Tag: AI models

McKinsey’s State of AI survey found that while 88% of organizations use AI, only 6% qualify as high performers, where AI actually delivers bottom-line impact. The bottleneck isn’t the model. It is the engineering discipline that moves a system from demo to deployment, and nowhere is that gap more visible than in payments, where a confident wrong answer costs real money.

To understand what that discipline looks like in practice, we turned to Ankit Rawat, a senior software engineer at Meta Platforms in Seattle, currently leading observability and performance work for WhatsApp Business Payment flows. A Computer Science graduate of Rajiv Gandhi Technological University, Bhopal, Rawat did not arrive at this role through a shortcut. Over twelve years, at Yodlee, Snapdeal, Amazon, Wayfair, and now Meta, he has built, debugged, and scaled payment systems where failure was never abstract. In this article, he discusses what it takes to get enterprise AI to production, how engineering disciplines built across commercial environments directly apply to it, and what engineers need to understand before they ship.

The problem most teams don’t build for

That understanding begins with a problem most organisations have the opportunity to solve early, but rarely do. A single digital payment passes through authentication layers, fraud detection, bank APIs, settlement engines, and often several third-party providers. What distinguishes the teams that succeed is a different capability: they can also tell you where users abandon a flow, which steps in a transaction sequence are slow under load, and why one bank’s definition of a “completed” payment doesn’t match another’s.

“Every component in a payment pipeline has its own idea of what success looks like,” Rawat explains. “A fraud engine might flag a legitimate transaction as risky, and the bank API returns a timeout that the settlement layer reads as a failure. You only find these mismatches if you trace the full journey, not just the individual services.”

Building that kind of infrastructure is exactly the work Rawat has spent his career doing. At Meta, the observability initiative he designed and implemented now directly impacts over 100 teams. It started as a collaboration between four groups: the billing engineering team, the payments product team, a data science unit focused on user behavior, and a growth analytics squad tracking conversion funnels. Rather than measuring whether servers were “up” in the traditional sense, Rawat’s approach traces real user journeys step by step. Data scientists and product stakeholders suddenly could see patterns that no standard dashboard captured. One of those patterns revealed that Instagram’s payment surface, previously unmonitored at the product level, was generating significant abandoned transactions, a finding that directly informed what became a multi-year payments program for the platform.

Seeing the problem before the customer does

If failures are often invisible, the obvious response is to build better ways of watching. Yet traditional monitoring, “Is the server up? Are response times normal?” misses most of what goes wrong in a payment flow. A server can be perfectly healthy while users give up at a confusing authentication step or a misconfigured timeout.

At Meta, where Rawat arrived in October 2023, he designed a cross-functional observability initiative spanning four teams: billing engineering, payments product, a data science unit focused on user behavior, and a growth analytics squad tracking conversion funnels, around a fundamentally different approach. Rather than measuring infrastructure health, it tracks real user journeys step by step: where people abandon a flow, which steps are inefficient, and where time disappears unexpectedly. Data scientists and product stakeholders suddenly had visibility into patterns that traditional dashboards never captured. One revealed that Instagram’s payment surface, previously unmonitored at the product level, was generating significant abandoned transactions, and the initiative eventually grew into a multi-year program reaching well beyond its original scope.

“Best practices for product-level observability are highly organization-specific and not well documented publicly,” Rawat explains. “We had limited external references, so we defined our own approach and aligned stakeholders. Getting from four teams to a hundred meant making the tooling extensible enough that other groups could plug in their own journeys without rebuilding the whole framework.”

The parts of payment infrastructure nobody celebrates

While everyone benchmarks processing speed and tracks uptime, the failures that actually cost money tend to hide in places nobody thinks to look. At Meta, Rawat designed a log optimization solution that reduced network data packet size by roughly 20% while preserving data integrity – saving terabytes of storage every day. Without that kind of efficiency, the cost of observing payment systems at scale becomes prohibitive, and teams lose the very visibility they need to catch problems early.

A separate cross-team initiative he led tackled billing and payment error rates directly. Working with three or more partner teams, Rawat’s effort cut failure rates by approximately 60% across multiple products supporting around $20 million in annual business value.

“What surprised people was that we didn’t need to rearchitect anything,” Rawat says. “Most of the improvement came from finally being able to see where errors actually originated. Once you separate infrastructure noise from real user-side failures and build feedback loops tight enough to validate fixes in days, the error rate drops fast. The hardest part is getting three teams with different priorities to agree on a shared definition of what ‘fixed’ means.”

What India’s AI moment actually requires

India is building the payment infrastructure that will test these principles on a national scale. On March 2, India and Israel signed a memorandum of understanding to link India’s UPI (Unified Payments Interface) with Israel’s MASAV system, as reported by Electronic Payments International – making Israel the eighth country to adopt India’s real-time payment protocol. UPI already surpassed Visa in daily transaction volume in 2025 and now handles over 700 million transactions a day. Every new country added to the network introduces new settlement protocols, new regulatory requirements, and new categories of failure that domestic testing never anticipates.

“Getting a system to work in a demo is the easy part. The real challenge begins when it goes live, when real users depend on it, and a single bad answer, a single failed transaction, is unacceptable. You have to know, before it ships, every way it can break,” Rawat says. “India has the scale and the ambition. What it needs are engineers who understand from experience where these systems break, not because they read about it, but because they shipped something that failed and learned from it.”

For India’s payments infrastructure to function at the scale it is being designed for, it must be underpinned by engineers who understand from experience where these systems need to be strengthened – engineers who learned by shipping and were held accountable for the outcomes.