AI driven predictive maintenance (PdM)(opens in new tab) has become a buzzword in the maintenance world, but not all PdM is created equal. While AI-driven PdM has set a new standard for performance, many solutions on the market today still rely on traditional, non-AI methods. These techniques have value, but they don’t offer the intelligence or automation many organizations assume they’re getting.
In this blog, we’re touching base with our subject matter experts to draw a clear line between what’s actually predictive and what’s just marketed as such.
The long history of non-AI predictive maintenance
Predictive maintenance didn’t begin with artificial intelligence. As Mike Cooper(opens in new tab) highlights, “for decades, maintenance teams have used tried-and-true methods to anticipate failures before they happen. AI is relatively new for maintenance teams; there are a lot of traditional forms of prediction that aren’t always talked about.”
The traditional forms Mike mentions include:
Condition-based or threshold monitoring This is when an alert is triggered for a variable like temperature or vibration that exceeds a predefined limit.
Statistical trend analysis This is when you are spotting anomalies by analyzing changes in equipment performance over time.
Physics-based models An example of this is using mathematical models to simulate how and when components are likely to degrade.
OEM guidelines and lifecycle models This is when you are estimating failure timelines based on historical data and manufacturer specs.
Each of these approaches laid the foundation for PdM, and they’re still being used today. Mike adds that “the catch with these four approaches is that they’re not truly predictive in the ways AI can be, and that’s important for the users of a CMMS to understand.”
AI driven predictive maintenance isn’t what you think
After speaking with some of our industry experts, one thing has become abundantly clear: many CMMS providers are claiming predictive AI capabilities, but it’s mostly statistical or threshold-based logic.
What we see in the market is the bold claim that a CMMS has AI-driven predictive maintenance capabilities, and that’s simply not true.
Mike goes on to explain that true AI-driven PdM uses machine learning (ML) to identify certain patterns. For example, it can:
Recommend optimal maintenance timing based on real-world behavior and not just predefined rules
“When predictive maintenance is truly AI-enabled, it changes how teams think, work, and measure success. It doesn’t just detect problems; it helps prevent them altogether, with fewer false alarms and more confident decision-making,” says Mike.
But what form can AI-enabled PdM take? What does it look like?
“For one thing, AI-enabled means it can look at specific, measurable, machine details,” Mike adds. For example, these can be:
Remaining useful life (RUL): Instead of guessing when an asset might fail, AI calculates how much time is left before a breakdown occurs.
Failure rate: AI models continuously learn from operational data to estimate how often similar failures occur.
Mean time to repair (MTTR): With earlier warnings and clearer fault diagnostics, teams can reduce the time it takes to fix issues.
Unplanned downtime reduction: AI helps maintenance teams prioritize interventions before failures happen, cutting costly disruptions.
Condition indicators: By analyzing data like vibration, temperature, and pressure, AI detects subtle shifts that precede failure, often weeks in advance.
Non-AI vs. AI based PdM approaches
Below is a table that outlines the non-AI and AI based predictive maintenance approaches:
Does predictive maintenance really work?
In short, yes. But it needs to be implemented correctly in order to work effectively for maintenance teams.
“Since PdM uses data from sensors, ML models, and historical trends to predict things like failures, that data needs to be accurate, and not only does it need to be accurate, but it also needs to be available,” Mike highlights.
Success for PdM really depends on the data quality and availability, the technological maturity (i.e., AI/ML models are trained and validated properly), and of course integration with operations (i.e., PdM is embedded into the workflows and decision-making process).
Why is predictive maintenance so important today?
Predictive maintenance is important because it changes maintenance from a reactive or scheduled task into a strategic, data-driven process. For industries like manufacturing, energy, and transportation, PdM can lead to thousands of dollars in savings and productivity gains.
Improving safety (e.g., preventing hazardous failures in critical systems)
Increasing operational efficiency (e.g., keeps production lines running smoothly)
Not all predictive solutions are made the same
In an industry crowded with predictive claims, it’s easy to assume all solutions offer similar value. But when predictive maintenance doesn’t use AI, you’re still doing most of the heavy lifting like manually setting thresholds, analyzing trends, and reacting after the fact.
With a true AI-powered system, your maintenance strategy becomes proactive, adaptive, and continuously improving.
If your PdM strategy still relies on old-school rules or statistical models, you’re missing out on the full potential of what AI can do, not just for assets, but for uptime, productivity, and peace of mind.
That’s where solutions like Fiix CMMS AI features stand out.
The 10th annual State of Smart Manufacturing Report from Rockwell Automation recently dropped, and one thing is clear: Artificial intelligence (AI) isn’t the future—it’s the present. Manufacturers are doubling down on digital transformation (DX), and smart maintenance is front and center.
From quality control to cybersecurity, manufacturers are turning to AI and SaaS tools (like a CMMS(opens in new tab)) to help navigate uncertainty, close skills gaps, and build operational resilience. In this blog, we’ll cover what maintenance professionals need to know from the report and how Fiix CMMS is helping teams get ahead.
Smart manufacturing is here to stay and will continue to grow
According to Rockwell Automation’s global survey of 1,560 manufacturing leaders:
95% have invested in or plan to invest in AI/ML, GenAI or Causal AI within five years.
41% are introducing AI and automation to address labor shortages and skills gaps.
50% plan to use AI/ML for quality control this year.
Yet, for all of this vested interest in AI tools, only 44% of collected data is used effectively, showing room to improve how we operationalize insights.
Smart manufacturing is no longer optional. With labor and quality pressures on the rise, tools like Fiix Asset Risk Predictor and Fiix Foresight analytics engine help manufacturers turn underused data into actionable intelligence—fast.
The report also highlights five top use cases for AI and machine learning (ML) in 2025:
Quality control (50%)
Cybersecurity (49%)
Process optimization (42%)
Robotics (37%)
Logistics (36%)
Each of these directly impacts how maintenance is planned, scheduled, and executed. The good news is that AI-powered CMMS tools can help flag anomalies, prevent downtime, optimize resources, and protect plant-floor systems. Fiix CMMS for example, helps maintenance planning by surfacing inefficiencies and organizing team workflows and assets.
Tools like Fiix Foresight leverage AI to track equipment failure patterns, suggest optimizations, predict stockouts, and boost asset reliability—all without complex configurations or data science teams.
Smart maintenance still needs smart people
Despite all the AI excitement, Rockwell Automation’s report reveals a critical truth: technology doesn’t replace people—it empowers them.
83% say analytical thinking, communication, and teamwork are top skills when hiring.
Nearly half of manufacturers plan to repurpose or hire more workers in response to digital transformation.
AI upskilling jumped 10% year over year as a critical organizational capability.
Don’t forget: AI removes tedious, manual day-to-day tasks, surfaces useful insights anyone can act on, and expands workers’ capacity to focus on higher-value work, helping them achieve their goals.
Fiix CMMS focuses on user-friendly AI for fast onboarding. With templates, training tools(opens in new tab), and no-code customization, maintenance teams can adopt AI without the steep learning curve.
Cybersecurity is a growing concern
Cybersecurity rose to become the #2 external risk in 2025. The integration of connected systems (IT/OT) and smart devices increases exposure and therefore, increases the importance of AI for threat detection and prevention.
38% are already using operational data for cybersecurity protection.
Ransomware attacks in manufacturing are 3x higher than in other industries.
Fiix CMMS in action
As a cloud-based software, Fiix CMMS adds layers of protection through secure data hosting, top security certifications, permission controls, and integrations that align with your IT/OT architecture.
Data is everywhere we look, but it still needs context
One of the most telling stats from this year’s report? Less than half of the data collected by manufacturers is used effectively. The gap between collection and action can cost manufacturers lost insights, reactive repairs, and inefficient processes.
Fiix CMMS in action
With real-time dashboards, custom reports, and API integrations, Fiix CMMS helps you capture, contextualize, and act on your data.
In summary, maintenance remains at the heart of smart manufacturing
Rockwell Automation’s 2025 report highlights a global push toward smarter, faster, and more resilient operations. But it’s the intersection of technology and people, like AI-enhanced maintenance teams, that will define true success.
With Fiix CMMS, you’re not just adopting technology. You’re building a maintenance program that is:
Artificial intelligence (AI) and chatbots are nothing particularly new to the maintenance sector. Most sectors have begun using chatbots as part of their overall business operations. We have recently developed a similar innovation, the Fiix Maintenance Copilot, a Gen-AI-powered chatbot designed to resolve asset issues faster with instant answers to your maintenance questions. In this blog, we’ll review Fiix Maintenance Copilot and how it fits into our other product lines. We’ll also discuss the other ways that chatbots are making their way into maintenance and manufacturing.
What is Fiix Maintenance Copilot?
Managing day-to-day activities for maintenance teams can be complex and time-consuming. However, Fiix Maintenance Copilot(opens in new tab) is here to simplify and enhance the maintenance process by capturing your maintenance knowledge in one place, surfacing answers to your maintenance problems faster, and being a conversational and convenient operator for every maintenance team. It works like other chatbot experiences in that it’s a window that pops up a chat-style expertise for team members to ask questions regarding an Fiix Asset Risk Predictor (ARP)(opens in new tab) enabled asset. The information from the asset is stored in a knowledge base for the team to retain.
Fiix Maintenance Copilot can quickly answer questions about your ARP-enabled assets without having to manually review all of your data and documentation. This brings me to my next point: reviewing Fiix ARP and how it fits into the bigger picture of your maintenance.
What is Fiix ARP and how does Fiix Maintenance Copilot fit into it?
According to Mohammad Esmalifalak, Lead Data Scientist at Fiix, ARP, Fiix Prescriptive Maintenance(opens in new tab), and Fiix Maintenance Copilot should be distinct from three separate products, but one. “Fiix ARP detects anomaly operations, and then Fiix Prescriptive Maintenance prescribes solutions to help resolve the issues and prevent potential failures; our latest addition to this product line is Fiix Maintenance Copilot. They work together, not separately,” says Mohammad. He added that the Fiix Maintenance Copilot solves some of the common challenges that maintenance teams face when quickly finding information on an asset. Some benefits of Fiix Maintenance Copilot are:
Time-saving: It streamlines maintenance processes by providing quick access to information, interactive troubleshooting, and around-the-clock support. This can reduce downtime and increase productivity.
Accessibility: It offers support to technicians anytime, anywhere, 24/7, giving teams flexibility in their work environments.
User-friendly: The chatbot functionality makes it easy to interact with and answer questions, and it can access accumulated knowledge about maintenance procedures for the most critical assets.
Adaptability: The chatbot gets better over time and has machine learning (ML) capabilities, which lets it learn from user interactions and adapt responses over time.
“With this, product maintenance teams can get quick access to information versus searching and digging through the computerized maintenance management system (CMMS). It’s easy to use, and it gets better over time. The chatbot is equipped with machine learning capabilities, so it learns from user interactions and adapts,” says Mohammad. But if the product is learning and adapting, how do we keep data and information safe?
“An important question we often get is about customer safety with chatbots. For one thing, we’re not using an open-source platform for our chatbot, and like all Rockwell Automation platforms and products, our customer data and safety are there. We make sure that we don’t use any customer information for training any AI models that are open access or live outside the Rockwell secured instances as an extra measure of safety,” Mohammad added.
Fiix Maintenance Copilot’s versatility and adaptability make it a robust solution for maintenance teams looking to improve their operations.
How are chatbots benefiting maintenance teams?
Since chatbots are growing in popularity, many teams have begun using them internally and externally to manage their clients or partners. Internally in the manufacturing sector, teams often use chatbots to help with real-time troubleshooting and support, predictive maintenance, enhanced communication, and even sharing data-driven insights. Let’s review how some of these features are used in maintenance and manufacturing with examples:
Real-time troubleshooting and support: When machinery malfunctions, every minute of downtime translates into lost productivity and revenue. Chatbots provide real-time support by:
Offering diagnostic assistance: Analyzing symptoms and providing potential causes and solutions based on historical data and industry best practices.
Accessing knowledge base: Quickly retrieving information from manuals, past maintenance records, and technical documentation to assist technicians on the spot.
Guiding repairs: Providing a step-by-step for common repair procedures, ensuring that even less experienced technicians can perform tasks correctly.
Predictive maintenance: Preventing unexpected equipment failures can save teams time and costs, chatbots play a role by:
Analyzing data: Using machine learning (ML) algorithms to analyze data from sensors and other sources to predict when a machine is operating abnormally and is likely to fail.
Scheduling proactive maintenance: Recommending and scheduling maintenance activities before a failure occurs, reducing unplanned downtimes.
Optimizing maintenance schedules: Balancing maintenance activities to minimize disruptions to the manufacturing process while ensuring equipment reliability.
Enhanced communication and collaboration: Effective communication helps maintenance teams organize their day-to-day routines and tasks. Chatbots enhance this by:
Centralizing information: Providing a single platform for sharing updates, reporting issues, and tracking the status of maintenance activities.
Sending notifications: Alerting team members about upcoming maintenance tasks, critical issues, and status changes in real-time.
Facilitating collaborations: Enabling technicians to easily share insights, ask for help, and collaborate on complex issues through the chatbot interface.
Data-driven insights: Data is a powerful tool for improving maintenance strategies. Chatbots help in leveraging this data by:
Generating reports: Creating detailed reports on equipment performance, maintenance activities, and failure trends.
Analyzing trends: Identifying patterns and recurring issues to inform proactive maintenance strategies.
Providing recommendations: Offering actionable insights to optimize maintenance schedules, improve equipment reliability, and reduce costs.
Although Fiix Maintenance Copilot isn’t quite at the level of the above examples yet, it may still be a potential future for the product. “We’re just in the first stage of what’s possible with chatbots like ours. Right now, we’re just accessing from an internal knowledge base, but what happens if we go outside to something like Google? Now we’re expanding our knowledge reach, but we’d need to do so in a smart and safe way,” says Mohammad.
There’s so much potential for chatbots in the maintenance space, Fiix Maintenance Copilot is just one example,” Mohammad added.
By automating routine tasks, providing real-time support, enabling predictive maintenance, enhancing communication, and delivering data-driven insights, chatbots are becoming indispensable tools for maintenance teams.
Fiix Maintenance Copilot is designed to streamline maintenance teams’ tasks
With the number of tasks maintenance teams need to organize and keep track of new tools like chatbots can help teams keep up with the demands of their operations. Fiix Maintenance Copilot is a powerful Gen-AI-driven chatbot that offers a range of features to help teams access their knowledge base easily and quickly, ultimately saving them a lot of time and being a tool, they can lean on 24/7.
Generative artificial intelligence (AI) applications like ChatGPT are dominating the news, and knowledge workers—professionals who use information, creativity, and critical thinking skills—are paying close attention.
Knowledge work categories include creatives (writers and artists), intellectuals (professors and economists), and experts (architects and computer engineers). Anyone interested in generative AI can try to predict the impact of this new technology. AI and automation have the power to reshape the future of work through knowledge work automation (KWA).
AI and automation
Knowledge work automation uses various technologies to empower knowledge workers to optimize their core skills. KWA speeds up tasks like document management, content management, workflow management, and security and compliance. KWA powers new best practices and automation, letting knowledge workers generate more value for their employers.
The average knowledge worker spends 40 percent of their day on tasks that do not require their core skills. Over a billion people worldwide spend almost four hours a day drafting emails, organizing documents, and checking regulatory compliance rules. These business processes don’t maximize a knowledge worker’s time. Knowledge work automation assists with these chores, and more, allowing knowledge workers to focus on important and more stimulating activities.
What does knowledge work automation do?
People often misunderstand the main use of AI in knowledge work and automation. First, different types of structured tasks can be automated without AI. Second, AI can help with less structured and creative tasks like creating art, writing code, and designing buildings in tight collaboration with people. Finally, the combination of AI and automation helps reduce the number of tasks that humans don’t need to do.
Here are four vital tasks that knowledge work automation can take off a professional’s plate:
Document management: Capturing, tracking, and storing electronic documents—PDFs, word processing files, schematics, and legal documents. Such tools are faster and more efficient than manual data entry. Document management software can track metadata, handle data integration and data validation, organize storage, and more without requiring time-consuming input from knowledge workers.
Content management: Collecting, retrieving, delivering, and governing institutional information in any format. Content management handles every phase of the document lifecycle from creation to storage or deletion. Document management, meanwhile, focuses on storing and sharing documents internally within an organization.
Workflow management: Identifying, organizing, and coordinating a given set of tasks to produce assets. Workflow management optimizes a company’s best practices and operating procedures to increase productivity, remove repetitive tasks, and eliminate errors. New projects require knowledge workers to develop their own best practices and workflows. Doing so through trial-and-error is inefficient and unrewarding.
Security and compliance: All companies face security risks and must follow several sets of regulations and compliance standards. Maintaining regulatory compliance and data security can be a full-time job for a trained expert. Knowledge workers are often undertrained for compliance and security tasks. Such assignments waste time better spent on actual knowledge work.
The future of knowledge work
Knowledge work automation leverages artificial intelligence, machine learning, and large language models, automating everything that’s not true knowledge work. AI eliminates information chaos, captures institutional knowledge, and analyzes existing documentation, creating and finalizing documents, emails, standardized forms, and more.
KWA gives knowledge workers document summaries in any language and generates content based on unstructured samples or prompts. KWA automates workflows that facilitate best practices. An industry-leading knowledge work automation platform creates an employee experience that attracts and stimulates skilled professionals, and eliminates nearly four hours of less interesting and low-value work per day.
FAQ: What you need to know about automation
What is the difference between an office automation system and a knowledge work automation system?
As its name implies, office automation helps to efficiently run an office. It supports data workers and project managers and enables video conferencing. Knowledge work automation empowers knowledge users to work more effectively and leverage knowledge of their organization. KWA fills knowledge gaps, guides knowledge intensive workflows, and ensures knowledge access for all team members.
Why should we use knowledge work automation?
KWA is the future of work and the future of automation. Automation tools help thought workers perform at higher levels and improve productivity by automating workflows. The long-term benefits of knowledge automation will strengthen efficiency in any work environment. KWA is already reducing human error and saving time for any business operation.
Why is the automation of knowledge work so disruptive?
Any kind of work automation is disruptive and knowledge work automation is no exception. KWA uses advances in computer science and information technology to build and make large-scale changes to thought work. A thought worker uses automation software to handle non-essential tasks and reduce costs on any product or service.
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