I meet with marketers every month in small groups I've named "Marketing Therapy," as an outlet to talk candidly about the challenges we face in our jobs. We talk about leadership, strategy, changes in the industry—you name it. But, no surprise, lately everybody wants to talk about AI.
All the marketers in those groups are already using AI in some way. Usually, they're at least chatting with one of the popular LLMs as a sort of collaborator.
It's great to people pushing themselves to try something new and learn while in motion. Unfortunately, the motivation behind it all seems to be fear of falling behind—not confidence in the opportunities AI presents.
How do I know that? Most members can't articulate their AI strategy. Instead, they're reacting to demands for "more AI" or to their perception that others are doing more with it. The result is a lot of "spaghetti method" use of AI that might feel innovative and look like progress but doesn't actually move the needle.
To quote Sun Tzu, "tactics without strategy is the noise before defeat."
I won't pretend I've not fallen into the same trap myself. And there isn't one path to AI-adoption maturity that works for every marketing leader. But I can tell you how to find the ways AI can work for you without spending time spinning your wheels.
The smartest thing you can do right now is slow down, think critically about where AI fits into your goals, and make a plan before you start throwing every prompt you hear about at your LLM of choice.
Why the Gap Between AI Adoption and Value?
AI has been hyped and adopted incredibly quickly. So lots of people are using it.
But because most of those people are simply chatting with an LLM, not many are getting a ton of business value from it so far. Some have hit the "trough of disillusionment" stage in Gartner's hype cycle: Their interest is waning as AI fails to deliver on their (very high) expectations.
Consider that acknowledgment my validation of your feelings if you've hit this point. Now, keep going.
There is meaningful, incremental value to using AI in your marketing workflows. I see it firsthand every day. But if you're not seeing that value yet, that's normal; you just haven't found your path yet.
Right now, just 30% of brands and agencies have fully integrated AI across their media campaign life cycles. Half of those who haven't say they'll achieve full integration by next year but don't have a strategic road map to get there.
But just adding AI across every existing workflow in your campaign lifecycle isn't enough to get past experimentation and into durable value.
To generate true impact, you need to plan for three changes.
- Adopt AI across the entire campaign lifecycle.
- Embrace AI as a collaborative strategic partner rather than just a shinier version of legacy tools.
- Shift from reactive execution to a real-time, future-oriented mindset.
Those aren't minor adjustments. They are foundational changes that require deliberate planning and long-term commitment that will get you to where you want to be.
The AI Road Map Starts With Your Goals
Starting with the tech, not pain points and desired business outcome, is a big mistake I see people make.
Before inserting AI in any stage, you need to know what success looks like, which enables you to map your current processes and goals to the best AI tools for the results you want to achieve.
To help build momentum and confidence in your AI-adoption journey, start with ideas that are safe to test but which also have strategy behind them. Definitely don't set goals that would require a complete rewiring of your tech stack or investing immense amounts of time or money to develop.
Here are two examples of what realistic goals look like:
- Increase the number of creative tests you run each week by 20% using generative AI to iterate on approved creative and launch them quickly.
- Reduce CAC by 10% compared with baseline on Meta and Google using cross-platform bid and budget optimization AI.
So, how can you build a plan once you identify your goals?
1. Mapping a Complete AI Campaign Lifecycle Integration
To truly optimize your ads, AI needs to be adopted across the entire campaign lifecycle, not in isolated pockets.
As you narrow in on the goals you want to achieve across your campaign, you'll realize you can't simply rely on one type of AI to do it all. Every type of AI plays a distinct role—and using the wrong AI, or even only one type, will leave massive gaps in your strategy.
Take GroupM as a real-life example.
GroupM noticed bid and budget inefficiencies across many client campaigns, but couldn't use just one solution that fixed them all. Instead, it used machine-learning to analyze customer behavior to uncover trends and segment audiences more effectively. With those insights, the team used optimization AI to fine-tune media spend in real time, maximizing each client's budget and driving stronger engagement across segments.
The potential of AI in GroupM's campaign optimization doesn't stop there. With generative AI, it could transform raw creative into hyper-segmented creatives tailored to its now more-granular understanding of its audiences. Once the entire process is dialed in, agentic AI can act on some of the steps without needing manual intervention.
From insight to execution, there's really no stage in the campaign lifecycle where AI can't streamline and scale efforts.
2. Treat AI as a Collaborator, Not a Speedy Automator
The true value realized from AI comes from when it makes us sharper, creative, and more strategic, not just more efficient. Don't think of your AI (especially your LLM) just as tool you use, but as a partner you collaborate with.
Imagine telling ChatGPT that your brand's Meta ads are underperforming. In the prompt, you give the AI a data set that shows where metrics are falling flat and ask how to troubleshoot the issue. You'll get a response, likely on where to move your budget, but it may not be correct or it may not be what you need.
On the other hand, when you engage in conversation with AI to give it more context—what targeting techniques you use, the creatives in rotation, what past campaigns have been winners—you work through the problem together, refine theories, and develop a plan to adjust. Here, AI identifies the root of the problem—creative fatigue. On top of that, it goes into action to generate new creative to A/B-test and see what resonates.
A hack I like to use is the Socratic method. Tell AI not to just give you the answer but, rather, to ask you questions that will help you uncover your own answer. You're still in the driver's seat, and I can guarantee you'll walk away with better insights.
3. Move From Reactive to Proactive
For years, AI in marketing was equated with glorified automation, and I've seen that idea bleed into how marketers select AI tools today. Think about it this way: If you have to fix your "AI" manually every time something changes, it's not AI. And that reactive approach of adjusting your approach each time isn't sustainable.
Brands that win are those that focus on capitalizing on opportunities, and their tech stack needs to do the same. Because many AI tools use probabilistic and causative models trained on marketing-specific data, they're well-suited to help you anticipate and pre-empt changes that would otherwise leave you flat-footed.
You cannot design a playbook for every event, whether that's tariffs, supply-chain shock, or a new rival model. But you can design a system that learns in real time and constantly predicts new likely outcomes to drive a proactive marketing strategy primed for resilience.
The Shift From AI-Ready to AI-Native
I'm often asked by digital marketers how they can realize the value of AI fast. It's possible, and it happens all the time; but speed without strategy only leads to failure.
Too often, AI is shoehorned into marketing efforts for the sake of it. It doesn't work.
Obviously, a road map is a path to a goal, not the end goal itself. Treat it as a living experiment, using what you learn along the way to move closer to your objectives and keep yourself accountable.
Even as you focus on results, your biggest concern shouldn't be failing—because everyone will fail. This is new territory for all of us. But the ones who fail also have opportunities to learn and do better the next time.
So put the fear of failure aside, focus on achievable outcomes today, and learn from them so you can tackle more ambitious goals tomorrow.
More Resources on AI Adoption and Use in Marketing
'Human-Ready Marketing': The Power of Human-AI Synergy
Why Marketers Shouldn't Wait for the Perfect AI: Lessons From Apple's Delay
AI Belongs in Your Marketing Toolkit, but Save Space for Humanity, Too
Navigating AI Adoption and Use in Marketing: A Strategic Approach
How CMOs Can Use AI to Make Career-Changing, Strategic Business Impact
