Push-Button Marketing Is Making Experienced Operators More Valuable Than Ever

Only 15% of CEOs believe their marketing leaders are currently AI-savvy. That confidence gap points to a broader problem: plenty of organizations have adopted AI tools without building the judgment it takes to use them well. A language model will run an SEO audit, draft an ad, and critique a campaign on request, which convinces a lot of people that anyone can do the work. To a marketer, though, AI is closer to what a power saw is to a carpenter, and owning the saw is not the same as knowing how to build.

Jonathan Giner, Founder of Marsense, has worked in digital marketing since 2006, early enough to have watched search engines reshape the discipline the first time. He reads the current moment as the same kind of media shift, the biggest in decades, playing out again around answer engines and language models. The lesson he takes from 2006 is that the tool never carried the marketer, and this shift is no different.

No one is an expert yet, so learn by doing

The field is too young for anyone to claim mastery, which reframes the whole question of whose advice to trust. Giner puts even its leaders in the same position as everyone else, learning in real time by building instead of theorizing. "I dare say that no one is really an expert yet," he says, pointing to the leaders he knows personally who tell him the same about themselves. The clickbait economy on social platforms rewards the opposite posture, selling certainty about what AI can do and leading followers down rabbit holes that end in disappointment when the promised result never lands.

Giner's own method is to test a claim directly instead of taking the post or the video at its word. When a question comes up, he takes it to the tools and works through the problem until the answer is clear. "The tech is set up so it educates you. You'll understand what's possible and what's not," Giner says. The habit also protects against the misinformation that has always trailed digital marketing, where someone can walk into a room, use enough jargon, and pass as an expert to a buyer who lacks the background to check.

Experience matters more now, not less

The tool makes a skilled operator faster but does nothing to install the skill. AI is to a marketer what a power saw is to a carpenter, a multiplier on knowledge that already lives in the operator's hands. The trouble starts when the hardware store opens to everyone. "Now we have AI being the Home Depot of the world, where anyone can go out there and basically buy a saw," Giner says, and the market fills with people who mistake owning the saw for knowing how to build the house.

That distance between owning the tool and wielding it shows up in the corrections Giner makes constantly when he lets AI run ahead of him. Schema placed wrong, a weak ad headline, an optimization that reads plausible and fails in practice, each caught only because he knows what right looks like. Businesses that followed the model's instructions and watched the results fall flat are the ones now coming back for an expert who can course-correct. Market curiosity is running higher than it has in years, and that attention is the opening for practitioners who can turn it into results.

Companies are hiring the wrong way

Companies continue searching for a single hire who can command every channel, and that person rarely exists at a price they will pay. The pitch sounds right on paper: bring in a CMO to manage SEO, pay-per-click, meta ads, and AEO strategy under one roof. Giner argues that genuine command of all those pillars at once is vanishingly rare, and the few who have it can earn far more elsewhere than a salary offers. "This person that we're describing to some extent is a unicorn," he says. Even a strong generalist hire leaves an open question about whether one person can track what is currently working across every discipline at the pace the field moves.

So companies bring in an outside advisor to check the in-house team's work and catch what it gets wrong. Giner recently brought on a client spending well into seven figures a year, twenty-two weeks in, for exactly that: an internal team that does not know what it does not know. The role often means advising the CMO directly, a second read on decisions the leader is too stretched to pressure-test alone. Gartner reports that most organizations call AI leadership a priority while only 30% report the readiness to scale it, and that shortfall is what sends companies looking outside.

Early adoption compounds

Being first has been the reliable edge in every media shift, and this one pays out the same way. Giner's read across nearly two decades is that the advantage accrues to whoever moves early and keeps moving, so the lead widens while slower competitors are still deciding. His instruction is plain: get into the language models now, run real work through them, and build the fluency that only comes from use. Waiting for the tools to mature is its own decision, and it hands ground to whoever started sooner.

The compounding runs through mentorship, too. "Find one person you can listen to," Giner says, describing a guide who can serve as a true north through changes that are not going to slow down. The signal-to-noise problem does not solve itself as the tools improve; more capable tools generate more confident-sounding noise. This is why AI literacy has become a control-room skill rather than a nice-to-have. AI has lowered the cost of producing marketing, and it has not lowered the value of knowing what good marketing looks like. As more businesses reach for the same tools, that judgment is the one thing the tools don't hand them.