Moving Faster Without Breaking Everything - AI, Risk, and the Human Side of Change with Andrea Tarrell

Hi, I'm Michael Hartman.

I'm Naomi Liu.

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Hello everyone, welcome to another episode of OpsCast brought to you by marketingops.com and powered by all those MoPros out there. I am your host, Michael Hartman. Today I'm joined by Andrea Tero, President of the Tech Services line at Trilliad and CEO of Circante, a Trilliad company. Andrea has spent years helping organizations navigate marketing operations, Salesforce ecosystems, data strategy, and large-scale technology transformation. But our conversation is going to be less about the technology itself and more about the human side of change in the AI era. We talk— we are going to talk about why so many organizations are struggling to move quickly, how fear and risk tolerance are shaping AI adoption, why governance models may need to evolve, and what separates organizations that are actually activating AI from those that are stuck debating it. Should be a thoughtful conversation about speed, trust, process, and the trade-offs companies are making right now as AI shapes how go-to-market teams operate. So with that, Andrea, welcome to the show.

Thanks for having me.

Yeah, this is great. Well, it's been a while for us to get this started, so, When we talked before, you know, that we've talked about that for years, the hard part of operations work was building technology, but now it seems like technology is getting easier, at least more democratized maybe, while people are, you know, while the people and process side is getting harder. So, A, do you think that's true? And then B, like, what do you think has fundamentally changed that has kind of led us to that?

Yeah, we see this everywhere. And obviously kind of being in the technology consulting space, like we have a lot of respect for like the very smart technical minds and technologists that build awesome solutions for customers. But that soft side of change, like the human side, is more relevant than ever. And I think we're seeing that right now with AI and just Like convincing people to use the tools and to its fullest potential. That's where a lot of the hard work is happening right now. And it's not a limit of the tech platform that's holding us back, but just kind of where the rubber meets the road with the team. And something that illustrated this for me recently was like, I think the stereotypical tech founder used to be like the antisocial, dude in a hoodie that was a coding expert and was respected by all the developers. But the Wall Street Journal had a headline last week where Silicon Valley VCs are sending their tech founders to etiquette school to figure out how to talk at a cocktail party and social etiquette and social norms. And I think that really emphasizes how important it is to pair The human element with the tech element if you really want to have change that sticks.

Yeah, it's interesting. It's funny that you bring that up. I didn't see that headline or that article, but having kids who are in that kind of college age, right? I think it's interesting to see how the interactions for them are different than what I remember when I was their age. And I'm, you know, free internet and all that kind of stuff. And there's all kinds of books like I just finished reading a few months ago, The Anxious Generation. Well, truth be told, I didn't read it. I did the audiobook, but I, you know, and I told my kids, it's like, you need to read this because this is about you, right? This is, but a big part of that is about how just in general, like communication is not happening on a regular basis. There's another article I referenced a couple of times in recent podcasts that somebody shared with me that And I haven't dug into the research, but somebody's done some research that says it's something like over the last 10 years, and I don't remember exact 10 years, it wasn't through 2026, but say 2024 or something. There, um, there's been a re— like a 30% reduction on the average number of words spoken by people. And I think it's fairly global if I remember right, but certainly in the, in the, in the North American space. So all that makes sense.

That was so fascinating. Fascinating. And honestly, like, the— I think, like, ops folks have been on the front lines of this for a long time, but, like, you can build the most perfect tech solution in the world, and if people don't use it the way that you expect, or you can't win over hearts and minds with the change that you're looking to drive, like, it's just, it's never going to succeed. And I think we're just, we're seeing that now more than ever.

Yeah, I think that's true. I mean, it's a combination of this, like, rapidly evolving technology, plus So these other social economic kind of things playing into it. So, so, oh, so let's go there. Like, so how much of this is, is for AI adoption? And I guess specifically is like, how much of it do you think is a technology one versus a trust one or change management? Or is it something else completely that's like causing it to be a little bit, because it feels like it's very uneven how it's being adopted across different organizations.

Yeah, I, I think it's primarily a change in a leadership problem. Um, I mean, okay, the tech tools are like, they are what they are. They're not perfect. There's certainly like still gaps in LLMs and some of the, the tooling that's available to us. But in general, like the potential of the tools has far outpaced our ability to effectively leverage them within our, within our organizations. But I think too many leaders are coming at AI with the angle of like, okay, this, we're gonna use this to eliminate jobs, or we're gonna use this to like work so much harder. And it's just, it's not really falling the, they're not striking the right tone with their team.

Yeah.

And individual contributors are looking at their heavy workload and the tools that they're being provided to do it. And when they're asked to innovate with AI, They're thinking like, who is this for? Like, is this—

How in the world am I gonna fit this into my day-to-day job? Well, I—

Yeah.

Like, yeah.

Yeah. Is this so the CEO can get a raise? Is this so I can like, like produce more hours? Like, like who, who is this for? And, um, I feel like there's not enough messaging and communication and like trust building around like, this can be a tool for all of us. Like, it doesn't have to be a, a tool that lets you Turn 40 hours of work into what used to take 60 hours to output. Like, this can be a tool for us to all be more effective, to live more balanced lives, to generate better outcomes for the company, but also for individuals. And I feel like we're overdue for a narrative shift around like, what is AI and what, who is it really for?

Yeah. I mean, and it feels, that point about individual contributors, I don't even think it's individual contributors. I think also middle managers are under a lot of pressure. I think, and you know, it feels like we're recording this late May 2026, and it feels like in the last few weeks, it feels like almost 2, 3, 4 a week, there's announcements of companies cutting workforce. And on the surface level, they're saying a part of that is AI driven, right? So if you're coming in as a leader saying we need to be doing more with AI and you're hearing as a person in these organizations about all these jobs being lost. It's like, why would I spend the time if what you're trying to do is eliminate my role?

Yeah, 100%. And I feel like those headlines of the mass layoffs because of AI are not doing any favors for the cause because the vast majority of those layoffs don't even really appear to be AI or efficiency-led. It's just like, A convenient label to excuse why you're having to trim your workforce. And I think that, like, AI is gonna take your job narrative, like, has a lot of team members rolling their eyes because it's like, okay, the person in the C-suite that's making that proclamation doesn't even know what their day-to-day job is. Like—

Right.

So, like, how are you gonna automate away a job that you don't even understand or don't even, like, Don't even understand why it exists. In thinking about AI adoption in an organization, like, I think it's less about like resistance to change in general and more like, how do we give people a reason to wanna change? Like, what's the pot of gold at the end of the rainbow if folks are leaning in? And how do we create space for that change? Because—

Right.

If your plate's already full, you have like, X number of hours to execute like the, the tasks that are on your plate.

Yeah.

You're, you're not gonna be able to pull up and spend an hour or 2 like redefining your workflow or testing different LLMs or like researching new tools or learning new things. Uh, we have, we have to create that space for learning and evolution. Um, and it, it, it can't just fit in the cracks of like what else is happening day to day.

I would love to get your take. So there was somebody who I admire, a CMO on LinkedIn, I guess it was last week. It may have been over the weekend, but certainly within the last week, it posted something about how like adding that extra layer of work on top of an already busy person is not likely to be successful. And the claim he was making was that essentially it feels, because I kind of pushed back, I was like, to me, this is not all that much different than other technologies that have come in. And it could innovate and make a job more efficient or whatever. And his take on it was like, this feels fundamentally different. And I'm torn because I don't know if it feels fundamentally different because it's like where we are in the middle of it, or if it truly is different than say, I don't know, the steam engine, right? If you go far enough back. What's your take?

I mean, one of the pieces that feels different to me is Like a lot of talk about like making work more efficient assumes that like the outcome is like the output will be higher. So like if I have 4 clients and now I can use AI to be 30% more efficient or save X number of hours, then maybe I can service 8 clients in like the same amount of time. But what we're seeing happen more often is like small tasks are getting compressed. So what used to take 30 minutes now takes 5. Um, and what's being done with that extra space is not more output, but higher quality. And that's really hard to quantify and really squishy. And so I think like measurement frameworks and how we think about productivity is going to need to evolve to, to keep up with that.

Yeah.

Um, because I, like many of the stats that I see about from AI tools are like, Okay, we took this workflow from 3 hours to whatever timeframe. And, but CEOs are looking and CFOs are looking at that and saying like, where did that time efficiency go? Like, it doesn't mean we are all of a sudden hiring X percent less people or people all of a sudden have 8 hours of free time that they never had before. Like, there, there's a very long backlog of things that can fill people's plates once things are made more efficient in the day-to-day. And so it feels like it's harder to capture some of those efficiency gains or measure it than it has been before.

Yeah. I mean, to me, one of the things I've seen, or I think would be a benefit of sort of getting that more of that time back. So I'll tell you, I'll put it in the terms of like a demand gen marketer, right? Who's got to build out campaigns. And say they've got this great idea and it ends up working well and they go like, oh, this is our audience. So rather than sort of segment that audience into smaller chunks, it is because there's time constraints, you sort of cast a wider net that's a little less, a little more generic, a little less on point for any one of those subsegments. And then you kind of hope for the best where I think like, now if you could take that and go like, actually I can now, because I've got more, I've got a time benefit from using AI, I can then narrow those into 4 separate things. They're all part of one big thing and get better messaging and better results for each one of those. Like, it's a multiplier effect on output or not output, but results. Like, and I think that to me is like, but you're right. Like, how do you measure that?

Yeah. How do you say that thanks to AI, like we moved the needle on on click-through rate, or we move the needle on like opportunity influence for this certain content channel. It's really hard to draw those like super crisp conclusions.

Yeah. And then you get into things, if it's things in the marketing, right? You get into the whole like, is attribution and like, what should marketing be measured on anyway? Right. And who gets credit for it? And I think it's like, It's a little bit of a circular problem that doesn't have a real good answer. I hadn't thought about the, that squishiness of being able to measure the impact outside of the efficiency one. Like, that's a really interesting point there. So do you, like, do you think that some of this challenge is really, is it because this is such a different thing and that there's this fear of replacing jobs, or do you think it's just Is there inertia in some of these organizations that is preventing that? Like, to your point, like not creating the space to build on those skills and capabilities?

Yeah, I mean, I'm torn, 'cause there definitely are some parts of this shift that are like new and novel and unprecedented, but there's a lot of similarities with like every wave of tech change that has come before it. And I think maybe the primary thing that's different is it's just faster and the fear factor is dialed way up. Like the narrative around like, you're gonna be left behind, you're missing an opportunity, your job's gonna be eliminated, you're gonna become irrelevant. Like there's so much negative talk about it that it, it's just, it's a lot of noise. So—

Yeah.

I'm torn on like, is it, is it, Same old, same old? Is it like new and novel? I think it's a little bit of both.

Yeah. So like one of the things that I hear people say is, right, AI won't replace your job, but, you know, people who use AI more efficiently could replace your job. Like, what's your, do you think that's like, I think we talked about this before, but like, how do you think about that? Does that resonate with you? And then how are you seeing that play out in reality with the way leaders are working with their teams.

Yeah, I think that that is a very true statement when you think about it at the organizational level. And we've tried to talk through this with our team internally at Circandate and Trilliad as well, because if you're not evolving, somebody else is evolving. And we're not looking at our team saying like, oh, if you're not using AI, we're going to replace you with someone who is. But the reality is like, our competitors are, are racing to figure out how to be more efficient, be more nimble, um, charge higher rates, deliver the same outcomes in less time. And if they crack the code before we do, like, we're all, we're all gonna be outta jobs. Like, we're, we're just slowly gonna start losing more deals. We're not like, so, and we can't control the future unless we actively participate in it. So we wanna disrupt ourselves So that we as an organization don't get disrupted by people that are more nimble and able to embrace change.

Yeah. So, and then there's this pressure, like it feels like everyone's being told like, go do AI or incorporate AI into your day-to-day activities and to do it fast. But then there's, I think then there's, you hear stories about IP being exposed and things like that, right? So how are you seeing people manage that, I guess, tension, for lack of a better term, between, yeah, wanting to move fast and to minimize the risk you're talking about while also minimizing the risk of potential downside of AI being incorporated?

Yeah, I feel like AI is challenging us to redefine how we think about security and governance more generally, because organizations that are super conservative in their posture towards that, they're just, they're not innovating. Their teams are frustrated because they're filling out 14-page questionnaires about the security of a tool that they want to just pick up and tinker with. And so IT and security are becoming bottlenecks and barriers to innovation rather than leading the organization through this change.

Yeah.

What we see happen more often than not is like shadow IT popping up in response to that. Like if it's too hard to get approval for a new tool, people are just going to sign up for a license with their personal email address and use it on the side. And That to me, that's the worst possible outcome because you're not mitigating risk, you're just removing your visibility to it. And that's not the solution.

It's so funny because I was thinking, I know some companies that like, they restrict access to like LLMs in general, right? Through IP stopping. But here's what they don't stop, right?

100%.

You know, our personal devices, And it's funny you bring up the shadow IT. It's like, this reminds me, the reason I was sort of laughing is because this reminds me exactly the same thing I dealt with when I was doing, I was asked to take on an initiative to try to reduce what I would call is like, not reduce, it's sort of a similar thing, right? The initial was like, go reduce cost on support as revenue was increasing and typically it had been in parallel, right? The way I framed it was, Twofold, right? One, I want to bend the cost curve for sure, but that doesn't mean we are going to cut costs. We're just going to change the rate of growth, which is a different thing, and we're going to capture more of it. But in the process of doing that, working with the support teams and the leaders, like it turned out because there was no centralized sort of knowledge base of ways to provide answers quickly to customers when they came up, is they all had these, they had a a document on their desktop or a spreadsheet, or they had their own little database. So they already had the solution that we were proposing, even though there was resistance at the top.

Yeah.

That we should do something centralized. It feels like, this is why I'm like, like, it feels, this is why I struggle with like, is it really different or not? To me, 'cause I've gone through something like this personally where I'm like, yeah, like I think the way you address it is not to say, It's going to replace your job. It could, right? We do need to adapt to it and adopt it. But the way that I, in that case, right, it was like, this is going to free you up to work on the more interesting, challenging issues, right? If that's what you care about. Now, if all you want to do is come in and check a box and, you know, you know, we solved some things, then it's probably not the right thing for you. This is probably the same case for AI tools. So, I mean, are you seeing stuff like that too? Like where that messaging is kind of not going through the way they expect it to as leaders are communicating it?

Yeah. Yeah. No, I, I see exactly what you're, what you're seeing there. And, um, the example of like reps with their own spreadsheets and Google Docs and like things saved to their desktop or saved to their like notes on their phone or whatever the case, like Um, I mean, everyone has to find a way to get their job done. And yeah, if you make it too difficult to go through the, the, the, the main channels, people are gonna find side doors to accomplish what they need to accomplish and keep things moving in their job.

So, no, I mean, that feeling about this stuff as being hidden from, like, if you don't open up that, then this stuff is gonna happen, it's gonna be hidden from you, is a really big thing. I don't think a lot teams think about?

Yeah. Yeah. I think that like the best hope is to make like the, the secure way of doing things the easy way of doing things. Like give your team the tools that they need to do their jobs. Give those tools access to the data, um, needed to provide context to, to power some of those LLM conversations and like pro— make that the default versus Like forcing people to find these side doors to get done what they need to do.

So I guess to some degree this implies they're using frameworks for governance and whether it's data, technology, things like that that are rooted in, do we call it pre-AI? Is there a term for pre-AI world models that seem like they don't work as well today because there's, I mean, the one thing it feels like there's a proliferation of this, like these small one-off tools with like purpose-built with AI underneath the hood that solves specific problems that people can get used very quickly. So is that, I mean, do you think that's part of the problem is that mindset of like how you manage this from a governance standpoint and the technology and risk management? is going to change?

Yeah, I do think the pendulum is swinging like the, the, a different way than it has for the last decade on the talk of point solutions. Because for the last several years, we've been talking about, okay, how do we eliminate point solutions, bring everything onto a central platform or system of record and like eliminate tools that do the same thing? But with the way that AI has moved, To your point, like, there's a lot of individual things that do a task super well. And so instead of like, okay, it has to be all on a single platform, it's like, how do we connect sort of all this infrastructure so that we're not copying data, we're not moving things around in an insecure way, but we're picking like the right tool for the task? And also recognizing that with how quickly things are moving, the right tool for the job in 6 months from now may be totally different than what's the best like now.

Yeah.

And how do we make it, like, how do we prevent, like, being handcuffed to vendors? How do we make it easier for our teams to experiment and move quickly? So, the narrative on that has totally shifted.

Yeah. So, for the audience that we have is ops people, like, how should they be thinking about managing this speed versus agility versus control and risk management? those trade-offs between those things? How should they be thinking about doing that?

Yeah, I mean, I feel really strongly that speed and agility are like, that's the top priority right now.

I'm with you.

And too many organizations operate in like a slow, very waterfall thinking, decision by committee type of approach. And it's just, it's resulting in organizational gridlock where nothing gets approved and Yeah. Folks have to work around the system. If it's like a 6-week approval process to run an experiment that takes like all of 6 minutes, you're on like the wrong side of the risk to speed trade-off.

So, I don't think that's just a theoretical thing. It feels like that's been a real experience that you've seen.

Oh, yes. We see that a lot, unfortunately, in our customer orgs.

Oh, that's crazy.

And something that our team talks about internally that I love as a concept is like asking yourself, what is the blast radius of a mistake? Like if it's—

Oh, that's an interesting way of thinking about it.

If the impact can be contained, cleaned up, triaged quickly, it's probably not that big of a deal. And you don't need to have sort of a really robust posture around like mistakes that will cost you very little.

Yeah.

Building an email in production versus a sandbox is an example of that. Like, the maximum damage of doing it wrong in prod is usually not that high. But on the other hand, if an oops moment is like, okay, you're losing data, you're getting fines, you're the next big headline, like, obviously, you need to have your ducks in a row on it from a security perspective. So, like, what are— another, an alternate term for that is like, Amazon uses the term like one-way door versus two-way doors. Yeah.

Yeah. Like, I think you're the one who told me about this now that I think about it, 'cause I've been, that's been in my head a lot lately. Yeah.

I love it. It's like if you could just walk back through like, oop, I don't wanna do that. Like, don't overthink it. But if it's like, all right, this is a permanent decision that's gonna have lasting consequences, right? You, you obviously need to think about those a little bit differently.

Well, I love the, I love the addition to that model or framework, the, the blast radius one, because I think They're kind of addressing 2 different sort of components of a decision-making process. One is, if I make this decision to move forward, can I undo it and go back to where I was or go to another door? The other one is, if I go through that door, am I exposing me or our organization to a risk that is actually one that is, even if I could go undo it, right? Once it's out there, it's out there.

Yeah. Yeah, 100%.

Uh, okay. Yeah, I like that combination. That's a, that's a really interesting way to do it. There's probably a good, someone's gonna come up with a good acronym for that. Um, kind of, uh, some sort of good matrix will come outta that, you know?

Yeah.

Um, yeah, so that's interesting. The, um, like I still struggle with, like, I don't think a lot of companies or organizations think about trade-offs like that, right? They think they, they don't think of 'em as a kind of a spectrum or in this case, right, 2 dimensions at least. So how are you trying to get your clients, or it sounds like you do a lot of this internally too, applying what you do with your clients. Like, how are you doing that with clients? Like getting them to start to think a bit differently? Because I did, like the one thing, like I said, I'm struggling with, like, is this a fundamentally different thing than past big changes? Like the one thing that does feel like it is different is that The amount of new stuff, your point about like 6 months from now, things are different. Like I've been here struggling. I invested in building out a lot of deep knowledge about me and how I work with ChatGPT. It just happened to be the one I used. I keep hearing all these great things about Claude. I'm like, should I jump and move and like all that work? And I'm like, I can't decide, but then I'm like, they're going to leapfrog each other. So like, is that, I mean, are you like, is that really the fundamental change that's happening now is it's just changing so quickly and it's hard to like go like, Am I locking myself into something I can't undo?

Yeah. Yeah. I think the concept of like shelf life is rapidly evolving. 'Cause I mean, we used to talk all the time doing CRM and marketing automation implementations, like you wanna build for the long term. Like you wanna pick a platform that you're gonna be on for the next 3 to 5 years and not make architectural decisions that you're gonna have to undo. Uh, I just don't think that's the, the reality anymore. I mean, like the, the big LLMs like ChatGPT, Claude, Gemini, um, it's been fascinating to watch how like one pulls ahead, one drops behind, one pulls ahead, one drops behind. And yeah, where my head is at with that is like, I don't wanna pick a winner or—

Right.

Like make a long-term commitment to any platform until I absolutely have to. So like, right, shorter-term contracts, allowing our team to choose the tools that they wanna use to, for different types of tasks, um, and testing the models against each other.

Mm-hmm.

Um, feel, feels like where we need to be right now. Um, I, I've gotten to a place where most sort of like routine queries, if I, if I have the time, I, I'll put it in the Gemini, Claude, ChatGPT, and it's fascinating to see where where some miss and where some don't. And, um, it's just a great sanity check that like we're not really ready to hand over the full keys to the, yeah, the AI overlords just yet. Like, the human in the loop definitely needs to still be in the picture, um, because no, no model is getting it right 100% of the time.

Yeah, it's interesting. I've, I've only done what you described a few times. I should probably get back to trying that. But one thing I did do recently was collaborating on a project with someone, and I was using ChatGPT, that person was using Claude. And, uh, generate something out of Claude. I used ChatGPT to evaluate it, tweaked it. Like, I think the combination ended up being something better than either one of them would've done by itself too.

Yeah. Yeah. I think there's, there's a lot of value in that, like, like multiple team members using different models and, yeah, uh, using it to come up with the best possible output.

Which like now gets back right back to this, like, how do you, like, this is why that idea of giving people a little more freedom to pick and choose what they're comfortable with. As long as the— I think this is where the problem of these people who are thinking that middle managers are going to be less and less relevant, right? I think this is where they can play a role is like evaluating the output and guiding and helping people Independent of whether or not they know how the LLMs all work, which they probably should know at least to some degree. But like, that to me is one of these fallacies that's out there. It's like, oh, this work is going to come out and it's going to be better, more efficient, et cetera. But is it really? I mean, I think— Yeah.

Who's the judge of that?

Right. I mean, somebody I heard talking the other day, and I think he kind of alluded to something like this earlier in our conversation today, Is that if you are a low, you know, a bottom 50% kind of person of whatever it is you're producing stuff that you can now use LLM, like you're gonna, your work is gonna get elevated. But if you're in the top 50%, it's actually gonna bring it down because the models essentially are geared towards getting to the middle, right? What's average or typical? And like, I think that's When I started hearing that, I was like, oh, that fundamentally describes my experience, right? When I'm dealing with something that I know really well, I don't find the output as good.

Yeah.

Are you seeing that? Like, does that ring true with you too?

That does ring true with me. And that I do think that the power of human discretion paired with these models is really important. And on that like bottom 50%, like we, I see, I see some folks that maybe weren't the highest volume producers in, in the past era. Like now you can get Claude to spit out a like 50-page, 50-slide deck for you that like looks really smart, sounds really smart, it's really beautiful. And, but when you start digging into it, it's like it, it just looks smart. Like it isn't, it isn't actually smart.

Yeah.

And like that discretion of like, okay, the LLM's gonna take us through the first 70, 80% or whatever, but that human is refining the last 20% to make it really commercially viable and impactful and just sense checking it like that, that 20%, we're not, we're not gonna lose the, the value of.

Yeah. At least I, I always, I, I wanna typically like, Doesn't feel like that's anywhere in the near term, right? Maybe one day. Yeah.

And honestly, I wonder if this problem actually gets harder over time because to your point on like the draw towards the average.

Yeah.

Like if I'm writing an email in ChatGPT and you're writing an email in ChatGPT and we're just sending them back and forth to each other and the models are getting trained on like more AI output, at one point, at what point does, do we start losing variability and like everything just sort of becomes like Mid. Um, yeah, it'll be, it'll be interesting to see how we navigate that.

Yeah. I mean, that's kind of, um, a term I like to use is like this, there's a fallacy of best practices, which feels like it's a similar thing, right? Where people are like, oh, this is the way everybody does it, so we should do it that way. Yes, but—

Yeah, I'm with you 100% on that. Yeah. I love that term, the fallacy of best practices, because they're best practices for a typical use case or an average scenario. It doesn't mean that it's the silver bullet that's going to work for you in your exact situation that is probably different than the person sitting next to you.

Yeah. I mean, the way I like to think about them is what are the principles that were in that quote best practice end quote, or the thing that was successful that can be replicated and applied creatively in different ways.

I, I love that. Like the, the principles, like why did that make that successful? And are those, those same variables true in this era? If they're not, that best practice is dead. And yeah, um, I think we're, we're on the front lines of a lot of that happening in marketing right now where like the content marketing, the ABM playbook, like things that have just been like, oh, this is, this is just how we do it. They're becoming less effective. And so we're going to need to rewrite the rules and yeah, it's a brave new world for marketing.

Yeah. So maybe not the best book to refer to at this point. Yeah.

Yes.

I agree. Yeah. It's a little bit too close to the reality maybe. I don't know. Maybe not that close. No, but so When we talked before, you had talked about the difference between, I think, the term governance and activation. It feels like maybe that's what you're talking about, right? Is there's like a new way of thinking about how to manage this change? Like, what does that mean to you? And like, how would it apply for the people who are listening?

Yeah, I have to give credit to my friend Corey Snow for this paradigm shift, but like reframing like governance as a function to the activation team. So instead of seeing governance as like, the department of no. It's like the department of how-to.

Yeah.

Governance implies that you need to be governed. You're creating rules, you're blocking things from happening. But many of our users are pretty ungovernable. So instead of trying to make them not do the things that they want to do and doing governance to them, how do we give them the training, the tools, the knowledge, the data to do what they want to do in a like safe and compliant way. So, getting the governance team or the activation team or whatever, however you wanna frame it, to ask like, what are the business outcomes that you're looking to achieve? And what is the safest, most compliant way to get you there? Versus like, oh no, that doesn't pass our 14-page questionnaire, so we're not doing it.

Right, right, right. Like, it needs to be, like, I think there's a lot of governance approaches, even ones that I've built before that I have, I think a relatively mild assessment of the costs and benefits or risks and things like that, but they still take time. And it feels like that needs to be even lighter weight than what I've thought about to be like, what is enough information that can be defended to say, yes, we can move forward with this, right? That feels like it needs to be a pretty quick conversation as opposed to a lengthy Like you said, 14-page form to fill out or whatever.

And maybe that initial like blast radius assessment dictates like what path it goes down. Like if it's something that's not a big deal, like, like put the 300-question secure security questionnaire down. If it's something that is like significant and mission critical, absolutely do the due diligence, but like not over-processing for process sake.

Yeah. Well, and so now I'm going to, I'm kind of laughing at myself here a little bit because like now I think the matrix that I said could be a part of this, right? The, the, can I go through the door and come back or do I, is it a permanent door that I'm going through or, and the blast radius, that combination, like if it's one that I can't come back from and the blast radius could be big, like that, that should get more scrutiny.

Yeah.

Right.

Yeah. It should be hard to request that data or request that platform or whatever. Um, like users should have to do work to, to walk through that door if, if that's the real implication of it.

Well, and I love anything that's going to get people to start thinking about things in terms of the risks and trade-offs, because I think there's very few things in, in life in general that are clearly, you know, right and wrong, yes and no, right? There's a lot of gray area in life, and I think that's the, like, This feels like the exact scenario where that plays a role, right? Where you've like, you need to have this mindset to evaluate things honestly, right? Just because you think it's a great idea doesn't necessarily mean it's not a high-risk one or, you know, even if it's an easy thing to do, right? And I think that's a framework and a model that I would totally support if I was in a leadership role. Like, because if you're teaching people to And maybe that's really like what needs to happen is these organizations, we need to be thinking about how do we get our teams to be thinking about like the criteria like that in a general sense so that when they're thinking about things, it's not like yes or no, I should do that. It's like, is the, if we take the step forward of doing this thing, you know, are we, do we feel like the upside is going to be better than the down— potential downside? And can we come back from it?

Yeah. Yeah. I, I feel like there's a, there's a slide in the making with like a visual for that.

I've got like in my head, I've got the same thing. Yeah. Um, but I do, like, I think that you're right that like that idea of activating and becoming more of a yes with, with guardrails and sort of clear expectations about How things are going to operate and when you're going to make decisions. I mean, this gets back to a little bit of like one thing, a lesson I learned from a decision-making class I took years ago at a company that provided a lot of training was this like the sunk cost fallacy, right? I see that so often where companies are like, oh, we spent $1 million or whatever it was, right, on this thing and it's not quite there. We should like keep spending more money. It's pretty, people are looking at it like usually know like it's not gonna work. Like, and so spending more is not really the right thing to do even though you've already spent $1 million. Yeah.

Yeah. I think the sunk cost fallacy is like where it's like front, front and center. 'Cause the, a lot of ops teams have infrastructure that they've spent years building out and refining and perfecting and Um, now there's a whole new set of market entrants that have different capabilities and different implementation timelines. And like, if it's gonna take you $200 grand to optimize your existing platform to do the thing versus buying something off the shelf that, um, there, there's, yeah, you, you can't, you can't be anchored to the past and thinking about, yeah, like what are your decisions for the future?

Yeah, I've always been a fan of trying to keep things as simple as possible because like you, like I am a big fan, I'm a big believer that speed and agility can trump, you know, even a poor strategy, right? Because if you have enough strategy and you can move quickly and you can adapt or cut it if you like, and then move to the next thing, I think you've got an advantage over people who spend a lot of time on a great strategy that ultimately may work, but if it's too slow, they may miss the opportunity.

Yeah. Yeah. I, speed and agility win every day in my book.

Yeah.

Because, and I feel like we see this trade-off in like on a lot of marketing teams and even in a lot of sales organizations, like you start the year with your strategy deck of like, okay, this is, these are the activations we're gonna do, these are the strategic initiatives. And then the rubber meets the road of just life and like your customers are doing new things, your competitors are doing new things and like the faster you can adapt systems, get the data, get data in the right hands of your internal teams, get campaigns out the door. Like the organization that moves the quickest has major advantages over one that has a perfect strategy, but is slower to react to what's coming down the pipeline from the, like the broader universe that we all exist in kind of thing.

Right. Yeah. Yeah. So if, for the people who are listening who are just like, if they're an organization are trying to think, okay, how, maybe they're actually working on a strategy for how do I incorporate AI into our go-to-market operations and our workflows? What, like, what are the things you're seeing that the best organizations are doing that seem to be, I'll use that same, like, have principles that can be adopted by other places, maybe not best practices.

Yeah. Yeah. I, I mean, the, the organizations that, that seem to really be hitting their stride with this, have like top-down support and alignment for the need to innovate and the potential of AI in the organization. So I do think there's an aspect of like, you gotta work for a team that sort of gets the vision and sees the light kind of thing.

Yeah.

I'm also seeing like high-performing teams, like they're providing their team with tools, but they're also teaching them to fish with those tools. So teaching them the principles, not just like making rules, but explaining like, okay, this is what we're trying to do. This is how we think about these things. This is how we make decisions in this era. So that like, as process changes are being pushed out top-down, bottom-up, the team is innovating and being agile and embracing change from their seat. So, it's kind of a— so, if I could boil it down to like the 3 things, it's like leadership alignment, like top-down process evaluation and change. So, like these are tools and processes that we can standardize across our whole team. And then that teaching people to fish, bottom-up innovation, where you're giving people the tools to be their own change agents and improving their own workflows. Versus waiting to be told like, okay, this is the process that's changing because of AI.

Yeah. Okay. I mean, I love that anyway. Can I just want to clarify on the leadership alignment piece? Is that, that the leadership agrees that there's an AI benefit and that they agree on what they expect that benefit to be and then that's communicated? Or is it more of a mandate kind of thing? Like, how do, like, what do you, what do you 'Cause I, I could see lots of different interpretations of what that means.

Yeah. And I, um, I, I feel like it has a lot of legs. 'Cause, I mean, like, I feel like every CEO on the planet right now is getting the message from their board, like, we gotta do more AI, we need to do more with AI. And then many of them are just turning around and repeating that message to their organization without a lot of clarity on like, what does it mean to do AI? And like, how are you measuring that? And Like, what does good even look like? I think also we're seeing a lot of CEOs beating the, like, we gotta do AI drum, and then, like, security and InfoSec don't get the memo. And so, it's like, we gotta do AI, and the teams are trying to embrace innovation, and then they're getting, like, handcuffed to these giant security questionnaires again.

Yeah, totally.

And if it's, like, ops versus security, and ops is responsible for convincing security that they need to loosen their posture, that's usually not a winnable battle. Like, you really do need the C-suite to be aligned on, like, we have to be agile, we have to lean into innovation, we have to change, or it just doesn't end up happening.

Yeah.

So, it's a tough one. Like, I feel like The C-suite in general is getting a lot of pressure around AI and postures need to evolve for their teams to really successfully partner with them on that change.

Yeah, it's just like something just popped in my head. I mean, it feels like this, the challenges with evaluating the ROI or sort of the financial benefit of adopting AI do seem like really difficult. Kind of similar to what marketing has faced for years, right? How does marketing measure itself? Is it from a financial standpoint? And it almost feels like there needs to be, I don't want to say no accountability to the financial performance because that's got to be realistic. And like one of the things like I have avoided using a lot of automated AI stuff is because I've, when I've tried it, like unexpectedly high volumes of transactions that each have a cost to them.

Right.

Um, and, but I do think there almost needs to be a, what are the, again, I'm going to use the word principles, but like, what are the principles we believe we're, uh, in terms of like the major things we're going to get as a benefit? And do we feel like we're making progress towards that in kind of a larger, yeah, larger point of view?

I think the analogy to, to marketing's measurement problems actually is super relevant because, I mean, we've been chasing like perfect marketing attribution for eternity. And some things you can very clearly point to like, okay, this is the pipe generated or the pipe that was matured by this marketing initiative. And like, Right. we have better metrics than we ever had before, but there's still this whole category of stuff that's like, you either see the value or you don't. And like, you're not gonna get a dashboard element that tells you like, yep, you sent a client newsletter. And like, you either believe in a client newsletter or you don't. Like, you either anecdotally see the value and you like see the metrics that it's performing. But like, there's no amount of data that can convince you that like talking to your customers is a good thing. And similarly, like with AI, I mean, There's some things we can measure, but there's, there are also things like, all right, a search that used to take me 15 minutes now takes 2 minutes. Do we believe that faster search is valuable? Like, I would say yes. And yeah, but like, are we gonna see that drop to the bottom line? Probably not.

Like, at least not in the, in the near term. Right. And I think that's the, that's the challenge. Yeah. It's been, uh, this has been a fun conversation. I guess maybe I'll leave it like, wrap up here? Like, is there anything we haven't covered yet? Do you think that, um, you know, our audience, primarily operators, would benefit from hearing that we hadn't covered?

Um, I think we covered a lot of ground. Um, yeah, I mean, I, I think the, the bottom line is like the space is evolving really quickly. The human aspect of change is equally important, if not more important, than the actual tech itself. Um, and ops teams who are agile and deliver, like, focus on time to value of the things that they're shipping. I think those are the teams that are gonna pull ahead and win.

I love it. This has been so much fun, Andrea. Thank you so much. If folks wanna continue with you or learn more about what you're doing or what's happening at your organizations, what's the best way for them to do that?

I'm on LinkedIn. Love to chat on LinkedIn. And then we, trilliad.com, we have a great blog and Full webinar lineup and a lot of great content to share with listeners.

Terrific. That's been great. Well, again, thank you, Andrea. It's been a lot of fun. I've really enjoyed the conversation. It's actually kind of crystallized some thoughts in my head. So awesome, awesome stuff. I appreciate it. Thanks to our audience out there for continuing to support us. If you have ideas for topics or guests or want to be a guest, you can reach out to Naomi, Mike, or me. We'd be happy to get the ball rolling. Till next time. Bye everybody.