Monitoring one pump with a vibration sensor and catching a failure two weeks early is straightforward. Doing the same thing across hundreds of assets in multiple plants, with different machine types, different maintenance histories, and different data systems, is where most predictive maintenance programs stall. During a panel at IIoT World’s AI Manufacturing Day 2026, experts from TDK SensEI, AWS, Omron Automation, and Acerta identified the specific patterns that separate programs that scale from those that stall after a successful first project.
Why Predictive Maintenance Fails at Production Scale
Predictive maintenance on a single asset is a contained problem. One machine, one set of sensors, one data stream, one maintenance engineer who knows the equipment. The business case is clear and the data science is manageable.
Scaling to production means connecting maintenance records, operating histories, and failure modes from separate systems across different machine types and different plants. That integration work is where programs stall, well before the algorithms are ready.
How to Implement Predictive Maintenance in Shorter Cycles
Organizations that try to solve the integration problem all at once are the ones that stall. The length of the initial time box matters more than any single technical factor. Programs scoped as two-year enterprise transformations tend to lose organizational momentum long before they deliver measurable results. By the time the first model is validated, the business priorities have shifted, the budget is under review, and the internal champions have moved on.
Programs scoped in four-to-six-week increments follow a different trajectory. A team picks a specific asset, collects data, builds a baseline, and demonstrates whether the approach can detect real anomalies within that window. If it works, the team iterates. If it does not, the scope was small enough that the organization learns without losing two years of investment.
The scope of that first project also matters. A single pump or motor that protects a critical process, a chiller whose failure would halt production or spoil product, is a better starting point than a CNC machine. The cost savings on these assets can justify the entire program. CNC machines run multiple different parts, each with different operating parameters, and the variability makes them a poor candidate for a first predictive maintenance deployment. A pump or motor operates with more consistent patterns and can still represent millions of dollars in potential downtime savings.
Where Predictive Maintenance Delivers the Highest ROI
Choosing the right asset type is only half the selection. Predictive maintenance does not create value from nothing. It takes an existing operational strength and protects it.
If an organization produces precision bearings and its polishing tools deliver surface quality that competitors cannot match, predictive maintenance on those tools protects the competitive advantage that already shows up on the P&L. The starting question should be: “where does unexpected downtime hurt us most, and what is the dollar value of that pain?”
Organizations that skip this prioritization step end up spreading sensors and models across machines that do not justify the investment, while leaving their highest-value assets unmonitored.
Build or Buy a Predictive Maintenance Solution
Once an organization knows where to focus, the next question is how to get the capability. Not every organization needs to build its own predictive maintenance capability from scratch. Smaller manufacturers without a dedicated data science team are better served by purchasing a purpose-built solution from a maintenance software or automation partner. Larger manufacturers with complex, varied equipment and multiple sites may justify building an internal center of excellence that combines data science, process engineering, and maintenance expertise.
“Don’t be afraid. You don’t have to invent everything in-house. There are expertise out here that can help you bridge that gap,” said Sundeep Ahluwalia, Chief Product Officer at TDK SensEI.
It comes down to whether the organization’s machines and processes are standardized enough that a packaged solution covers them, or varied enough that custom model development is required. Both paths work, but choosing the wrong one for the organization’s actual situation is what creates stalls.
Related from IIoT World
- 15 Real-World AI in Manufacturing Use Cases: From Predictive Maintenance to Agentic AI
- Predictive Maintenance: The Hidden ROI Driver Manufacturers Can’t Ignore
- Why Smart Vibration Sensors Still Use Wires
This article is based on a panel discussion at IIoT World’s AI Manufacturing Day 2026, sponsored by TDK SensEI. Panelists: Sundeep Ahluwalia, Chief Product Officer, TDK SensEI; Steve Blackwell, Head of Product Engineering & Services Center of Excellence, AWS; Thomas Kuckhoff, Sr. Product Manager, Omron Automation Americas; and Greta Cutulenco, Founder and CEO, Acerta. Moderated by John DiPaola. AI tools were used to help summarize and organize the content. Reviewed and edited by the IIoT World editorial team.
Sponsored by TDK SensEI. Editorially Independent.
FAQ
1. Why do most manufacturing AI projects fail?
Most manufacturing AI projects stall because the scope is too broad, the timeline is too long, or the data foundation is incomplete. Programs structured as two-year transformations lose momentum before delivering measurable results. Programs scoped in four-to-six-week increments around specific assets demonstrate value quickly enough to sustain organizational support and funding.
2. How to implement predictive maintenance in a factory?
Start with a single, critical asset whose failure would cause significant production or financial impact, such as a pump or motor protecting a key process. Collect sensor data, maintenance history, and operational context for that asset. Build a baseline and test whether the model detects real anomalies within four to six weeks. If the approach works, expand to similar assets. Avoid starting with complex, variable machines like CNC equipment.
3. How much does predictive maintenance save manufacturers?
Savings depend on the criticality of the monitored asset. A single pump or motor protecting a chiller or critical process can represent hundreds of thousands to millions of dollars in potential downtime costs. The value comes from converting unplanned breakdowns into scheduled maintenance performed during planned outages, reducing emergency repair costs, extending equipment life, and preventing production losses.
4. What is the best first asset for a predictive maintenance project?
A pump, motor, or similar asset with consistent operating patterns and high downtime cost is the best starting point. Avoid CNC machines or equipment that runs multiple products with variable parameters for a first deployment. The goal is to demonstrate measurable anomaly detection within weeks, not months, using an asset whose failure would have clear financial consequences.
