AI Speeds Up Your Marketing 3x. But Have You Thought About How You'll Prove It's Worth It?
A 2026 Knak survey of 333 marketing decision-makers found that 69% track click-through rates while only 41% measure revenue impact. Only 29% consider themselves advanced AI users, and 88% say AI-generated content still requires moderate or heavy revision.
A while back, a friend who heads up marketing was venting to me.
He said that ever since his team adopted AI, the speed at which they produced emails and landing pages was visibly faster. A piece of copy that used to take two days to grind through now produced a first draft in two hours.
He was feeling good about it. Then his boss asked him one question, and he froze.
His boss said: How much faster? And after getting faster, how much more revenue did it bring in?
He didn't have an answer.
And he's not the only one caught in this awkward position.
Knak, a company that builds marketing production platforms, led a research study in 2026. They had Datalily run a survey through Centiment, polling 333 marketing decision-makers across the United States, the United Kingdom, and Canada. The respondents came from companies with annual revenues of at least $50 million, sending at least 5 marketing emails per month, all using enterprise-level marketing automation platforms.
This study uncovered a number that cuts deep:
When marketing teams measure the effectiveness of their emails and landing pages, 69% are looking at click-through rates (CTR).
Meanwhile, only 41% track how much revenue an email actually generated or how much it influenced the deal pipeline.
That's a gap of nearly 30 percentage points.
What Are Click-Through Rates Lying to You About?
You might wonder — what's wrong with looking at click-through rates?
Of course click-through rates are useful. They can tell you whether a headline is compelling, whether a button is in the right spot, whether an image makes people want to click.
But click-through rates can't answer one question: what happens after the click?
A user clicks through, browses around, leaves. Three months later, they convert through a different channel. Who gets the credit for that?
You report on click-through rates, and your boss might think things look decent. But you know in your gut that you have no way to connect those clicks to the final contract signature.
You're measuring the buzz, not the business.
The Survey Revealed Something Even More Brutal
One question on the survey asked: Do you consider your company an "advanced AI user"?
Only 29% said yes.
Think about it — AI has been the hot topic in marketing for this long, and fewer than three in ten actually feel they're using it well.
So what's the other seventy-plus percent doing? Using AI to write a first draft, generate a few images, handle some odds and ends.
Most teams haven't genuinely unlocked AI's productive power yet.
What Makes Those 29% of "Advanced Users" Actually Better?
Good question.
The study found that these self-identified advanced teams share something in common — but it's probably not what you'd expect.
It's not that they're using more sophisticated models.
It's not that their prompts are flashier.
It's that the way they work has fundamentally changed.
Advanced teams are more likely to use structured project management tools and follow standardized native approval workflows. And here's a particularly interesting detail — when they identify a capability gap, they're more willing to spend money on a dedicated tool rather than forcing their existing platform to do something it was never built for.
You see, these are really two completely different things.
One is "I have AI."
The other is "I've embedded AI into my workflows, my governance framework, and my measurement standards."
The first is tool procurement. The second is organizational capability.
Hold Off on Popping the Champagne
Even if you're already in that 29%, the study throws some cold water on the celebration.
88% of respondents admitted: AI-generated content still requires moderate or heavy revision before it's usable.
88%. Nearly nine in ten.
What does that mean?
AI can absolutely help you lay bricks faster. But whether the house you build is sturdy, looks good, and meets compliance standards — that still requires human oversight.
You saved time writing the first draft, but now you need to spend some of that saved time on reviewing, revising, and compliance checks.
AI accelerates production, but it doesn't eliminate judgment.
So, What's the Real Problem Here?
The problem was never "whether you use AI."
It's 2026. That question is already outdated.
The real question is: Are your workflows, your governance framework, and your measurement standards mature enough to convert "faster production" into "better business outcomes"?
There's a number in the study worth chewing on: only one-third of marketing teams consistently meet or exceed their own email and landing page performance targets.
One-third. Meaning the majority of teams can't even hit the goals they set for themselves.
And at a time like this, if you're still staring at click-through rates, it's like driving incredibly fast with a dashboard that only shows your RPMs.
The faster you go, the less you know where you've ended up.
Let's Do the Math
Most enterprise-level marketing automation platforms and CRMs can absolutely connect email engagement data with opportunity records.
Technically, it's not a problem.
The problem is whether you've actually built that workflow. Whether you've created that report. Whether you've trained your team to look past the click-through rate and dig one layer deeper into revenue impact.
The data has been lying there all along.
It's not that you don't have a measuring stick. You just can't be bothered to pick it up and measure.
So let's go back to that friend from the beginning.
He told me later that the first thing he did when he got back was change his team's weekly report template. They still look at click-through rates, but they added one new line: How many opportunity leads did the content produced this week influence?
At first, they couldn't fill it in. That's okay. The important thing is to let that question take root in the team's minds.
Because only when you start measuring "business" instead of just "buzz" does every dollar you spend on AI and every hour you save truly pay off.
What you measure determines what you can manage.
And what you can't manage, you can never change.