How Agile Saves Marketers from AI Chaos

Episode Transcript

There are a lot of really urgent reasons for marketers to adopt Agile in 2025, but I don't know if any are as really mission critical as its enabling capabilities around AI. I'm gonna unpack exactly what I mean by that over the next 30 minutes or so, but let me maybe get your attention by being very, very clear about why I think this topic is so important. Your AI. adoption efforts are doomed to fail without an Agile marketing framework to support them. Yep, I'm just coming out and saying it, no qualifiers, no modifiers. I' just think this is completely and totally true, and here's why.

AI is simultaneously the most powerful and most overhyped phenomenon marketing has ever seen. But somehow, we've gotta use it without getting sucked into the hype. We cannot postpone implementing it, but we also have literally no idea what it's gonna be like to use it in six months. AI has accelerated marketing's metabolism to beyond, like, hummingbird level. If we cannot move fast, learn fast, and evolve fast, we're gonna be left behind and we just might die. But it is a well-known psychological phenomenon that humans work a lot harder to avoid pain than pursue gain. So, I'm gonna spend the rest of this episode talking about the dangers of not having agility in the age of AI. Because, my fellow marketers, if you're chasing AI without Agile on your side, you are setting yourself up for a world of hurt.

Welcome to The Agile Marketing Edge, the first podcast dedicated to turning Agile theory into real world marketing breakthroughs. I'm Andrea Fryrear CEO of Agile Sherpas, and your guide on this climb to smarter, faster, outcome-driven marketing. Every week, we unpack the what, who, and how behind Agile marketing, from building high velocity workflows and slashing waste to measuring what really matters, and scaling success across teams. You'll hear quick hit strategies you can deploy today, plus candid stories from marketers who have traded chaos for clarity and never looked back. Hit follow wherever you listen, and let's carve the next switchback together.

Okay, I could probably go on for hours and hours and hours about all of the risks of trying to integrate AI without Agile marketing as your support system, but I'm gonna confine myself to my current top three in this episode. I cannot imagine that we will not be coming back to this topic as the situation continues to evolve, but for today, we're gonna stick to three. One, without the ability to rapidly experiment, you're gonna end up burning time, burning energy, and burning up political capital trying to implement AI, which means you're gonna be launched into this horrible downward spiral of repeated AI failures. Reason number two, nobody knows what is gonna happen with AI, but we do know that it's gonna change, and it's gonna change fast. So, if you don't have a system that can sense and respond in as close to real time as possible, you'll be so far behind that it doesn't really matter what you're doing anyway. And reason number three, marketers, in case you haven't noticed, are already ridiculously busy.

Without a framework that allows them to deliberately prioritize experimentation and innovation, there is simply no way that they're gonna have enough time and space to meaningfully integrate AI into their operations. All right, there's our top three for today. No doubt these are gonna change and evolve as time goes on, but, you know, those are the three we're diving into today. All right, so deeper into reason number one. If you can't really quickly sort out the positive AI experiments from the negative ones, then you're gonna end up burning up enormous amounts of time and energy and budget, and political capital because you burned all those other things chasing shiny AI objects.

And as you're burning up all of those things, that delays your ability to realize the ROI on any single AI experiment or group of experiments, which means that you are endangering not only those first few tentative forays into implementing AI, but now you're also endangering all of your future efforts because you couldn't show the impact of those first ones because they took so long to show any ROI. Because you were spreading them out over so many different ones, you were trying to do too many things at once, you could not test and learn fast enough. All right, so to really drive home what I mean by...This scenario, let's compare and contrast an Agile versus a non-Agile marketing team, and how they might approach this differently. Okay, let's start with how an Agile marketing team might approach this. Obviously, an Agile marketing team is accustomed to using tools like backlogs and deploying a mindset of ruthless prioritization.

And so, Agile marketing teams can brainstorm a huge number of possible AI initiatives and experiments without feeling compelled to try to do them all at once. They can think about them, and they can document them, put them in their backlog, and not try to tackle everything at once.

They're comfortable with prioritization. They're comfortable with saying, "Not right now." And so they're only going to focus on some, a small number, of high value experiments, which are hopefully gonna be designed to deliver them outcomes over outputs. Right? So the point of this kind of activity is not to just test AI or experiment with AI, it's to deliver a business outcome, and marketers using Agile ways of working should be comfortable with pushing themselves in this direction. So not just doing work for the sake of doing work, but doing work for the sake of delivering an outcome, a business outcome, a customer value delivery outcome. And in the name of that goal, they should be more likely to be able to zero in on an MVP, a minimum viable version of this test of an AI implementation.

What is the smallest thing that we can try that will validate or invalidate our hypothesis, that will tell us if we are moving in the right direction or not? So it's going to, one, deliver a rapid impact instead of having us wait six months to know if this thing is working or not, and it's gonna allow us to test and learn much more quickly. And two, it's gonna de-risk this whole process. So this is how an Agile team would approach their initial AI experimentation process.

Brainstorm a lot of things, but not feel like they have to tackle all of them. Visualize, prioritize, find the MVP, look for that quick delivery of impact, and then quickly move forward with the good stuff and abandon the bad stuff. So that's how an Agile marketing team would approach an AI implementation. But a non-agile team.

You've probably been here if you are a non-Agile listener, so, uh, apologies if this is a little bit painful to hear. But a non-agile team does things like spending tons and tons of time researching and scoping and getting buy-in, and maybe even chasing multiple huge bets simultaneously, because the idea of prioritization or MVP identification or finding ways to test and learn rapidly just isn't a muscle that they've developed. There's no capability or willingness to not chase something. There's such a fear that they're gonna get it wrong, that they feel like they have to do everything all at once.

And this is the same reason that non-agile teams say yes to every incoming request, and then end up with 25 projects in progress and never get anything done. It's that same kind of inability to postpone any work that leads them to try 25 AI experiments, and then none of them deliver any kind of impact because there's just too many things demanding time, attention, budget, et cetera.

And the terrible thing is, in the case of an AI experiment or implementation, this launches you into this downward spiral, because if you invest a ton into a new AI tool of some kind, or you put a whole bunch of time and money and energy into getting everybody trained up on a particular AI capability or platform or approach or a technology, or whatever it is, only to see no impact whatsoever, then you are burning all kinds of political capital.

And in some cases, you might even experience a decline in productivity or impact if the tool or the technology that you are trying to implement just serves to highlight the already broken parts of your existing operations. And this has happened over and over and over again, because AI is not this magic wand that is gonna fix everything.

This happens with Agile as well. People expect it to come in and just be this panacea that magically transforms any and everything. Agile doesn't do it and AI doesn't do it. You still have to do the hard work of fixing your processes if you want these new tools and techniques to be able to work.And so in the case of AI, if you go and make all these investments to new tools and trainings and techniques and capabilities, but there's no visible measurable impact, and maybe it even makes things worse, of course you're gonna create a whole lot of skepticism in your organization, whether that's with your boss or your manager or your budget holder. So the next time you come with a request for another round of training or a new tool or an investment in a new capability, you're gonna get an extra high level of scrutiny. And if you get it approved, you're probably gonna get a lower budget, limited scope, or some other restriction that's gonna hamstring your efforts, and you're gonna have a lower chance of success. It's like they're strapping weights to your feet for the next race that you're trying to run, which means that the next round of implementation efforts you're gonna try has an even worse chance of showing an impact.

And now the downward spiral continues because your next round doesn't show an impact, so the next time the scrutiny's even higher and the budget's even lower and the weights are even heavier, and down and down and down we go until eventually you've lost all willingness for people to help invest in your team to keep trying these new tools and techniques and you've lost.

So having an Agile approach to this prevents getting trapped in this downward spiral. Okay, reason number two why you must have Agile in your corner in order to effectively implement AI in marketing: Action is necessary but it has effect to be taken in an environment of extreme uncertainty.

We have no choice but to try and make AI our best friend. It has so much potential. All of our competitors and our customers are expecting it, our bosses are expecting it, our shareholders are expecting it. We have to try and make it work for us. However, we have to do so in the midst of huge numbers of unknowns and traditional waterfall-style process management was not designed to work in these kinds of environments.

These traditional management approaches assumed minimal change would take place once a plan was created and set in motion. Agile processes on the other hand were developed to help work evolve in parallel with a changing environment. Customer wants and needs evolve, the world that we are building things for evolves, competitor offerings evolve. Agile is made to allow us to quickly react to those changes, so it is made for these kind of dynamic uncertain environments. Traditional ways of working, not so much.

So let's come back to our two hypothetical teams again and compare them side-by-side. So let's pretend that AI has been identified as a strategic priority by the executive team during an annual planning or quarterly planning moment, right? That's, that's been brought up and mandated, "Everybody's gotta figure this out." Well, because Agile has a lot of really frequent touchpoints, sprint reviews and sprint planning, backlog refinement, all of these kinds of moments come up frequently, every couple of weeks typically, that means that when these kinds of initiatives come up, new work can be proposed, it can be scoped, and it can even be started within a couple of weeks of being surfaced by executive team, marketing leadership. Wherever the source of it is, it can be brought into the team's workflow very, quickly. And then once the work has been started, all of those same touchpoints, sprint planning, sprint review, retros, backlog refinement, all of those kinds of things, are also moments when performance data could potentially be surfaced. How are the tools working? What are the KPIs? Is a competitor doing something new? Is a new tool out there that might give us a better chance at success? Is there a new security capability that w- has been developed in-house that's gonna give us more options for implementing AI more completely? A lot of us are limited by our own internal options, and as those get eliminated, we have more freedom and flexibility, and because we have this faster metabolism in an Agile environment, we can take in that information and make adjustments to what we're doing every couple of weeks, and at just about any of these new points, new work items can come into or out of an Agile team's workflow. So if we're working on something AI-related, whether that's testing the way that we are using AI in workflow... in our work development, or we're testing how we're using it in our workflows itself, we're testing out a new tool, whatever it is. If it's not working, if it's not delivering the ROI we want, or if our investigative activities revealed that a particular tool or tactic just isn't a fit for us, we're gonna figure it out really fast and we stop that work very quickly. So this is that increased metabolism that we really need to be able to keep up with how quickly the world is moving right now. You cannot go six months between making decisions anymore. That is simply just not feasible, and you can't wait for 16 directors to weigh in before you make a move, because, uh, let's, let's move over to our waterfall team and see kind of what that might look like. Okay, same thing here. We have AI gets identified as a strategic priority during annual or quarterly plan, but in this case, it takes another four, six weeks for our various leaders in marketing to come together and agree on how that strategic directive is gonna translate into actual marketing projects. Because we have the inevitable political jockeying, and there's no rapid recurring cycles to lean on like we would have in an Agile system. And of course, we don't have any real team level empowerment where an Agile team could put their hand up during a sprint planning or doing a retro and advocate for work that they see as being valuable to be brought into their backlog and into their next round of work. That doesn't exist in most waterfall systems. And so because we're waiting for alignment at this leadership level, it takes a long time for any real actual work to make it into anybody's queue. The real actual hands to keyboard work doesn't start for a really long time. And then of course, as soon as we've finally agreed on what the projects are gonna be, we have to do the whole documentation thing before anybody can start on anything. We have to collect data and we have to build slide decks so that we are all aligned on who's gonna do what, who gets to take credit for things, and who's gonna be in charge of which things. So many slide decks. We have to build so many so that we're all aligned, and we have to have meetings about the slide decks to review them. and prepare them so that we can present them to other people who give feedback on the slide decks. And then we have to iterate on the slide decks and meet again about the new iteration of the slide decks, and somehow all of those directors and marketing leaders have to somehow agree on whose teams are going to do what based on that original strategic directive that AI is important. And by the time all of this is happening, we are probably months down the road from that original moment of AI is important, let's figure out how to use it. Months, we are months down the road now. All the decks and all the meetings and no real work has happened. And in 2025, 2026, the world is completely different in a few months, which means that the work that we have so meticulously documented and now has so much political capital thrown behind it, is almost certainly no longer the right, because the world is so different. The technology has evolved. The competitive landscap- scape has evolved. Everything's different.

But we've spent so much time and effort and therefore money in figuring out what to do, and we've identified this work and it's got so many slides related to it. We're gonna do that work no matter what. That's the work that we're gonna do 'cause we spent all this time and all this effort and money agreeing that that's the work. So we're gonna do that work for the next six months no matter what. Which of those two scenarios do you think is likely to deliver the best outcomes in the rapidly evolving world that we all live in?

Yeah, the Agile one. I hope that you got that. Okay, third and final reason that you need Agile in your corner if you are gonna try to implement AI. This final one is maybe the saddest of all, but it's kind of the realest as well. Non-agile teams just do not have the time to experiment properly with AI. I mean, most of them can't even manage their existing workload. So how on earth are they possibly supposed to make space for exploring all the new tools, for upskilling themselves on the new technical capabilities that they need, piloting new processes, or any of the other new steps that they've gotta figure out in order to properly and fully integrate AI into their marketing operations?I mean, you bring this up to some of the most overworked marketers out there, and you can see, like the tears behind their eyes of just trying to think about, how are they supposed to do this? It's ridiculous. It's overwhelming and insane. I mean, we hear from clients and prospects all the time. Interestingly, th- this has become an inflection point for finally changing ways of working and adopting Agile, because they've realized that AI isn't optional, it is critical to their future success.

But then they've also realized that they cannot possibly integrate it successfully without first improving their current levels of productivity, 'cause there's just no space.

Without being able to get more done with the resources and time that they have, there's no space for AI implementation.

We have some clients who did try to bring in AI without first improving productivity or operations, and they had their old traditional operational models totally implode. They just couldn't handle it.

And what we realized is this is kind of like if you had a really old operating system on your phone and you tried to install this really cool shiny new app. It's not gonna work. Both of them are just gonna, like, break down. You have to upgrade your base operating system first, and then you can install the cool new app. And this is why Agile marketing and Agile frameworks in general are so critical right now, and this is why we talk about Agile marketing as an operating system, because it has to be there as that foundation.

Because today, it's AI that's disrupting our ways of working, but tomorrow, it's gonna be something else. But if you have an operating system that is designed to continuously evolve and improve, then it doesn't matter what app it is that you have to install next.

Right now, it's the AI app, but whatever the next one is, you'll be ready for it. So, do not put your expensive and essential  AI rollout at risk by trying to install it on an outdated and buggy and glacial operating system.

Upgrade to Agile and make sure that you become a success story and not a cautionary tale.

If you have no idea how, to do that and you would like some help to make sure you get that operating system installation right, of course AgileSherpas is here for you. You can tell us a little bit about what you're struggling with and what you hope to accomplish at AgileSherpas dot com slash contact-us, and then one of our experienced sherpas will reach out and we' can collaborate with you to develop an action plan.

Until next time, I'm your host, Andrea Fryrear. Remember, the struggle is real, but so is Agile marketing.