2x–50x ROI on AI implementations
Zendesk's internal AI POC and platform implementations have delivered between 2x and 50x ROI.
Snapshot · The MongoDB Podcast
Don't Build Your Own AI (Unless You Have To)
Explore episode
Mar 6, 2026
Where this was said
AI Washing: How to Spot It and Why It Matters
At 35:43 · chapter starts 34:50
AI washing, Akshaya explains, is what happens when 'AI' becomes a marketing suffix rather than a functional description — and he opens with a disarming example: a rice cooker bearing an 'AI Powered' sticker. In the enterprise context, the pattern is more insidious: a vendor adds a thin LLM plug-in to an existing product without re-architecting the solution or reimagining how value is delivered. The test for legitimacy comes down to four questions. Is the AI solving a specific, well-defined problem? Are concrete KPIs and ROIs attached to the solution, and is it clear how AI achieves them versus the prior approach? Is the claimed improvement consistent with realistic AI outcomes — Zendesk's internal POCs see 2x to 50x ROI, so a vendor promising 10% CSAT lift should raise eyebrows? [1] — Akshaya Murthy "2x–50x ROI on AI implementations: Zendesk's internal AI POC and platform implementations have delivered between 2x and 50x ROI." 35:43 And finally, is the vendor claiming a universal 'one size fits all' solution, which is almost always a red flag? Ticket summarisation might be one of the few truly general-purpose AI tasks, but beyond that, domain specificity matters enormously. If those four boxes aren't checked, you're looking at a rule-based engine wearing an AI costume.
What AI Washing Actually Looks Like
AI washing is a buzzword salad with no specifics. If a vendor can't show you the KPIs, the defined problem it solves, and a measurable ROI, they're masquerading a rule-based engine as AI. The rice cooker with an 'AI Powered' sticker is not a joke — it's a category.
Architectural Principles for Robust AI Products
The architecture decisions that will make or break your AI product: clean data pipelines, a modular API-first model layer that lets you hot-swap LLMs, CI/CD for models themselves, and continuous monitoring with human review. These aren't nice-to-haves — they're the difference between a product and a liability.
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