Diagnosing a payment conversion dip used to mean multiple dashboards, systems, and a lot of manual reconciliation.

Mayank Vats, Head of Product Analytics at Tira (India’s leading beauty retail platform), remembers this process all too well. The internal logs showed how many payment requests were sent. The payment gateway showed how many were received. When you saw a conversion anomaly, you pulled data from both and tried to make sense of what was happening. The process took hours, and that didn’t include all the cross-functional coordination once you had the story in hand.

“Analysts were stitching journeys using SQL, exporting to Excel, doing lookups, creating presentations, and then sharing those with leadership. Leadership would review it and eventually take action. That entire process often took more than a week.”

The cost of that delay isn’t abstract. As Mayank puts it: “If you’re doing 100 orders a day and, because of a payment or API issue, orders drop by 80% for an hour, the difference between fixing that in one hour versus eight hours is huge.” In his experience, the speed of diagnosis is the strongest predictor of revenue impact.

Building an AI-monitored analytics stack

Tira attached Amplitude AI Agents to dashboards aligned with each functional KPI. For a product team, that means step-level funnels and error rates. For marketing, it’s traffic by source, conversion rates, and new customer acquisition. Agents monitor those signals continuously and surface what needs attention. Teams don’t go hunting for problems anymore.

Alerts fire automatically to the people closest to the data, and AI-generated daily summaries go to a broader leadership audience. Instead of opening a dashboard and interpreting charts, leaders get direct statements: conversion dropped here; the likely source is there; here are the affected segments.

Not every function needs every insight, though. Feedback agents are relevant to customer success and marketing, while finance has different needs altogether. Part of making this work at scale is being deliberate about who receives what, because otherwise you’re trading one kind of noise for another.

Tira didn’t roll this out to everyone at once. They started small, validated summary quality, fixed instrumentation gaps, and expanded access once the signal-to-noise ratio was proven. That sequencing was intentional. “We know there are gaps in instrumentation, so we focus on fixing those first before exposing summaries more broadly,” Mayank explains. “The real value is in quickly surfacing what needs attention, while still applying human judgment.”

The result: an analysis cycle that used to take more than a week now runs in a day or two.

Asking questions instead of building views

Most analytics workflows still have the same bottleneck: someone needs to build a view before anyone can get an answer. Model Context Protocol (MCP) changes that. It’s an open standard that lets AI systems connect directly to external data sources and fetch live information on demand—no export, no handoff, no waiting.

Tira uses Amplitude MCP alongside its AI agents to bring downstream data into one platform. The result is exactly what it sounds like: leadership asks, “What were day-on-day sales for the last seven days?” and gets an answer without dashboard requests or raised tickets.

The bigger shift, however, is behavioral. When the barrier to getting an answer drops low enough, people start asking more questions. Teams that previously avoided dashboards because they felt too technical are now engaging directly with their own KPIs. At the scale of an enterprise like Tira, that kind of adoption compounds quickly.

Governance wasn’t an afterthought

Speed matters. But Tira wasn’t willing to trade governance for it.

“One thing we were very strict about is not passing any PII [Personally Identifiable Information] to Amplitude,” Mayank notes. All interactions run on internally generated user IDs. Analysts can walk through an individual user journey in full detail, but they cannot identify the person behind it. Role-based access controls are enforced across the board, with single regional admins managing permissions.

That architecture was in place before the rollout, not added to it afterward.

For Mayank, the destination is clear: “AI in analytics is about minimizing time spent extracting data and maximizing time spent acting on insights.” As Mayank shares, “The goal is to spend as little time as possible creating dashboards, reviewing dashboards, or staring at line charts. Instead, teams should consume summaries and act on them.”