Your bottle cooler knows more than your data team: Here's how it unlocks agentic AI

You're the CEO of a beverage bottler, sitting down with your leadership team to talk about AI. Where does the discussion start? 

Most conversations begin with tools; which platform, which model, which vendor. But I'd argue the first step should be examining whether you have data that AI can do something meaningful with. For most beverage companies, the honest answer is "not yet". 

AI is only as good as the data behind it. Without a foundation of accurate, high-frequency operational data, even the most advanced models will fail to move the needle.

In the beverage industry, data exists in abundance where the customer and product finally meet; the bottle cooler. Unfortunately, most bottle coolers have been overlooked as source material for AI-powered analytics. They remain unconnected and unmonitored, with beverage companies relying on estimates to gauge how they are performing, let alone capitalising on their AI opportunity.

Around a decade ago, beverage bottlers in Latin America were losing as much as six percent of their cooler fleets every year. Broken coolers relegated to stock rooms, stores changing hands and issues with theft were leaving valuable assets unaccounted for. When it became apparent that field teams were unable to physically investigate every missing cooler in person, an answer emerged. If a cooler could report information via Bluetooth or cellular transmission on its location, asset managers could track it down. And thus, our connected bottle cooler platform was born.

Once coolers were connected, the scope for generating data grew exponentially. We could centralise data on things like temperature, door openings, compressor cycles and faults into a single view cloud platform, ready for analysis at scale.

Today, our data moat spans more than 3.2 million connected coolers worldwide, built incrementally since we launched our first IoT offering in 2017. With almost a decade of real field data behind it, our software platform's machine learning algorithms can surface connections between equipment behaviour and performance that no human analyst reviewing individual fleet data could reliably detect.

Take predictive failure as an example. By analysing patterns in compressor behaviour and temperature variance, we could identify which coolers were likely to fail weeks before they actually did. For a bottler managing tens of thousands of coolers across a distributed territory, this completely changes the economics of fleet maintenance. Every point of uptime recovered is retained sales. Every avoided field dispatch is an hour redirected to revenue. IoT connectivity stops being an IT project and becomes a P&L conversation.

Agentic AI at the cooler level will take this a step further. A sensor detects an anomaly, like a compressor running hot or a door seal losing integrity. The system flags it, raises a work order, and dispatches the nearest technician with the right part already in hand. The outcome is logged, the model updates, and intervention thresholds self-recalibrate. No ticket queues, no manager chasing reports. The cooler is back at temperature before the next restock.

Camera-based sensing is now adding a new dimension to cooler insights, unlocking commercial intelligence on which SKUs are selling, whether planograms are being followed, and how displays perform across different store formats and regions. Once combined with external data sources such as weather patterns, local events, and promotional calendars, AI will move from describing what happened to actively shaping what comes next.

There are strong signals from global brands about how AI will drive progress in the beverage industry. 

PepsiCo has publicly committed to becoming agentic AI-first, deploying systems that don't just report on field conditions but act on them, adjusting inventory and responding to retail signals in real time. Coca-Cola has spoken publicly about using AI to sharpen demand forecasting and help retail partners manage stock more precisely. And yet, the vast majority of bottle coolers in the field still have no connectivity, no data flowing, no signal to act on.

Bottle cooler data is the gold that will fuel the agentic future the beverage industry is building towards and will compound in a way that revenue or market share will not. A late mover cannot buy the years of real-world signals that can train a model to know the difference between a cooler compressor fault and a seasonal temperature spike.

My advice to the beverage bottlers thinking about AI? Stop waiting for the perfect model and start treating your operational data as your most valuable asset. Your coolers are generating signals every hour of every day. Prioritize connecting your entire fleet. The window is open, but it won't stay that way.