10 do’s and don’ts for deploying predictive AI in manufacturing
Predictive AI can transform manufacturing, but only when manufacturers understand what to look for and ensure systems, data, and expertise are fully connected, according to Tom Clayton is the CEO and Co-Founder and of IntelliAM.
Artificial intelligence (AI) and machine learning (ML) can transform manufacturing, from reducing downtime to optimising plant productivity. Yet many manufacturers struggle to separate genuine technology from hype.
Underhand approaches, technical jargon, and empty promises from some tech providers are creating doubt and confusion around AI, undermining confidence in what, when implemented correctly, can deliver real operational improvements.
With mounting pressure to do more with less, manufacturers can also be vulnerable to solutions that promise the earth but deliver very little.
The key to adopting AI and ML effectively lies in transparency and due diligence – knowing what to look for in a technology partner, identifying systems that genuinely deliver insights, and ensuring that any solution enhances long-term productivity rather than creating technical debt.
1) Do choose a system that can access a world of sensors
In truth, a predictive AI system is only as good as the data it receives. So, to gain real value, manufacturers need systems that can connect to a wide range of sensors, both new and legacy, rather than being restricted to a single proprietary setup.
A system limited to a small number of sensor types provides only a partial view of production, when in reality, different types of failures require different sensors, and no single device can capture every potential issue. However, many manufacturers still find themselves constrained by narrow, closed systems that overpromise on capability while underdelivering on actionable insights.
More data isn’t automatically better either. Without intelligent filtering and contextualisation, engineering teams can drown in meaningless alerts while missing the problems that actually threaten uptime.
The goal should be a connected network that integrates multiple data sources into a single view and produces actionable work orders, not dashboards that look good but don’t explain next steps. This way, predictive AI can identify real risks and keep production running smoothly.
2) Don’t invest in long-term contracts
Lengthy, restrictive contracts, often five years or more, lock manufacturers into technology that may rapidly become outdated, stifling innovation and increasing risk.
Manufacturers should seek transparent partnerships based on measurable improvement, and mutual accountability, where performance is demonstrated through clear KPIs and incremental improvement, not just one-off fixes. Flexible contracts support continuous optimisation rather than long-term technical debt.
3) Do prioritise real case studies
When choosing a predictive AI partner, it’s essential for manufacturers to look beyond broad figures, logos, or one-off examples. Specific and relevant case studies are the most valuable because they demonstrate real-world performance improvements over time, such as reduced downtime or failure rates..
Be cautious of claims such as “ML eradicates failure.” ML cannot remove all failures; it reduces failure rates and improves performance gradually. That’s why short-term tech trials should also be treated with caution, as they’re insufficient to capture meaningful insights.
The most valuable case studies show measurable results and they’re backed by real quotes from real customers.
4) Don’t Rely on Systems Limited To Predictive Maintenance Sensor Data
Predictive maintenance (PdM) sensors monitor individual variables such as vibration, but factory environments are rarely uniform. Variability in speed, load, and process conditions means single parameter monitoring often misses the full picture.
Without contextualisation across multiple sensors and operational parameters, PdM-only systems risk missing emerging issues, failing to optimise production, and leaving technology that doesn’t scale.
5) Do Prioritise Domain Expertise Over Purely Software-Driven Solutions
Predictive AI is most effective when it is guided by industry knowledge and practical experience, not just software capability.
With experienced engineers retiring and skills shortages increasing, embedding operational expertise into connected systems helps reduce reliance on specific individuals, giving access to a broader ‘skills space.’
AI should support staff with contextual intelligence, enabling informed decisions across shifts and experience levels while keeping critical insights accessible across the workforce.
6) Don’t Expect PdM Data Alone To Optimise Productivity
PdM data alone doesn’t provide insights on throughput, line setup, or supply chain performance. Systems limited to PdM often miss broader opportunities to optimise production, reduce waste, and account for variability across the plant.
Manufacturers should seek platforms that integrate multiple operational data sources to enable proactive, plant-wide decision-making. Without this broader perspective, AI cannot guide improvements in overall productivity or supply chain efficiency, leaving operators reactive rather than proactive in managing the factory floor.
The key is to look for systems that deliver actionable insights across production and supply chain processes, not just isolated machine alerts.
7) Don’t Use Software Without a Unified Namespace
Manufacturers are exposed to a sea of buzzwords to do with AI, so it’s no wonder this can create noise and confusion when try to determine the right solution for their site
“Data lakes,” are often promoted as a solution, but collecting raw data without structure creates noise. Actionable insight depends on how data is stored, indexed, and connected.
This is where a unified namespace (UNS) is critical. A UNS structures and connects data while metadata provides context, showing how readings interact with other variables and their relevance to throughput. Without this, AI can see data but not understand it.
8) Do Retain Data Ownership
Manufacturers must retain ownership and control of their operational data, with access protected by their own protocols. Restrictive contracts that compromise data control risk intellectual property loss.
9) Do Challenge Unreasonable Claims
Claims of “billions” of alarms are a red flag, indicating poor baselining and alert management.
An effective ML system filters out false positives, presenting only the need-to-knows and intelligently guiding next actions without drowning teams in white noise.
10) Do Ensure Flexible Security Options
Accreditation alone is not enough. Security must align with a manufacturer’s own protocols and operational requirements. Flexible, demonstrable security measures backed by transparent case studies allow manufacturers to protect their data while safely integrating the system.
Ultimately, predictive AI success depends on how it’s applied. It’s not a one-off project, but a journey.
Manufacturers that question claims, prioritise transparency, and select solutions that can evolve will be best placed to future-proof operations.
The connected factory isn’t a distant vision, it’s the outcome of thoughtful, phased deployment. By focusing on actionable insights, clear performance metrics and continuous optimisation, manufacturers can move beyond hype to unlock the true productivity and operational intelligence that AI and ML promise.
