Nita Song, president and chief momentum officer of IW Group, opens her Marketing Dive guest piece with a claim she says is unprecedented in her 30-plus years in the agency business: a major technological shift is not rewarding scale. A 10-person boutique and a 1,000-person holding company shop can access the same AI platforms, generate the same research summaries, automate the same workflows, and produce the same first drafts. The tools don't know the difference — and the industry's oldest unspoken rule, "size wins," is cracking.
Song's core argument starts from a structural limit. AI is exceptionally good at looking backward — it learns from what already happened: what people clicked, purchased, shared, responded to. Culture doesn't move that way. It's happening now, in conversations, communities, and experiences that haven't made it into a dataset yet. A large language model can summarize what Asian-American consumers purchased last quarter. It cannot tell you why a message lands differently with a second-generation Chinese-American household in Los Angeles than with a first-generation Vietnamese-American household in Houston.
Multicultural agencies never had the luxury of waiting for data to catch up. They've had to understand communities before they become trend reports and recognize behavior shifts before they appear in dashboards. That proximity is an asset technology can't substitute — all the more because multicultural consumers don't simply consume culture, they create it. Many of the trends that eventually shape mainstream marketing start in these communities long before the broader market notices.
The long-standing frustration for multicultural shops wasn't creating value — it was proving it within measurement systems and procurement models that couldn't quantify it. AI is changing that. Research that took months now takes days. Campaign adaptation that demanded heavy production now scales efficiently. Multi-versioning delivers culturally nuanced creative across audiences at a fraction of historical cost. Most importantly, advanced attribution and AI-powered analytics increasingly isolate audience behavior and connect multicultural efforts to business outcomes with real precision. The measurement systems themselves got smarter — making visible the value this discipline always generated.
The piece's reach extends beyond multicultural marketing. If marketing's future is more fragmented, more personalized, and more culturally nuanced — speaking to multiple audiences simultaneously, adapting messages across communities, creating individually relevant experiences — then that future looks exactly like what multicultural agencies have practiced for decades. A specialty capability is becoming a core business requirement.
The implication holds for agencies and in-house teams alike: advantages in what AI commoditizes (research summaries, first drafts, localization volume) are evaporating, and what's not in the dataset — community proximity, accumulated relationships, judgment about what "good" looks like — becomes the substance of competitiveness. That converges with the conclusion of What Socar and Samjjeomsam Learned the Hard Way — the AI Gap Is Accumulation, Not Tools, and with the client-relationship honesty argued in Saying 'I Don't Know' Is the Strongest Pitch Right Now. Song's closing line carries the thesis: AI didn't create this advantage. It revealed one that was there all along.
Previous technology shifts favored agencies with bigger production budgets and research teams. AI hands every shop the same tools regardless of headcount, erasing scale advantages in research, drafting, and localization volume — and moving the competitive axis to what tools can't provide.
AI learns from the datafied past — clicks, purchases, shares. Culture forms in present-tense conversations and communities that haven't entered a dataset yet, so shifts are legible only to people close enough to see them before the data catches up.
Yes. As marketing fragments into more personalized, culturally nuanced work, the ability to read and address multiple communities simultaneously is becoming a core requirement — and every organization should audit what proprietary, off-dataset understanding it actually holds.
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