How Generative AI Is Reshaping
Consumer Goods Operations

The consumer goods industry has always moved fast — but the arrival of large language models has fundamentally changed what "fast" means for back-office operations. At RealTowers, we've been at the intersection of human expertise and AI capability for years, and the shift happening right now is unlike anything we've seen before.

We recently spoke with Marcus Oyelaran, RealTowers' Head of AI Operations, about how generative AI is reshaping the work our teams do every day — and what it means for consumer goods businesses looking to scale smarter.

The Old Model Is Breaking Down

"Three years ago, a brand would come to us and say: we need 50 people to label product images, categorize returns, and flag quality issues in supplier data," Marcus told us. "Today, the same brand says: we need 10 experts who can train, monitor, and audit AI systems doing the work of those 50."

It's not a story of replacement — it's a story of elevation. The RealTowers teams working in data and AI aren't doing less work. They're doing higher-value work: catching edge cases the models miss, flagging bias in training sets, and ensuring that the outputs businesses rely on are actually accurate.

Where AI Falls Short (and Humans Step In)

Generative AI is extraordinarily capable at pattern recognition and content generation — but it struggles with cultural nuance, low-frequency exceptions, and anything requiring true judgment. In consumer goods, that matters enormously.

"We had a client whose AI model was categorizing a traditional West African food product as a beauty item," Marcus explained. "It kept happening because the packaging looked similar to a cosmetics brand the model had seen many times. A human caught it in the first week of auditing. The model had been making that mistake for months."

This is exactly why the human-in-the-loop model isn't going away. It's becoming more important.

What Consumer Goods Brands Should Do Now

Marcus laid out three priorities for consumer goods brands wanting to get ahead of the curve:

• Audit your training data first. Bad data in means bad outputs out. Before deploying any generative AI in your operations, invest in a human-led data quality review.
• Don't eliminate your operations team — evolve them. The brands winning with AI are the ones upskilling their people to work alongside it, not replacing headcount wholesale.
• Build feedback loops. Your AI systems should get smarter over time. That requires structured human feedback at every stage — something RealTowers builds directly into every AI operations engagement.

RealTowers' Role in the AI-Augmented Future

RealTowers now dedicates a significant portion of its global workforce to AI-adjacent roles: data annotation, model evaluation, red-teaming, and output review. Our teams work embedded within client AI pipelines — not as a separate vendor, but as a core part of the operation.

"We're not just doing BPO anymore," Marcus said. "We're building the quality layer that makes AI trustworthy. That's the most important job in the industry right now."