2026 Predictions for AI in Marketing: I Asked the Experts

The hype phase is over. We've moved past the breathless excitement of "AI can write things!" and into the harder, messier work of figuring out what AI actually does well and where it falls short. As we head into 2026, the marketers who thrive won't be the ones chasing every new tool. They'll be the ones who understand where AI fits into strategy, where humans remain essential, and how to navigate the regulatory and ethical landscape that's rapidly taking shape. I reached out to friends and colleagues for their best predictions. Here they are, along with mine! 

PS: We are now the newsletter of The Marketing Metrics Association. Last year, I founded The Marketing Metrics Association to foster data analytics best practices. I'm bringing this newsletter under the MMA umbrella to offer you more content for free. Happy reading! 

Best,

Christina

Christina J. Inge, EdD

AI Analytics Will Dominate, But Trust Is Still Catching Up

Gartner predicts that 80% of all marketing analytics workflows will involve AI by 2026. That's not a distant future scenario; it's next year. AI will mine our marketing data for insights humans can't easily spot, surfacing patterns in customer behavior, campaign performance, and attribution that would take analysts weeks to uncover manually.

But anecdotal evidence from my conversations with marketing leaders at Fortune 500 companies reveals significant mistrust, both in the insights AI generates and in the analysis itself. Don't assume you won't still be working in Excel this time next year.

The shift will be gradual. As AI analytics proves its predictive power through measurable ROI improvements, trust will grow. We'll start relying on AI not only to tell us what our data says, but to recommend how we should act on it in straightforward scenarios. The vision remains human. The insights are increasingly AI. Your job is to bridge that gap. As Wafaa Arbash, Techstars mentor and AI founder notes: "with all the new AI marketing tools and bots, it's going to be challenging to actually reach the real customer and understand their problems since a lot of the bots will give false information."

We Stop Tolerating AI Slop

When generative AI first emerged, we were so entranced that it could produce anything resembling human language that we didn't examine the quality too closely. AI makes pretty groups of words. But humans create the original ideas those words communicate.

We're moving away from generic AI content, you know the kind: with obvious ideas and emojis scattered everywhere like spots on a leopard. We can still generate content with AI, but it needs to be based on our ideas, with AI adding polish, suggesting directions, and smoothing flow. Think of it as an editor, not the person behind the ideas. And we still have to edit what AI generates to ensure brand alignment, human oversight, and the authentic tone that makes content resonate.

Blade Kotelly, Senior Lecturer at MIT, puts it bluntly: "When people see the output of GenAI they believe it's 'high fidelity'—it looks really good, or reads really well. However, given the context, it's almost always low fidelity before hand editing."

The trap, he explains, is falling in love with surface polish: "We easily fall in love with the really well written, internally consistent, well structured text output from ChatGPT. But when we apply it—sending the ChatGPT-written email to someone, using the AI art in an advertisement—the thing that looked high fidelity to us on inspection lacks true connectivity because a ton of small issues are waiting right below the surface."

The meanings aren't aligned with core messaging or brand. The phrases lack rhythm. The whole ends up less than the sum of its parts. Kotelly's advice: "Don't fall in love with the beautiful, but shallow, output. But do use it to create more clay quickly from which to sculpt a beautiful message."

Personalization Has a Regret Problem—And AI Made It Worse

Here's a paradox: 71% of consumers expect personalized interactions, and 90% want more personalization than they're currently getting. But a June 2025 Gartner survey tells a different story. Among B2B buyers, personalized marketing generated negative experiences for 53% of customers, who were then 3.2 times more likely to regret their purchase and 44% less likely to buy again.

The distinction matters: most "consumers love personalization" research comes from B2C studies. B2B buyers are the canary in the coal mine. It is more deliberate, more research-driven, and quicker to recognize manipulation disguised as helpfulness. If B2B personalization is generating regret, B2C likely has the same cracks. 

Gartner's research found that customers experiencing personalization were 1.8 times more likely to pay a premium, but also 2 times more likely to feel overwhelmed and 2.8 times more likely to feel rushed. We're extracting short-term revenue while eroding long-term loyalty.

The fix isn't abandoning personalization but refocusing it. I still believe in Next Best Experience (NBX), but we've let "experience" become an afterthought while optimizing for conversion. Gartner's data shows that active personalization - helping customers reflect and build confidence rather than pushing offers - makes them 2.3 times more likely to complete purchases confidently. 

In a tough economy, customer trust is worth more than a single transaction. Audit your personalization for pressure tactics, and redefine success beyond conversion rate and AOV.

Regulation Is Coming. Get Your Data House in Order

Laws are emerging to regulate AI in ways that directly impact marketers. The U.S. Copyright Office has already published guidance clarifying that AI-generated content is not copyrightable—only human-created works qualify. If your content strategy relies heavily on AI generation, you need to understand what that means for your intellectual property.

Meanwhile, the EU AI Act takes a risk-based approach, focusing on society-wide harm reduction rather than business implementation guidance. It's designed to protect consumers, not make your marketing workflows easier.

Here's the bottom line: AI in marketing depends on consumer data to predict, target, and inspire customer actions. Being on top of your data compliance isn't optional anymore—it's the must-have for 2026. If you're not already auditing your data practices, you're behind.

GEO Is Not Replacing SEO—They Coexist

Let's be clear: Generative Engine Optimization is not making traditional SEO obsolete. The two exist alongside each other, serving different purposes in the customer journey.

ChatGPT gets much of its data from Google searches—only 22% of ChatGPT results come from web pages that aren't also visible in Google SERPs. What I'm seeing is that SEO remains vital for local searches and ecommerce, while informational search intents are migrating to AI tools like ChatGPT, Perplexity, or Google's own AI Overviews.

Your strategy needs to account for both. Optimize for traditional search where transactional intent dominates. Build authority and clear, citable content for AI discovery where informational intent rules. Neither approach works in isolation anymore. Wafaa urges us all to learn to write content for LLMs, not just the search engines.

Platform Consolidation Is Inevitable

Every tech adoption curve brings consolidation, and marketing technology is no exception. We're already seeing mergers between legacy tools - witness Adobe's acquisition of SEMRush in November.

Expect this trend to accelerate. The fragmented landscape of point solutions will give way to integrated platforms. For marketers, this means fewer vendor relationships but deeper platform dependencies. Start evaluating your tech stack now with an eye toward which tools are likely acquisition targets versus long-term players. The decisions you make about platforms in 2026 will shape your operational flexibility for years.

Data Stewardship Is the New Professional Mandate

Alice Stein, past president of the American Marketing Association, Boston, and founding member of Women Applying AI, frames the responsibility clearly: "As we move into 2026, marketers must recognize that their profession has become one of the most important stewards of data in the age of AI."

The access we have is unprecedented. Mobile applications, ad identifiers, and persistent digital tracking give marketers enormous volumes of sensitive data that is often gathered passively and invisibly. Stein sees both opportunity and obligation in this reality: "This access brings extraordinary potential: to expand products that support human health, empower more sustainable choices, and enable personalized learning or medicine. But it also concentrates great power and creates profound responsibility."

Her call to action is direct: "Marketers must build fluency in AI, understand evolving global data regulations, and design with transparency and consent at the center. They must treat data not as an entitlement, but as a privilege—one that carries ethical obligations."

Ethical marketing is no longer a "nice to have." It's the new professional mandate.

AI Can't Replace Human Originality

For all the capabilities AI brings, there's a fundamental limitation worth remembering. Jeremy Pesner, Policy Director at AI for All, offers a crucial reminder: "AIs, by their nature, can't come up with anything truly original. Any ideas or strategies it generates will be rehashes of what it has in its training data."

That's not a flaw to work around but a feature to understand. AI excels at the "fairly by-the-book" work: summarizing, reformatting, optimizing existing approaches. But anything truly unique and differentiated? That still requires human minds.

Your competitive advantage in 2026 won't come from having better AI tools than your competitors. It will come from having better human ideas that AI can help you execute.