We’ve all seen the charts: AI is saving marketers thousands of hours on copy, creative, and coding. But while efficiency is through the roof, the impact on the bottom line is still surprisingly silent. To figure out why, I sat down with David Kohl, founder of Morgan Digital Ventures. AI Marketers Guild partnered with his team to field the Q1 2026 AI Adoption Survey. David joined us to explain why doing things faster isn't the same as growing faster in this first-ever, exclusive interview with AI Brief.

You surveyed 55 marketing and media professionals, and the efficiency story is pretty clear: People are saving time. But when you look at revenue impact, it gets quiet fast. What do you think is actually going on?

David Kohl: Nobody's figured it out yet. Efficiency is the easy win: You save time, you save money, you can feel it in your week. But translating that into top-line growth requires a different set of decisions that most organizations haven't made yet. They've optimized the work without changing the work.

"Time saved" isn't a revenue strategy. It's a starting point that gives marketers some headroom. The organizations that take those recovered hours and deliberately point them at something that drives growth — better creative, better media, faster iteration on what's actually converting — will pull ahead in ways that make it hard for others to catch up. The marketers that crack this first will have solved the most important unsolved problem in marketing AI right now.

Content creation is the clear bright spot in the data, but media planning and buying is barely moving. What's behind that gap?

DK: Media planning runs on proprietary data that's fragmented across systems and often contractually siloed. AI needs clean, connected data. Content creation doesn't have that problem: Give it a brief, get an output. Media planning is more like asking AI to solve a puzzle where half the pieces are in someone else's box.

There's also a risk asymmetry. A bad creative can easily get swapped out. A misallocated $2 million upfront commitment is a very different conversation. The respondents furthest along on media planning, buying, and optimization didn't try to solve everything at once. They started with one channel, one data source they actually controlled. That's the model.

Here's where I'm most concerned: Digital advertising already has a transparency problem. Marketers often don't know exactly where their ads ran, what drove performance, or how their budgets were actually allocated. When AI starts mediating planning, buying, measurement, and optimization — and it will — that mystery gets compounded. The marketers positioned to benefit from AI in media are the ones getting control of their data now, before they hand the wheel to a system they can't interrogate. In my view, this needs to be a near-term business priority before anyone should be letting AI take the reins.

Skills and team readiness ranked as the top barriers, more than the technology itself. But plenty of organizations have smart people and adequate budgets and are still stuck. What separates teams that move from teams that don't?

DK: Clarity. The organizations stuck in experimentation almost always have leadership that's said "go use AI" without saying "use it to do this specific thing, by this date, measured this way." That ambiguity stalls everything, because without a target, every experiment is equally inconclusive. It's basically throwing spaghetti at the wall and hoping something sticks.

Before investing another dollar in training, define what winning looks like in 90 days. Make it small enough to be achievable and specific enough to be measurable. The re-skilling question gets a lot easier once you know what skill you actually need.

A meaningful percentage of organizations report moving fast with AI but not measuring impact in any real way. This feels related to the skills and team-readiness barrier. Is it the same underlying problem?

DK: It's the same root cause. If you never defined what winning looks like, you can't measure whether you won. The ambiguity that stalls execution is the same ambiguity that makes measurement feel optional.

There's also a self-protection element. When you can't measure something, you can't be held accountable for it. And right now there's enough social pressure just to be seen as an AI organization that measurement feels like a future problem. The trouble is, the future will arrive faster than people expect. Someone in the C-suite is going to ask what all of this produced; I'd put that 12 to 18 months out. The teams that built even imperfect measurement discipline now will have a significant advantage when that conversation happens. The ones that didn't will be scrambling to justify investments they can no longer reconstruct.

The data shows agencies are ahead of brands on AI maturity across almost every dimension. Is that a durable advantage?

DK: There are two competing explanations here.

The benefit of the doubt answer is that agencies are under tremendous pressure to demonstrate value to clients, and that competitive pressure creates a natural forcing function for AI adoption. With their governance cycles and procurement layers, brands are simply taking a more measured approach.

The other explanation is that some of this is drinking the Kool-Aid. Agency respondents described their organizations as well into the AI implementation stage, which means we should be seeing real results in short order. Those agencies that are truly ahead of the curve will shine, but the ones overstating their maturity are going to have a hard time explaining it to clients when the receipts come due.

Either way, the agencies I'd bet on are treating AI as a product development opportunity. The ones treating it as an efficiency play are the ones with something to worry about.

There we go. Efficiency is a gift of time, not a guarantee of growth. If you’re saving hours on content creation just to fill that time with more meetings about AI, you’re missing the window of opportunity. The brands that win in 2026 won't be the ones with the most automated workflows, but the ones that reinvested their AI dividends into the high-stakes human work. Thanks, David, for the insights.

Now dive into the data, and let me know what jumps out at you.

— David Berkowitz, Chief Community Officer, Marketecture Media

Who: Performance Marketers, B2B Growth Leads, Legal, Small Business Owners

What: The Federal Trade Commission (FTC) reached a settlement with Air AI, banning the company and its owners from marketing business opportunities. The agency alleged the company used deceptive claims about AI-driven earnings potential and business growth to mislead small business owners and entrepreneurs.

Why it matters: The "AI as a magic money maker" narrative is officially in the crosshairs; marketers must vet their performance claims for AI tools as strictly as they would any financial product.

(FTC)

Who: Retail CMOs, E-commerce Teams, Performance Marketers, Fashion Brands

What: Gap is the first major fashion retailer to let shoppers browse, get styling help, and complete a purchase inside Google’s Gemini experience. That turns AI chat from a discovery layer into a conversion layer, which is a much bigger deal than just better search.

Why it matters: Marketers should treat AI assistants less like referral traffic and more like emerging storefronts. Product data, fit guidance, and checkout readiness are becoming media strategy.

(The Verge)

Who: Media Buyers, Agency Leaders, Programmatic Teams, CPG Marketers

What: Butler/Till tested a programmatic media-buying agent on a campaign for Geloso Beverage Group and reported an 82% drop in supply-chain costs, a 30% lower CPM, and a 40% lift in impressions delivered. Human teams still approved the process, but the workflow shows where the puck is going.

Why it matters: This is the kind of case study marketers have been waiting for: less theory, more operating leverage. If these results hold up elsewhere, AI in media buying moves from deckware to procurement conversation.

(Digiday)

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