Maintenance, Repair, and Overhaul has always been a numbers game. How long does an asset stay operational? How quickly can a repair be completed? How often does the same component fail? How much inventory is sitting on a shelf, and how much is missing when a technician actually needs it? How much does an unexpected breakdown really cost?
These questions have traditionally been answered using maintenance schedules, historical records, technician experience, and increasingly, connected equipment data. Now AI is adding another layer to the equation. It can process large volumes of operational data, identify patterns, flag anomalies, and help maintenance teams decide where attention is needed before a small problem becomes a major disruption. But there is a difference between saying AI in MRO can improve it, and proving that it actually does.
In this article, we’ll look at what the data actually says about AI in MRO, from predictive maintenance and downtime to inventory, technician productivity, and the connection between AI insights and real maintenance workflows. We’ll also look at where AI delivers measurable value and where organizations can run into problems when the technology is disconnected from the systems managing their operations.
Why MRO Performance Is So Difficult to Improve
MRO performance sounds straightforward until you look at everything happening behind a single repair. A piece of equipment may need attention because of a scheduled service, an unexpected failure, a sensor alert, a customer request, or a recurring problem that has appeared several times before. The maintenance team then has to determine what happened, identify the right technician, find the required parts, check the asset history, manage the work order, consider warranties or contracts, and eventually document the result.
Maintenance Teams Are Managing More Than Equipment
The equipment itself is only one part of the equation. A modern MRO operation is also managing inventory, technicians, suppliers, customers, service contracts, warranties, repair histories, compliance requirements, and financial information.
That creates a data problem before it creates an AI problem. The information needed to make a good maintenance decision often exists, but it may be spread across an ERP, an MRO system, spreadsheets, PDFs, sensor platforms, emails, and individual employees’ experience.
Deloitte has pointed to this fragmentation as one of the challenges preventing organizations from getting the full value from predictive maintenance. Its research describes the opportunity to combine sensor information with ERP data, historical maintenance and repair records, production data, and reports from employees in the field.
Once those sources are connected, the maintenance team has something much more useful than a collection of individual data points. It has context.
The Cost of Getting Maintenance Wrong Adds Up Quickly
The consequences of poor MRO performance can extend far beyond the cost of replacing one component. A failed asset can interrupt production, delay a customer delivery, create emergency procurement costs, increase overtime, consume technician capacity, and potentially cause additional equipment damage.
Deloitte estimates the annual cost of unplanned downtime for industrial manufacturers at around $50 billion. Its research also reports that manufacturers using preventive or predictive maintenance practices have reported substantially less unplanned downtime, fewer defects, and reductions in other operational impacts compared with reactive maintenance.
That is why maintenance has gradually moved from being viewed purely as a repair function toward being treated as a business performance function.
The question is no longer simply, “How quickly can we fix this?” It is increasingly, “How can we make sure this failure does not happen again, and how can we know which problem deserves attention first?”
What the Data Says About AI and Predictive Maintenance
Predictive maintenance is where the case for AI becomes easiest to measure. Instead of waiting for equipment to fail or servicing every asset according to a fixed calendar, predictive maintenance uses operational information to identify signs that an asset may require attention.
AI Can Identify Problems Before They Become Failures
Traditional preventive maintenance operates according to time or usage. A component might be inspected every six months or replaced after a certain number of operating hours.
That approach is useful, but it assumes equipment behaves according to a schedule. Real equipment does not always cooperate.
Two machines operating under different temperatures, loads, environments, and usage patterns can experience very different rates of wear. One may require attention earlier than expected while another may still be operating normally long after its scheduled maintenance date.
AI driven predictive maintenance changes the equation by analyzing actual operating conditions. IBM describes predictive maintenance as using operational data and real time condition monitoring to identify when assets are likely to fail. AI and machine learning can analyze information from IoT sensors, work orders, maintenance records, and environmental monitoring systems to identify patterns that may otherwise remain hidden.
The practical benefit is not that AI knows the future with perfect accuracy. It is that maintenance teams can receive an earlier indication that something has changed.
That extra time can be extremely valuable.
The Potential Savings Are Significant
IBM’s 2026 research on AI in predictive maintenance reports potential maintenance cost reductions of 25 to 30 percent and potential reductions in total downtime of 35 to 45 percent when AI driven predictive maintenance is implemented effectively. Those figures should be treated as potential outcomes rather than universal guarantees, but they illustrate the size of the opportunity.
McKinsey’s more recent research into asset heavy industries also points to significant opportunities. Its analysis estimates that organizations can potentially achieve maintenance cost savings equivalent to around 10 to 15 percent of total maintenance conversion costs, with additional gains possible through improved asset availability.
The important point is that these numbers are not really about AI as a standalone technology. They are about what happens when better information changes maintenance decisions.
If a team knows an asset is beginning to behave differently, it can plan an intervention. If it knows which part is likely to be needed, it can prepare inventory. If it knows which technician has the right expertise, it can schedule the work appropriately. The prediction is useful because it changes what happens next.
AI and IoT Give MRO Teams a Better View of Asset Health
AI needs information to work with, and physical equipment can provide enormous amounts of it. That is where IoT becomes an important part of the MRO conversation.
Sensors Turn Equipment Into a Source of Operational Data
An IoT enabled asset can continuously produce information about its operating condition. Depending on the equipment, that could include temperature, vibration, pressure, acoustics, energy consumption, operating hours, or other measurements.
A single reading may not mean much. A pattern across thousands of readings can mean something very different.
IBM describes AI driven predictive maintenance as a combination of IoT data, condition monitoring, historical performance information, and machine learning models that identify changes in equipment behavior.
That gives maintenance teams a much more dynamic view of equipment health. Instead of asking when an asset is scheduled for its next service, they can start asking whether the asset is actually showing signs that service is needed.
The Value Appears When the Alert Becomes a Workflow
This is where many AI discussions become too focused on the technology itself. A sensor can detect an anomaly. AI can interpret it. But neither one repairs an asset. The value comes from what happens after the prediction.
An alert might need to become an inspection. The inspection might become a work order. The work order might require a specific part. That part might need to be ordered. A technician might need to be assigned. The repair might need to be completed under a service contract or warranty.
This is why Epiphany‘s MRO approach is particularly relevant to the AI conversation. Its eService MRO capabilities bring together preventative maintenance, repair management, equipment history, parts and consumables, warranties, inventory, technician scheduling, and other operational information within the NetSuite environment.
The important idea is not simply collecting more data. It is connecting the data to the work.
AI Can Improve More Than Failure Prediction
Predictive maintenance gets most of the attention because it is easy to visualize. A machine starts showing signs of trouble, AI identifies the pattern, and the maintenance team intervenes before failure.
But MRO performance extends much further than predicting failures.
AI Can Help Maintenance Teams Prioritize Work
Most maintenance teams have more potential work than they have time to complete immediately.
There may be several open work orders, multiple equipment alerts, scheduled preventative maintenance, customer requests, inspections, and repairs competing for the same technicians.
AI can help analyze those competing priorities by considering factors such as asset criticality, historical failure patterns, current equipment condition, customer impact, repair history, and operational consequences.
McKinsey’s 2026 research describes organizations using AI for reliability workflows, including identifying problematic assets, analyzing failure histories, and recommending actions. One example cited by McKinsey involved an airline using AI driven health monitoring models for components that frequently contributed to aircraft grounding, with the models reducing reliability events by around 30 percent compared with the baseline.
That is an important shift. AI is not simply saying, “Something looks unusual.” It can help answer the more useful question: “Which issue deserves attention first?”
AI Can Help Reduce Unnecessary Maintenance
Preventive maintenance solved one problem by moving organizations away from waiting for equipment to fail.
But it introduced another problem. Sometimes equipment gets serviced because the calendar says it is time, not because the equipment actually needs attention.
That can mean replacing components that still have useful life, taking equipment offline unnecessarily, and spending technician hours on work that may not have been urgent.
Predictive maintenance can potentially reduce that unnecessary work by using actual asset conditions to influence maintenance timing. IBM describes this as a shift away from fixed maintenance intervals toward interventions based on real time operating information.
The result is not necessarily less maintenance overall. It is more targeted maintenance. That distinction matters because MRO teams are not trying to eliminate maintenance. They are trying to spend maintenance resources where they create the most value.
AI Can Connect Maintenance With Inventory
A maintenance prediction is only as useful as the organization’s ability to act on it. If an AI system identifies a likely failure but the required part is unavailable, the organization has gained information without necessarily gaining availability.
This is where maintenance and inventory planning start to overlap.
Spare Parts Are Part of the Maintenance Decision
Consider a component that has historically failed after a particular operating pattern. If AI can identify that pattern early, the organization may have time to determine whether the replacement part is available and prepare for the repair.
Deloitte has specifically identified procurement and spare parts as areas that can benefit from predictive maintenance. Knowing when a replacement part may be required can improve planning and potentially reduce the amount of unnecessary spare equipment held in inventory.
That matters because inventory creates its own balancing act. Too little inventory can delay repairs. Too much inventory ties up capital and can leave organizations holding parts they may not need. AI does not eliminate that tradeoff, but better forecasting can give maintenance and procurement teams more information to work with.
The Bigger Opportunity Is Connecting Asset and Inventory Data
An asset record by itself tells you about an asset. An inventory record tells you about a part. A work order tells you about a repair. The real operational picture emerges when those records are connected.
That is one of the reasons integrated MRO systems can become increasingly important as AI adoption grows. The AI needs access to the underlying operational context, and the maintenance organization needs a way to act on the resulting insight.
Epiphany’s approach brings equipment, maintenance, repair, inventory, contracts, warranties, and service workflows into the same broader operational picture. That creates a stronger foundation for applying intelligence because the prediction does not have to exist separately from the process it is meant to influence.
AI Can Help Technicians Spend More Time Fixing Problems
The maintenance workforce is another part of the equation that often gets overlooked when people talk about AI.
Technicians do not spend every minute physically repairing equipment. A significant amount of time can also go into finding information, reviewing previous repairs, documenting work, looking up procedures, checking parts, and trying to understand what happened before they arrived.
Maintenance Knowledge Is Often Trapped in Unstructured Information
A technician’s experience can be incredibly valuable, but organizations can struggle to capture and reuse it.
A previous repair might be documented in free text. A troubleshooting procedure might exist in a PDF. A useful observation might be buried in an old work order. A technician may remember a recurring problem that never became structured data.
McKinsey’s research highlights how AI can help organizations use historical maintenance records and digitized knowledge to accelerate reliability work. One example involved using generative AI to update asset hierarchies, develop failure mode analyses, and draft preventive maintenance plans from historical information, reducing the time required for a site from six months to six weeks.
That is not about replacing the technician. It is about making the technician’s information easier to find and use.
Generative AI Could Become a Maintenance Assistant
Generative AI introduces another possibility. Instead of asking technicians to search through hundreds of records, an AI assistant could potentially summarize relevant repair history, surface documentation, identify previous failures, or help organize information around a particular asset.
That becomes particularly valuable when experienced technicians retire or move into different roles. The organization is not simply losing a person. It risks losing years of accumulated knowledge.
AI cannot perfectly reproduce human expertise, but it can help make documented knowledge more accessible.
This is one area where Epiphany’s broader focus on operational data and document processing fits naturally into the discussion. Turning information trapped in documents and historical records into usable operational data can make that information more valuable to both humans and AI systems.
The Data Also Shows That AI Is Not a Magic Fix
The case for AI in MRO is strong, but there is a part of the story that needs to be taken seriously. AI does not automatically improve maintenance simply because an organization installs an AI tool.
In fact, current research shows that many organizations are still struggling to move AI from experimentation into everyday asset management.
Many Companies Have Not Fully Operationalized AI Yet
IBM Institute for Business Value research published in September 2026 found that, at the end of 2025, only around 12 to 17 percent of organizations across chemicals and petroleum, utilities, and mining were operating AI in asset lifecycle management or at scale. That means more than 80 percent in each of those sectors had not yet fully operationalized AI.
That gap is revealing. Companies are clearly interested in AI, but interest and operational adoption are two different things. An organization can run an impressive pilot and still fail to integrate it into daily maintenance operations. The difference usually comes down to data, workflow, integration, people, and the ability to measure results.
Bad Data Can Limit Good AI
AI cannot reliably find patterns in information that is incomplete, inconsistent, or poorly structured.
If the same asset is recorded under different names across systems, historical analysis becomes more difficult. If technicians describe the same failure in completely different ways, identifying recurring patterns becomes harder. If important repair information exists only in disconnected documents, the AI may not have access to it.
McKinsey has previously highlighted data quality and accessibility as fundamental enablers of digital reliability, noting that inconsistent asset identification, free text records, spreadsheets, and paper documentation can make analysis and integration difficult.
This is why implementing AI sometimes starts with work that does not look particularly exciting.
- Clean up the asset data.
- Connect the systems.
- Standardize the workflows.
- Make the information accessible.
Then apply AI.
That may not sound as impressive as announcing an AI transformation, but it is often much closer to what actually creates sustainable results.
The Biggest MRO Gains Come From Connecting AI to Execution
This is perhaps the most important point in the entire discussion. AI can provide intelligence, but MRO performance depends on execution. A prediction has to lead somewhere.
Knowing Something Will Fail Is Only the Beginning
Imagine that an AI system identifies an 85 percent probability that a particular pump will experience a failure within the next month.
That sounds useful. But what happens next?
Someone needs to evaluate the prediction. The equipment may need an inspection. A replacement component may need to be sourced. A technician needs to be assigned. The maintenance window needs to be scheduled. The work needs to be documented. The cost needs to be recorded.
If the AI prediction sits in a dashboard while all of those activities happen in separate systems, the organization still has a fragmented process.
IBM’s recent research makes this exact distinction, describing the challenge of moving from AI generated insight to trusted action within day to day maintenance operations. That is where the next generation of MRO platforms has an important role to play.
This Is Where ConnectX Becomes Relevant
Epiphany’s ConnectX positioning is built around the idea of connecting operational information, AI intelligence, analytics, and execution rather than treating them as separate layers.
The distinction is useful because an ERP can remain the system of record while an operational layer can help coordinate the intelligence and workflows surrounding that data.
For MRO organizations, that can mean connecting information about assets, service, contracts, inventory, repairs, AI insights, and operational decisions.
The goal is not to replace the ERP. It is to make the information inside and around the ERP more actionable. That is increasingly important as AI becomes more capable. The better AI becomes at identifying opportunities and problems, the more important the execution layer becomes. Otherwise, organizations end up with very intelligent alerts and the same old operational bottlenecks.
How Companies Should Measure AI’s Impact on MRO
The easiest way to make an AI project difficult to evaluate is to start with a vague goal such as “use AI to improve maintenance.” The better approach is to identify a specific operational problem and measure what changes.
Start With the Metric, Not the Technology
If the problem is downtime, measure downtime before implementing the new process and compare it afterward. If the problem is repair turnaround, measure the average time between equipment arrival and return to service.
If the problem is parts availability, track stockouts, emergency purchases, and repair delays caused by missing components. If technicians are spending too much time on administration, measure how much time is spent searching for information, documenting work, and handling repetitive tasks.
The technology then has a clear job. McKinsey’s current research recommends focusing AI and technology across specific maintenance areas such as reliability, maintenance planning and execution, shutdowns and turnarounds, spare parts, and contractor management rather than treating AI as one broad initiative.
That approach also makes it easier to determine whether an investment is actually producing operational value.
MRO Success Is About Better Decisions
The most useful AI metric may not always be the number of predictions an AI system generates. What matters more is the number of better decisions those predictions help teams make. Did the maintenance team intervene before a failure occurred? Did a repair happen during a planned maintenance window instead of becoming an emergency? Was the right part available when the technician needed it? Did the technician find the relevant repair history faster? Did the organization avoid replacing equipment unnecessarily, or prevent the same failure from happening again? These are the outcomes that ultimately determine whether AI is improving MRO performance. AI is simply one of the tools that can help maintenance teams achieve them.
Where Epiphany Fits Into the Future of AI Driven MRO
The direction of MRO technology is becoming increasingly clear. Organizations are moving toward connected equipment, richer operational data, predictive analytics, AI assistance, and workflows that can respond to information faster.
Epiphany sits naturally within that transition because its platform is already focused on connecting many of the operational pieces that make MRO complicated.
MRO Needs More Than an AI Model
Epiphany’s eService MRO capabilities address areas such as preventative maintenance, repairs, equipment history, inventory, parts, warranties, technicians, and service operations. Its wider product ecosystem also addresses contracts, costing, depot repair, rentals, allocation, and other processes that sit around the asset lifecycle.
That matters because the value of AI increases when it has context. An AI system that knows an asset is behaving abnormally is useful.
An AI system that also understands the asset’s repair history, contract status, warranty, location, available parts, previous failures, and service requirements can potentially provide a much more actionable recommendation. The difference is context.
ConnectX Extends the Conversation Beyond ERP
This is also why ConnectX is an important part of Epiphany’s future positioning.
The ERP remains critical because it stores financial and operational records. But MRO operations frequently involve processes that span multiple systems, data sources, people, and physical assets.
ConnectX is positioned as an operational layer connecting ERP data with AI intelligence, analytics, workflows, and execution. That becomes increasingly relevant as organizations adopt AI.
The more intelligence an organization introduces, the more it needs a reliable way to move that intelligence into the operation. Otherwise, AI becomes another place where information accumulates. The real opportunity is to make it part of how work gets done.
Wrapping Up: AI Can Improve MRO, But Integration Is Where the Value Lives
The data increasingly supports the idea that AI can improve MRO performance. Predictive maintenance can help identify equipment problems earlier. AI can help maintenance teams prioritize work, improve spare parts planning, analyze historical failures, reduce administrative effort, and make operational information easier to use. Industry research has documented potential reductions in downtime and maintenance costs, while newer examples are showing how AI can extend into reliability engineering, maintenance planning, inventory, and execution.
But the strongest lesson is not that every MRO organization needs an AI tool.
It is that AI works best when it becomes part of the maintenance operation.
The sensor provides the information. AI identifies the pattern. The MRO system provides the context. The workflow determines what happens next. The technician applies expertise. The result is recorded and becomes part of the organization’s future knowledge. That creates a continuous loop between equipment, data, intelligence, people, and action.
For organizations managing complex assets, that loop can be far more valuable than simply adding another dashboard.
This is ultimately where Epiphany’s approach fits into the larger MRO conversation. By connecting MRO processes, ERP information, operational workflows, AI capabilities, and execution, Epiphany is addressing the part of AI adoption that is often overlooked: what happens after the prediction. Because improving MRO is not really about making machines smarter.
It is about helping the people responsible for those machines make better decisions sooner, with the right information in front of them. And when that intelligence is connected to the systems that actually manage maintenance, repair, inventory, contracts, and service, AI has a much better chance of becoming an operational advantage rather than another technology experiment.
Sources
- IBM: The Role of AI in Predictive Maintenance
- McKinsey: Maintenance Meets AI: A Proven Approach for Asset Heavy Industries
- IBM: What Is Predictive Maintenance?
- Deloitte: Using AI in Predictive Maintenance
- IBM: Industrial Maintenance in the Age of AI
- McKinsey: Digitally Enabled Reliability Beyond Predictive Maintenance
- Deloitte: Predictive Maintenance
- Deloitte: Predictive Maintenance Solutions
- Deloitte: Maintenance Operations: Transition From Reactive to Predictive
- Epiphany: ConnectX and MRO Solutions