What's up everyone, today we have the pleasure of sitting down with Tata Maytesyan, Growth Consultant, Keynote Speaker, and AI Trainer.

(00:00) - Intro
(01:08) - In This Episode
(01:46) - Sponsor: GrowthLoop
(02:50) - Sponsor: Attribution App
(04:34) - Which Marketing Tasks Are Actually Worth Automating
(13:07) - Why Deep Generalists Outperform Channel Specialists in Marketing
(26:07) - Sponsor: MoEngage
(27:04) - Sponsor: Knak
(35:06) - Why Marketing Org Charts Are Not Getting Flatter
(43:01) - Why Change Management Determines Whether AI Adoption Actually Sticks
(48:03) - The Fear of Automating Yourself Out of a Job
(53:13) - The Voice Diary Technique for Tracking Your Own Energy at Work

Summary: Tata Maytesyan runs an AI bootcamp for marketers on Maven and consults with scaling companies across Europe. In this episode, she breaks down why the best AI automation targets are the boring, repeatable tasks nobody talks about on LinkedIn, and why the specialist-to-generalist shift in marketing is already happening whether your org chart reflects it or not. She also gets direct about what's really going on inside companies claiming to go flat, the 100-hour threshold for building genuine competence across domains, and the self-preservation fear she hears from leaders every week. If you have ever wondered whether you are building your career around the right foundations, this episode is worth your full attention.

About Tata Maytesyan

Tata Maytesyan is the founder and CEO of Grow Global Tech, where she builds AI-powered marketing systems for tech scale-ups and runs a hands-on AI bootcamp for marketers on Maven. She spent 15+ years leading growth inside Nike, Deloitte, and Picsart, including a stint as Head of Product Strategy and Operations for Picsart's content and AI division, a platform with over 100 million monthly active users. She has since advised more than 40 companies across 12 countries on go-to-market strategy and AI adoption, and consults primarily with CMOs and CEOs at companies between a few million and $200 million in annual revenue.

Which Marketing Tasks Are Actually Worth Automating

The wrong starting point for AI adoption in marketing is inspiration. Most marketers scroll LinkedIn for jaw-dropping use cases: ad creative generated at scale, competitive analysis in 10 minutes, entire campaign briefs written by agents. It looks impressive. It's also almost never applicable to your specific job on any given Tuesday. Tata has spent years watching this pattern play out with consulting clients and bootcamp students. Her fix is deliberately boring.

At the start of every engagement, she asks everyone in the room to close their AI tools. Then she opens Miro and maps how the team actually works. From there, 3 questions run against every process on the board: how often the task repeats, how acceptable an imperfect output would be, and whether it's something you actually enjoy doing.

Those 3 questions quietly eliminate most of what people think they want to automate. Frequency kills off exciting-but-rare workflows not worth touching. Risk tolerance separates contexts where imperfect output is acceptable (most content tasks) from those where it isn't. Tata advises a healthcare client where certain work is patient-facing, and mistakes there carry real consequences. The enjoyment filter protects the parts of the job people actually like, because automating something you love is just spending money to make work less interesting.

Her own example from the day this episode recorded: she built a script to pull LinkedIn post metrics (impressions, comments, likes) into Notion. Before that, an assistant handled it. Before that, she did it herself. She describes the task with open contempt, which makes it the perfect candidate: something done constantly, where imperfect output is acceptable, and which requires 0 joy to hand off. She calls it boring is sexy. "Figure out the workflow you do repeatedly, and then if mistakes are manageable and you're okay with them, delegate and automate with AI."

People get frustrated when they hear this. You show up to a bootcamp or hire a consultant expecting to leave with something impressive. Instead someone hands you a whiteboard. But Tata is direct about the tradeoff: "It takes time and it slows you down, sort of feels like it slows you down. In fact, it speeds you up."

The same logic applies to how people first explore AI tools. Pure tinkering has value: testing a new model, playing with a capability outside any work context. That's curiosity, and it's worth protecting. But when something needs to work reliably in your actual job, setup is non-negotiable: context files, folder structure, clear instructions. The AI can't fill in what you don't give it.

The most durable AI workflows come from people who got honest about which parts of their week are boring, repetitive, and low-stakes. LinkedIn will give you inspiration. Your Miro board will give you your actual starting point.

Key takeaway: Map your actual workflow before opening any AI tool. For each repeated task, ask whether mistakes are acceptable and whether you actually enjoy doing it. Frequent, low-risk, low-joy work is the right first target. Build from there.

Why Deep Generalists Outperform Channel Specialists in Marketing

There's a running debate in marketing about whether to go deep in a specialty or build broad across domains. The specialist argument has genuine weight: if you've never actually run an SEO campaign, how do you know when an AI is confidently producing garbage? Tata sees the point. She also thinks the framing is wrong. Specialization built around channels is the vulnerability, and channels keep changing.

Her term for what marketers should actually become is "deep generalist," a phrase she found on the internet and adopted because it captures something the T-shaped marketer framework mostly misses. A deep generalist has real expertise in at least 1 domain but deliberately builds breadth around it. The depth is still there. The difference is the deliberate horizontal stretch.

She watches this compression play out in her bootcamp every cohort. At the start of cohort 6, a participant said her team of 4 had been cut to just her. As the remaining content writer, she was now responsible for everything: SEO, social, website, the whole thing. That's not a future prediction. It's already the operational reality for a large share of the marketing workforce, and the people who trained deep in a single channel with no adjacent experience are the ones struggling most.

The channel argument is where Tata's case gets sharper. An "SEO specialist" built around Google search has a real problem now that AI Overviews are reshaping how search works. Nobody building a "TikTok specialist" career a few years ago expected it to become a top-performing B2B SaaS ad channel. But 1 VP of business development recently told Tata that's exactly what's happening at their company. Channels are fluid. Betting deep on any specific 1 locks you into an increasingly narrow position.

Her own example: at Picsart, 1 division had no SEO function and no budget for an agency. Tata spent 2 months doing the SEO work herself, learning enough to direct AI through the process. When the business eventually hired an SEO agency, the agency was impressed by what was already in place. She had put in enough time to know what good SEO looked like and how to direct AI against that standard effectively.

The underlying skill that makes all of this work is judgment. Generating an image is table stakes. Knowing whether it's good, whether it fits, whether an agent's output is trustworthy enough to use: those require domain awareness that a speciali...