A global, stretched team
Resources were spread across regions, and turnover had thinned the bench.
Case study · AI Capacity on Demand
Turning ad data into currency, at market speed
A global CTV advertising leader is transforming into an AI- and data-driven business, with specialized capacity from AI Collaborator to deliver AI across data, monetization and sales, without expanding its permanent team.
Overview
The story at a glance
A global CTV advertising leader faced a make-or-break challenge: modernize its legacy advertising operations by turning the audience and campaign data it captures every day into a monetization engine.
The business, the data and the opportunity were all big. The capacity to find, map and deliver the AI use cases was not. With a global, stretched team and firm budget limits, leaders needed specialized expertise they could add without hiring.
The challenge
A big opportunity, not enough hands to seize it
The transformation was committed. The specialized capacity to deliver it was missing.
The company had a large global advertising network, deep data assets and a clear chance to monetize them. It had committed to an AI and data transformation in a fast-growing industry. But it lacked the capacity to generate and map AI use cases, let alone deliver them. Hiring its way there was slow and costly. Specialists who know both AI and the ad market are scarce and expensive, and juggling several partners to fill the gap adds overhead of its own.
A transformation at market speed
The shift to AI demanded specialists across data, monetization and sales use cases, all at once.
A firm line on headcount
Budget limits ruled out building a larger permanent team.
The solution
Many use cases at once, with the right specialists
MARCO® added specialized AI capacity without adding headcount. Forward-deployed engineers with ad-market expertise worked alongside the company’s own team and vendors, moving many use cases at once. Leaders gained a board-ready view of the portfolio, and every deployment made the data more valuable.
How it worked
Domain expertise, on demand
ML and data engineers who know the ad market joined solutions architect–led teams. They worked data, monetization and sales use cases at the same time, and scaled as priorities changed.
Many use cases, one extended team
Multiple use cases moved forward at the same time, each with the specialists it needed. Forward-deployed engineers worked alongside the company’s own team and existing vendors, so nothing had to be replaced or rebuilt.
Board-ready visibility
MARCO® put every use case in one ranked view, by financial value and strategic fit. Leaders could show the board what was funded, what was moving and why.
Compounding intelligence
Each initiative’s data foundation and results informed the next. Every deployment made the data more valuable, building the currency the business set out to create.
The impact
Speed to value, without an army of hires
A predictable path from AI ambition to deployed, revenue-generating initiatives.
The company now has a repeatable way to deploy AI and see a return, sized to its budget. Multiple initiatives are already in production, helping advertisers measure campaign results and reach new audiences. New initiatives keep coming, without a larger permanent team.
Speed to value
Forward-deployed engineers started on priority use cases right away, putting the first initiative into production in weeks.
A predictable path to deploying AI
Every use case follows the same route from ranked priority to production, with named owners and clear release decisions. Leaders know what ships next and when.
A return without the hiring burden
Capacity is added as each use case needs it, instead of recruiting, onboarding and retaining a large permanent team. Spend tracks delivered work rather than headcount.
“Working with AI Collaborator enabled us to significantly improve audience segmentation, drive stronger campaign outcomes, and increase the overall currency of our data. Their team brought both technical depth and real understanding of our business.”
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