Reframing AI as Career Transformation

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"How many people can this replace?" is the wrong question to ask about AI.

The better question is, "What could my team do if all the busywork disappeared?"

Today's featured guest unpacks how he's embedded AI across a 12-year-old agency, why it's increased hiring instead of reducing it, and what it actually takes to make AI stick culturally, not just technically.

Gilad Bechar is the founder and CEO of Moburst, a global digital transformation agency that started as a mobile marketing shop and evolved into a full-service growth partner for some of the biggest brands in the world, Google, Microsoft, Uber, Samsung, and more.

Over the past 12 years, Moburst has completed five acquisitions and continues to acquire two to three companies per year, intentionally expanding capabilities to become a true one-stop growth shop. In our previous conversation, we talked about acquisitions and scale. This time, we focused on what Gilad calls the next major accelerator: AI.

In this episode, we'll discuss:

• AI is NOT a side project.

• AI adoption could result in more hiring, not less

• How your agency team could see AI as a career transformation

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Treating AI as a Strategic Priority, Not a Side Project

One of the biggest mistakes agencies make with AI is delegating it too low in the organization.

Gilad knew early on that AI wasn't a trend; it was an operational shift. Instead of hiring a junior "AI manager" or tasking a developer with experiments, he hired a VP of AI and gave that role real authority. The mandate was simple but uncomfortable: if you're doing things in 2026 the same way you did them in 2024, you're already behind.

That level of change creates friction, especially in senior teams with decades of experience. Gilad was clear that AI adoption couldn't be optional or political. A manager shouldn't have to "fight" a director or VP to change how work gets done. By putting AI leadership at the VP level, Moburst removed that bottleneck entirely.

AI wasn't framed as "your work is wrong." It was framed as "your work could be 10x more effective if we rethink the process."

They backed this up structurally. Every team has an AI Champion, someone who spends 20–30% of their time driving AI adoption within their department while still doing real client work. On top of that, there's a central AI team building protocols, agents, workflows, and even new products.

The result: AI becomes part of how the agency operates, not something people dabble in when they have extra time (which no one ever has).

Why AI Led to More Hiring, Not Less

There's a persistent fear among agency teams that AI equals layoffs. Gilad's experience has been the opposite.

The original internal goal was to increase billable capacity per employee by 50%. On paper, that could mean doing the same revenue with fewer people. In reality, what happened was far more interesting: revenue per employee increased and demand exploded.

When Moburst started showing clients what was possible, new automations, new AI-powered offerings, faster insights, smarter execution, it unlocked more budget. Clients didn't just buy services; they bought innovation. They talked about it internally. They shared it with peers. And that momentum brought in larger, more sophisticated opportunities.

Gilad shared an example where Moburst won two Fortune 10 companies in Q4, one of which came in looking for a media agency. Media alone would've won the pitch. But what sealed the deal was showing how the brand could improve visibility and positioning across AI-driven discovery platforms like ChatGPT, Perplexity, and Claude.

This is the key shift: AI freed up time and raised the ceiling on value. Instead of spending hours exporting spreadsheets, building decks, or manually stitching reports together, teams could focus on thinking, collaborating, and creating new growth levers for clients.

That's not a cost-cutting story. That's a growth story.

Using AI to Upgrade People, Not Replace Them

Another overlooked benefit of AI is internal career transformation.

Gilad talked openly about roles that are likely to disappear as platforms automate more of the execution. Media buying is a great example. When Google and Meta are telling the market that campaigns will soon require little more than a credit card and a website, the writing is on the wall.

Instead of pretending that isn't happening, he decided to lean into it.

Media managers, content managers, and BI specialists were given the opportunity to reskill, moving into AI-focused roles where their domain knowledge still mattered, but their output multiplied. A content manager could become an AI workflow designer. A media buyer could evolve into someone who builds and manages intelligent systems instead of manually tweaking campaigns.

This reframes AI from a threat into leverage. Employees aren't stuck defending outdated tasks; they're learning future-proof skills. That mindset shift alone changes morale, retention, and performance.

Building a Culture of AI Sharing and Experimentation

At his agency, Gilad made sharing AI knowledge non-optional. Every week, AI Champions review what's new in the AI world and translate it into what this means for our teams right now. Monthly hackathons focus the entire team on eliminating one manual process at a time. And then there's AI Week—a multi-day internal event where every team presents what they built, what worked, and what failed.

The presentations aren't dry. Teams tell stories. They demo workflows. They show where things broke and how they pivoted. Some even use AI-generated video to walk through the narrative.

That transparency matters. Failure isn't hidden, it's shared. And that creates trust, speed, and cross-team learning.

One team's solution becomes another team's shortcut. Ideas jump departments. People start asking, "How could this apply to my work?" That's when AI stops being a tool and starts becoming a multiplier.

The Bigger Takeaway for Agency Owners

You don't need a VP of AI tomorrow. You don't need hackathons or AI week or 16 champions.

But you do need to take the first step.

AI adoption doesn't start with tools. It starts with ownership. Someone has to be accountable for asking, "If we rebuilt this process today, would we still do it this way?"

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