AI has dramatically expanded what marketers can personalize – across audiences, creative, channels and customer journeys. But greater capability has also exposed a harder question: can the organization actually operate personalization at the same scale that technology now makes possible?
The constraint is increasingly less about generating another variation or identifying another audience. It is about whether data, creative, media and measurement can work together quickly enough to make personalization repeatable across the business.
For CMOs, that shifts the conversation from what AI can personalize to what the marketing organization needs to change to make personalization a sustained source of growth.
The Results Are Promising. Repeating Them Is Harder.
Recent examples shared by marketers participating in MMA Global’s AI personalization work show why brands are pursuing the opportunity.
Indeed, the global employment platform, began by asking whether more relevant creative could influence a meaningful business outcome. The team chose Germany rather than its largest revenue market, the US, and used audience understanding, modular creative and AI-led optimization to personalize its approach. The result was a 126% increase in account creation. The work has since expanded into five global markets.
Other brands have reported meaningful gains. Choice Hotels saw a 24% lift when AI personalization was switched on, while Travelers reported a 171% increase in engagement and a 188% increase in ticket conversion from personalization around the Travelers Championship.
The numbers establish the potential. The experiences behind them reveal the harder part: reproducing those gains requires changes well beyond the AI itself.
More Data Doesn’t Automatically Mean Better Personalization
Indeed illustrates the problem particularly well. The company had 610 million job-seeker profiles, alongside extensive employer and jobs data. Yet its marketer explained that the data was not sufficiently organized or clean to be used externally for the personalization work. The team first had to turn that information into addressable audiences and usable signals.
This becomes critical when AI is optimizing decisions at speed. The system can only optimize toward the signal it receives.
During the MMA session, one example highlighted the danger of optimizing for webpage visits when the business may actually care about quote starts, orders or sales. AI can become highly effective against the metric it has been given without necessarily improving the outcome that matters commercially.
For CMOs, that makes the question more fundamental than whether enough customer data exists. Is the business outcome that matters actually available to the systems making personalization decisions?
Creative Scale Needs the Right Amount of Variation
Choice Hotels encountered a different scaling challenge.
The hospitality company operates 22 brands, with much of its portfolio in the mid-scale segment. It wanted to understand whether enterprise marketing could also generate greater interest in Cambria, its smaller upscale brand. Using an existing media buy and enterprise audiences, the team tested more aspirational creative for Cambria.
Three images, three copy options and three calls to action created 27 possible combinations for AI to optimize. The objective was not to find one universally winning ad, but to understand which combinations worked for different audiences. The exercise delivered the 24% lift—but also exposed the operational demands of taking the approach further.
Every creative combination also needs different platform sizes and formats. More importantly, there is a balance to strike. Too little variation gives AI little to optimize; too much can spread conversion data across so many versions that the system lacks enough information to learn effectively.
Creative scale is therefore less about producing endless content and more about supplying enough useful variation for continuous learning.
That also requires media and creative teams to move together. Choice Hotels’ marketer described the need for connected tools, systems and workflows so creative supply can keep pace with media optimization.
Measurement Has to Get Closer to the Business Outcome
Travelers offers another useful perspective.
Insurance is a complex purchase, with requirements that vary by state. So when Travelers began exploring creative personalization in 2025, it chose a more contained environment: ticket sales for the Travelers Championship, the PGA Tour event it sponsors.
The resulting 171% engagement lift carried through to a 188% increase in ticket conversion. But the company’s next step is arguably more instructive. Travelers is now applying the approach to its core insurance business and moving toward test-and-control measurement using its own quoting data, allowing it to examine incremental impact closer to the purchase journey.
That progression captures how measurement needs to evolve with personalization. Initial results establish whether the approach creates lift. At scale, measurement needs to identify which signals and combinations should change the next decision—and whether they are improving an outcome the business actually values.
What Changes for the CMO
These brands faced different problems, but together they expose the organizational dependencies behind personalization.
Indeed had to connect teams, budgets, audiences and fragmented data across markets. Choice Hotels needed creative and media workflows capable of supporting continuous optimization. Travelers is moving measurement closer to commercial outcomes.
Indeed’s experience shows how far that change can reach. The company moved from teams with competing OKRs, separate budgets and fragmented signals toward shared audiences, connected data and a common learning agenda. Optimization signals now feed what the company describes as a broader “flywheel,” carrying learning into future decisions. Its marketer said the process ultimately changed “the whole operating model and the way we work.”
For CMOs, that is the more useful definition of personalization at scale. Data needs to provide the right signal. Creative needs to provide enough meaningful possibilities. Media needs to act on them. Measurement needs to carry the learning forward.
Scale is reached when those capabilities stop operating as individual campaign inputs and start working as a connected system.
Turning Proof Into Repeatable Growth
AI will continue to increase the number of marketing decisions that can be personalized. The larger opportunity is to ensure the organization becomes better at making those decisions each time.
A strong result proves what personalization can achieve. Repeatable growth comes when the learning survives the campaign—informing the next audience, the next creative decision, the next investment and, eventually, the way marketing operates.
That is where personalization moves from an impressive result to a capability the business can repeatedly use.
