Introduction to AI in Marketing
Marketing teams have spent the last three years experimenting with generative AI, with some discovering genuine efficiency gains, while others have accumulated tool subscriptions, leading to frustration. There’s a gap between AI’s promise and its practical value, with many "AI best practices" not leading to real outcomes. Meanwhile, clicks and organic traffic are in decline.
The Value of AI in Marketing
At Contently, we believe in the value of AI as a force multiplier for great teams. Used thoughtfully, it can streamline research, tighten workflows, and help people ship higher-quality content faster. However, there are persistent "marketing myths" about what AI can realistically do for content programs and how to use it effectively. These myths tend to take root because AI marketing advice swings between extremes: hype merchants promise transformation without effort, while skeptics dismiss everything as a fad.
Common Myths About AI in Marketing
This year, it’s essential to get clarity on what works and what doesn’t. Here are five myths that deserve to stay in 2025:
Myth 1: More AI Tools Automatically Mean More Efficiency
On paper, it sounds logical: add more AI, get more done. In practice, it often works the other way around: instead of replacing manual steps, many teams end up layering tools on top of one another. The takeaway isn’t "use fewer tools," but rather that true efficiency comes from connected workflows. When AI lives inside the places work already happens — your briefs, your CMS, your editorial calendars — the gains start to show up. Good training and clear guidelines can also do more for productivity than chasing the newest feature set.
Myth 2: AI Content Performs Just as Well on Its Own
Thanks to AI, we’re no longer short on content. Most teams can publish more than ever. The real challenge is creating work that actually sounds like you — and earns more trust than the nearly identical post your audience saw five minutes earlier. Performance now hinges on expertise and perspective, not volume. Search engines and readers both look for signals that someone who knows the topic is actually behind the keyboard, but generic AI text often lacks the lived experience and perspective that makes content persuasive.
Myth 3: AI Will Solve Bad Strategy
AI optimizes execution. But it cannot fix fuzzy positioning or off-base business goals. Speed amplifies direction, including the wrong direction. Teams use AI to publish more, faster… and the metrics that matter don’t budge. Traffic goes up, but conversions stall. The content ranks for keywords, but it doesn’t speak to real buyer pain. Without clear positioning or a path to conversion, all that new visibility simply evaporates before it reaches pipeline.
Myth 4: Everyone Needs to Adopt AI for Everything Immediately
FOMO drives bad technology decisions. Teams adopt tools because competitors are using them, not because they actually solve identified problems. Those wrong-fit tools then create cost, confusion, and cynicism that makes future adoption harder. The teams that make AI work may not move the fastest, but they do make those moves deliberately. They start by identifying a problem worth solving, define what success should look like, and only then pick the technology.
Myth 5: AI Search Is Basically the Same as SEO
Marketers understand visibility through rankings. So it’s easy to assume AI-powered answers are just another extension of Google’s algorithm. They aren’t. Traditional SEO metrics like site structure and performance remain foundational. But AI Search works differently. Instead of ranking pages, language models compress and rewrite information across multiple sources. Visibility in AI Search depends on whether your content is structured clearly and rich with credible context.
Best Practices for Using AI in Marketing
To get the most out of AI, focus on the following best practices:
- Map your current process end to end and look for bottlenecks AI can realistically remove.
- Use AI to speed research, outlines, and first passes, then layer in human editing for accuracy, voice, story, and differentiation.
- Get crisp on messaging and conversion paths before you scale production.
- Look for a single, high-impact use case where AI can remove friction or cost, and run a contained pilot.
- Maintain traditional SEO foundations while adding practices designed for AI visibility — clear entity definitions, structured data, and question-driven content formats.
Conclusion
If the last few years were about experimentation, the next one should be about discipline. Use AI where it helps, skip it where it doesn’t, and focus on outcomes instead of promises. By understanding what AI can and cannot do, and by avoiding common myths and misconceptions, marketers can unlock the true potential of AI and drive real results for their teams. With the right approach, AI can be a powerful tool for driving efficiency, improving content quality, and increasing visibility in search. By being thoughtful and intentional in our use of AI, we can create a better future for marketing and drive real success for our teams.