Three layers: from sensor data to agentic action

The IntelliAM platform consists of three components that stack on top of each other, each adding a new capability layer. IntelliAM 53 handles machine and asset intelligence — it ingests sensor data, PLC outputs, and SCADA feeds to build a trusted picture of what each machine is doing. Decipher layers operational context on top: it correlates reliability data with production schedules, quality metrics, and cost information so teams can see not just that a bearing is vibrating, but whether that vibration is actually costing them output or creating waste.

The third layer, Enigma, is where the "agentic" part comes in. Unlike conventional industrial AI dashboards that stop at showing you a red alert, Enigma uses the combined data picture to recommend — or in some configurations, automatically trigger — the next operational step. "Manufacturers do not have a data problem; they have a decision problem," IntelliAM CEO Tom Clayton told Drives & Controls. "The opportunity now is to use agentic AI to turn trusted industrial data into better operational performance."

Real numbers: Müller and the 215% MTBF gain

The most cited figure from IntelliAM's early deployments is a 215% improvement in mean time between failures (MTBF) over a 12-month period at a Müller Milk & Ingredients site. That is not a modeled projection — it's reported from a live production environment. Müller's group operations manager Matt Wilson said the platform helped the company move from reactive maintenance to proactive control: "We didn't know how to identify these bearing failures until IntelliAM came along. Now, we've not had a single failure that we don't know about."

Critically, the platform also cut diagnostic time. What once took engineers hours of searching through drawings and manuals, Wilson said, now happens "at the click of a button." Müller is now expanding the deployment to use production line data, machine settings, and process parameters for broader performance optimization — not just failure prediction.

Separately, IntelliAM recently expanded an existing partnership with Mars to cover six UK sites, including a Wrigley plant. The contract is worth approximately £425,000 (€498,000), with nearly half as annually recurring revenue — suggesting a SaaS-like pricing model layered on top of the initial deployment.

The economics: what a 2% productivity gain looks like

IntelliAM is targeting the food and beverage sector first, and the numbers help explain why. The UK food and drink manufacturing sector contributes £37.3 billion (€43.7 billion) in gross value added to the economy. A 2% productivity uplift — a modest goal by industrial standards — would inject £746 million (€873 million) of additional annual value. At 5%, the figure reaches £1.87 billion (€2.18 billion).

These are not small numbers, and they explain why IntelliAM pitches its technology as an alternative to building new factories. "Britain's future productivity gains will come less from building new factories and more from improving the performance of the assets we already have," Clayton said. The platform is designed to work with existing sensors, PLCs, and historians — no major capital investment required.

IntelliAM claims it already works with half of the world's top 12 food and drink manufacturers. Its client logos displayed on the company site include Diageo, PepsiCo, Mars, Müller, Bacardi, Kerry Group, and Weetabix, among others.

How this differs from "predictive maintenance"

Industrial AI is not new. Predictive maintenance — using machine learning to forecast equipment failures — has been marketed for over a decade by companies from Siemens to Uptake. What makes IntelliAM's approach distinct is the decision-to-action loop. Instead of stopping at "bearing X will fail in 14 days," Enigma evaluates the operational context — production schedule, available maintenance windows, spare parts inventory, the criticality of that specific line — and recommends whether to schedule a replacement now, defer it to the next planned downtime, or adjust operating parameters to extend remaining life.

This is the "agentic" distinction that has been dominating AI discourse in 2026 — the shift from AI that analyzes to AI that acts (or recommends action with sufficient confidence). In a factory context, the stakes are higher than in a chatbot: a wrong recommendation could stop a production line unnecessarily or, worse, let a failure happen. This is why IntelliAM emphasizes that Enigma works with human engineers, not as a replacement. The platform captures existing expertise and makes it consistently available across shifts and sites.

It is also why the SKF partnership matters: the Swedish bearing manufacturer contributes its century of lubrication and engineering knowledge to validate the platform's recommendations against real-world mechanical constraints. An algorithm might spot a vibration anomaly, but knowing whether that anomaly signals a lubrication issue or an impending catastrophic failure is where domain expertise becomes essential.

European angle: SKF, the AI Act, and the post-Brexit dynamic

For European manufacturers watching this space, several factors are relevant. SKF is headquartered in Gothenburg, Sweden — an EU company — which means the partnership carries weight for EU-based factories already using SKF bearings and maintenance services. The platform's approach to blended human-AI decision-making also aligns with the EU AI Act's requirements for human oversight in high-risk AI systems, though industrial machinery AI classification under the Act's risk categories is still being interpreted in practice.

Data residency is another practical concern. IntelliAM processes billions of industrial data points from sensors, PLCs, and production databases. For EU manufacturers operating under GDPR, any such platform must handle data within compliant infrastructure. IntelliAM has not published a detailed GDPR compliance statement publicly, but its registration as a UK company (number 14992634) places it under the UK's own data protection regime, which maintains adequacy status with the EU.

The company is also expanding its European presence. IntelliAM blog posts indicate attendance at Smart Manufacturing Food & Beverage Europe 2026, signaling intent to grow beyond the UK market. For European food and beverage manufacturers — from German dairies to Italian pasta producers — a platform proven at Müller and Mars represents a tangible reference point.

The labor angle: force multiplier, not replacement

One underappreciated aspect of IntelliAM's pitch is its focus on labor shortages rather than labor replacement. Vacancy rates in UK food and drink manufacturing remain well above the national average. The platform is designed to capture scarce engineering expertise and make it available to less-experienced teams — what IntelliAM calls a "force multiplier" for existing staff. An engineer with 20 years of experience can have their diagnostic patterns baked into Enigma's recommendations, making that knowledge accessible to a new hire on a night shift.

This framing is politically smart in an era where "AI will replace workers" is a sensitive topic across European labor markets. It also reflects a practical reality on factory floors: there simply are not enough experienced maintenance engineers to go around.