Editorial
Research Report
Business Leaders CX Report: What’s Working and What’s Not in AI-Driven CX
The Gist
- AI success isn’t about tools. Teams that focus on platforms over process consistently underperform, no matter how advanced the tech stack.
- Agile teams are pulling away fast. They integrate AI at 3x the rate of non-Agile peers, and the gap is widening year over year.
- The real advantage is operational. Test-and-learn cultures turn AI into strategic leverage, not just incremental efficiency gains.
Every conversation about AI in marketing eventually lands in the same place: the tools. Which ones to use, which ones to trust, which ones are worth the budget. Teams spend months evaluating platforms, running pilots, building prompt libraries.
And then they wonder why the results feel underwhelming.
Here's what nine years of running the State of Agile Marketing Report has taught me: the tool is rarely the constraint. The way the team works is.
The 2026 data makes this harder to ignore than ever.
Table of Contents
- The Gap Is Already Here, and It's Accelerating
- They're Not Even Solving the Same Problem
- The Outcomes Aren't Just About Speed
- What This Means for Your Team Right Now
The Gap Is Already Here, and It's Accelerating
Agile marketers are three times more likely than their non-Agile counterparts to have AI fully integrated into their marketing processes. Not experimenting. Not exploring. Integrated. Thirty-nine percent of Agile marketers have reached that level, compared to just 13% of non-Agile marketers.
That gap would be notable on its own. What makes it alarming is the trajectory. One year ago, fully integrated Agile marketers sat at 27%.
They jumped 12 percentage points in a single year. Non-Agile marketers? Still at 13%.
This isn't a gap that closes on its own. It’s a discrepancy that compounds.
If that trend continues — and there's no structural reason it wouldn't — in 2027 Agile teams will have spent another full year building AI into how they plan, create, test and measure.
Meanwhile, their non-Agile peers will have barely figured out if they’re allowed to get started. The distance between those two groups isn't measured in tools or budget. It's measured in reps.
Related Article: Stable Teams, Flexible Capacity: A Smarter Model for Marketing Agility
They're Not Even Solving the Same Problem
The most telling data in the 2026 report isn't about AI adoption rates. It's about AI barriers; specifically, the fact that Agile and non-Agile marketers are stuck on completely different problems.
For non-Agile marketers, the top obstacle to effective AI use is accuracy and quality concerns. Forty-one percent cite this as a barrier. That's a trust problem. Those teams are still in the "does this actually work?" phase – evaluating, second-guessing, waiting for proof before committing.
Agile marketers' top barrier is a lack of clear policies or guidelines (31%).
They've already moved past "does this work?" They're now asking "how do we govern this responsibly?" That's not a skepticism problem. That's a scaling problem.
Two groups. Same technology. Completely different conversations.
The reason for the divergence isn't a mystery. Agile marketers have a deeply embedded test-and-learn culture: 90% say experimentation, testing and learning are built into how their team works.
When a new technology shows up, they don't deliberate. They run a small experiment, observe, adjust and go again. That's the same muscle they use on campaigns, on processes, on planning. AI is just the latest thing it gets applied to.
Non-Agile teams don't have that muscle. So every new tool becomes a bigger decision, with higher perceived stakes and less infrastructure for learning fast. The caution is understandable, but it's also expensive. And the costs are getting higher every quarter.
AI in Marketing: Agile vs. Non-Agile Reality
Editor’s note: The real divide in AI adoption isn’t about tools or budgets — it’s about how teams operate, learn and scale.
| Theme | What the Data Shows | What It Means for CX and Marketing Leaders |
|---|---|---|
| AI adoption gap | 39% of Agile marketers have fully integrated AI vs. 13% of non-Agile teams — up from 27% a year ago for Agile. | The gap isn’t static — it’s accelerating. Agile teams are compounding experience while others are still evaluating. |
| Speed of progress | Agile teams gained 12 percentage points in full AI integration in one year; non-Agile teams showed no movement. | This is a reps problem, not a resource problem. Teams learning faster will outpace competitors regardless of spend. |
| Core barrier (non-Agile) | 41% cite accuracy and quality concerns as the top obstacle. | These teams are stuck in validation mode, delaying adoption while waiting for certainty that never fully arrives. |
| Core barrier (Agile) | 31% cite lack of clear policies or guidelines. | Agile teams have moved past “does it work?” and are now solving governance and scale — a higher-order problem. |
| Operating model | 90% of Agile marketers say experimentation, testing and learning are built into how they work. | AI success follows existing habits. Teams with embedded experimentation loops adapt faster to new technology. |
| Output gains | 53% of Agile teams report higher-quality output (vs. 48%); 51% report faster production (vs. 45%). | Efficiency gains exist — but they are not the biggest differentiator between teams. |
| Strategic impact | 35% of Agile teams say AI frees up time for strategic work (vs. 14% non-Agile). | The real win is repositioning marketing as strategic, not just faster execution. |
| Collaboration | 33% of Agile teams report easier cross-team collaboration (vs. 15% non-Agile). | AI amplifies connected teams. Without alignment, it simply accelerates silos. |
| Mindset shift | Agile teams experiment quickly; non-Agile teams deliberate before acting. | The difference is cultural infrastructure — not access to tools or talent. |
| What matters now | Success depends on test-and-learn systems: sprints, retrospectives, and structured experimentation. | The question is no longer “which AI tool?” — it’s whether your team knows how to learn and iterate fast enough. |
The Outcomes Aren't Just About Speed
You might expect the AI advantage for Agile teams to show up in output volume or speed. And it does — 53% of Agile marketers say AI helps them produce higher-quality work (vs. 48% non-Agile), and 51% say they produce work faster (vs. 45%).
But that's not where the statistically significant differences land. The biggest gaps are in outcomes that matter at the organizational level.
Agile marketers are more than twice as likely to say AI helps them spend more time on strategic work — 35% vs. 14%. And more than twice as likely to say AI helps them collaborate more easily across teams — 33% vs. 15%.
Those aren't efficiency wins. Those are positioning wins.
Marketing's perennial struggle is to be seen as a strategic function rather than a reactive one. AI, deployed inside an Agile operating model, is apparently helping close that gap.
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Deployed without one, it's mostly just making the same work go a little faster.
Related Article: The Maze of AI in Marketing: What Should We Do First?
What This Means for Your Team Right Now
If your team is still in "should we use AI?" territory, the honest answer is that the conversation has moved on without you. The question worth asking is not whether you should buy this tool or spend time learning that one; it's what operational infrastructure you have for testing, learning and iterating.
A test-and-learn culture is not a personality type. It's a set of practices.
It's how you build your sprints, how you hold retrospectives, how you make space for experiments that might fail, and how you capture and share what you learned when they do.
Teams that have those practices in place can absorb AI — and whatever comes after AI — because they already know how to learn fast. Teams that don't are starting from scratch each time.
This compounding advantage isn't magic. It's just the result of the same learning loop, running faster and longer, every sprint, every quarter, every year. The teams building that loop now will have more iterations behind them, more institutional knowledge baked in, and more capability to absorb whatever changes next.
That's a hard thing to catch up to once it gets going.
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