Why AI Rewards Cultural Knowledge, Not Scale

According to Nita Song, president of IW Group with more than 30 years in advertising, this is the first time in her career that a major technology shift hasn't rewarded scale. A ten-person independent agency and a thousand-person holding company use the same AI platforms, pull the same research findings, automate the same workflows, and generate the same first drafts. The tool can't tell them apart. That's forcing the advertising industry to rethink an unspoken rule it followed for decades: bigger wins.

AI makes a lot of things easier, but it doesn't make understanding culture easier. As the author observes, AI looks backward — it learns from what people have already clicked, bought, or shared. Culture doesn't work that way: it emerges right now, in conversations, communities, and lived experience, before any of it ever makes it into a database. Agencies serving multicultural audiences never had the luxury of waiting for data to catch up — they had to understand a community before it turned into a trend report.

The author offers a concrete example: a large language model can summarize what Asian American consumers bought last quarter, but it can't explain why the same message lands differently with a second-generation Chinese American family in Los Angeles than with a first-generation Vietnamese American family in Houston. AI can't replace years of built community relationships, cultural knowledge, and lived experience — which means multicultural audiences don't just consume culture, they create it: many of the trends that later define mainstream marketing originate in these communities long before they reach the broader market.

At the same time, AI is solving a long-standing economic problem for multicultural agencies: proving the value of their work in terms traditional marketing organizations and budget models understand. Research that used to take months can now be done in days, and creating culturally adapted creative for multiple audiences has become far cheaper than before. Most importantly, advanced attribution models and AI-driven analytics make it easier to segment audience behavior more precisely and tie multicultural marketing efforts directly to business outcomes.

The author stresses that this trend's significance extends far beyond multicultural marketing. The market is growing more fragmented, personalized, and culturally sensitive — brands now need to speak to multiple audiences at once, adapting their message community by community. That's a situation multicultural agencies know well: for years they've worked across cultures, languages, and generations, translating between communities. A skill once considered a narrow specialty is becoming a core requirement for the entire marketing industry.