Amway Content Ecosystem & AI Enablement

the problem

Amway's content ecosystem was fragmented by design. Content was created both centrally and across dozens of global markets, spread across a mix of SaaS platforms — a DAM, a CMS, creative tools, and numerous local repositories. Each market had its own way of storing and distributing assets, from microsites to shared folders to local content libraries that Amway Business Owners (ABOs) relied on for customer marketing.

The result was a broken content supply chain. Internal content teams spent hours searching for assets that already existed and, unable to find them, recreated content from scratch. ABOs faced the same problem from the other side — struggling to locate relevant marketing assets across scattered websites and libraries, which dragged down adoption and reuse.

the approach

The obvious answer looked like centralization: migrate everything into a single enterprise DAM. Deeper analysis said otherwise. The problem wasn't where assets lived — it was that most of them lacked meaningful metadata and tags. Even a perfectly centralized repository would still return poor search results without that.

Instead of committing to an expensive, multi-market migration, I shifted the strategy toward the actual bottleneck: build an AI-powered semantic search layer trained to understand Amway's products, campaigns, asset types, and marketing terminology — one that could surface relevant assets from natural-language queries instead of depending on manually applied tags.

what happened

The semantic search layer plugged into the existing fragmented ecosystem rather than replacing it, which meant no full repository migration and no forcing dozens of markets to adopt a single system on a fixed timeline. It worked across content sources for both audiences that had been struggling — internal content teams doing production work, and ABOs searching for marketing assets to use with customers.

Findability improved enough to change behavior on both sides: less duplicate content creation, more reuse of what already existed.

the outcome

Better discoverability lowered content production costs by roughly 5% in the first quarter. For an organization spending approximately $10M annually on content production in a single major market, that translated to roughly $500K in annualized savings — without the cost or disruption of a full DAM migration, and with a better day-to-day experience for both internal teams and distributors.