Every board has funded AI by now. Dashboards, copilots, a predictive-maintenance pilot or two. Ask what any of it returned last year and the honest answer is usually the same word: promising. The technology works. The payback is still a slide.
You already know this, because you signed the cheque. So the useful question isn't whether AI belongs in the factory. It's which AI project comes back with a number and a date on it, and which one comes back with another pilot. This is the piece about the first kind.
Industrial AI is the shelf: the established category, from Siemens to Augury to Microsoft, for applying AI to real industrial processes rather than the back office. Liquid process intelligence is Industrial AI for the liquid phase of the factory: the CIP cycles, the changeovers, the product transitions where dairy and beverage plants actually lose water, energy and yield.
It pays there for a simple reason. The liquid phase is the part of the plant nobody has been measuring at the second. Flow meters and conductivity smear over the exact moments where product becomes water. Point AI at data that vague and it guesses. Point it at a real-time reading of the liquid itself and it acts. That's the difference between a pilot and a payback.
A European beverage producer runs Collo Insights on a single CIP line, 250 cleaning cycles a year. The results are published. €542,000 recovered annually. Twenty-six minutes of cleaning handed back on every cycle. CIP time down 23%, water down 1.55 million litres a year, and 6,500 minutes of production capacity gained back annually.
The minutes come off every phase of the clean: 4.8 from the pre-rinse, 5.1 from the caustic circulation, 15.1 from the final rinse. Nobody lowered the cleaning standard. The line stopped cleaning to a timer and started cleaning to the true state of the liquid.
The plant's Operations Director put it plainly: half a million euros from one production line, and a clear view of what guesswork actually costs. When the number comes from the person who owns the line, measured rather than projected, the sign-off gets easy.
One result is a case study. Three is a method. Alongside the beverage producer sit two more published proofs: Valio's Joensuu plant, where Plant Director Petri Liukka credits the work with “a remarkable difference in our margins”, and a European cheese plant that recovered more than €300,000 from its changeovers from just one line. Different companies, different liquids, different countries — the same move each time. Measure the interface, show the loss, stop it.
That's what separates this from an AI bet. You're not funding a hypothesis about what the technology might do. You're buying a repeatable method three plants have already run.
There are two returns to name. Collo Insights delivers the recoverable one: operators see exactly where the loss happens and act on it, and the value comes back through the changes they make. Collo Signals delivers the avoidable one: the same real-time reading feeds the plant's automation directly, so cleaning and changeovers run on the true state of the process, not a timer, and the loss stops at source with no operator effort.
Insights shows the loss. Signals stops it. Most plants run them in that order: prove the number with Insights first, then wire Signals into the automation for full state-based control.
Scaled the way an executive reads it, the case compounds. On the lines they treat, operations directors target 8–12% EBITDA improvement, and Collo's own modelling, from the loss and CIP calculators on European dairy and beverage averages, puts the combined CIP and changeover potential at €6.12 million per plant per year. That figure is modelled potential, not a delivered outcome, but it rests on the same mechanics the beverage producer just proved on a real line.
And the sustainability line moves with the money, not against it. 1.55 million litres of water saved from a single line: a stronger ESG position and a lower cost base falling out of one investment. Green is gold: the plant that wastes less and the plant that costs less are the same plant.
Don't fund another pilot that reports “promising” at the next board. Fund the platform that reports a number in a quarter. Bring Collo onto your highest-impact plant, read the payback off real data, and roll it out line by line, plant by plant across the network.
The technology question is settled. Three named and anonymous plants have settled it. What's left is a rollout, not a trial: one platform across every line that loses water, energy or yield. Start where the payback is clearest, and turn every drop into value.
Ready for a number of your own? Talk to us about your highest-impact plant.