By Mandar Kulkarni, VP & GM, Findability Sciences 

From Opinions Desk

Walk into any well-run manufacturing plant today and the intelligence is already there. It lives in the experience of operators who can hear a failing pump days before it shows up on a vibration sensor, in supervisors who know which batch of raw material will cause trouble downstream, in quality engineers carrying two decades of pattern recognition that no SOP fully captures.

The problem is not that factories lack intelligence. The problem is that this intelligence does not scale, does not transfer when an experienced operator retires; and does not reach the planner’s screen three floors above. Modern AI, applied correctly, finally solves this, not by replacing human judgement, but by capturing it, multiplying it and distributing it across the plant in real time.

Three Streams of Data – Almost No One uses them Together

Every plant runs on three very different kinds of information. There is OT data from the plant floor, mill operations, evaporation, centrifuges, utilities, lab readings, energy meters. There is IT data from enterprise systems, SAP and other ERPs, production planning, supply chain, inventory, finance. And there is external data from outside the gate, weather, raw material supply, market prices, regulations.

In most factories these three streams never meet. The plant manager looks at OT data, finance looks at IT data, procurement looks at external feeds. The cost of that siloed shows up as yield loss, energy waste and quality incidents nobody saw coming.

Building the AI Factory Inside the Factory

At Findability Sciences, the approach to fixing this is what we call the AI Factory, a structured way to convert raw plant data into actionable decisions. It runs on a framework we have shorthanded as I-CUPP – build the infrastructure, collect data from every available source, unify it across OT, IT and external streams, process it through AI models; and present it in a form the people running the plant can act on.

This sits on top of the existing foundation, PLCs, SCADA, historians, IIoT sensors and edge gateways on the OT side, servers, Cloud, data lakes and APIs on the IT side. The AI Factory does not replace any of this. It draws from it.

The AI Brain has Four Jobs

At the centre of the AI Factory is the AI Brain. It plays four distinct roles, each answering a question manufacturer have been trying to answer for decades.

Predictive AI answers what is going to happen, forecasting yield, recovery, quality, or downtime, early enough that there is still time to act.

Interpretive AI answers why it happened, converting a yield drop into “the cane mix from Field 14 combined with lower steam pressure in the second-stage evaporator.”

Generative AI answers what to do about it, a copilot that drafts reports, surfaces recommendations. It lets a plant head ask, “Why did energy consumption spike in Line 2 last shift?” and get a coherent answer pulled across five systems.

Agentic Workflows close the loop, triggering alerts, raising approvals, even initiating corrective actions without waiting for a human to assemble the picture manually.

The output is not another dashboard nobody reads. It is three things the plant can use: visualisation that ties KPIs to decisions, anomaly detection that flags issues before they become incidents, and optimisation across yield, energy, throughput and cost. For a typical sugar mill, this kind of unified intelligence, proven through our work with Grupo Pantaleon via Stomata Labs, can unlock tens of millions of dollars annually. For dairy plants, our LactaAI platform delivers similar gains.

Bottomline

None of this works without doing the unglamorous work first. Most plants are not AI-ready on day one. Data sits in disconnected systems, naming conventions are inconsistent. Operators are rightly sceptical of consultants who arrive with slideware. The first 90 days of any serious deployment are almost never about models. They are about plumbing, getting data to flow, agreeing on definitions, earning trust on the shop floor. Done well, what emerges is a factory that learns. From plant to product, AI becomes the connective tissue, and the operator who can hear a failing pump now has a system listening alongside him.