Christine Royston: 0:00

So certainly as as I went into planning for for this year, one of the things that I am thinking about is how do we how do we upskill a team that is an existing team? We are a non-AI native organization. Rike has been around for many, many years. And so I had to think about how do I make sure I'm upskilling and enabling the team so that they're able to do more themselves. And so, you know, I created a dedicated role on my team reporting to me that is focused on AI and automation specific to marketing. We've got an amazing team. And so we're not building the team from scratch. We're looking at how do we move from sort of traditional marketing to AI-enabled marketing without requiring that everybody becomes a data scientist overnight. So I had someone in the organization who was naturally, naturally drawn to thinking about how they could use AI within their current marketing role. And as I was looking at what do we need for the whole organization, I thought having that person move into this role would help scale their expertise and also just enable the whole team. And so, you know, this is somebody who is a marketer, but they're deeply technical as well. I think, you know, if I had to, if I had hired sort of a standard engineer to build out marketing automations, it would have taken forever to get them up to speed.Kevin Kerner: 1:25

Hello, everyone. This is Kevin Kerner with Tech Marketing Rewired. This episode, well, I was fortunate enough to speak to Christine Royston, who is the CMO of RICE. It was a really fascinating conversation. I had I had reached out to Christine a couple months ago or so after seeing one of her posts about her really beginning to try to operationalize AI into her organization. I think it's really interesting dynamic that she works for one of the top uh at least project management automation companies in the world. And they've implemented a lot of AI inside of their technology. So it was a really great conversation. I was asking Christine just how she has uh is organizing this year. Um she's really doing some amazing, amazingly innovative things, uh hired a uh AI uh efficiency expert on her team. I think she promoted someone into that role who was really responsible for now activating a lot of the automations that they're doing and is really a guide for their uh internal staff to use. She has a 10% OKR across all of her team to use uh to 10% efficiency goal across all of her team inside of their OKRs. And um she's moved to a pod structure for a lot of her teams to work within. So instead of organizing by departments, they're organizing by pods. So we talk a lot about that. But really, the idea was how do you get a uh large organization, large marketing organization to go from how it used to be done to how it needs to be done now? So it's really great conversation. So I'm really looking forward to you, and I'm really thankful for you hearing it, and I'm really thankful for uh Christine to be on. But before we get started, I wanted to mention that uh this podcast is made possible by my company, Mighty and True. We are a growth marketing agency that helps CMOs identify gaps in their strategy and then help uh put teams in place as well as workflows and automation that help them uh become more efficient and also drive growth faster. And we do a uh 45-minute uh blueprint um session with companies to help them identify where their gaps are and how at least we've seen companies solve for these gaps with um better processes, systems, and automation. So if you're interested in that, let me know. You can email me at Kevin at MightyYintrue.com, or you can uh simply contact us at www.mightyandrue.com. Okay, this one's gonna be a good one. If you're a CMO that's looking to restructure or thinking through your organization, this is a definite must-listen. So let's get to it. This is tech marketing rewired. Christine, welcome to the podcast.

Christine Royston: 3:55

Thank you for having me.

Kevin Kerner: 3:57

Yeah, so good to have you here. I I think we go way back, but we hadn't talked in quite a bit of time.

Christine Royston: 4:04

It has been a while, so I was definitely happy to be reconnected.

Kevin Kerner: 4:08

Yeah, yeah. I think I think even think it might go back to the Salesforce days.

Christine Royston: 4:12

I think so. Yes. Yeah. At least at least that long.

Kevin Kerner: 4:16

Yeah, quite a while ago. Well, I've had all these friends that have that I've met through the years that have gone up through organizations and now they're CMOs and you know, doing all this amazing stuff. I've kind of stuck where I've always been, unfortunately. My friends have gone pretty far, so it's kind of fun to see. But I've always enjoyed working with you, and I was really pleased to to uh do the podcast here with you today. I'm going to just kick things off. There's a lot to talk about here. One of the reasons I reached out to you when I did was they was around the topic of toward 2026 and the fact that we're now, you know, we've been using AI for a few years here now, and it seems like most of the CMOs that I know and have talked to are now in the operational, like they're now they're operationalizing things. And so I think I'll start by just saying, like, what was the specific like as you enter 2026 or maybe 2025 into 2026, what was the specific light bulb moment that was like, okay, we've tried this stuff. Now we need to sort of put real operational strength behind it.

Christine Royston: 5:15

Sure. I would say, you know, it's definitely that drive for continued growth. Um, I feel like in 2025, we still had high growth targets to hit. Uh, and we were using AI in what I'll call experimentation mode. Um, and as we go into this year and looking at our own growth targets, we have to move to operational. I think also just, you know, the trends in the market. Everybody is looking at how do they become more efficient with AI. And if you're not doing that, you're not keeping up. And so I want to make sure that, you know, we are keeping our own team's skills up to date, my own skills up to date in terms of what it means to be a modern marketer.

Kevin Kerner: 5:57

Yeah. Yeah, totally. That's interesting. It's efficiency because there is a lot of pressure now from growing faster, trying to grow faster, but also the budgets are you know not exactly overflowing right now.

Christine Royston: 6:07

Right.

Kevin Kerner: 6:08

How do you keep the there's so much hype around this stuff. If you go on LinkedIn, it's almost like it's better to avoid LinkedIn in a lot of ways, but how do you filter what people are really doing and what they're not doing? Like, how do you how do you keep control of the hype cycle out there?

Christine Royston: 6:22

Sure. So, you know, I definitely look to my own network to to figure out how are people actually using AI, what results are they seeing? Um, there's certainly a lot of great blog posts out there, great LinkedIn posts, but I kind of look to my trusted network to say, okay, I'm hearing that people are able to do this. What have you been able to do? What tools are you using? What change management does it require in your team as well? Because I think anytime new technology pops up or you you have to use technology in a different way, the the other side of the coin is how does it change the way you work, the way your teams work, the skills that they need?

Kevin Kerner: 7:03

Yeah. Yeah, let's get into that a bit. So you mentioned efficiency as one of being the being the primary driver. You mentioned to me on the pre-call something about AI efficiency lead or trying to put centering some roles there that are around driving efficiency. How tell me a little bit about that.

Christine Royston: 7:18

Sure, sure. So certainly as as I went into planning for for this year, one of the things that I am thinking about is how do we how do we upskill a team that is an existing team? We are a non-AI native organization. Right has been around for many, many years. And so I had to think about how do I make sure I'm upskilling and enabling the team so that they're able to do more themselves. Um, and so, you know, I created a dedicated role on my team reporting to me that is focused on AI and automation specific to marketing. We've got an amazing team, and so we're not building the team from scratch. We're looking at how do we move from sort of traditional marketing to AI-enabled marketing without requiring that everybody becomes a data scientist overnight. So I had someone in the organization who was naturally, naturally drawn to thinking about how they could use AI within their current marketing role. And as I was looking at what do we need for the whole organization, I thought having that person move into this role would help scale their expertise and also just enable the whole team. And so, you know, this is somebody who is a marketer, but they're deeply technical as well. I think, you know, if I had to, if I had hired sort of a standard engineer to build out marketing automations, it would have taken forever to get them up to speed. We would have had to explain a lot. So this sort of hybrid type of role means that we were able to get him and the team up to speed much quickly and in execution mode already.

Kevin Kerner: 8:52

That's cool. If you had to look at the type of things that they're doing on a day-to-day basis in their role, like what type of stuff are they doing?

Christine Royston: 9:00

Sure. So, you know, I'd say they wear a couple of different hats. One is one is an internal consultant. So the first thing that I had this person do when they moved into this role was sit down with each of my leaders and really dive into their workflows, dive into the pain points, dive into where where are the opportunities that we could use AI to optimize. So they are sort of looking at all the opportunities and partnering with the leadership team on where where to focus. The second kind of key thing they're focused on is building. So, you know, this person is technical, and so they can actually build solutions and look at how are we using the tools that we have, how are we using the AI that's built into Rike as a product, which runs our marketing operations, how do we think about connecting those things? And then another key piece, which I think is something that's key for any team that's thinking about using AI, is looking at the data. So is the data in the right structure that we can actually use AI so that we've got consistency and results, we can access the data that we need. If we're building an agent, it's pulling from this structured single source of truth, not disparate data sources.

Kevin Kerner: 10:13

Oh man, you're speaking my language. That's so that's so awesome. The tool part of it, like the tech part, I think is really smart. How did you find this person? Did they did they just happen to have some AI tech curiosity, or did they have a technical mindset or background beforehand?

Christine Royston: 10:28

I'd say both. So definitely technical background in terms of their career and and they had moved into more of a technical marketing role in terms of using technology for performance marketing, really focused on the data and the analytics. And the thing that his manager and and his marketing leader observed was that he was naturally drawn to this and just building out solutions for that one team. Um and so we looked at, you know, is there an opportunity for us to broaden that role and make sure that we're able to do this across the whole team? So we we were lucky enough to have somebody in our organization already. But I think that, you know, if if there are leaders out there who are looking to build this type of role and don't have that skill set in-house, I think that there are people out there that you can kind of look at where's that balance of marketing knowledge and understanding of what you're trying to do as a marketer, but also the technical chops in order to be able to build and actually get hands-on with the tools.

Kevin Kerner: 11:28

Yeah, 100%. Like builders are very valuable right now. Someone that's maybe doesn't necessarily know all the coding language, but it's kind of fearless, smart enough maybe to run security checks inside the code that they're building, but it's a very valuable thing. I'm curious, are you getting in any of the tools? Are they teaching you like, hey, check this out? I'm the CMO. I can get in and actually wrestle with some of these tools.

Christine Royston: 11:54

So we have recently instituted um a regular AI enablement session for the whole marketing team. And I join every single one of those myself. Um, our CEO also asked all of his executive team to get hands-on and start to use the tools ourselves. So we want to make sure that we're, you know, if we're if we are setting expectations for our teams, we're actually in those tools ourselves, looking at how they can help us from an executive perspective.

Kevin Kerner: 12:22

Yeah.

Christine Royston: 12:22

And making sure we're we're sort of showing the team that that we're getting into the details and and getting hands-on.

Kevin Kerner: 12:29

Yeah, super cool. What do you have most fun like playing around with? Like what's been surprising to you, like, oh, that worked.

Christine Royston: 12:36

I think the I think some of the tools, like I'll I use Nobook LM to take all these disparate disparate data sources and pieces of information to try to figure out how do I consolidate all of these points of view that are out there. As you do too. You know, there's so many articles, podcasts, LinkedIn posts that have a certain point of view on something, and you want to sort of figure out, okay, how do I take this and and consolidate it and and figure out what my point of view is? And so being able to use a tool to be able to take all of that and kind of give me, all right, what are the differences? What are the commonalities? How do I think about where I want to move the team next? I think that has been super interesting. I mean, certainly, you know, being able to also analyze a ton of different data points internally. You know, if I'm looking at what are the sales teams talking about, what am I seeing in the data, how are certain channels performing, how do you take all these data points and figure out how that changes your strategy or your focus?

Kevin Kerner: 13:40

Yeah. Yeah, super cool. Yeah, it's just, it's an amazing time to be a thought, have a thought partner like we have now. And I think does Rike has AI built in the platform, but is there a MCP access as well in some way? Yeah, so you can literally talk to Rike, right?

Christine Royston: 13:56

Yes, yeah. So we're we're um, you know, it's something that we launched um and mid-end of last year to to think about how do we make sure that people can can access Rike and make sure we know people are are using our tool as a source of truth for their workflows, but you need to always connect to other tools. Uh so you know, moving beyond sort of traditional integrations to think about how do you open up access.

Kevin Kerner: 14:23

Yeah, really cool. When you think about efficiency, because that word can be, you know, tossed around. It made actually made some of the, maybe take the team a little nervous. How do you measure, how is he defining and measuring efficiency? Like what are the factors that you're looking at to say, okay, this is working, this isn't working.

Christine Royston: 14:42

So ultimately we look at time saved.

Kevin Kerner: 14:46

Yeah.

Christine Royston: 14:46

And that time saved can help people focus on different types of work, more strategic work, more creative work. It can help us execute faster, it can help us execute more. And so last year, I set an OKR for the entire marketing team to improve efficiency by 10% for each of their workflows. And I left it open because I wanted them to identify what are those workflows that have the biggest opportunity for efficiency with AI. And I'd say, while specific metrics change whether you're in product marketing or you're in demand gen or you're in content creation, ultimately it turns into time that you can use in other ways. And ideally, we want people to automate the repetitive tasks so they can be more creative and focus on the work that they love.

Kevin Kerner: 15:35

Yeah, I don't know if you've you've experienced this with your team, but it's like there's a you really don't get in your head the efficiency gain through AI until you actually experience it. But then when you create an automation and it takes something away from you and you've got, let's say, an hour or a couple hours back, it's like clicks. And that for our team, it's as a leader, that's one of the toughest things is making sure that we're giving everyone the opportunity to see the efficiency gain. I wonder if you're I wonder if your experience of the same thing in your team. I guess you have this efficiency lead who can help educate and actually build and drive some of the efficiencies. Um, but you're seeing the same thing in your team.

Christine Royston: 16:12

Yes. Yes, because we don't, you know, even though we have this partner for each of the teams to work with, we don't remove the responsibility of leveraging AI from the teams. So we want the teams to be constantly looking at how do I leverage AI? How do I move from experimentation to operation? How do I save the time so that I can spend it in other ways? And so we ask the teams themselves to be on the hook for measuring, you know, what that efficiency is and looking at, you know, what are the hours you get back? What can you apply those hours to? We have the teams share updates in our marketing all hands. We want them, you know, as soon as the light bulb goes off, we want them to share an update in Slack about what they've tried and what tools they're using. So we're constantly trying to drive that broader internal communication among the teams so they can also learn from themselves and think about how they could apply something that, you know, this team has done to the work that they're doing.

Kevin Kerner: 17:11

Yeah. And I suppose the activator for you is they can literally go to the efficiency lead and say, hey, I'm I have an idea to build something. Can you help me build it? And they go to this person. And they don't have to have the fear of actually getting in the tools. They can do it themselves, but they have this additional role that helps them get there.

Christine Royston: 17:27

Right. Right.

Kevin Kerner: 17:28

Yeah, that's super cool. That's a way to act because the toughest part is getting a bigger team activated. You know, getting them on the boat is just having them see what can actually happen. And sometimes technically that becomes a challenge for people. Right. Okay, so let's talk about the or the rest of the organization. You got this efficiency lead, you're doing OKRs around this stuff. Have you done anything else to structure around AI usage? I think you mentioned something about pods on our initial conversation. Um is there other ways that you're thinking about organizing the team to optimize for efficiency and speed to outcome?

Christine Royston: 18:05

Yes. That that um sort of changing the way we work and creating these pods to drive more agility is one one of the big bets for this year. So we have not restructured the reporting lines, we haven't renamed functions, we still have typical functional teams. But what we're doing is moving away from these silos toward building these cross-functional pods that have specific objectives. So we want to make sure that we're driving more collaboration. We're looking at how does a combined team drive more impact than passing it down this conveyor belt of of production. And so, you know, we're looking at what are some big objectives we're trying to drive as an organization. Do we want to drive brand awareness? So creating a pod around that. Do we have a specific audience or industry that we want to drive more pipeline and more bookings from this year, creating pods around that? And what I've done is I've asked my leadership team to identify, you know, a possible lead for each of those pods, but then a member from their teams to participate in every pod. So really looking for teams to make sure that they are comprehensive in terms of, you know, we've got somebody from the design team, someone from the demand gen team, someone from product marketing who's part of this pod. They're part of weekly meetings, they're part of planning, they're part of setting the targets. And, you know, really the a lot of this was around how do we stay agile? How, how do we become more nimble as an organization? So we don't want the teams to wait for strategy to get created by one team and then hand it off to another and then another and then another. And those pods really allow us to act in in real time.

Kevin Kerner: 19:56

Yeah, super smart. Well, we're organized similarly by pods, and it just, it just the silos of traditional like strategy, creative, measurement, all those things, they're seems like you put something. If you put a coin in that thing, you're just gonna get each each like step of the way, you're gonna get something. But when the pod has the responsibility for the outcome, it just activates the whole thing. Does that change the planning process cycle or process? Yeah, how does that work?

Christine Royston: 20:27

It does. And I I'll say we're we're we're still learning there. So one of the things that we recognized was by moving to this model, the way that we're thinking about creating workflows, the way that we're thinking about putting projects in Rike will change. And so we're asking those pod leads to partner closely with our AI lead, partner closely with our operations team as well to say, all right, how does this change the way we think about adding projects into Rike? How does it change the way we we get sign off for certain ideas? We still ladder all of our work up to business outcomes, OKRs, but the team that's building those plans is now a cross-functional team. Instead of teams saying, well, this is the demand gen project, this is the product marketing project, now this is the project about launching this big new feature, or this is the project that is about driving more awareness with this audience in Q2. And so one of the other sort of operational changes that we made was moving to a rolling planning process. So we had been operating on sort of this quarterly cadence of plan for the next quarter, plan for the next quarter, but along the lines of remaining agile and nimble, we're we're just moving it to ongoing three month planning cycles. So we end January, we start to plan for the next three months. It doesn't mean if you've got something that's needs to start being executed. Or planned for in six months, that you don't get it on the calendar. But what we want to do is make sure we're not drawing these artificial lines of a fiscal quarter to kind of hamper the way we are thinking about planning for the future.

Kevin Kerner: 22:13

Yeah, that's really smart. One of the toughest parts of being a marketing leader is having the strategy in your head and the gap between what's in your head and actually getting it done. And the pods seem like a way, a catalyst to be able to say, okay, this quarter, we're working on these things. Could a pod be on a short-term initiative and eventually they're moved to another initiative?

Christine Royston: 22:34

Yes. Yep. So we, while we set the year with, you know, I think we've got five, five uh sort of permanent pods that we've we've identified. We've also flagged to the team that there are going to be these, these short-term or temporary pods that need to happen for, say, you know, our big annual event. We've got to bring a group of people together when we start planning for that to execute it, to measure the results. If there are other things that pop up in the year and we say, wow, that's something that wasn't on our annual plan, but we've got to rally around it. How do we create a team or maybe borrow people from another pod to say this is high priority? We've got to have these resources here. And so I think that that also adds to the agility.

Kevin Kerner: 23:20

Yeah, that's really smart. So you have this command center view of like, here's the pods I have running. They all ladder up to your, let's say, primary initiatives. But if an initiative pops up that you need to get covered, you could spin up a pod and have that pod working on it with their own objectives. It's really I haven't heard of anyone else. I've never I've heard that in a client-side environment before. It's really smart.

Christine Royston: 23:44

Yeah, I I I like it because I expect it's going to drive results, but I also like it because one of the observations I have when teams operate in silos is that people don't always get a chance to see how their work is connected to driving the final outcome or how it's connected to company or team objectives. You know, they kind of do their piece of the project and then it moves on to the next piece. So I like it because people are able to see the impact of their work. And then I think another benefit is great ideas come from everywhere. And I want to make sure I'm unlocking the creativity of my whole team to come up with new ideas, propose them in these pods, even if it's something that's, you know, not part of what their job title says that they do, because now they're they're kind of thinking, they're thinking more about well, what can the whole team do in order to drive this objective?

Kevin Kerner: 24:37

Yeah. Yeah. There's more uh the innovation around a specific objective that's really it's really cool. And like like if I'm the copywriter on a project and I'm in the old way, you're just gonna write your copy and then you're gonna be out and you're gonna, you know, do it. Now you're in in a pod and you can get to the end of the end of the road with the whole thing. So it's really cool. And I I guess I could be in more pods than one pod, right? I could be, I could work cross-functionally.

Christine Royston: 25:01

Yes, yeah. Cause because certainly we have, you know, we've got teams that are small or a single person running a function and and they just have to make sure they're balancing their time. And I'm asking the leads to help with that because I don't want that one person to have to join five meetings a week. But when they need, you know, a certain outcome that this person is is responsible for driving or a certain piece of content, uh, content type, pull that person in, give them a heads up, you know, hey, next two weeks, we're we're looking at this, we want your input on it, we're gonna add you to next week's agenda. And so you can come in and kind of consult with the pod.

Kevin Kerner: 25:35

Yeah. So you're asking a lot of people to be in a pod, but also to have this sort of AI transition, let's say, this AI mindset. Like, is it changing? Are you are you doing things to upskill the people that you have for these things? Sounds like you are with the efficiency lead, but are you also thinking that the profile of a successful marketer in your organization just sort of fits a certain type of person now?

Christine Royston: 26:03

You know, I don't know that the kind of profile has changed. Maybe it flexes in different ways. Um I have always looked for people who are curious. And I think in the age of AI and the speed that we are seeing change, I really need people who are curious and want to learn and want to be able to kind of self-upskill. Certainly, I want to make sure I'm providing the forums, you know, making sure they understand learning is expected. But the type of person who's going to be able to be successful in in the world of marketing that we are moving into, I think you have to be curious. You've got to be flexible. You have to be a good collaborator because you know, you're probably working with even more teams than you have in the past. So I think it's it probably makes some of those core things that I have always hired for even more important.

Kevin Kerner: 26:58

Yeah. It's really, really uh interesting. I do think that skill set being more of a T-shaped or a pie-shaped individual is becoming more important. Okay, so you've been at this now for all of a month and a half in 2026. Are there any use cases or surprises that you've run up against so far as you're starting to implement this?

Christine Royston: 27:20

You know, I think the uh I think the one the one surprise has probably been that I think some teams, I think some teams naturally saw results with AI last year early on. And so they are way further down their journey in terms of how how to use AI and and the opportunity there. And other teams, you know, maybe just haven't had as much experimentation. And so what I need to think about is, you know, it's not the entire team moving from experimentation to scale. There are some teams who are already on the scale piece, and there are some teams that are maybe, you know, toward the end of that experimentation. And so I have to figure out, you know, how do I recognize where people are in their journey and make sure that I'm helping them to accelerate as quickly as possible. I think one of the other things that we have found as a core requirement is before implementing AI at scale, you have to look at your processes first. So do you have the processes documented? Are you revisiting those processes? Because in some cases, you know, those processes could have been built two predecessors ago. And now's the time for you to relook at it.

Kevin Kerner: 28:39

And then without AI too, without any thought for systems or AI.

Christine Royston: 28:43

Exactly. And then apply AI on top of it. So we we are looking at all of our processes. We're looking at how we use Rike to move those workflows along. And then how is AI going to change those processes? Do we need to look at can we tighten up the the number of people that are are reviewing something? Now I'm definitely a proponent, proponent of human in the loop. I think you still have to be involved, but maybe it allows us to shorten that review cycle. Um, you know, maybe it allows us to skip a step that we had to manually do in the past because now AI can push it up, push it even further before it gets to a person for review. And so it's it is something that we are, we're looking at. It's not only rolling out AI, but it's thinking about how you work, how can you optimize how you work, and also when you're applying AI, making sure you've got really clear definitions around, okay, who owns the input for this model, who owns creating the messaging for this ICP, who owns the templates of what does a good LinkedIn post look like? Really making sure that you're creating that clarity around ownership when you're using AI. So you don't have three different people using a new AI-enabled workflow in different ways.

Kevin Kerner: 29:59

Yeah. And you know, the other thing that gets me thinking is you're kind of in a little bit of a superpower position being at a project management sort of workflow tool. It's like you have the keys of the kingdom. And I'm I'm a I'm gonna be amazed to watch uh that category of tools advance over the next probably, I don't know how long, six months to a year or a couple years. It's gonna be hard to imagine how much the use of AI can actually influence workload, task assignment, communications across different tasks and subtasks. It's just like there's a lot that's been done in those tools, but there's still so much to do. Is there innovation that's happening in your team because you're at one of these innovative like software platforms? Are you using the tool to do that in unique ways?

Christine Royston: 30:50

Well, so for us, everything is in Rike. It starts and ends in Rike. You know, we like to say if it's not in Rike, it doesn't exist. And so we we certainly I think benefit from the fact that we are using our own tool every day and looking at how we can use our own AI innovation, how do we use AI in general in terms of how we think about workflow? And like you said, it is a fascinating place to be right now because so much of the way that AI impacts work is how work gets done and how workflows are built or optimized or changed. And so I feel like we're in this great spot where we're looking at, you know, the humans and the technology coming together with the data and the government, governance that you need, especially if you're thinking about supporting, you know, complex use cases, regulated industries, any place where you've got to make sure that you're thinking about scale plus governance and making sure AI is is used appropriately.

Kevin Kerner: 31:51

If I went, if I was able to walk the halls there and go into like the back room, like the special like experimental room that's somewhere then with all the engineers and scientists, what are they working on? Like what is the next, what is the next thing that you could actually tell me? Is there some advancement that's coming that is gonna just blow everyone's brains?

Christine Royston: 32:10

So I I mean I'll talk about something that just launched recently, was is just around uh, you know, our agents in Rike and thinking about how how are you using those agents in places where humans might have had to push something along or analyze our all of the inputs in this brief. And I think you know, it's something that uh that we previewed at our collaborate event at the end of last year. It went GA just recently, and it is something that I'm super excited to see, you know, our general customers use. The customers that have been deep into it already have some pretty amazing use cases, and certainly our our teams are looking at it. And as a marketer, I look at all the things that it can do from intake to flagging risk, and I just think it is definitely going to help at all levels.

Kevin Kerner: 33:03

Yeah, I am uh I'm rooting on your engineering team. I'm like, yeah, bring it as much automation as possible. Because the whole thing about like moving a task from here to here or seeing if something's done and not done, and reminders, a lot of that's done through dependencies right now, but it's just seems like there's I know it's gonna happen. It just can't happen fast enough for someone that uses, you know, we use project management a bunch, but it's I'm excited for to see what's coming, that's for sure. Has there been any, just a final question, has there been any uh big use cases of AI on your team that you've you've seen like this, we did this and it really improved speed to outcome or efficiency in some way? Like what's been the biggest success that you've had? And maybe too early.

Christine Royston: 33:48

I mean, I think if I look at some specific places where we have reclaimed hundreds of hours, um, there's some functional specific examples. And of course, my goal for this year is to look to look at, okay, how do we how do we actually estimate the hours across the whole team? How could we take this connected workflow, something that a pod is working on, and and say we save this many hours uh combination of AI plus just operational efficiency to deliver a project from beginning to end. But if I look at some of the the specific teams, so on our SEO and web team, we looked at using AI and and automated redirect mapping. So this is something that, you know, basically the team was able to see a 90% efficiency gain.

Kevin Kerner: 34:39

Yeah, it takes forever to remap map redirects.

Christine Royston: 34:42

And it's not fun.

Kevin Kerner: 34:43

No, it's just a pain. It's like, why am I doing this?

Christine Royston: 34:46

Yes, exactly.

Kevin Kerner: 34:47

That's a good one.

Christine Royston: 34:48

Um, also, you know, on a totally different side of the marketing spectrum, on our analyst relations team, um, you know, we we look at answering the requests for information that come in. It's it's important to think about how are you getting the right information to the analysts. And the team was able to save 25% of their time using our own internal AI portal to draft responses that experts internally can review rather than just starting the writing from scratch.

Kevin Kerner: 35:18

That's awesome.

Christine Royston: 35:18

So just being able to leverage all the data that we have out there in a single place in our AI portal and and create these responses. So that team has saved a ton of time. Again, being able to use that time for conversations with the endless or or thinking about different types of areas that we want to move into in terms of conversations.

Kevin Kerner: 35:39

Yeah, that's great. Early results like that are fantastic. And I guess your sounds like your CEO is pretty bought in in the leadership team. So they're probably interested to see, hey, marketing is saving X percent, 10% over the course of the year, whatever.

Christine Royston: 35:52

Yes.

Kevin Kerner: 35:53

If you if you come up with that metric, it'd be really interesting for you to build it in public and show people the actual attributes that because a lot of people are, I think, wrestling with what is efficiency, what are the what are the inputs to get to an efficiency number? Um, and there's of course every company's different, but yeah.

Christine Royston: 36:11

Yeah, and and certainly as people are using our own tool, we're working to figure out, you know, how can we help our customers understand the impact of using AI in their workflows? So we're looking at how we can think about that calculation to be able to show people that impact.

Kevin Kerner: 36:29

Yeah, really amazing. I'm sure a lot of CMOs are going to be listening to this, getting a lot from it in terms of the plan for 2026. If you had a, you know, we're sitting down over a drink with a fellow CMO and they wanted to get started, where would you point them to? Like do the do these two things first.

Christine Royston: 36:46

Uh you know, I think I think the making sure that you are vocal, you know, internally about the expectation with your team that this is a tool that if we are not using it, we're behind. And so I expect you to be leveraging AI and thinking about how you can use AI. And we, you know, last year we we put some practices into place to make sure it's not just me saying it in an all hands or me having a one-on-one with my leadership team and saying this, but we spotlight it in our marketing all hands. We certainly put it out via blog posts and external content. We also created a team award um around people who are innovating with AI. So trying to make sure it's even more visible. So I think setting that expectation that it's not, you know, it's not a nice to have, it's must, it's a must-have. And one of the things I tell my teams is if you're working in tech, you've seen, you've seen these transitions throughout your career. This is the fastest, most exciting technology transition I have ever seen. Yeah. And so I think to be a marketer in a company that is uh, you know, all in on this, we're building it into product. This is great from a career perspective. You've got an opportunity to look at how do you build your skill set, how do you um drive change in this time of technology transition, because there will be another one in, you know, the next couple of years. It doesn't stop if you're working in tech. So I think that's one is just making sure you're you're vocal and you're setting those expectations. And then I do think, you know, if if you can identify a resource, and maybe it's not a full-time resource, but it's someone on your team, even part-time, who can be kind of that go-to to help upskilled team members, help to build out the technology. I mean, I think, I think um, you'll be able to show the ROI on that type of role very quickly to build the case for your CFO or your CEO to think about funding that type of resource.

Kevin Kerner: 38:50

Yeah, great advice. I mean, you just need some people just won't get their hands in the tools till they see it work. The person, the putting that person in place is really smart and have them actually be a builder. Because it's scary sometimes if you're not technical. Like, I don't, I don't know. But and some of the tools are a little advanced. And so, yeah, having that as a catalyst is great. It's kind of funny you said the the like this is the biggest change of our time being in tech for so long. It's it's it's not great being as old as I am, but I at least have the context of what it used to be like back in the day. So I know how big this change is, and it's a really big one. She did some people, maybe that are younger, might be, huh, these things must come along all the time. But it's not not really that way. Kind of crazy.

Christine Royston: 39:32

Right.

Kevin Kerner: 39:33

Okay, so I have one more thing that I wanted to do with you. It's called our AI roulette question. And so I have loaded your profile and some of the questions we're going to talk about into perplex, or actually Gemini. We changed from perplexity. So it's basically gonna give you a question. It's gonna be the best question that I asked today because the AI wrote it. So let me press sun and I'll tell you what it is here.

Christine Royston: 39:53

Right.

Kevin Kerner: 39:56

Okay. Christine, if you had to conduct a performance review for your collective fleet of AI agents today, what is the one soft skill or personality trait you wish you could program into them to make them a better teammate for your human staff?

Christine Royston: 40:13

That's a great question.

Kevin Kerner: 40:15

I told you. You know me. That's it's gonna be the best question.

Christine Royston: 40:19

So I would say, given where we are with our AI journey, I think the soft skill that I would ask our agents to use, I think it would be collaboration because I think we are still building and I want to make sure that the AI agents are working with the humans to build. Yes, they they are gonna be able to come up with a lot of new ideas, but I I want the humans involved to say, okay, but at what point do I come in to review something? Or you've proposed something that may technically look like a great idea, but uh, you know, the way our organization is structured today won't quite work yet. Or, you know, the way that we think about getting approvals from customers aren't are on something, it won't quite work yet. So I think collaboration would be the key skill.

Kevin Kerner: 41:19

It's a really good one. I was just in um, I build some stuff in Claude Code and I always try to ask the agent in Claude Code how I'm doing. And and every time it codes something at the end of it, I ask it for how about what's happening? Do you agree with the plan? Should we be doing something different? I run run security check on the code, all the things, because if you can get the agent to actually give you advice, no, or if the agent knows it can give you advice and it's you're not, it doesn't need to just agree with you. Right. It gives some really good stuff. It rebuilds stuff entirely. So I think that's really good. I mean, if you're building GPTs or whatever, you can easily build that in. Super smart. Love it. Well, Christine, this has been great. I hope to catch up with you again sooner than I have in the last uh couple of years or so. But um it's been great talking to you. I know people want to follow you in the Reich journey. Uh, how should people get a hold of you if they want to um get more details on what you guys are doing?

Christine Royston: 42:13

Sure. They they can reach out to me on LinkedIn uh or they can follow, follow Reich on LinkedIn as well. We try to put out some interesting content and and share our own journey as we continue to evolve here.

Kevin Kerner: 42:25

Well, thanks so much, Christine. Great seeing you.

Christine Royston: 42:28

You too. Thanks so much.

‍