“We need to figure out AI.” That’s the number one thing we’re hearing from our clients. Whether it’s coming from a C-suite mandate, pressure to do more with less, or the desire to keep up with the competition, “use AI” is the critical mission. But many teams don’t know where to start or where to allocate resources. If this is the boat you’ve found yourself, we get it. But you should know that it’s impossible to revolutionize any marketing operation overnight. And “using AI” doesn’t change anything if you don’t have the infrastructure or confidence to implement it effectively.
We recently sat down with Column Five AI Strategist Julien Palliere to chat about the barriers to AI implementation, the common mistakes we’re seeing organizations make, and how marketers can master AI without losing their minds (or muddying their brand voice). Julien works directly with marketing teams to integrate AI into their workflows, and he’s seen firsthand what works and what doesn’t—both with clients and in our own systems. His biggest piece of advice? Stop trying to boil the ocean. The key to successful AI adoption isn’t some grand transformation; it’s starting small, being strategic, and building confidence one repetitive task at a time.
The Biggest AI Marketing Mistake: Trying to Do It All
When marketing teams get the green light (or the mandate) to explore AI, there’s a natural impulse to think big. Why not automate the entire content calendar? Why not rebuild your whole creative workflow? Why not create a fully personalized content engine from scratch?
But as Julien says, “Getting onto the curve is the hardest part.” And that curve gets a lot steeper when you’re trying to climb a mountain instead of taking the first step.
The teams that succeed with AI aren’t the ones with the most ambitious plans. They’re the ones who identify one highly repetitive, time-consuming task and build a simple system around it. Then move on to the next. As each piece is implemented, confidence builds quickly, and teams start naturally expanding into other areas.
So if you’re feeling overwhelmed by AI, or coming up against constant blockers, here’s Julien’s roadmap to deploy AI and see real results.
1. Identify your team’s biggest time drain.
Before you even think about tools or platforms, you need to understand where your team is actually spending (or wasting) time. Julien’s approach is simple: review your existing processes and look for the bottlenecks.
“What’s something super repetitive that you’re wasting a lot of resources on?” Julien asks. That’s your starting point.
The best use-cases for AI are tasks that are highly structured and repeatable. Think:
- Reformatting content for different channels
- Writing first-draft social captions
- Pulling data for reports
- Transcribing and summarizing meeting notes
These are the activities where AI can make an immediate impact without requiring you to rebuild your entire operation. They can also often address tasks that you never even realized were a time-suck (like synthesizing meeting notes).
Tip: Don’t pick aspirational projects. Start with the annoying, repetitive tasks that are currently blocking your team’s productivity. The goal is to create space for more strategic work, not to add another complex system to manage.
2. Enhance what exists—don’t rebuild from scratch.
Here’s where a lot of teams go wrong. They get excited about AI’s possibilities and decide to create entirely new workflows from the ground up. But Julien’s experience working with clients has taught him a key lesson: once teams implement a built-from-scratch option, they often don’t do a second one because it simply isn’t worth the effort or because adoption is frustrating.
Ultimately, adoption is everything. If your team is already comfortable with a process, adding AI to make it faster or easier is a much smoother transition than asking them to learn a completely new system. Plus, when you try to make things too modular—where changing one thing breaks something else—you end up creating more problems than you solve.
This is a lesson we’ve learned the hard way at Column Five. But, as Julien points out, we have a pretty good system for knowing what needs to be fixed at every level precisely because we’ve learned from those over-engineered failures.
Tip: Map out your current workflow step-by-step. Identify where the friction points are, then figure out how AI can slot into those specific moments. Look to enhance existing processes rather than replace them entirely. The goal is to remove the tedious parts that slow everyone down.
3. Start with an MVP and build confidence.
Once you’ve identified the task and figured out where AI fits, resist the urge to make it perfect. Build the simplest possible version that solves the immediate problem.
“Start really small because confidence is built super quickly with just a little bit of familiarity,” Julien says. And he’s seen this play out repeatedly with clients. Once teams get that first small win, they quickly start seeing other opportunities.
In fact, Julien says clients tend to accelerate and go in multiple directions once they gain familiarity with the tools. That’s not a bad thing; it’s a sign that the learning curve has been conquered and creativity is taking over.
The key is getting past that initial hesitation:
- Run a pilot.
- Test the system with a small group.
- Get feedback.
- Iterate.
- Then expand.
Tip: Choose one simple use case, prove it works, then expand based on what your team actually needs (not what you think they might need six months from now). The goal is momentum, not perfection.
4. Do a cost-benefit analysis before scaling.
Just because you can automate something doesn’t mean you should, which is why Julien is adamant that you run a cost-benefit analysis before investing. The truth is not every process needs AI. Sometimes, the manual approach is actually more efficient, especially when you factor in the time it takes to set up, maintain, and troubleshoot an automated system. Similarly, just because a process is repetitive doesn’t mean it’s worth the investment to automate it.
Tip: Measure time saved versus setup and maintenance time. If it takes 10 hours to build a system that saves you 2 hours a month, you’re not creating efficiency; you’re creating technical debt.
Remember: Getting Started Is the Hardest Part
AI adoption isn’t about grand transformations or overhauling your entire marketing operation. It’s about making small, strategic improvements that compound over time.
If you want your team to succeed, start somewhere—anywhere—and build from there. Focus on repetitive tasks, enhance existing workflows, and prove value before scaling. And don’t forget that these tools are evolving rapidly, so be thoughtful about where and how you’re investing.
Most importantly, remember that the technology itself isn’t what makes you successful. Your brand is your biggest differentiator, and that doesn’t come from workflows or prompts. It comes from the stories you tell, the value you create, and how well you communicate that to your audience. AI is just one tool to help you bring those stories to life.
For more tips to master AI strategy and brand marketing, listen to Julien’s full episode of the Best Story Wins podcast, where he shares insights on everything from personalization at scale to why tech doesn’t make you generic (bad marketing does).
