5 ways AI changed marketing strategy in just one year

What a difference a year makes, especially when it comes to artificial intelligence. Scott Brinker and Frans Riemersma released their “Martech for 2026” report yesterday and the differences between it and the 2025 edition say a lot about how AI and marketing have evolved over the last 12 months.

Here are five key changes in how AI agents are being used and understood across the marketing landscape.

1. From efficiency to growth

Last year, most marketers focused on squeezing more out of their teams and tools. That made sense — AI was new, and the easiest wins were around productivity. Tools for content ideation (used by 69%) and copywriting (62%) were among the most common applications.

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Today, the bar is higher. Efficiency is taken for granted. Now the focus is on growth — using AI to drive innovation, unlock new revenue streams and create differentiation. Instead of “do more with less,” it’s “do more with more.” As the 2026 report puts it: “More is not better — better is better.” In other words, it’s not about how much AI you use — it’s about what you do with it.

2. From competing platforms to AI-empowered buyers

In last year’s report, the main disruption was inside marketing: established vendors vs. upstart AI-native tools, or the challenge of managing ever-expanding martech stacks.

This year, the main disruption is outside of marketing’s control: The consumer use of tools like ChatGPT, Claude and Gemini for research and buying decisions is turning everything upside down. Half of all consumers already use AI-powered search, putting as much as 50% of traditional search traffic at risk. That’s an enormous power shift.

To respond, marketers are beginning to embrace optimizing content for AI engines. Whether you call it AEO for AI engine optimization or GEO for generative engine, it means applying relevant schema types — such as Product, FAQPage, Review and Article — that make details like features, use cases and customer ratings easier for AI to extract and display in results. It’s still early, but the trend is clear: SEO alone won’t get you seen anymore.

3. From replacement to augmentation

There was a lot of noise last year about AI replacing martech as we know it. Would AI agents “eat SaaS”? Would traditional platforms become obsolete?

Turns out, not so much. The data from 2026 shows that most companies are using AI to augment what they already have — not to rip and replace it. In fact, 85.4% of respondents said they’re using AI to enhance existing tools, and just 30.1% reported replacing any major parts of their stack.

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What’s emerging is a hybrid model: traditional, rules-based software paired with AI that can reason, generate and adapt. That blend of deterministic and probabilistic systems is fast becoming the new standard.

4. From data collection to data quality

In 2025, the most significant data challenge was getting access to it. Marketers were all about setting up cloud data warehouses, centralizing information and building a universal data layer.

Now, it’s about ensuring that the data is actually useful. The biggest hurdle in 2026? Poor data quality. More than half of marketers reported struggling with missing, outdated, or inconsistent data. And when AI agents rely on that data to deliver insights, quality matters more than ever.

To solve this, marketers are leaning into Context Engineering to ensure AI gets the right inputs at the right time. That includes connecting CRM and CDP data, tapping into DAMs and CMSs and enriching insights with both internal and external signals. Without reliable context, even the smartest AI can get things wrong.

5. From keeping systems running to driving business impact

AI may be changing the role of marketing operations faster than anything else. MOps now has strategic responsibilities in addition to managing tools and ensuring campaigns run on time.

In 2026, MOps teams are becoming “business value engineers.” That means translating AI’s potential into outcomes that matter — like top-line growth, customer expansion and strategic insights. It’s a mix of technical know-how, cross-functional collaboration and the ability to turn data into direction.

The bottom line

In just a year, marketing has gone from “how do we use this?” to “how do we lead with this?” And expectations for marketing teams are rising accordingly.

AI is now a new layer in how marketing operates. That means rethinking how stacks are built, customer journeys shaped and teams create value. Like it or not, marketing is becoming the leading change agent for organizations. The rewards and challenges are enormous.