Future Impacts, Governance, and Personal Routines

What’s up everyone, today we have the pleasure of sitting down with Olga Andrienko, Former VP of Marketing Ops at Semrush.
 

• (00:00) - Intro
• (01:24) - In This Episode
• (03:55) - How AI Agents Reshape Marketing Ops Roles
• (08:53) - How To Beat AI Imposter Syndrome And Start Using Custom GPTs
• (13:28) - How AI Content Agents Generate Drafts Using Internal Context
• (24:29) - How to Use a Risk and Reward Grid to Prioritize AI Projects
• (33:19) - How To Use Google Workspace To Skip AI Vendor Approvals
• (40:00) - How To Decide Which AI Agent to Use
• (46:44) - How To Build an AI-First Reflex in Marketing Ops
• (51:59) - AI’s Endgame: Play-to-Earn and Mandatory Human Quotas
• (01:03:58) - What Happens When You Optimize Your Body Like a Martech Stack

Summary: Olga thought she was ahead of the AI curve, but a weekend course on autonomous systems showed her she was thinking too small. She pitched a shared internal AI stack at Semrush, built systems off APIs, skipped procurement by using already-approved tools, and tracked hours saved instead of promising vague ROI. She started with the work she already knew, made it faster, and used that time to build better systems. Now she’s looking ahead, watching work blur into participation, prepping for human quotas, and making sure ops teams aren’t caught off guard while the rest of the company is still testing prompts.

About Olga

Olga Andrienko spent nearly 12 years at Semrush, where she helped build one of the strongest B2B marketing brands in tech. She started by leading social media, then expanded into global marketing, eventually becoming VP of Brand and later VP of Marketing Operations. She helped guide the company through its IPO, launched brand campaigns that drove massive reach, and scaled AI systems that saved her teams hundreds of hours.

Most recently, she built out a marketing and AI ops function from scratch, automating reporting, content feedback, and influencer analytics across the org. Recently, Olga announced she was leaving Semrush to go out on her own. She’s now building a marketing SaaS product while advising companies on how to use AI agents to rethink marketing operations from the inside out.

How AI Agents Reshape Marketing Ops Roles

Olga had already logged countless hours with Claude and ChatGPT. She was building chatbots, fine-tuning prompts, and staying sharp on every update. Then she joined a weekend course on agent-based AI. At first, it felt like overkill. By the end of day two, she had completely changed direction. That course forced her to realize she had been spending time in the shallow end. Agent AI wasn’t just a smarter assistant. It was a structural overhaul. It changed what could be automated and who was needed to do it.

Agent AI builds systems instead of just responding to inputs. Olga described a clean divide between tools that help you finish tasks faster and agents that actually run the tasks for you.

How agent AI differs from task-level tools:
Traditional tools require manual input for each use
Agent systems operate autonomously and initiate actions
Tools accelerate individual work
Agents orchestrate end-to-end processes
Tools help you move faster
Agents help you step away entirely

She saw use cases stacking up that didn’t fit inside marketing’s current playbook. Systems could now operate without manual checkpoints. Processes that once relied on operators could be built into fully autonomous loops.

“I went into panic mode. Even with our tech stack at Semrush, I realized we were behind. Every company is behind.”

The realization came with a cost model. Internal adoption of Claude and ChatGPT was rising fast. Olga noticed growing subscription bills across teams, with everyone spinning up individual accounts. She ran the numbers and saw the future expense curve. Giving each person their own sandbox didn’t scale. What made sense was building shared tools through APIs, designed to solve repeatable tasks. That way you can maintain quality, cut costs, and still give everyone access to powerful AI systems.

Timing mattered. Olga was coming off a quarter where she had high visibility, internal trust, and a direct line to leadership. Instead of waiting for AI priorities to come down from the top, she used that leverage to move. She pitched a new team and made the case for shifting from brand to ops. She had technical interest, political capital, and an urgent belief that velocity mattered more than perfection.

Key takeaway: Marketing ops leaders are uniquely positioned to build agent-level systems that scale across teams. Instead of waiting for strategy teams to greenlight AI plans, use cost data to make the case for shared infrastructure. Build with APIs, not individual tool access. Push for automation at the system level, not just task-level assistance. If you understand the workflows, know the tools, and already have trust inside the org, you are the one who should be building what comes next.

How To Beat AI Imposter Syndrome And Start Using Custom GPTs

AI imposter syndrome shows up fast. It tells you the developers will handle it, the data team will figure it out, and you should stick to writing copy or launching campaigns. Olga ignored that voice. She opened up ChatGPT, looked at the most repetitive task on her plate, and started testing. No credentials. No roadmap. Just frustration, curiosity, and a weekend.

“Anybody who says they have figured AI out or that they’re on top of this, they’re lying to you.”

She did not wait for a manager to assign her an AI project. She looked for work she already understood. Rewriting vague marketing text. Fixing formatting issues. Translating copy into other languages without sounding robotic. These were not moonshot experiments. They were annoyances. She built a custom GPT for each one.

That work gave her traction. It also gave her time back. She found herself reclaiming an hour a day just by handing off the small, repeatable parts of her job. That time opened up new space to build more. The learning came naturally because it was grounded in daily tasks she already owned.

“If we look at this like a Maslow pyramid, the repetitive tasks are the base layer. That’s where you start.”

Confidence grows when the work starts to feel useful. That shift does not come from reading whitepapers or watching LinkedIn demos. It comes from applying the tool to one thing you do every week and watching it cut your time in half. That is how you build fluency. Not all at once. One custom GPT at a time.

Key takeaway: Choose a task you already know well and automate it with a custom GPT. Keep the instructions specific and tied to your current workflow. Run it repeatedly until it saves you real time. Then build another. Confidence in AI tools comes from using them to solve work you already understand, not from waiting until you feel qualified.

AI Use Cases in Marketing: AI Agents Creating Drafts from Context That Humans Perfect

AI content agents are getting better, but they are not off the leash. Olga built two systems to test how far automation can go without turning content into generic filler. One starts with human writers. The other starts with a structured form. Both rely on real performance data, brand knowledge, and experienced editors.

The first system runs inside Google Docs. Writers draft copy. The AI overlay scores it using past campaign performance, conversion data, and hand-labeled examples of strong and weak copy. It flags weak headlines, vague CTAs, bloated structure. Then it explains why. Olga’s team noticed that when the starting draft is weak, AI only sm...