The uncertain economic landscape presents a challenge to marketing leaders: how to invest in technology that drives results while navigating frozen or reduced budgets.
The answer? 2025 is not the year for bold leaps into expensive platforms but a time for strategic caution. With AI promising to revolutionize marketing, but its full impact still unclear, the focus must shift to optimizing existing systems and laying the groundwork for an AI-driven future.
Here’s how to make 2025 the year of smart, strategic martech decisions.
Why conservative budgeting makes sense for martech in 2025
Is your technology budget frozen or reduced for next year? You’re not alone. Due to economic uncertainty, most companies are cautious about spending and taking a conservative approach to budgets.
Normally, I’d be lamenting the spending limitations, but this year is different. We’re at a pivotal point in the evolution of marketing technology.
On the one hand, many companies, particularly in B2B, are finding that their traditional programs and technologies no longer deliver the results needed. On the other hand, AI is emerging with the potential to reshape marketing, though its full impact is still unclear. The path ahead is murky, so proceed cautiously.
2025 is not the time to spend heavily on traditional martech, especially costly platforms like marketing automation, email, or CDPs. These tools require long-term training and investment to see a return, and there’s a high risk they could quickly become outdated.
Dig deeper: AI is poised to disrupt the world of martech vendors and users
Building a foundation for AI adoption in 2025
2025 should be a foundation-building year that sets the stage for AI adoption and growth. It will be necessary to do the following.
Prune and optimize existing martech tools
Streamline and optimize your current technology to ensure it maximizes value and aligns with your business and marketing goals. This will free up resources to explore new AI-driven technology.
Lock in your phase 1 AI strategy
Define your use case priorities and assess your in-house skills against what’s needed to implement your AI use cases. Determine a training and hiring plan to ensure you can translate your strategy into action.
Up to 72% of U.S. CEOs say genAI is a top investment priority despite uncertain economic conditions, per KPMG research. This is the time to explore generative AI and other AI-enabled technologies. Establish guardrails to ensure whatever you’re doing with AI adheres to clear usage and compliance directives.
Document your data architecture and governance plan
Ensure that you have quality data that can be used by the AI solutions you develop or implement. Up to 70% of leaders felt data quality was their biggest challenge when trusting AI with their business success, per Zenhub’s recent survey.
You may need to acquire data management technology that provides a means to manage the integrity and governance of your data. Half of all governments worldwide will regulate the use of AI by 2026, per Gartner’s prediction, so make sure you have a compliance framework in place. Data management is where technology investment makes sense in the coming year.
Experimenting with AI: Opportunities and challenges
Though AI in marketing is still in its infancy, companies are moving quickly and experimenting with AI-driven content generation, chat assistants, and search interfaces. According to BCG:
“AI-mature companies are generating 72% of their AI value in core functions like operations, marketing, and sales…Of the companies that are on their AI transformation journey, 68% have reshape plays in motion, transforming their support functions with AI before moving to the core functions critical to their industry.”
Early use cases for AI are centered around process automation, efficiency, content generation, and improving the customer experience. These use cases are all about improving and enhancing current operations.
Experimentation is key when implementing new AI applications and solutions. Initial experiments can fail, but it’s an iterative process to ensure systems are properly trained and produce the right outcomes.
Mary K. Pratt writes:
“Consider some figures from the 2024 report “Scaling AI Initiatives Responsibly,” published by research firm IDC. It found that organizations with mature AI practices – dubbed AI Masters – still have a 13% failure rate on average. Those considered AI Emergents have an even higher failure rate, at 20%. There are multiple reasons for those failure rates, according to the report and numerous executive advisors. Reasons range from poor data quality to cultural aversion to AI use.”
Dig deeper: AI readiness checklist: 7 key steps to a successful integration
AI in action: Case study of Revmatics
In the near future, we’ll see AI-driven products in familiar categories offering better performance through advanced algorithms and data processing. Case in point, I spoke with Ricky Ray Butler, founder of Revmatics, an AI-powered ABM platform for optimizing B2C conversions. Given Butler’s extensive experience in media and AI, his decision to create this product piqued my interest.
Revmatics can be classified under the ABM category because it focuses on creating personalized experiences at scale. Its first product, Revmatics CRO, aims to boost ROI on media spending. As audience fragmentation increases, reaching and converting new customers has become more challenging and costly. Revmatics addresses these challenges by using AI-driven personalization to create custom, high-converting landing pages rapidly and at scale.
The platform uses real-time factors — such as location, device type, referring platform, and user behavior — to generate millions of dynamically personalized landing page variations in minutes. Key features include:
- Multivariate testing for continuous optimization.
- Advanced bot detection to ensure clean, actionable data.
Butler says this tool delivers improved conversion rates of 15%-50% by learning and adapting over time.
The tool generated 1.2 million personalized landing page variations for one brand in just 15 minutes, tailored to 10 personas across three timeframes (breakfast, lunch, dinner) and covering over 41,000 U.S. zip codes. The result was highly targeted messaging, increased conversions, and a 19% lower cost per acquisition — a scale and efficiency only possible through AI.
While Revmatics falls within the traditional ABM category, its speed, scale, and level of personalization set it apart from existing platforms. This could lead to the evolution or division of the category itself. The uncertain future of key vendors in this space underscores the need for cautious investment decisions.
Avoiding the hype: Why AI-enabled products require more than genAI
There’s a misconception that products can simply be “AI-ified” by integrating existing genAI models like OpenAI, Gemini, or Anthropic. Adding a genAI chatbot might enhance a platform, but it won’t transform a product to deliver the speed, efficiency, and precision that AI promises. As Butler puts it, “You can’t sprinkle AI on an existing product like hot sauce.”
To fully utilize AI, products must be built from the ground up using diverse models and supported by specialized AI scientists and engineers. Some vendors will adapt to this challenge, while others will fall behind.
This is complex and expensive work, which explains the significant investment in AI-related companies. In 2023 alone, generative AI startups raised $21.8 billion across 426 deals, according to CB Insights. Although there has been a surge in generative AI products, most improvements so far focus on doing what we already do — just faster and, in many cases, better.
Dig deeper: AI is a game changer, but not generative AI
The future of martech: Innovation amid uncertainty
We are still in the early stages of this transformative cycle of innovation. Just as we couldn’t foresee the impact of the internet, increased bandwidth, or the smartphone, we can’t yet predict how AI will fully reshape marketing and other functions.
However, tools like Revmatics offer a glimpse of the future — where personalized experiences at scale could replace today’s static websites, tailoring each interaction to the visitor’s unique needs and interests. This vision, long a goal for marketers, is now becoming achievable through AI.
For marketers, the coming years will be a mix of challenges and opportunities. Dependable programs may no longer deliver predictable results while new AI-driven technologies emerge for experimentation.
The key challenge will be balancing the pressure of meeting goals with the need to test innovative approaches. Success in 2025 will depend on building a strong foundation that supports experimentation. This should be a top priority.
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