Talk to a maintenance lead at any enterprise and the same theme comes up: they know their team generates real value, but they struggle to make it visible in numbers the CFO trusts.
The operational story the data tells is not what actually happens on site. Notifications come in half-filled, time bookings get rounded to the hour, schedules live in Excel and reconcile to SAP once a week, and downtime is not assigned to the correct entity.
That gap between reality and SAP (the world's most popular EAM platform) is what makes maintenance look like a cost line in the budget even when it is carrying revenue, reliability, and labor productivity.
What the research says about the stakes.
The financial weight of getting maintenance right is well-documented. Siemens' True Cost of Downtime 2024 put a number on the global cost of getting it wrong: roughly $1.4 trillion a year across the world's 500 largest industrial companies, or around 11% of their combined revenue. ABB's Value of Reliability survey put the average cost of unplanned downtime at $125,000 an hour.
For producers running close to capacity, every recovered hour of uptime is revenue that would otherwise be lost.
Two other value dimensions are less visible but compound over time. Reliable equipment lasts longer, which moves capital replacement decisions years out. And when planners send technicians in with the right parts, history, and instructions, the first-time fix rate climbs and the overtime and subcontractor spend that absorbs reactive work falls.
None of these dimensions register in a budget conversation without reliable data over time.
Three places the data trail breaks.
I see the same three failure modes across SAP maintenance organizations regardless of industry. Each breaks a different part of the operational data the business case depends on.
The first is at the technician layer:
SAP GUI was not designed for someone in PPE on a factory floor or out servicing assets in the countryside. The result is end-of-shift reporting, where technicians enter what they remember rather than what happened. Time gets rounded, context from images rarely makes it in, and secondary damage gets folded into the primary failure code.
The second is at the operator layer:
Creating a notification in SAP typically requires a credential, training, and a desktop. Most operators have none of these. So small issues get reported by phone, by paper, on whiteboards, or at a shared terminal when possible. A notification that should have been the first signal of a developing problem never enters the system.
The third is at the planning and scheduling layer:
Most SAP planners I meet are doing the real work in Excel and reconciling back to SAP. Teams that adopted SAP's Multi-Resource Scheduling are facing its retirement (2027 for SAP ECC, 2030 for S/4HANA), with Resource Scheduling and Field Service Management positioned as successors. In the meantime, capacity views are stale, schedules trail reality by days, and a meaningful share of the planner's day goes to reconciliation rather than planning.
None of these are failures of SAP; they are gaps between SAP's capabilities and the experience that people executing the work actually need.
Deployed software is not necessarily useful software.
Buying the right software is the first half of the job. The second half, getting people to actually use it, is the half that decides whether you have the data to show maintenance value. And it's the half that gets underestimated.
Gartner's 2024 Tech Trends in Manufacturing survey found that 48% of manufacturing software buyers regretted a recent technology purchase. Higher-than-expected costs and implementation issues were the top two drivers, both of which often trace back to a rollout that looked very different in production than it did in the sales cycle.
Deloitte's 2025 Smart Manufacturing and Operations study, which surveyed 600 executives at manufacturers with $500M or more in annual revenue, points to the why. Human capital scored lowest of any dimension in the entire smart manufacturing maturity model. The bulk of the investment goes into technology; less of it goes into the people who have to use it every day.
The World Economic Forum's Global Lighthouse Network 2025 report names the investment ratio explicitly. The manufacturers who excel at implementing new technology spend roughly $5 on scaling and adoption for every $2 they spend on the technology itself.
Your alternatives when launching new software and processes:
●Low adoption produces a partial data trail.
●High adoption produces data the business can act on.
That difference is what separates maintenance proving its value from maintenance continuing to live on the cost line of the budget.
Why this matters for SAP's autonomous enterprise future.
SAP's platform roadmap is increasingly built around AI agents and autonomous workflows.
The Autonomous Asset Management scenario introduced at Sapphire 2026 has agents analyzing thousands of past incidents to generate pre-filled work orders with the right tools and proven fixes.
It's part of the broader Autonomous Enterprise vision rolling out across SAP's industry portfolio. All of it runs on operational data from the notifications, time bookings, and equipment history that today's data trail captures unevenly.
The maintenance organizations closing those data gaps now are the ones that will get the most out of what comes next.
A practical place to start.
Getting started with closing your data gaps rarely needs lengthy strategy discussions. You should already know where the trail breaks: where are technicians keying data in from memory? Where do issues get reported by phone? Where does the schedule lag reality by more than a day? The biggest, most painful gap is a good place to start the project.
Maintenance has always generated business value. The work in the next budget cycle is making sure the people deciding what to fund next can see it.
