Building a truly connected factory

CONNECTIVITY ISN’T a technology milestone, it’s an operational outcome shaped by how effectively data and AI inform everyday decisions.

With sensors installed, data flowing across the site, and, in some instances, predictive AI tools in place and generating alerts, manufacturers may believe they're already operating a connected factory.

Yet the reality often looks different. Unplanned downtime persists, engineering teams remain stretched and decision-making is reactive. Valuable data sits in silos across maintenance, operations and process systems, so while the factory may be digitised, it’s not truly connected.

Dashboards alone don't drive decisions

One common reason predictive AI initiatives fail is that they are deployed as isolated projects. A model is trained to predict a specific failure mode, alerts are configured and a dashboard is built to visualise risk. However, little changes in how the factory is run.

If AI is not integrated into maintenance planning, production scheduling and engineering workflows, it becomes another layer of information rather than driving improvement. Engineers interpret outputs manually, alerts accumulate and confidence declines. That’s because predictive AI only delivers real value when connected to operational processes, with clear ownership and defined pathways from insight to intervention.

The goal is not to predict more failures but to prevent disruption and improve asset performance to support throughput, quality and cost targets. When intelligence is embedded into routine decision-making, it shapes how the plant runs rather than simply reacting to breakdowns.

Cutting through hype

Many manufacturers struggle to separate genuine capability from marketing noise, with technical jargon, overpromising and opaque delivery models creating scepticism. Under pressure to do more with less, some manufacturers invest in AI tools that promise rapid transformation but deliver little measurable value.

Moving towards a connected factory requires transparency and due diligence. Leaders need clarity on what a system does, how it integrates with existing infrastructure and what outcomes it has delivered elsewhere. The focus should be on long-term productivity gains rather than short-term demonstrations that create technical debt.

Inflexible, closed systems are a common barrier. A predictive AI system is only as good as the data it receives, which means it must be capable of connecting to a wide range of sensors, including both new and legacy equipment. Different failure modes require different data sources, and platforms restricted to narrow, proprietary setups provide a partial view of production and limit scalability.

A genuinely scalable approach unifies multiple data streams into a single contextualised view of operations. It links process conditions, asset behaviour and maintenance history so that insights reflect how the factory actually runs.

But more data is not automatically better. Without filtering and context, engineering teams can become overwhelmed by alerts while missing the risks that genuinely threaten uptime. The goal should be a connected network that produces clear, prioritised work orders and recommended actions, not dashboards that look impressive but fail to explain next steps.

Shifting AI from prediction to prevention

Traditional maintenance models focus on fixing what breaks. Predictive AI was expected to shift the emphasis towards prevention, yet many deployments focus on responding to alerts.

When data is properly integrated and contextualised, AI can move beyond prediction and start to play a role in shaping day-to-day operations. This positions AI as a core tool that supports strategic objectives rather than a technical add-on. Over time, as models learn from outcomes and interventions are refined, results compound. What begins as targeted predictive maintenance evolves into broader optimisation across assets and lines.

Carrying over engineering expertise

Many sites rely on a small number of experienced engineers whose knowledge has been built over decades. Their ability to interpret subtle signals and anticipate issues is invaluable, yet much of this insight remains informal and difficult to transfer.

Connected data allows organisations to capture this expertise systematically. By embedding operational knowledge into models and decision-support systems, manufacturers can reduce dependency on individuals and build a repeatable capability that supports less experienced teams. The aim is to buttress engineers with contextual intelligence, ensuring vital knowledge is diffused across the organisation.

Measuring success beyond alert volumes

Dashboards and alert volumes are not reliable indicators of success; genuine value shows in reduced unplanned downtime and improved asset utilisation. It’s also visible in behaviour, as maintenance plans become more proactive and teams spend less time firefighting.

Building a connected factory is not a one-off project but a capability that matures over time. For leaders at both board and plant level, the challenge is not whether to adopt AI, but how to turn machine and operational data into decisions that improve reliability, reduce downtime, cut waste and extend asset life, delivering measurable results today while building stronger operational capability for tomorrow.

Tom Clayton is CEO of IntelliAM AI

For more information: 

intelliam.ai

Tel: 0114 299 5007