AI That Works: Why Diligence Beats Hype
Generative AI is having its moment—but not always for the right reasons. Headlines often focus on hallucinations, misinformation, and failed pilots. Yet beneath the noise, some organizations are quietly building systems that work. Amazon’s Catalog AI is one of them.
Featured in Harvard Business Review, Catalog AI is a generative system that creates and tests millions of product page hypotheses annually. It doesn’t just generate content—it learns, improves, and scales. And it’s already driving measurable revenue impact, with 8% of its suggestions boosting sales.
But here’s the real story: this success didn’t come from blind optimism. It came from careful engineering, rigorous quality control, and a culture of experimentation.
Amazon’s Approach:
Audit first: Before deploying AI, Amazon benchmarked its performance against known data to understand baseline reliability.
Guardrails matter: Instead of limiting AI inputs, they layered in rules, statistical profiles, and even AI-on-AI review systems to catch errors and inconsistencies.
Test everything: Every output is A/B tested. If it doesn’t move the needle, it doesn’t go live.
Learn continuously: The system improves itself using feedback loops, multivariate experiments, and concept tests—even challenging long-held assumptions about customer preferences.
This isn’t just a tech story—it’s a blueprint for how AI can create real value when approached with discipline.
✅ Experimentation is essential.
✅ Quality systems are non-negotiable.
✅ Strategic alignment is the multiplier.
Let’s move beyond the hype cycle. Let’s build AI systems that learn, adapt, and deliver.
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