When we think of Artificial Intelligence in shopping, we usually imagine a chatbot helping us find a pair of shoes. But for Bhasker Goel, a Software Engineer at Google, the real magic isn’t in the conversation. It is in the infrastructure that makes the modern world run.
Goel specializes in building massive, reliable systems that handle millions of tasks at once. In the Food and Beverage and Retail sectors, he argues that the biggest hurdle for AI isn’t intelligence, but speed and scale.
"In a global supply chain, every second of delay is a wasted resource," Goel says. "If an AI takes too long to calculate a price change or reroute a delivery truck, the window of opportunity is already gone."
Traditionally, retail data moves in slow batches, often updating inventory only once a day. This creates a stale data problem. If an AI agent makes a decision based on inventory numbers that are even six hours old, it might authorize a shipment to a warehouse that is already full.
AI needs to shift to event driven systems. In this model, data is processed as it happens, allowing AI to authorize instant actions, such as automatically moving fresh produce to a store where demand just spiked. This transition requires a fundamental rethink of how databases are structured. Instead of waiting for a scheduled update, the system must be capable of handling a constant, high speed stream of information from thousands of sensors and point of sale terminals simultaneously.
A major part of making this work is shifting heavy computation to the edge. This means processing data locally in stores and warehouses rather than sending everything to a distant, central data center.
"If you have to send a data packet from a local grocery store in Mumbai to a central server and wait for a response, you’ve already lost the battle against latency," Goel explains. By keeping the thinking close to the physical action, companies can reduce the time it takes for a system to react from minutes to milliseconds.
This is particularly critical for "low latency authorization." When an autonomous system needs to approve a high value logistics change, it needs a specialized software layer that can verify the decision against safety and budget rules instantly. Without this, the AI is effectively flying blind.
Over the next five years, Goel predicts that the winner in the AI race won't be the company with the flashiest app, but the one with the sturdiest backend. The industry is moving toward a world where AI manages physical logistics in real time without human intervention.
"The goal is to make the technology so efficient and reliable that the consumer never even notices it working," he concludes. "When your favorite snack is always in stock despite a sudden surge in local demand, that is the invisible engine at work."
