The future of predictive maintenance Part 1: Mass adoption
Predictive maintenance, or PdM, shifts the maintenance paradigm from reactive maintenance and preventative tactics to proactive potential failure detection. This model addresses specific machine failures before they happen. These improvements directly contribute to enhanced operational efficiency while helping organizations reduce costs and minimize downtime across their facilities.
The future of predictive maintenance depends on how today's technologies improve, and which new ones emerge, to make PdM more effective and accessible to facilities of all sizes across many industries.
Current research and trends indicate that predictive maintenance programs vastly decrease machine downtime, equipment breakdowns, and maintenance costs. Case studies from ifm show how predictive maintenance:
- Avoided $500,000 in prevented maintenance costs
- Prevented five weeks of press downtime
- Saved over $230,000 annually in scrap costs per line
- Generated $30,000 in savings on replacement parts annually per location
This three-part series explores the future of predictive maintenance through the lens of current trends and how technologies can evolve to drive mass adoption.
These forecasts are informed by:
- More than 30 years of sensor innovation and automation technology in the U.S.
- Field experience implementing predictive maintenance with dozens of facilities
- Current, industry-wide research and white papers
This is the first installment of a three-part series exploring the future of predictive maintenance. Read Part 2 and Part 3.
Predictive maintenance today
Analysis of predictive maintenance coverage across various industries versus the number of facilities adopting it reveal that relatively few organizations implement PdM today.
For instance, the predictive maintenance market was valued at $13.65 billion globally in 2025 and could reach $91.04 billion by 2033. However, only around 12% of industrial companies apply predictive maintenance-based data analysis in 2025, making it third behind preventive maintenance and run-to-failure.
Most that do deploy PdM initiate small projects, often using battery-powered wireless sensors retrofitted to a few machines.
However, this method doesn’t collect and integrate enough data to effectively train statistical models to produce meaningful predictability. Additionally, it is often too expensive to scale beyond a few critical machines. Facilities that want to expand a pilot program often have to start over after a few years.
Obstacles to mass adoption
Consultations with hundreds of PdM candidates suggests that one of two factors hold back many predictive maintenance launches:
- Organizations find it too complex
- Purchasing and installing equipment and resources is too expensive
In the short-term, an organization can simplify the process by:
- Partnering with the right solutions partner
- Harnessing improvements to the technology
Expanding predictive maintenance for machine builders
Mass adoption of predictive maintenance will likely occur when machine builders include the essential sensors in the equipment design. This approach can eliminate current barriers to recording and analyzing real-time sensor data:
- The expense of buying equipment
- The resource drain of installing and running extra cabling
But, this transition has not occurred yet because:
- Most end-users still see vibration monitoring and PdM as “nice to have,” not essential.
- Project leaders responsible for new facilities want everything coming in under budget.
A machine builder who`marginally raises its prices to add sensors risks losing sales, even on $500,000 machines in new $10 billion facilities. However, that may change as facilities shift from seeing PdM as a bonus to a necessity.
For example:
One prominent semiconductor manufacturing company now sees the value in vibration monitoring and predictive maintenance. But, retrofitting equipment is a heavy resource drain. So, they’re working with their vacuum pump OEM to integrate this technology into new models, thus building it into new facilities under construction.
This suggests a trend where hardware and software costs drop and built-in cabling eliminates the expense of retrofitting wired sensors.
How AI can make predictive maintenance more accessible
Any organization recording and analyzing sensor data requires a high degree of in-house expertise to understand the information and make predictions. Advances in AI and machine learning, combined with deeper integration in analyzing sensor data, can make predictive maintenance accessible to more facilities.
AI technology in PdM uses machine learning to:
- Analyze large amounts of machine health data
- Establish baseline metrics
- Interpret anomalies to predict failures
Then, a large language model, or LLM, autonomously creates work orders to prevent failures.
These intelligent management systems can predict potential failure scenarios and optimize maintenance staff schedules to minimize downtime.
As this technology improves, it can make PdM attainable for organizations who currently don’t have the staff or resources to implement a program.
For example:
Vibration monitoring requires specialized skill sets to interpret and apply the metrics. AI can provide all that expertise while also analyzing data far more efficiently and accurately than human data analysts.
Addressing major AI concerns with custom LLMs
Customized, on-premise LLMs should provide security and functionality aimed at overcoming two typical AI concerns:
- Data privacy
- Incorrect information (“hallucinating”)
AI models used for predictive maintenance should be private, closed systems, unlike popular LLM’s such as ChatGPT, Perplexity, and Claude. The differences are:
- Mainstream LLMs are open to the public. They learn from almost everything written on the internet, regardless of quality or accuracy. They can also store and surface information entered by a user, which makes sharing proprietary data risky.
- A custom LLM can be private and secure. Data should never leave an organization if they use a secure, private LLM. The program would provide consistent, objective predictions and interpretations by working only from sensor data and specific rule engines and models.
Predictive maintenance can be more easily adopted when it requires smaller teams to implement. Improving and applying more AI technology can bridge that gap.
Continue reading this series:
How to get started
The best predictive maintenance strategy is simple to implement and maintain. It also prepares a facility for the future. The best way to create a customized preventative maintenence program is to start with a proof-of-concept project. For a free consultation with a Solutions Architect, fill out the form below.
For immediate assistance, contact our service center:
info.us@ifm.com
800-441-8246