In this episode
Jonathan Moran, Senior Manager, Horizontal Solutions Marketing at SAS, defines experience debt as the customer-facing cousin of technical debt: the cost customers pay when outdated data, fragmented systems, and manual processes quietly degrade a journey that looks fine on every dashboard. He and Greg Kihlström look at why that debt surfaces during peak demand, why post-event analytics usually stop at diagnosis instead of decisions, which early signals expose inconsistency before cart abandonment does, where AI should act autonomously versus predict and flag, and what has to change structurally for one person to own the experience end to end.
Key takeaways
- Experience debt is what the customer pays for your technical debt. Aging reservations platforms, outdated payments infrastructure, and legacy CRM show up to the customer as friction, inconsistency, and stale messaging.
- Experience debt accumulates quietly. Each campaign adds small inconsistencies that degrade the experience over time, even though nothing appears broken.
- The brand under load is your real brand, not a distorted version of it. Brand building happens under ideal conditions; at peak, there is no time for people to compensate, so customers see only what the systems can do on their own.
- Manual processes are the first thing to break at scale. Spreadsheets, hand-built segments, and human approvals that were added as Band-Aids often turn out to be load-bearing.
- Most post-event analytics stop at insight. “What broke” (volume, latency, drop-off, error rates) is diagnostic and should be automated; “what to do about it” requires a unified customer view and an end-to-end picture of the journey.
- All-green channel dashboards are why the journey stays broken. Each dashboard measures a channel against its own goals, and none measures whether the customer experienced one coherent brand.
- Measure the signals that expose strain, not just outcomes. Moran points to message and offer contradiction rate, time to consistent context across channels, repeat contact or re-explanation rate, and recovery time after a spike.
- Autonomous AI action needs three conditions at once. The decision is low-regret and reversible, the data feeding it is trustworthy and current, and the failure mode self-corrects. Otherwise, predict and flag.
- Culture follows structure. End-to-end ownership needs a shared real-time customer record, at least one metric that spans teams, and a journey owner with real authority.
Chapters
- 00:00 Why CX platforms fall apart at the moment that matters most
- 03:13 Jonathan Moran’s background: SAS Customer Intelligence and SAS Viya
- 03:43 Experience debt: the customer-facing cousin of technical debt
- 05:39 The gap between the designed brand and the brand under peak load
- 08:54 Diagnostic vs. prescriptive analytics: what broke vs. what to do
- 12:55 Why green channel dashboards keep the journey broken
- 17:01 Sponsor break
- 18:37 Early signals: contradiction rate, time to consistent context, re-explanation rate
- 21:45 When AI should act autonomously vs. predict and flag
- 24:41 What has to change for teams to own the experience end to end
- 28:09 Looking ahead: quantum in martech and decision intelligence
- 29:01 How Jonathan stays agile
What is experience debt?
Moran frames experience debt as the customer-facing cousin of technical debt. Technical debt is what your systems owe; experience debt is what the customer pays because of it. It builds up when outdated data, fragmented systems, and manual processes compromise the journey, and the key word is “quietly”: it doesn’t announce itself, and each campaign can add inconsistency, friction, or outdated messaging while nothing looks broken.
Why the brand under peak load is your real brand
Brand building usually happens under ideal conditions, but experience debt becomes visible under load. When an error message appears at peak, the brand has no time to compensate, so what’s left is whatever the systems can do on their own. Disconnected data turns personalized messaging generic and stale, fragmented systems produce contradictory decisions across channels, and manual workarounds are the first things to fail at scale. Moran’s advice is to ask during slow periods what breaks first when people aren’t fast enough to compensate — usually the data layer, the integration layer, or a manual process someone patched and forgot was load-bearing.
How do you get from “what broke” to “what to do”?
Moran separates two lines: from data signal to insight, and from insight to action. Most post-event analytics stop at insight — the queue spiked, cart abandonment jumped, a channel choked. That diagnostic work should be automated, since a model can flag where friction concentrated faster than a team combing logs. Deciding what to do is harder because it depends on the unified customer view and integrated systems that experience debt erodes. Insights stay trapped in individual tools like the ecommerce platform or the CDP, and teams skip from raw data to reacting, patching symptoms instead of determining whether a failure was a one-off spike or a sign of deeper debt.
Why green dashboards keep the customer journey broken
Every channel dashboard measures itself against its own goals: email opens healthy, web metrics healthy, service resolution time healthy. None of them measures whether the customer experienced one coherent brand or five disconnected systems firing in sequence. Every component can work exactly as designed while the journey is still broken. Moran’s fixes: measure the customer’s path across channels instead of the channels, trace a small number of real journeys end to end manually, make integration and context sharing an explicit success metric, and decide who owns the journey as a whole.
Which signals expose experience debt before customers abandon?
Brands tend to measure outcomes like conversion and cart abandonment rather than signals of inconsistency and strain. Moran’s list includes message and offer contradiction rate (how often a customer sees two different prices or offers in one journey), time to consistent context (how long an action in one channel takes to show up elsewhere — seconds under normal load, possibly hours or days at peak), and repeat contact or re-explanation rate, which is invisible to any single touchpoint but deeply felt by the customer. He also cites rules and override frequency, personalization decay under load, journey completion versus journey coherence, and recovery time after a spike or outage.
When should AI act on its own at peak moments?
A wrong autonomous decision is most expensive at peak, when there’s no human in the loop and no slack to absorb the mistake. Moran draws the line on reversibility and on how much the brand trusts the data and integration layer feeding the decision. Autonomy earns its place when the decision is low-regret and reversible, the data is trustworthy and current, and the failure mode self-corrects. Predict and flag is the right posture when the action is hard to undo or carries brand or trust risk, when signals are ambiguous or contradictory, or when the situation is new at scale. Right before the holiday peak is not the time to test autonomous decisioning in high-volume systems.
What changes so one team owns the experience end to end?
Marketing, IT, operations, and service each optimize for what they’re measured on, and telling them to collaborate more doesn’t fix that. Moran calls for a shared real-time customer record that no single team owns exclusively, at least one metric that spans teams (such as repeat contact rate), a journey owner with actual authority to say an ops rule is breaking what marketing promised, and treating customer experience gaps as serious incidents. Coordination has to be built into systems and incentives, because culture follows structure.
FAQ
What is experience debt? Experience debt is the customer-facing cousin of technical debt: the friction, inconsistency, and outdated messaging customers experience because of a brand’s aging systems, fragmented data, and manual processes.
Why do customer experiences break during peak demand? Brand experiences are usually designed under ideal conditions. At peak there’s no time for people to compensate, so gaps in data, integration, and manual processes become visible to customers.
What metrics reveal experience debt early? Jonathan Moran recommends message and offer contradiction rate, time to consistent context across channels, repeat contact or re-explanation rate, and recovery time after a spike or outage.
When should AI act autonomously in customer experience? When the decision is low-regret and reversible, the data feeding it is trustworthy and current, and errors self-correct. Otherwise, AI should predict and flag for a human.
How do organizations get end-to-end ownership of the customer journey? Through a shared real-time customer record, at least one metric that spans teams, and a journey owner with the authority to fix conflicts between functions.
About Jonathan Moran
Jonathan Moran is responsible for global marketing activities for SAS’ marketing solutions. He has more than 20 years of marketing technology and customer analytics industry experience. Jonathan has designed, developed and implemented analytical software solutions that helped Fortune 500 customers solve unique analytics business issues.
Jonathan Moran on LinkedIn: https://www.linkedin.com/in/jonathanmoran/
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Transcript
[00:01:03] Greg Kihlström: Hi, I’m Greg Kihlström, host of the Agile Brand, and here’s a question for you. Brands have spent millions building out customer experience platforms. So why does the experience so often fall apart at the exact moment it matters most? Agility isn’t just about moving faster or scaling up. It’s about staying recognizably yourself when the pressure is highest, so the experience still holds together when every system is under strain. Today, we’re going to talk about what happens when the experience you designed on paper collides with the reality of peak customer demand. Specifically, we’re gonna cover the idea of experience debt and why high traffic moments are where the hidden fractures in a customer journey finally show up, turning digital intelligence into actual decisions, not just dashboards, so friction gets fixed at the root instead of patched at the surface, and where AI should predict and flag a problem versus act on its own, and what has to change across teams to own the experience end to end. To help me discuss this topic, I’d like to welcome John Moran, Senior Manager, Horizontal Solutions Marketing at SAS. John, welcome to the show.
[00:02:56] Jonathan Moran: Thanks, Greg. Thanks for having me on again. It’s, great to be with you and, and talking to you about this topic.
[00:03:01] Greg Kihlström: Yeah, absolutely. Yeah. And yeah, great to, great to see you again. Always, always great talking with you. Before we dive in, and for those that weren’t able to catch the last time you were on, why don’t you give a little background on yourself and your role at SAS?
[00:03:13] Jonathan Moran: Yeah, sure. So I’ve been working in MarTech for 25 years now, for analytics companies, optimization companies, and even data warehousing organizations. So today, I manage a team of solution product and content marketers that focus on SAS’s MarTech solution, which we call SAS Customer Intelligence I also have marketers that focus on, fraud detection, marketplaces, managed cloud services, and components of our base analytics and AI platform, that’s called SAS Viya.
[00:03:43] Greg Kihlström: Got it. Got it. Okay, great. Great. So yeah, let’s, let’s dive in here then and, you’ve, you’ve talked about this, this idea of experience debt, and, it’s certainly something I think top of mind for, for many of us here dealing in not only marketing, but customer experience and, and related areas. But could you talk a little bit about, what ex- what exactly does experience debt mean, and what does it look like, before maybe a peak moment exposes it?
[00:04:13] Jonathan Moran: so simply put, Greg, I would frame experience debt as the customer-facing cousin of technical debt, right? We’ve all heard about technical debt. It’s the payment your system owes, whether it comes in the form of an aging reservations platform, outdated payments infrastructure, legacy CRM. experience debt is what the customer pays because of your brand’s technical debt. So it accumulates when, outdated data and fragmented systems and manual processes quietly compromise the, the customer journey, right? And so I think the key word there is quietly, right? Experience debt doesn’t really announce itself. It kind of builds in the background in every, campaign that’s run by your brand, introduces
[00:04:59] Jonathan Moran: kind of compounding inconsistencies, friction, outdated messaging that just, it just degrades the overall, customer experience, over, over time, right? Even though nothing really appear, appears to be or looks broken. So, I think that, it, it’s, it’s something that, is a thing that brands deal with and they’ve been dealing with for a number of years. and, and it’s something that I think we need to start to a- a- address more actively and, and us being MarTech and CX practitioners should, should certainly, help to do that.
[00:05:39] Greg Kihlström: Yeah. Yeah. And I, I gotta say, I feel like I’ve experienced, experience debt [laughs] a few times even over the last, last few weeks in my, in my travels and, and things like that. So definitely, definitely know what you’re talking about there. A- and I think, brands so often talk about this idea of real-time customer experience and, and things, and yet, you know- there, there’s still– Th-this is I think where experienced debt really comes into play, right? Is systems under maximum stress, so the customer actually, they, they feel experienced debt from, a queue, an error message, all these things which is actually the
[00:06:24] Greg Kihlström: brand behaving in, in real time, right? As, as opposed to what they might wish it were [chuckles] would– how or how they wish it would behave in real time. How should leaders think about the gap between the brand that they’re so carefully designing and the one that actually shows up when that, when, when that high maximum stress, time occurs?
[00:06:44] Jonathan Moran: Yeah. I think there’s, there’s one very important thing to consider here, right? And that is that brand building usually happens under ideal conditions, right? And so experienced debt becomes visible under load, right? And so when that, error message pops up, that’s, that’s the brand with no time to compensate in real time. So what’s l- what’s left and what the brand has to then show is whatever the systems can do on their own. And so, the sources of experienced debt, right? bad data, n-disconnections, l- bad integrations, fragmented systems. a-all of those things kind of predict what shows up under stress, right? So if you’ve got outdated or disconnected data, that means, the brand voice that’s personalized is replaced with something generic and, and perhaps stale. If you’ve got fragmented systems, that means channels are operating independently and, the decisions that are made, by the technologies that the brand implements are often c- contradictory or disjointed. how many times have we had to, repeat the same information to multiple, channels n the, in the ex- duration of one interaction with a brand. manual processes, right? They’re, they’re the first things that, that break under scale or load, right? If, if a brand is compensating for system gaps with spreadsheets and hand-built segments and human approvals, it’s, it’s, it’s r-ripe for failure. So I think that leaders need to think about it in this way, right? The, the brand under load isn’t a distorted version of your brand. It’s actually your real brand. It’s the truest test of your brand, and it’s how you’re represented, right? And so, that’s something to consider, right? If leaders want the brand experience to hold at peak, then they have to ask in the slow times, what breaks first
[00:08:42] Jonathan Moran: when the humans aren’t fast enough to compensate? And that’s usually the data layer, the integration layer, or those manual processes that someone Band-Aided and forgot were very much load-bearing.
[00:08:54] Greg Kihlström: Yeah. Well, a-and I think to, to the point about the, the data availability, I don’t, I don’t know of any of the teams that I, that I talk with or, or, or work with, I don’t know any teams that don’t have enough data or don’t have, the data. It’s not a, a matter of availability. It’s about being able to do something with it, right? I mean, especially after a major traffic event or something, then you’re, you’re probably drowning in data, but it’s that idea of, what, what’s the line between having even, even the, the analytics a-and the data to tell you what broke versus analytics that tell you what to do about it. how do you, how, how do you bridge that gap?
[00:09:40] Jonathan Moran: Yeah. I think, I think the what broke analytics in my mind are diagnostic style analytics and are easy to determine, right? The harder answer to determine is the one concerning what to do about it, the s- the prescriptive piece. What medicine do I prescribe to make things better, right? As far as lines, I think there are a couple. One, one exists between data signal and insight, and one exists between insight and taking action, right? So most, most post-event analytics, stop at insight. They tell you that the queue spiked, that, cart abandonment jumped, that a channel choked out, right? That’s diagnostic. It’s forensics on what broke. direction, right? Translating insight into action, that second line requires connecting that diagnosis to that decision intelligence about what the system should do differently next time, and that’s a much rarer capability because it depends on the very things that experienced debt erodes and that systems under load lack. Things like a unified customer view, integrated systems, end-to-end picture of the, of the journey. So I, I think in the analytics layer, measurement and optimization stall because there’s no end-to-end view of the customer journey, and insights often stay trapped inside of these individual pools, right? Whether it’s, inside of the e-commerce platform or inside of the CDP, each one tells you a piece of the what broke story, but none of them alone can tell you what to do because that requires understanding all of the pieces in concert, right? So I think that, where I, where I draw the lines, right? What broke is really, volume, latency, drop-off, error rates, channel-by-channel performance. This is largely diagnostic, descriptive systems questions. It should be automated, right? A lot of that work should be automated and I think a, a model can flag, here’s where the friction concentrated faster and more reli- reliably than a, than a team c-combing through logs. What to do about it? I think that’s where it gets harder because it’s not one decision, it’s two different decisions layered on top of each other. So I think you’ve got to tune decisions and decision intelligence within your existing architecture, and I think you’ve really got to understand, from an, an architectural perspective, what caused the failure. Is it a one-off spike? Is it a symptom of a deeper experienced debt issue? And how do you, how do you fix that, right? And I think what we see most often is that- Team skips straight from raw data to being reactive.
[00:12:17] Greg Kihlström: Mm-hmm. Yeah.
[00:12:18] Jonathan Moran: patching the symptom versus looking at the root cause, right? And, and I think, if you’re able to really understand how to add in guardrails and, exceptions and adjust the experience, to, to b- better improve things year over year, that’s what’s gonna make them, the, the most im- important move for you as a brand. So I think it’s about, fixing and improving that underlying decision intelligence architecture, back to your point of making decisions, how you make them and, what, what is the best one based on the data that you do have.
[00:12:55] Greg Kihlström: Yeah. Well, and I think another, another part of this, the, the, you touched on briefly, is just this idea that, in, in any enterprise, there’s gonna be different teams focused on different parts of the journey and parts of it may look great, they may perform great, and yet, you’ve, you’ve mentioned the, the last mile, so to speak, of the, of the customer journey is a frequent failure point, where earlier parts in the journey may be performing well and customers aren’t giving any negative feedback there. Customers don’t think in terms of, oh, well, this part worked great, [chuckles] but the, this other part was, was terrible. They generally think of either the whole thing is terrible or, things like that. So how do you, how do teams start to see the journey as one connected thing, even if they’re responsible for a piece of it, but, start to see things a- as connected rather than these independently optimized pieces?
[00:13:56] Jonathan Moran: Yeah. That’s the million-dollar question. I think that gets right to the point of, of what the hardest structural problem is, right? And it’s worth naming, honestly. Brands within organizations, measure based on individual dashboards, being green, right? And, and all of those dashboards showing as green is the reason that the journey stays broken. So every channel dashboard, by design, is kind of measuring itself against its own goals. email opens are healthy, web metrics healthy, service resolution time healthy. But none of these dashboards measure what, what really needs to be measured, right? Which is, did my customer, did the, did the person on the other end of the line, experience one coherent brand or five disconnected systems that all happen to fire in sequence, right? So, are my channels competing? Are they coordinating, right? are the, are the experiences inconsistent? they, they might be, and my systems are working exactly as designed, and that’s the,
[00:14:57] Jonathan Moran: that’s the trap, right? Is every component can be doing its job perfectly, and the journey can still be broken because it’s, it’s, it’s being scoped too narrowly. So I think that it kind of goes back, to that root cause of, of an architectural issue, right? i- i- is your data disconnected? Is it making decisions based on only a partial view of the customer, right? i- is, is integration fragmented, right? Is it, is your decisioning static or rules-based versus adaptive in nature, right? Is, are things being compensated for in a manual, in a manual manner, right? Is a, is a human, having to repeatedly catch and fix conflicts between channels, or are they
[00:15:42] Jonathan Moran: just not being caught at all? and, and, that’s where, the, the, the root of the problem lies. So then the question becomes: how do teams start to fix this, right? How do they start, seeing things, cohesively? And I think it’s, the first is the channel issue. Stop measuring channels, right? Start measuring the customer’s path across channels. test and learn, right? Trace a small number of real journeys end to end manually on purpose. See it from the customer’s side. you mentioned your experience in your recent travels. I can only imagine, what, what type of brand you’re talking about, but I have a pretty good idea right? Make integration and context sharing an explicit success metric, not just kind of an IT project, right? So make sure the integrations actually work as designed. And then I think one thing that we also see all the time that’s so important is: who owns the journey, right? Who owns the, the journey that your customer takes, right? I don’t, I don’t care who owns the different channel touchpoints. Who owns things holistically? If no one’s job is to look across the whole thing, then no one’s gonna notice a gap between teams and, and their dashboards. And so that’s one of the biggest root problems of experience debt, in my mind.
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[00:18:37] Greg Kihlström: You’ve touched on this, but w- are there, are there initial… So the, there’s, there’s measurements of, of journey completion and, and success overall. Are there, some of those things you mentioned even, are, are some of those some of the signals that tell you along the way, like, “Hey, something’s not right,” or, “This isn’t holding together”? are there, are there initial signals that can kind of point to issues along the way that then leaders can, can take a deeper look at just, before they get to cart abandons and, lack of journey completion and stuff like that?
[00:19:13] Jonathan Moran: Right. Yeah, so I mean, we’re, we’re so used to measuring kind of outcome-based signals, like you s- like you said, cart abandonment, conversion, things like that. But what we, what brands don’t measure as much are, the, the signals that expose inconsistency and expose strain or load, right? So a few that I point to, message and offer contradiction rate. How often is a customer of yours shown two different prices, offers or recommendations across channels within the same journey? if that’s happening, that’s a major problem, right? Time to consistent context, this is another one. How long does it take for an action in one channel to be reflected elsewhere, right? I buy something, how long does it h- take for it to show up in a, maybe a service or support channel so that I can, be, that product or service can be maintained? under normal load, this might be seconds. Under peak strain or load, it might stretch to hours or days. and this one, this one might be my favorite, repeat contact or re-explanation rate kind of already, already referenced this one. not was the ticket resolved, which is what brands measure for, but how often does a customer have to repeat information that they already gave someone, somewhere else along their journey? this is one of the, clearest signals of, of fragmentation and lack of integration, because it’s individual, it’s in- excuse me, invisible to any single touch point, but it’s deeply felt, by that customer. and there’s, there’s a lot of other signals as well that, that brands can measure that they probably don’t measure, right? rules and override frequency, personalization decay under load or strain, journey completion versus jou- journey coherence, things
[00:21:05] Jonathan Moran: like that. recovery time after a spike or a- an outage or something like that, right? How long does a system take for it to return to its normal decisioning quality? these are all things you should measure that, that certainly, impact the, the experience, debt or lack of debt that your, your consumer might feel.
[00:21:28] Greg Kihlström: Yeah, yeah. De- definitely the, the repeat one, [laughs] that’s, definitely… I, I know it’s a pet peeve of mine. I know it’s, I’m sure it’s, it is of many, many others. Just, how many times do I need to give my account number, right to the bank or whatever? Yeah.
[00:21:45] Greg Kihlström: so let’s talk a little bit about, certainly, AI is, is, is thrown out a lot in, in these conversations and, I wanna talk a little bit about, at, not only at peak times, but at important times in, in the customer experience. sometimes automation is the best possible thing. I just wanna get what I need, I want self-service, I want, I want that tool, to, to just give, give me what I need when I need it in, in the moment. How do you look at or how do, how do you recommend that, organizations think about automating y- something in a, in a peak moment for a customer versus, human in the loop
[00:22:31] Greg Kihlström: moments? how, how should an organization think about, what to basically set ought to be autonomously, autonomous versus, have that human there in the loop?
[00:22:44] Jonathan Moran: Yeah. Well, I, I think, when, when experience debt occurs, right, when there is a peak moment, a- and you serve or deliver the wrong au- autonomous decision, that’s when it’s the most expensive, right? There’s no human in the loop of time to catch it. There’s no slack in the system to absorb the mistake gracefully, right? And so I would really, I’d really draw that line of when autonomy versus n- no autonomy based on, how reversible or, reparable is the action and how much does your brand trust the underlying data and integration layer that’s feeding the decisions that you’re serving, right? So I think autonomous action
[00:23:29] Jonathan Moran: earns its place when three conditions are true together. The decision’s low regret and reversible, the data feeding it is, is trustworthy and, and current, and the failure mode is self-correcting. Meaning if the model is wrong, does the next interaction naturally correct course, or does it propagate the, the, the bad decision or error? I think predict and flag is the right posture when the action’s hard to undo or customer facing in a way that carries brand or trust risk. I think, if the data signal is ambiguous or contradictory, which is, under peak load is a common call, is, is common because of the fragmentation problem that exists. And I think predict and
[00:24:14] Jonathan Moran: flag is the right posture when, it’s a new situation that perhaps your systems or, or models within your systems aren’t, prepared to handle or they haven’t seen at scale before, right? So good example is right before the holiday peak might not be the best time to test out, autonomous decision-making in your, in your high volume systems, right?
[00:24:35] Greg Kihlström: Yeah.
[00:24:35] Jonathan Moran: But I, I think, that’s, that would be my general framing of, of how I would approach that.
[00:24:41] Greg Kihlström: Yeah. Yeah. Well, and I think- A- another important part of this discussion is, certainly there, there’s a data availability, connectivity, reporting availability. all, all of that stuff has to be in place for teams to truly work and understand a- and analyze what, what needs to happen. But there’s also a cultural aspect to this and, and it’s not purely an abstract culture of like, “Hey, let’s all get along.” It’s, we need to actively work together and coordinate sharing intelligence. As, as we talked about earlier, there may be completely different teams involved in different stages of a customer’s journey that need to share information so that they can think of this holistically.
[00:25:27] Greg Kihlström: What, what, what needs to change in, how team… every organization is different, but they are all siloed in some way or, some fashion or other. What has to change so that the experience is owned end to end rather than in pieces?
[00:25:44] Jonathan Moran: Yeah, I mean, I think what we see today, right, is, marketing, IT, ops, service, they each optimize for their own function because that’s what they’re being measured on, and those are the tools that they own. telling them to collaborate more doesn’t really fix things, right? it, it’s all about, changing what they share, when they share it, who’s accountable for the gaps, you know. I, I think there are a, a few specific shifts that need to happen, right? So first is, there needs to be a shared real-time customer record that no single team owns exclusively, right? Right now, marketing’s view of the customer, services’ view, ops views are usually three different partial pictures updated on different schedules. That’s needs to be, shifted into a common data layer, that every function reads from and
[00:26:29] Jonathan Moran: writes into, right? And I think that, shared metrics only make sense, when they’re used e- end to end, right? So each function should keep its own KPIs, but at least one metric needs to span teams, right, like that repeat contact rate that I mentioned. if only one team’s measured on that, then, there’s no i- incentive kind of to fix things across the board. I think that, there needs to be a journey owner that has actual authority, not just a CX lead title. Someone has to be able to say, “Look, this rule in ops is breaking what marketing promised in this journey,” and have the s- they need to have the standing within the organization to fix that. And then I think that,
[00:27:14] Jonathan Moran: there needs to be, the ability to, make changes in an agile fashion, right? And, and, and being able to, affect those processes quickly, being able to review, incidents that do happen and, and treat, customer experience gaps like, like, major issues inside of the organization, ’cause they are, right? these big-time experience failures are, are serious impacts to, the bottom line for, for a lot of brands. So I– brands. I think that the common thread is that, communication and coordination has to be built into the systems and the incentives, not just left to people and their goodwill, right? Because culture kind of follows
[00:28:00] Jonathan Moran: structure, right? Once teams are, are, a- agreed upon and supporting of the structure, the culture will kind of, will, will follow that.
[00:28:09] Greg Kihlström: Yeah. Yeah. Love that. Well, John, thanks so much for, for joining today. got a couple last questions as we wrap up here. First one, if we were having this interview one year from today, what is one thing that we would definitely be talking about?
[00:28:24] Jonathan Moran: All right, well, I’m gonna go there, and I’m gonna go ahead and say, quantum in, in MarTech. I, I, I, I think that quantum gets a little bit closer d- day by day, but I think some of these large-scale marketing optimization problems can be eventually solved by quantum if we can get some of these cost structures w- work out, worked out. I think that, decisioning versus, decision intelligence is gonna be a hot topic for next year. It’s gonna be a battleground. I think we’re gonna see a lot of vendors in the space com- competing on that topic as well in 2027.
[00:28:53] Greg Kihlström: Nice. Love, love it. Well, yeah, we’ll have to have, have you back on and, and talk about both of those things. [laughs]
[00:29:01] Greg Kihlström: Great, and last question for you. what do you do to stay agile in your role, and how do you find a way to do it consistently?
[00:29:07] Jonathan Moran: Yeah, staying agile, right? That means carving out time to research and learn, right? Things are moving so fast right now with AI. you’ve gotta program learning into your day. tons of learning subscriptions out there. Go out and get one. That would, that would be my recommendation, and just always be learning. So-
