-off that you're willing to trade? I think most companies would take that. If it goes down 5 points but you save 8% of cost, you're probably not willing to take that. And the answers are going to vary depending on how customer-centric the company is, what their business model is. Like, Four Seasons may not say yes to that, but like Motel 6 probably would. And there's totally different ways to approach it. So I think what is really interesting about this time, and this is across more industries than just customer service, is people are having really kind of candid discussions around quality, reliability, sort of these, these sorts of things versus cost savings and cost impact. And how does that compare to ugly reality of people aren't always right. People need to be trained. They need to be coached constantly. Like, like it's, it's imperfect. So I think those discussions are happening more, but there's also lots of technical things that companies like, like us, but also other companies are implementing around like kind of observability of what is the AI telling people. Can you kind of give it feedback that that was good or this other answer would have been better? Like those sorts of things are being built into products, including ours, to, to help guide the answers, give them more specific kind of feedback. Tweak the prompts sort of that are being used to create this. Like there's these, all these hidden prompts and, and all these customer service solutions as well of like, oh, hey, keep this in mind. Keep this in mind about our company. This is one of our policies. This is the tone we want. All those sorts of things can be tweaked.

Michael Koenig: So customer service, this is an interesting application of AI. It's been long predicted that AI is going to completely revolutionize customer support. In addition to whatever accounting, legal, et cetera. But customer support is one of those that is heavily dependent on the number of people that you have to be able to handle the calls, handle the support tickets all at once. One of the things I really love about Customer is that there's a nice handoff where there's still an acknowledgement that the more difficult things are going to need that human touch. How do you all approach that? Is that kind of how you're thinking about it? And then how do you approach this with just customer support folks in general?

Doug Hanna: I think there's a gradient of human and AI interactions and customer support. So on one extreme, it's AI handles everything. So that could be your simple knowledge inquiry. What's your return policy? How fast is shipping? Is this more advanced? Is this date available for this restaurant booking or this hotel booking? Like, that's kind of just like sort of simple knowledge things. Like, if you did, if you did the research yourself as a consumer, you could figure it out. The other extreme is human handles the entire thing, and that might be due to system limitations, AI limitations, customer preference and prioritization, whatever. But there's certainly a category of issues that the humans handle. Like, I recently booked a honeymoon, and maybe they used AI in the background, but we definitely spent a lot of time talking to a human about how to do like a complex multi-country itinerary And I, I'm sure AI could have given me some guidance there, but I don't think it would have done the whole thing and gotten to know our preferences. Maybe, I don't know. Travel, I think, is another area where this is going to have a huge impact. And then there's the mix, which I think is really interesting, and what you alluded to of maybe human handles some of it, AI handles some of it, and there's handoffs back and forth. So in my travel example, maybe the humans and I figured out where we wanted to go and kind of roughly when and how to prioritize the ways we could spend our time. But then they're like, okay, I'm going to hand you off to the AI bot to come up with your itinerary for sightseeing in this particular city. Because like, I don't know, like the AI can tell you the 5 best things to see in a city and now like increasingly help you find the tour group or the tour guides that you want. So you can imagine there's ways to do it. Or you call your airline and you have questions about a flight or a change policy. They explain the policy to you, be it with a human, and they're like, okay, I'll transfer you to the AI bot that can then handle that. Where I think it gets really interesting and where the technology in some places has gotten to and others hasn't gotten to is also doing that across channels. Okay, you call, you get your policy clarification, or you figure out your itinerary. And then it's like, okay, I'm going to email you like a link to this AI chat or experience or something. And then on your own time, you can go deal with that and kind of take the next step and solve your inquiry that way. So I think we're going to see more of that. It's going to get more dynamic. I think right now as a consumer, I've not personally experienced, okay, I'm talking to a human, they're going to transfer me to an AI thing and then they're going to transfer me back. Like, I know some of our customers are doing it. I've not experienced it as a consumer, but it will be interesting when that happens and like how I feel.

Michael Koenig: Well, you just need to call customer support more or just become customers of your customers at customer. Oh gosh, I can't believe I just did that. Yeah, there's a lot of customers.

Doug Hanna: We do try to— sometimes we use the word client. I've always grown up using the word customer, so I have to break that habit sometimes. But we try to become customers of our clients just to support them and support our brands. And we have a lot of great brands as our clients. But, but yeah, I think there is this interesting idea that as customer service has less friction, people will just call more or email more, chat more because it's like really easy to get that answer in the same way that like you Google things really quickly now instead of like looking it up in your encyclopedia, like it drives more consumption of the thing because it's so much easier. And if the incremental costs are low for the company, they probably don't care. It's just like a different way to interact with the website. And then you get into all this sort of like interesting stuff of like, okay, is the internet going to be more like agent, different AI agents interacting with each other? Like, will my travel booking agent call the Priceline travel booking agent and they'll like talk together about what my travel plan should be? Probably. Like, I think there's something like that that will happen in the future.

Michael Koenig: Right. And then 99% of the time it's accurate. 1% of the time it isn't. Your honeymoon, you thought you were going someplace really tropical and now you're going to end up in Siberia.

Doug Hanna: Enjoy. Yeah, I hope you, you double-check your tickets and your packages that you get. But yeah, it's going to be imperfect. And again, humans make mistakes too. And the travel agent could fat finger what airport you fly into. And if you don't catch it, you might not know until you show up. So like these things do happen. But yeah, it's, it's a really interesting time. Like the last probably 10, 15 years of like online customer service has been really focused on deflection and self-service. Like, I always use the example of back in the day, probably when you want to do a return at Amazon, you— there's probably a book at the time, you probably had to call them or email them and be like, hi, I'd like to return this Walter Isaacson book that I bought. And they would say, okay, here's the label, please, like, send it to us, the book to us at this address, and you go do that. Obviously now that's a fully self-service experience. It's really powerful. Like where I live, it's like, do you want to drop it off at the UPS store? Do you want to drop it off at Whole Foods? Like, do you want to mail it to us? And it takes like 2 seconds to do it. And I would prefer that 100 out of 100 times than speaking to a human, even if it was like right available to me. And honestly, I prefer it probably over talking to an AI bot because it's just so It's so purpose-built for exactly that use case and it works extremely well. And I don't need like this unstructured thing to be like, I want to return this book that I bought and I want to do it this way. So AI is not the end-all be-all. It's just like, I think, a different way to approach customer service and it will make it more accessible to more companies. Whereas like Amazon, I'm sure, has teams of people working on the return experience. Your smaller retailer can't do that.

Michael Koenig: Well, let's talk about your operations. You're doing a lot of the commercial aspects. What have you learned over the years from

Doug Hanna: Just from an operations perspective?

Michael Koenig: Yeah, totally.

Doug Hanna: It's an interesting question. I think customer expectations are continuing to increase. And some things around uptime, stability, reliability in the world of AWS and super, and I'm biased here based on my experience at Grafana, but I think those expectations have gone up way more. People don't really tolerate their internet going down. People don't tolerate websites being down. It's like, well, every now and then AWS goes down and the whole internet is broken, and that happens for 10 minutes and everybody's like, wow. And then otherwise it's like things generally work. So I think people's expectations are, have increased, customer expectations have increased there. And there's a lot of things probably over the last 10 to 15 years that felt like a big deal at the time and now are less of a big deal. They're just kind of the expectation. Other like kind of technical things in that case, like I often use the analogy of like, I don't know, 20 years ago, maybe more, it was a big deal for an application to be SaaS. Like Salesforce was kind of the first like notable SaaS application and they had their like no more software, it literally in their logo. And like, that was a, that was a big deal. And now if I like went to a VC or I was like, you know, my application is a SaaS application and like, that's why it's a big deal. Like you would get laughed out of the room because that's just the default. And I think we might be going there with AI as well. Like everything is going to be AI enabled, certainly AI supported. And that's going to be the same sort of paradigm for, for software and kind of applications and companies even that, that we're used to. Uh, mobile is probably similar. Like again, I think if you went to a VC in 2025 and you're like, I have this idea for a mobile app, like no, no one cares. It's like, what is, what is the app? Like, what is it going to do? What's the business model? Now it's just kind of assumed that the platform like that exists. So I think those kind of like expectations around how products are built and sold and what they're, how they're consumed has gone up. And related to that specifically, I think for operations is there's like the whole toolset has changed and evolved. It's gotten way more complex. There's a million productivity tools. There's a whole data infrastructure kind of market now with like 5 or 6 different layers of like data infrastructure and like How do you handle that? And, and like the complexity in the operational space in, in technology companies that like when you think about at a high level, like they don't have inventory, they don't have physical products, like there, there, there's a lot of complexity that's been created, uh, and a lot of tooling to help with that, um, as these companies like continue to scale. So I think that's been an interesting observation over the last, uh, however many years.

Michael Koenig: And in like maybe 5 to 7 years you'll walk into a VC and say, I have an AI tool, and you'll get laughed out, like everything is just going so much faster.

Doug Hanna: Yeah, maybe, maybe like 2 years from now or a year from now, or maybe even now. I think like a lot of the VC funding activity is, is going to companies with some sort of AI strategy. I had a funny conversation probably at this point like 9, 9 months, maybe more ago with a recruiter, and he's like, I didn't realize so many of my clients were AI companies, but every spec they're sending me now is they're the AI forward whatever. And he's like, I'm pretty sure 3 months ago they were not. Uh, so there's definitely like some positioning that's taking place in the space because there's so much customer interest, there's so much hype, there's so much buzz around it.

Michael Koenig: Everything at least has to be positioned as AI forward for sure. Now you guys are AI native at this point. How does that translate into the toolset that you were talking about into your operations?

Doug Hanna: So I think across different parts of our business, we're trying to use AI much more extensively from simple things like we have ChatGPT Pro for all of our employees, uh, and we encourage them to use it. And people are building, instead of like internal FAQ docs, we're building like little, um, like AI sort of like Q&A tools where you can just ask it and, and you can get an answer about our new pricing thing or this enablement thing. So we're using that where we have like some AI sales enablement and kind of go-to-market team enablement that we're doing. Clearly we're using it heavily on the engineering side where engineers are utilizing kind of Copilot-like technologies to help write a lot of code. People team uses it for sentiment analysis and surveys. Customer support is using our own AI tools, of course. I think every function is touching it and you're starting to see it in every sort of every tool we use. Like one of the ones that we use that I really enjoy is Gong will do like an auto summary of the calls and we post those in a bunch of internal Slack channels and I can follow along. And instead of listening to a 30-minute call, I can read 4 sentences or 5 sentences about the summary. And it's reasonably accurate. Like it's certainly accurate enough to be useful. And like that, I do, I use that every day. So there's just, there's tons of things like that, that like, I think vendors are being smart about and kind of inputting into their products. And there, there's still so much opportunity for improvement of things that I use and I feel like they have not really changed even with the advent of AI.

Michael Koenig: We'll be right back. Hey, it's Michael. If you've been on a

Doug Hanna: I think because we're investing in so many areas right now, we haven't seen that as much as I think we would like or, or that, that we, we have. I think some of these like brand new companies that are starting today, like I was just reading something recently that like revenue per employee is probably going to go up amongst early stage startups. I think there is a lot of opportunity there. We're definitely seeing it in that. I think we're able to get more done with a similar number of people., not necessarily that we've like reduced our, our hiring plans as a result, but I think we're, we're hopefully seeing more, more output, higher quality output, better visibility, things that wouldn't have been done possible, like listening to every Gong call before. Now, like everybody can do, or like a great example of just like more capability is like we use the

Michael Koenig: Let's talk about how you're setting up your sales organization at Grafana. If I recall, it was largely regional-based, regional managers and sales directors, etc., etc., going all the way down. Is that a similar model that you're putting together for customer?

Doug Hanna: Yeah, pretty much. And like customer, we're smaller than at the time I was at Grafana, but Grafana kind of grew up in a similar way as well. Like you start with what I joke is like a big bucket of reps and then you start kind of regionalizing things and segmenting things a bit. So the same person working on a $20K deal is not working on a $200K or $500K deal. So we're starting like this fiscal year, we started segmentation and kind of regional alignment. So those are both good things. And yeah, similar to the evolution we had at Grafana, something we're doing a little different than what we did at Grafana that I think will work well for us now and kind of based on our current products and roadmap is Grafana, we had a combined hunter-farmer role. So the same AE that would sell into an account initially and land the logo would then kind of own the account on an ongoing basis and try to sell more. Interestingly, like in the last year or so, Grafana has split that out and now they have kind of growth slash acquisition, like new logo teams and existing kind of customer teams to help drive more new logo acquisition. What we're doing at Customer is similar to that structure. We have our, our sales, a lot of our sales team is focused on new logo acquisition. And then we have a specific kind of new account manager role that's focused on upselling to our existing customers. And they're really focused on upselling our AI products to those existing customers. And they will also find additional business units and use cases and things like that. But my, my belief is the big opportunity is selling AI products to them, and that's what that team is really focused on. So that's a difference. And again, we were just covering this in our board meeting the other day, and I think coverage models, which is what I group all this is, they evolve over time and we'll probably tweak it next year. Based on our learnings and experiences and where the business is at. But for where we are with a bunch of new AI products and a good-sized customer base, like, I think this is the right move for us.

Michael Koenig: Yeah, and I agree. I like that how you have— it's a different sales motion as well, right? From new logo acquisition to expansion and upsell. So very different. And I like that you guys are doing that. That's great. Let's talk about RevOps. Is RevOps sitting under your belt?

Doug Hanna: It does.

Michael Koenig: Okay. It's something I have not gone into a lot on this podcast. First off, what is in RevOps at customer?

Doug Hanna: We haven't had a centralized RevOps function, I think, in quite a while. Certainly when I joined, we had kind of sales ops and CX ops and like support ops, like all these different operational folks. And we also grouped enablement as part of this kind of centralized group too. And we had CX enablement and sales enablement., and my, one of my big goals is kind of operate together as one team, uh, on the go-to-market side in particular. And so our RevOps function is centralized. So all kind of like one group owns the tooling across the company that way sales doesn't have one enrichment tool and marketing another enrichment tool. There's clear ownership of something like Salesforce, uh, and tools that are shared across multiple groups. So, uh, that, that's been a big improvement. But, but broadly, I would say you have kind of like a few key functions within a RevOps team. Um, at our scale, you have kind of the business partnerships and think about like your sales segmentation, reporting, all that sort of thing. You have your tech and tools and managing your tool stack. And then we have like a deal desk pricing function and thinking through like, what is our, what are our offerings? How do we take them to market? How do we think about discounts, all that sort of stuff., and then there's kind of like a more centralized ops function of, right, and this is part of tech and tools as well, but like data quality, reliability, piping between them, how do we work across all that? So it's really like kind of the functionally aligned group, tech and tools, field desk. That's, that's kind of the core functions.

Michael Koenig: So is that a consolidation of sales ops and CX ops now into one rev ops function?

Doug Hanna: Correct. So we brought all the groups together and then we've also made some like incremental hires and kind of changed some roles around as well.

Michael Koenig: That's very cool. That's very cool. Now, you as a sales leader and one of your mentors, Tom Kaiser, back at

Doug Hanna: So we do a weekly forecast, which is the fact of our sales review. We're looking at the deals currently in flight and then the deals that are kind of marinating and becoming, becoming full-fledged deals. So we're definitely checking that weekly. And then we have a pretty good cadence here around also looking at our marketing pipeline on a very regular basis. And also looking at our kind of existing customer health and renewals at a pretty regular basis. A lot of this was set up before I joined, but as I come on, we've consolidated, sort of changed some of the purpose of these meetings as well and increased the rigor around it. And

Michael Koenig: Yeah. And you're known for running really efficient, great meetings that are on point. How do you run a forecast? What's your weekly forecast meeting look like? What's that structure? Are people coming in prepared? Uh, just take us through it.

Doug Hanna: Oh, I didn't know I had a, a reputation for that, but yeah, you got a little one.

Michael Koenig: You got one there. Uh, so when I was a Grafana customer, We just, you get to chatting with people and they're like, yeah, Doug's great. So there you go.

Doug Hanna: Good. Uh, yeah. Thanks, sir. Thanks for being, being a customer. Uh, we'll get you to be a client of customer as well. And yeah, I think so. So full credit. We, I have a sales leader on my team who the forecast meeting is his meeting. It's really the sales team's meeting, but some of the things that we're aligned on for that meeting. Is we want to look at kind of all the deals in flight regardless of stage. And we want everyone on the team to be able to speak about any deal that they're working on. And that includes their manager if they don't report directly to our sales leader and we're starting to build out kind of that next layer of management as well as kind of the individual rep. We also invite the SEs, so the technical counterparts on that into that call. And we want their feedback as well because they have a perspective and in a different kind of vantage point from, from their side. So, so the expectation is everyone should be able to speak about their, their meeting, the meetings, activities, etc. that are happening with the deal week over week. We use MedPic as a qualification criteria and to speak through kind of any of the variables there. And, and Andrew, our sales leader, does a good job of kind of asking questions and poking holes while still being supportive of where we're at. And in saying like, okay, this is what, this is what we need to know. This is what we need to go do. And critically, I think the tone that hopefully everybody gets from the forecast meeting, and we did something similar at Grafana, is yes, it's about transparency and accountability, but it's also about the sellers getting the support they need, whether that's from legal or product or engineering or sales leadership or pricing. Like that. If you're a seller, that's your time to say, hey, I'm waiting on these red lines and it's been too long, I need help. Or, hey, I need someone from product to jump on to this next call. And it's up to us as kind of leadership to suggest some of those things if they're not explicitly asking, but we think it could be helpful.

Michael Koenig: One of the things, as we've just talked through all the things that you've implemented at Customer and just the throughput of the different touchpoints here. You have taken certain practices from Grafana, from

Doug Hanna: So I think of that, I call out like generic playbook application. And I think that's a real risk that leaders kind of make or have where they were at some company, maybe not even in the same space, obviously not at the same time or in the same situation. And they're like, well, this worked for me before, it's going to work for me again. And I think generic playbook application can work better in some areas of like maybe how you approach leadership, management, hiring, running a meeting. Like some of those things are more like you've built experience in hiring and assessing talent, working cross-functionally, looking at data, having standards around certain things. Like some of that can work, but on big things like coverage model, strategy, marketing approach, product approach, like different companies are different and have different times and situations. Markets change. Like we were talking about how, how much AI has changed. Like when I was at

Michael Koenig: Yeah, that's great. I love that. Last question. As you think about how you spend your time, as you think about what you're working on, where, where the most important things are, what is the one thing you wish AI did for you? That would just be a huge time saver.

Doug Hanna: So I spent a lot of time with customers and I think it's really, really valuable. I don't know, I don't think I could send my AI bot to like sit across from an executive and talk to them about their AI strategy. That would be super helpful of like, how do you get more customer kind of knowledge, sentiment, context with existing customers? Like it's very time consuming to go on a plane and go see them spend time with them at their office and over dinner and stuff like that. But it— I also consistently find it's like amongst the best time I spend because I learn so much and I get so much context from like the perspective that really matters, which is the person like paying the bills and making the purchasing decisions, what I think doesn't matter relatively. So I wish AI could make that more efficient for me, but that, that I think is very long-term, very, very future-looking, agentic AI. And I, I'm getting some of the benefits of we do, I don't know, hundreds of calls a week across our customer base, if not more, and prospect base. And I can read all these Gong summaries in an hour a week and get a decent idea of what's happening. And we have all these like internal customer Slack channels and I can read the Gong summaries as they come up and like, that's super useful for me. So I'm getting some of it, but it's like maybe eventually we'll be able to have our AIs talk to each other about the sentiment and what we need to do and just read the summaries and go on from there. But we're not there yet.

Michael Koenig: Not there yet. Well, that's awesome. All right. Someone needs to develop it, but I guess we have to get to that full Ajahnic soon so that it can get there and we'll probably get there sooner than later. All right, Doug, thanks so much. It's so great to have you back.

Doug Hanna: Oh, I'm honored. Yeah. Really good to catch up with you.