Optimize existing martech systems, don’t expand or invest
In 2025, the smart money is on optimization, not expansion. With economic uncertainty tightening budgets, marketing leaders need to prioritize refining what they already have. This isn’t the year to go all-in on new, expensive platforms. Instead, organizations are tightening up current technology stacks and making sure they deliver more value.
The rapid expansion of AI presents a lot of excitement, but the full scope of its potential in marketing is still somewhat unclear. No one knows exactly how quickly or how far AI will push the envelope. What’s certain is that AI is here to stay, and the future is promising. But here’s the catch: Marketing tech is moving fast, and many of the traditional tools we rely on, like marketing automation, email systems, and customer data platforms (CDPs), are quickly becoming outdated.
Heavy investments now could lead to massive write-offs down the road when new technologies (AI-driven or otherwise) take over. The strategy for 2025 should involve working smarter with what you already have. It means minimizing risks while positioning your business for what’s coming next. Play it safe with your investments, focus on what’s working, and make sure your tech stack is as optimized as possible. That’s the play this year.
Laying a strong foundation for AI adoption
2025 should be your year to lay the groundwork for AI in your marketing strategies. But here’s the thing: You don’t want to rush in without a clear roadmap. The goal here is optimization and preparation.
Start by refining your existing systems. Cut down on any inefficiencies, and make sure that your current tools are aligned with your marketing objectives. When you get that right, you free up valuable resources to start integrating AI technology. But before you dive into AI, you’ve got to lay the foundation.
You’ll need to figure out your AI strategy. What are the top priorities for your company? Are your internal teams skilled enough to execute on AI use cases, or do you need to bring in external expertise? Make sure you assess your in-house capabilities and lay out a clear training and hiring plan to fill any gaps.
Then comes the data side of things. AI needs good data to thrive. Make sure your data is solid, clean, reliable, and structured in a way that AI can use it effectively. If your data infrastructure isn’t up to par, your AI implementation is going to struggle. You may need to bring in new tools for data management and governance.
AI regulations are coming fast. By 2026, half of all governments worldwide will have AI regulations in place. It’s time to start thinking about how to comply with those future regulations, even if they aren’t fully developed yet. Establishing a compliance framework now will save you time and headaches down the line.
The risks and importance of AI experimentation
AI is still in its early stages in marketing. There’s a ton of experimentation going on, everything from content generation to automating customer interactions. These early-stage applications are showing promise, but there’s one big caveat: they come with risks.
The failure rate for AI projects is high, especially for those just getting started with it. Companies that are early in their AI journey face risks in terms of underperformance, poor outcomes, and wasted resources. For AI to work in marketing, you’ve got to start experimenting and understand that initial failures are a part of the process. It means testing, tweaking, and getting better over time.
The first priority in AI adoption should be process automation, improving efficiency, and customer experience. These are the low-hanging fruits that can provide immediate value, but don’t expect perfection right out of the gate. AI needs time to learn and adapt.
Building products from the ground up
Many marketers hold the idea that AI can just be tacked onto existing products like some sort of quick fix. That’s not how AI works. You can’t just sprinkle a little AI on top of what you already have and call it a day. The reality is that AI-driven products need to be built from the ground up. This is where the real value lies.
Take Revmatics, for example. This platform was specifically designed to harness AI’s full potential in B2C marketing. Unlike traditional ABM platforms, Revmatics uses real-time data, like location, device type, and user behavior, to create personalized landing pages at scale. These AI-driven landing pages deliver results that would be impossible with legacy systems.
A glimpse into the future of personalized marketing at scale
Revmatics is a perfect example of what AI can do when it’s applied correctly. This platform creates personalized landing page variations on a scale that was unimaginable just a few years ago. The AI behind it can produce millions of custom landing pages in minutes, tailoring each one based on a user’s behavior, location, and more.
What does this mean for marketers? The days of static, one-size-fits-all landing pages are over. We’re moving into a market where experiences are highly personalized and dynamic,delivered at an insane speed and scale. For marketers, this kind of AI-driven efficiency could make or break your customer conversion strategy.
Key takeaways
It’s tempting to throw everything at the wall and see what sticks when a new technology like AI comes into play. But here’s the truth: You can’t afford to lose sight of your goals. The traditional marketing tools you’ve been using might not be cutting it anymore, but AI offers an entirely new avenue for experimentation.
The key here is to balance experimentation with clear, measurable performance goals. AI needs to contribute to tangible business results. Whether it’s improving conversions, cutting costs, or improving customer experience, you need to keep your eyes on the prize.
A solid foundation for experimentation is key. 2025 is about testing and iterating, but it’s also about staying focused on the outcomes that matter most. Keep the experimentation tight and the goals clear. With AI, the focus needs to be on learning quickly and adapting in real-time.
