Deliver successful CMMS implementations with Generative AI

For the sake of repetition, a CMMS (Computerized Maintenance Management System) is the hub for your maintenance operations. It tracks equipment, schedules repairs, logs work orders, and stores asset data. 

Yet despite these tools, most CMMS setups still rely on technicians typing notes, managers searching for answers, and teams making decisions manually. Thus, there is a strong need for a simplistic and easy-to-use CMMS in the market, as these CMMS tasks often become not only cumbersome for users, but in many cases, unnecessary.

That’s where Generative AI comes to the rescue.

Human Error - Root Cause for CMMS Implementation Failure

Depending on the source, CMMS vendors acknowledge that the failure rate for CMMS implementations is 50%, and can be as high as 80%. The problems don't end even when the CMMS implementation is considered a success.

Consider the following:

• Only 6% of companies claim to use 100% of their CMMS functions
• 61% of maintenance managers find implementing CMMS challenging
• 39% use CMMS consistently to track maintenance tasks

10% of work orders are not updatedUp to 80% of CMMS projects fail to deliver expected results due to poor user adoption, lack of planning, or insufficient resources dedicated to the implementation process. Of all this, poor user adoption emerges as a common reason across many poorly done CMMS implementations.

Low User Adoption

If maintenance technicians and operators perceive the CMMS as a burden or fear it will replace their job, they’re likely to resist using it. The standard reasons given are as follows:

• Lack of training
• The system feels complex or slow
• Fear of being monitored
• No visible benefit to their daily work
• It takes too long to use it, and I can get more work done without it

Often recommended as a solution is to involve users early, get buy-in, train them, show them how it makes their job easier, and reward usage. However, these fixes also don’t seem to solve the problem. Because in many cases, the reasons for poor CMMS implementations are related to deeper human psychological issues.

We are humans, after all.

We get lazy, we tire, we don’t follow processes consistently, we miss things, and we forget. Unfortunately, the CMMS doesn’t care. When it comes to low user adoption of CMMS, a significant portion can be traced back to natural human behaviors—not malice or incompetence, but instead how people are wired.

Let’s attempt to understand why this is so.

Laziness (or Conservation of Effort)

People tend to avoid anything that feels like extra work—especially if the payoff isn’t immediate or visible. This behavior is often subconscious. It’s not about being lazy; it’s about instinctively optimizing effort. After all, what is the need to log a job in the CMMS?

Not Following Process (Process Fatigue or Lack of Ownership)

People follow processes when they feel ownership or see value. If they see CMMS as “admin work” or “someone else’s job,” they skip it. If the process changes frequently or isn’t enforced consistently, they ignore it.

Forgetfulness

Humans forget. If CMMS updates aren't ingrained like muscle memory, people will forget to log tasks—especially under time pressure and unwanted distractions.

CMMS Feels Like "More Work"

The more a system competes with human natural behavior, the more likely it is to be ignored. Surprisingly, the fix implemented to address the above problems suffers from the same human issues as the problems.

• Nudges and reminders, such as push notifications and daily SMS prompts, can be used to support users.
• Forcing CMMS entries, without which preventive maintenance checklists can’t be marked complete.
• Reward or acknowledge those who consistently use it correctly.

Instead, the most successful CMMS implementations blend seamlessly into daily, everyday routines, feel helpful (not a burden), and show instant value for the user and not just the manager. This is where Generative AI CMMS comes into play.

Generative AI CMMS Solution

Gen AI is not just about getting operational tasks done, but also making decisions. Gen AI kills the inertia of action and the hesitation of decision-making. That is what makes Generative AI a game-changer.

Traditional AI can spot trends or trigger alerts when sensors go out of range. It is passive, at best. But Generative AI goes further: it can draft text, summarize information, chat naturally, and connect insights across data sets. It turns the CMMS from a database into a proactive digital teammate—helping you find solutions, predict problems, and streamline work.

Gen AI takes the work out of work.

Here is a use case example of how Generative AI can be applied to CMMS usage:

“You walk into your plant, grab a cup of coffee, and your maintenance dashboard already knows which machine might break next, what replacement parts to order, and even provides pre-filled work orders. All before your first coffee.”

Too good to be true?

Gen AI CMMS Use Cases

You can consider the following use cases in any order. Whether you are already using a CMMS or implementing one, this list serves as a helpful reference.

• Content Management
• Make Technicians’ Lives Easier
• Maintenance Operations Productivity
• Knowledge Management

Content Management

Intelligent search and dynamic documentation in Gen AI-powered CMMS streamline access to critical information, enabling faster troubleshooting, seamless updates, and thoughtful decision-making across maintenance workflows.

Search with content - Smart Search.

Generative AI possesses the capability to interpret context beyond mere words. Most CMMS Software includes a search function; however, the searches are static. Users must know the correct keywords related to maintenance to get relevant results. Even then, there is no assurance that the CMMS will display the desired results. Thus, a poor user experience (UX) leads to users becoming disenchanted with the system's output over time, resulting in decreased usage and ultimately abandonment.

This is the key reason for user frustration with search. Through Gen AI's Natural Language Processing features, it can comprehend not only the user's input keyword but also the search intent. For example, a static CMMS might interpret “machine not working” as plain text. In contrast, Gen AI understands that an issue exists. It can then prompt the user with specific queries, such as whether the motor has overheated or if there is another issue.

Gen AI achieves this by analyzing all relevant work orders, even if worded differently. It then suggests likely fixes based on the analysis. This provides quick and intuitive assistance, saving repair time and reducing recurring issues while improving user experience. Maintenance technicians do not need to remember specific keywords or exact matters; they can use natural language, typically English. Consequently, when users communicate effectively in English, Gen AI can respond to search queries with maximum efficiency.

Documentation

Product equipment manuals are central to the preventive maintenance process. However, they have often caused significant challenges for maintenance technicians, particularly during breakdowns. Although soft copies have largely replaced hard copies over the years, the issue persists and has transitioned into a digital problem. Locating the correct information promptly is frequently considered “pure luck.”

Maintenance technicians find it daunting to retrieve accurate guidance during breakdowns. Through tagging, linking, and summarization, Gen-AI transforms these documents into a virtual expert consultant with comprehensive answers. This technology enables technicians to quickly locate necessary information and receive real-time guidance from the document, which evolves from a passive reference to an active tool. By utilizing Gen-AI, technicians can efficiently transition from uncertainty about document contents to immediate access to required information. This capability significantly enhances document organization and utility in maintenance practices.

An on-demand chatbot on top of this documentation layer can be a sound amplification of Gen AI NLP capabilities.

Make technicians’ lives easier

Maintenance technicians have to juggle many tasks, such as performing maintenance work, handling paperwork, sending emails, and updating systems. These extra tasks can become annoying and lower their motivation. Using Generative AI could help improve their experience.

Talk to the CMMS

One way AI can help is by making it easier to interact with the CMMS. Instead of navigating through menus to find tasks, technicians could simply ask AI, "What's on my plate today?" The AI would then assign them tasks, such as inspecting a conveyor belt. This makes finding information and getting started much simpler.

Drafting emails

Another way AI can assist is with drafting emails. If a technician needs to email a vendor about a delayed part or contract negotiation, AI can write the email for them via automated content generation. It ensures there are no spelling or grammar mistakes and helps get emails sent on time.

Take a selfie with the machine

AI can also help with diagnosing issues during inspections. Technicians can take a photo of a tool showing wear patterns and upload it. The AI will diagnose the problem and document it quickly, reducing miscommunication.

Auto-fill work orders

Additionally, AI can auto-fill work order descriptions. Instead of copy-pasting templates, technicians can ask AI to generate detailed descriptions of tasks, parts needed, and safety procedures. Auto-filling standardizes work orders across teams and makes the process more efficient.

SOPs

Lastly, AI can create custom SOPs (Standard Operating Procedures) for one-off repairs or procedures. Technicians just provide prompts, and the AI generates an SOP instantly. Technicians can still make edits if needed.

In all these ways, generative AI simplifies tasks for maintenance technicians, making their work smoother and more enjoyable, increasing the chances of success of CMMS implementations.

Maintenance Operations Productivity

This section will focus on maintenance, operations, and productivity, highlighting several key aspects. 

Dynamic scheduling and resource optimization

The first aspect is dynamic scheduling and resource optimization. Scheduling preventive maintenance tasks is often a frustrating experience for all involved. It requires checking technician availability, assessing the criticality of parts to production, and minimizing disruption. Static schedules are particularly problematic in maintenance operations. Generative AI (adaptive task prioritization based on changing conditions) can address this by generating real-time schedules based on technician availability and equipment priority. It can also account for pending maintenance tasks and production processes to identify optimal time slots, ensuring objective scheduling and resource optimization.

Reports and compliance

The second aspect is reports and compliance. Documentation, including reports, is typically an unpleasant task due to its frequency—whether weekly, monthly, or daily. For example, OSHA safety compliance reports can take maintenance or production teams up to two weeks to complete. Additionally, preventive maintenance compliance must be documented and reported. The manual nature of these tasks makes them cumbersome. Generative AI can automate the drafting of these documents, sending them for review and ensuring deadlines. This automation helps maintain compliance with regulations, thereby preventing mishaps that could harm production.

Inventory forecasting

Inventory forecasting is another challenging area. High inventory ties up working capital, while low inventory risks missing orders and damaging customer service reputation. Generative AI analyzes usage patterns, seasonality, and repair trends to predict needed parts and quantities accurately. This adaptive forecasting eliminates guesswork, optimizes working capital, and ensures no loss of business.

Predictive Maintenance

The final aspect is predictive maintenance. Predictive maintenance aims to anticipate problems before they occur by using sensors combined with generative AI. This technology not only collects sensor data but also integrates it with past work history, parts performance, and external factors such as humidity and usage capacity. By synthesizing multiple data points, predictive maintenance can proactively alert teams to potential failures—such as a pump with a 50% chance of failure in a specific month—allowing for preemptive action.

These aspects illustrate how generative AI enhances maintenance operations productivity by improving scheduling, compliance documentation, inventory forecasting, and predictive maintenance.

Knowledge Management

Generative AI can significantly enhance knowledge management in maintenance management, particularly in training and upskilling new technicians. New maintenance technicians require training from senior technicians, which demands considerable time and coordination. The availability of senior technicians is often limited, leading to a strain on resources that could otherwise be directed toward production activities.

Generative AI can create tailored training modules, walkthroughs, and interactive quizzes specific to the assets that new technicians will work on. This content is available on demand and can adapt to the learning styles and preferences of the new technicians, ensuring that learning outcomes are met. Traditional training methods often compromise learning outcomes due to the limited involvement of senior technicians in assessing these outcomes.

Another critical aspect of knowledge management is knowledge retention after retirement. In many companies with substantial maintenance operations, valuable tribal knowledge is lost when senior technicians retire. Efforts to mitigate this loss through handover documents or knowledge transfer sessions are often insufficient.

Generative AI can address this issue by mining work orders, notes, and SOPs to retain insights and answer future questions. It can even emulate the style of a proficient senior technician, thereby preserving the wisdom and continuity of the maintenance setup.

Experiment.. and do More Experiments

While the use of Generative AI (Gen AI) in Computerized Maintenance Management Systems (CMMS) has been discussed, it is essential to recognize that this technology is still in its early adoption stages within the business context. According to the technology adoption curve, innovators and early adopters precede the early majority. By the time the early majority adopts a technology, approximately 50% of the journey has already occurred.

Although AI feels new, its growth has been phenomenal. For instance, OpenAI's valuation has skyrocketed from approximately $10 billion in 2021 to $300 billion today, alongside significant revenue increases (US$13 billion in annual revenue). This rapid growth makes it challenging to determine whether AI adoption is currently in the early adopter stage or has progressed to the early majority stage.

For those in maintenance management considering Gen AI, a strategy of incremental experimentation is advisable. As covered in various CMMS implementation blogs and articles, starting with small experiments allows for assessment of results and gradual adoption. A piecemeal approach is recommended over an all-in strategy, enabling continuous learning and adaptation.