What You’ll Learn

Why Your DSO’s Marketing Team Is Already Behind

Picture this. A DSO with 200-plus locations gets a directive straight from the top: become an AI-first organization. The marketing team salutes, rolls up their sleeves, and spends the next six months teaching themselves Claude from scratch. Six months later, they’ve made progress, sure, but a specialized engineer could have built the exact same automations in about two weeks.

That’s not a hypothetical. It’s a real client of My Social Practice, and it’s exactly the kind of story Adrian Lefler, CEO of My Social Practice, brought to a recent episode of The Dental Truth Project. Adrian has spent almost 17 years building a dental digital marketing company that’s worked with more than 7,000 practices, and he came on the show with a warning wrapped in a challenge. He says the org chart for most dental marketing teams hasn’t changed in years, but the actual work has changed dramatically in the last six months alone.

If your marketing department looks the same today as it did last year, this conversation explains why that might be a problem.

The Double Blind Problem, Explained

Adrian’s core idea is simple but a little unsettling. He calls it a double blind problem, and it goes like this:

Executives don’t know what’s actually possible with AI right now, so they can’t give clear direction. Meanwhile, marketing teams don’t know what they don’t know, so they keep doing the work the way they’ve always done it, just faster with a chatbot open in another tab.

Neither side can see the gap, which means nobody fixes it.

“If they are not literally neck deep in AI automations, then they have no idea how the work that they’re currently doing could be done, and neither do the executives,” Adrian explained.

That’s a tough pill for a lot of dental organizations. Most marketing teams are doing exactly what’s been asked of them, and doing it well. The problem isn’t effort. It’s that the job itself has quietly shifted underneath everyone’s feet.

Why Six Months Changed Everything

Adrian points to two specific software launches as the tipping point, citing Claude Code and Google Antigravity, both of which rolled out within the last six to nine months. Add in the broader explosion of large language model tools, and suddenly most of the repetitive tasks marketing employees were doing a year or two ago can be automated.

To be clear, Adrian isn’t arguing that AI should replace every human on a marketing team. He’s arguing that the tasks have changed enough that the roles built around those tasks need to change too. And most dental marketing orgs, DSOs especially, are still staffed and structured for a world that doesn’t exist anymore.

A Real Example: Cutting a Copywriting Team in Half (and Doubling Output)

Here’s where the episode gets tactical. My Social Practice writes somewhere around 700 to 800 pages of copy every month, from blog articles to service pages to full website builds. For years, that meant two full-time copywriters grinding away five days a week.

Adrian’s team tried the obvious first step by having writers use tools like ChatGPT, Gemini, and Claude to speed things up. But there was a catch. Search engines got very good at spotting generic AI output, fast. Typing “write me an article about dental implants” into a chatbot and publishing the result just doesn’t work anymore, and it hasn’t for a while.

So instead of prompting from scratch, My Social Practice built something more sophisticated and created an automation with roughly 50 steps that pulls real source material (old transcripts, Google reviews, existing content) and drafts an article using that actual, original information. The automation drops the draft directly into WordPress on the client’s site.

The result? One copywriter now produces close to 1,000 pages a month, nearly double the old two-person output, and the copy tests as more original and higher quality when run through AI-detection and originality tools.

The bigger shift wasn’t the automation, just the job. The remaining copywriter didn’t get laid off. They got reclassified into a brand-new role Adrian calls an editing automation specialist, someone who reviews and edits the AI-generated draft and flags patterns back to the technical team when the automation needs fixing. Same person, completely different job description.

The $182,000 Job That Didn’t Exist a Year Ago

If there’s one role Adrian wants every dental marketing leader to know about, it’s the GTM engineer, short for go-to-market engineer. This is someone who specializes in building marketing automations inside tools like Claude Code or Google Antigravity.

According to Adrian, the average salary for this role has already climbed to around $182,000, and plenty of GTM engineers are skipping traditional employment entirely to start their own companies instead. It’s a title that simply didn’t exist a year ago, which is exactly Adrian’s point about how fast the ground is shifting.

Hire the Skill, or Teach the Skill? The Six-Month Lesson

Back to that 200-location DSO client. Adrian walked through the real cost of their six-month, self-taught approach to building AI automations. It wasn’t just the calendar time. It was salary dollars spent training a marketing team to become part-time engineers, when hiring someone who already had the skill could have delivered the same result in a fraction of the time.

“There’s a learning tax, right?” Adrian said, describing the hidden cost of that decision.

His advice isn’t that every practice needs to hire a $182K engineer tomorrow. It’s that leaders need to make this decision on purpose, weighing the real cost of upskilling an existing team against bringing in someone who already knows how to build the automation.

Old Roles vs. New Roles: A Quick Comparison

What Should Owners and Operators Ask Their Marketing Team Today?

The Bottom Line for DSO Marketing

AI has changed what a marketing team needs to look like to stay competitive. The double blind problem means most executives and most marketing teams are each missing half the picture. Closing that gap starts with an honest conversation about what’s actually possible, followed by a real decision about whether to build that capability in-house or bring it in.