2025: The Year of Martech Refinement Over Growth

In an unpredictable economic environment, marketing executives are faced with a daunting question: how do they invest in technology that yields measurable outcomes while contending with stagnant or diminished budgets?

The solution? The year 2025 should not be marked by rash investments in costly platforms but rather by a strategy rooted in prudence. While AI holds the promise of transforming marketing, its full implications remain uncertain, necessitating a shift toward enhancing current systems and preparing for an AI-centric future.

Here’s how to ensure that 2025 is characterized by intelligent, strategic decisions in marketing technology.

Understanding the case for conservative budgeting in martech for 2025

Is your tech budget facing cutbacks for the upcoming year? You’re not alone. In light of economic instability, many organizations are adopting a cautious stance regarding expenditures.

Typically, I’d express frustration over spending constraints, but this year presents a unique scenario. We are at a crucial juncture in the development of marketing technology.

Many organizations, especially in B2B, are discovering that their conventional programs and tools are falling short of delivering desired results. Simultaneously, AI is on the rise, with the power to alter the marketing landscape, though its overarching effect is still not fully grasped. Under such uncertain conditions, a prudent approach is advisable.

2025 is not an opportune moment for significant investments in traditional marketing technologies, particularly high-cost solutions like marketing automation, email marketing, or customer data platforms. These instruments demand a strong commitment over time for a return on investment, and there is a considerable risk of them becoming obsolete quickly.

Explore further: The potential disruptions of AI in the marketing tech sector for vendors and users

Establishing a foundation for AI integration in 2025

2025 should serve as a year for laying the groundwork that facilitates AI integration and advancement. Focus on the following steps:

Streamline and enhance existing martech solutions

Refine and enhance your existing technology to guarantee that it provides maximum value and aligns with your marketing and business objectives. This effort will free up resources for exploring innovative AI-driven solutions.

Solidify your initial AI strategy

Identify your use case priorities and evaluate your internal team’s capabilities against what is required for implementing AI use cases. Develop a training and hiring strategy to translate your AI vision into actionable steps.

According to KPMG research, up to 72% of U.S. CEOs consider generative AI a pivotal investment priority, even amidst economic uncertainty. Now is the moment to delve into generative AI and other AI-driven technologies. Establish clear guidelines to ensure that your AI efforts comply with necessary regulations and usage policies.

Clarify your data structure and governance framework

Ensure quality data availability that can be utilized by the AI systems you intend to develop or implement. Findings from a recent survey by Zenhub indicate that 70% of executives view data quality as their foremost challenge in relying on AI for business success.

Consider investing in data management technologies that ensure data integrity and governance. Gartner predicts that by 2026, half of global governments will regulate AI use, therefore establishing a compliance framework is crucial. In the upcoming year, data management will be a key area for tech investments.

Exploring AI: Opportunities and challenges

As AI in marketing remains an emerging field, companies are quickly experimenting with AI-powered content generation, chatbot functionalities, and search mechanisms. According to BCG:

“Organizations that have matured in their AI practices derive 72% of their AI value from core operational, marketing, and sales functions…Of those undergoing AI transformation, 68% are making changes to their support functions before addressing critical industry functions.”

Initial AI applications are focused on automation, efficiency, content generation, and enhancing customer interactions. These applications aim to refine and elevate current operations.

Trial and error are essential in integrating new AI tools and applications. While initial attempts may falter, it is an iterative learning journey to ensure systems are appropriately calibrated for optimal results.

Mary K. Pratt notes:

“According to the 2024 report “Scaling AI Initiatives Responsibly” from IDC, organizations with advanced AI capabilities, termed AI Masters, have an average failure rate of 13%. Those categorized as AI Emergents face even higher rates, at 20%. Various factors contribute to these failure rates, as highlighted in the report and by several executive advisors, including subpar data quality and organizational resistance to AI adoption.”

Explore further: Essential steps for AI readiness and successful integration

AI in action: The Revmatics case study

In the near future, we can anticipate AI-enhanced products in well-known categories delivering superior performance through sophisticated algorithms and data processing. I recently talked with Ricky Ray Butler, the creator of Revmatics, an AI-centric ABM platform designed to optimize B2C conversion rates. Given Butler’s deep roots in media and AI, his venture immediately captured my attention.

Revmatics falls under the ABM umbrella by focusing on the creation of personalized experiences at scale. Its pioneering product, Revmatics CRO, aims to enhance the return on media investments. As audience fragmentation intensifies, attracting and converting new clients has grown increasingly challenging and expensive. Revmatics tackles these obstacles by employing AI-driven personalization to swiftly generate tailored, high-converting landing pages.

The system leverages real-time variables—like location, device usage, referral platforms, and user behaviors—to create millions of dynamically customized landing page variations within minutes. Noteworthy features include:

• • Lightning-fast personalization.
• • Comprehensive multivariate testing for continuous improvement.
• • Sophisticated bot detection to maintain reliable, actionable data.

Butler indicates this tool achieves conversion rate improvements ranging from 15% to 50% by learning and adapting over time.

One client benefited from 1.2 million personalized landing page variations created in just 15 minutes, targeting 10 personas across three meal times (breakfast, lunch, dinner) while spanning over 41,000 U.S. zip codes. The outcome included highly relevant messaging, increased conversion rates, and a 19% reduction in customer acquisition costs—an efficiency and scale only feasible through AI.

While Revmatics fits within the traditional ABM mold, its rapid and extensive personalization capabilities differentiate it from other existing platforms, potentially leading to a transformation in the category itself. The ambiguous future for primary vendors in this space stresses the importance of prudent investment choices.

Discerning the reality: The need for more than just genAI in AI-enabled products

There’s a prevalent myth that simply “AI-ifying” existing products via integration with established genAI models like OpenAI, Gemini, or Anthropic suffices. Incorporating a genAI chatbot may augment a platform but won’t fundamentally transform it to deliver the speed, efficiency, and precision that AI has the potential to offer. In Butler’s words, “You cannot merely sprinkle AI on an existing product like a spice.”

To harness the full capabilities of AI, products must be meticulously constructed from the outset, employing diverse models and backed by specialized AI experts and engineers. While some vendors will rise to this challenge, others may lag behind.

This undertaking is complex and costly, which elucidates the huge influx of investments into AI startups. In the year 2023, generative AI startups alone attracted $21.8 billion across 426 deals, as noted by CB Insights. Despite the proliferation of generative AI products, most enhancements to date center on optimizing current processes—just at a faster pace, and in many instances, a better manner.

Explore further: AI’s transformative potential extends beyond generative AI

The trajectory of martech: Innovating within uncertainty

We find ourselves in the nascent stages of this revolutionary cycle of innovation. Just as we couldn’t accurately predict the ramifications of the internet, increased bandwidth, or the smartphone era, we remain uncertain about how AI will ultimately redefine marketing and its allied functions.

However, platforms like Revmatics offer a preview of a forthcoming reality—where tailored experiences on a large scale could supplanted today’s static web pages, adapting each interaction to individual visitors’ specific needs and interests. This long-standing aspiration in marketing is now becoming a tangible goal due to AI advancements.

For marketers, the upcoming years will present a blend of challenges and exciting prospects. Time-tested programs might not yield consistent results, while fresh AI-driven tools present new avenues for exploration.

The central challenge will be finding the right balance between meeting expectations and experimenting with novel methodologies. Succeeding in 2025 will hinge on establishing a robust foundation that encourages experimentation, which must be prioritized.