Before AI Can Fix Plant Maintenance, Something Has to Fix the Clipboard

SAP Plant Maintenance (PM) teams looking to extract value from AI-enabled maintenance must first establish accurate, complete, and structured maintenance data at the point of execution. That begins with how technicians record notifications, labor, and failure information before those entries reach automation platforms.

This was the core message of a recent SAPinsider webinar, “Prove-It! Measuring Maintenance Modernization in an Outcome-Driven Market,” featuring Rajiv Kumar, SVP, Customer Operations at Sigga Technologies, and Caleb Jones, VP, Strategic Industry Executive at UiPath.

During the webinar, Kumar clearly outlined the AI constraint: “If your data quality is bad, then your AI automation is not going to deliver the value that you’re expecting.”

Jones added that AI business cases now face more scrutiny for these applications than ever before.

For SAP PM organizations in manufacturing operations, the implication is clear. If a current plant maintenance record cannot establish a baseline for a key performance indicator (KPI), it cannot support AI models.

Beyond enabling future AI initiatives, better maintenance execution also delivers immediate operational benefits by improving wrench time, asset reliability, and maintenance productivity.

The Clipboard Is Now an AI Problem

Plants may run SAP PM, but the execution layer often still relies on paper packets, spreadsheets, and improvisational workarounds. In these manual setups, the SAP record routinely fails to show what happened on the shop floor. The physical clipboard has effectively been promoted into the AI architecture, an absurdly critical role for a paper process.

Kumar noted this is a foundational governance issue. “Quite often these initiatives never start because the value case is not well defined,” he stated. “We’ve chosen the wrong outcomes, or we haven’t defined the outcomes. We don’t have outcome alignment across the multiple organizations that these initiatives impact, or the dollar amounts just are not believable.”

According to SAPinsider research, consistent asset data quality remains a top priority for 64% of SAP EAM decision-makers. The same study indicates that 80% of the effort in advanced analytics initiatives historically goes into understanding, standardizing, normalizing, and cleaning asset data.

Structuring the Case for Finance Review

Moving SAP PM modernization past the pilot stage requires a business case structured specifically for finance leadership. Maintenance leaders must translate proposals into terms that chief financial officers (CFOs) evaluate. Jones offered a tactical reframing device for the pitch. “Pretend you’re the CFO of the company,” he advised. “How would you talk about the business to an investor?”

That question shifts the narrative from technical capabilities to financial accountability. Finance teams require a transparent view of the current cost burden, the expected operating impact, and the baseline against which teams will measure improvement.

Jones also cautioned against overloading the business case with mobility, analytics, automation, and AI bundled into a single timeline. “The organizations that have attempted to do all of it have actually done none of it,” he noted. Kumar agreed, explaining that modernization efforts freeze when teams cannot clarify costs hidden within overtime, contractor spend, unplanned downtime, or manual data entry.

Mobility as the Foundational Data Layer

Webinar participants framed SAP PM mobile execution as the mandatory data layer for maintenance modernization rather than another productivity tool. Sigga Mobile EAM is not an AI product, but its relevance to an AI strategy is fundamental because it structures data flowing from technicians directly into SAP PM.

Kumar detailed this mechanism: “Having a technology, a mobile application where they have everything live, they’re connected to SAP real time, will actually help clean up some of that data without investing in a large IT data cleansing project.”

By capturing maintenance information directly in SAP during work execution, organizations can improve data accuracy, completeness, and timeliness of maintenance data while reducing manual entry and paper-based processes.

Organizations using Sigga have reported measurable operational improvements, including higher technician productivity, increased wrench time, and reduced reliance on paper-based maintenance workflows.

The Baseline Challenge

A missing data baseline limits AI viability and restricts vendor accountability. If a plant cannot establish a baseline for mean time to repair or maintenance expenditure before deploying a mobility platform, it cannot credibly prove what changed afterward.

Moreover, executives often ask whether a subsequent improvement resulted from new software or an updated process. Kumar provided a pragmatic perspective. “Would you have changed your process if you didn’t start on this technology initiative in the first place?” he asked. “If you’re just absolutely forced to measure both, I would align on an agreed number.”

This insight acknowledges that SAP PM software rollouts and process redesigns move together in tandem.

What This Means for SAPinsiders

Digital maintenance execution delivers immediate operational benefits while establishing the trusted data foundation needed for future analytics and AI initiatives.

Treat mobile maintenance execution as the foundational data pipeline for future AI projects. Enterprise architects must recognize that data captured accurately at the technician level using tools like Sigga Mobile EAM is exactly what downstream SAP PM analytics models inherit. Ensure mobile data capture mechanisms enforce standard failure codes, real-time validations, and structured formats to prepare the SAP PM system for predictive algorithms actively.

Audit current execution-layer data to build a defensible CFO business case. ERP program managers planning to upgrade to SAP S/4HANA or introduce advanced analytics must first document the financial toll of poor execution-layer data quality. Define exactly what the problem costs the enterprise today so that the ROI from the proposed modernization anchors firmly to operational margins rather than abstract technology promises.

Scope SAP EAM mobility, analytics, and AI as distinctly sequenced workstreams. Chief Information Officers must require program managers to decouple these initiatives. Sequence mobility execution tools first to fix the foundational data collection process, wait for the data flow to stabilize within SAP PM, and only then introduce the advanced AI overlays that rely on that accurate transactional history to succeed.