Smarter work execution: How AI chatbots supercharge asset maintenance

In asset-intensive industries, executing a work order isn’t just about ticking boxes in a system, it’s about doing the job with accuracy, efficiency, and confidence. Consider a frontline technician in the middle of a critical repair who suddenly needs to verify a safety procedure or a specific torque value from a manual. In the past, they might have to pause work, sift through a 200-page PDF, or call a senior colleague for advice. These delays don’t just hurt productivity; they can translate into significant downtime costs. Clearly, getting the right information instantly to the right people can have a huge impact on maintenance performance.

Yet, maintenance managers know that timely information is often not at a technician’s fingertips. Valuable data is scattered across equipment manuals, past work orders, and enterprise systems. Field staff and planners face daily frustrations that erode their efficiency. This post examines those challenges and explores how AI-powered chatbots, such as AskMai , are bridging the gaps. We’ll also look at real-world examples where these digital assistants improved efficiency, reduced downtime, and enhanced decision-making in maintenance operations.

Common obstacles for field technician

Frontline maintenance technicians often encounter delays not because they lack skill, but because they lack immediate access to information. They might need to confirm a lockout-tagout procedure or recall if a certain part was replaced during the last overhaul. Unfortunately, that information lives in disparate sources. Technicians end up losing time hunting through technical documents.

Studies show that “wrench time” (actual hands-on tool time) is on average only about 18–30% of a technician’s day. The rest is taken up by indirect activities- over 60% of a typical day can be spent searching for information, handling paperwork, or other admin tasks in fact, up to 39% of their day may be spent simply waiting on information, equipment, or support. This is wasted productivity that directly contributes to longer downtimes and growing maintenance backlogs.

Information overload, but no context

One root cause is information overload without context. Equipment manuals and standard operating procedures are often available, but not in an easily digestible form. For example, a maintenance manual uploaded to IBM Maximo might be hundreds of pages long with mixed content (text, tables, diagrams). Even if a technician knows the document exists, finding one key instruction or data point in the heat of the moment is like finding a needle in a haystack.

Technicians can’t afford to read lengthy PDFs on a mobile device in the field. Important details end up overlooked, leading to mistakes or rework. The result? Lost time, mounting frustration, and sometimes safety risks

Work History is there, but buried

Another challenge is that work history and tribal knowledge are buried. Maximo does track a wealth of historical data like past failure reports, work logs, parts used, who fixed what, and so on. The irony is that all the answers a technician needs might already exist in these records, but there’s no quick way to retrieve them during a job.

If a technician wants to know, “Was this same pump seal replaced last year, and how did that fix hold up?” they would have to manually pull up past work orders, read through long text entries, or call a planner to dig into reports. In a fast-paced repair situation, that’s often not feasible. Thus, technicians either press on without the info (risking a wrong fix) or halt the job to seek help, neither of which is ideal for operational uptime.

Planning and coordination pains

It is not just field staff, but maintenance planners and supervisors face their own information hurdles. Planners must scope jobs, estimate resource needs, and prevent duplicate or unnecessary work. They would love to leverage historical data to answer questions like “Has this issue occurred repeatedly in the last six months?” or “How much time did we actually need to overhaul this motor last time compared to the estimate?”

But getting those insights is cumbersome. If data is siloed across various Maximo screens or in experts’ heads, planners may rely on best guesses. This can lead to inaccurate estimates, misallocation of parts, or scheduling conflicts. A planner might overestimate time for a task (wasting resources) or underestimate it (causing firefights when technicians report it can’t be done in one shift).

In short, without easy access to consolidated knowledge, planners struggle to optimize work orders and often have to revise plans after execution feedback. The back-and-forth between planners and technicians increases when job information is incomplete, which further slows down the maintenance workflow.

Meet AskMai, the AI Powered Chatbot

To address these field challenges, we built AskMai, an AI-powered chatbot that can be embedded in EAM360 Mobile application or IBM Maximo or MAS. AskMai is not just a search engine; it leverages advanced NLP and even generative AI to truly understand a technician’s or planner’s question, search across various data sources, and return a useful, context-specific answer. The capabilities of AskMai are listed below.

On-demand answers from technical documentation

One major capability of AI maintenance assistants is ingesting large volumes of technical documents (manuals, schematics, specifications) and making sense of them. For instance, AskMai can parse documents in any format— whether it’s a structured PDF, a scanned image with tables, or a maintenance form with mixed media.

If a technician in the field asks, “What’s the tightening torque for this model’s flange?” the AI can go through the model’s service manual, find the exact table or line where that torque value is listed, and respond with, say, “The tightening torque for Model X flange is 55” Nm”—possibly with a citation or reference to the manual page. This saves countless minutes (or hours) that would otherwise be spent flipping through pages. It provides just-in-time knowledge, so the tech can proceed confidently with the correct data. In essence, the chatbot reads the manuals so the technician doesn’t have to, surfacing only the relevant snippet needed for the task at hand.

Instant recall of asset history and past fixes

Another powerful feature is querying historical maintenance data conversationally. An AI assistant like AskMai is connected to work order histories, maintenance logs, failure codes, and more. If a technician asks, “Has this motor experienced the same fault before?” the bot can swiftly scan the maintenance history for that asset. It might answer, for example, “Yes, this motor had a similar overheating fault on 2023-07-10. The issue was resolved by replacing the cooling fan and cleaning the vents.” This kind of insight— available within seconds— is game-changing. The technician can avoid redundant troubleshooting steps and check if the previous remedy was effective or if a deeper issue might be at play.

In complex repairs, having the full context (previous failures, parts replaced, who worked on it, how long it took, etc.) allows a more informed and faster resolution. Essentially, the chatbot serves as the technician’s memory bank, instantly connecting the dots that a human might take hours to correlate.

Contextual support for planners too

AskMai isn’t just for technicians using EAM360 application. The Planners, who typically use Maximo Manage can use it to validate whether a job needs more time, parts, or whether the same issue has recurred repeatedly. It helps improve work assignment quality and reduce back-and-forth revisions.

For instance, AskMai can be queried by a planner “Do we have any pending work orders on Pump #5, and how often has Pump #5 failed in the last 2 years?” Instead of manually pulling reports, the chatbot could return a quick summary: “Pump #5 has 3 work orders open (2 PMs, 1 corrective). It experienced 4 failures in the last 24 months, mostly related to the cooling system.” Armed with this, the planner might decide to bundle the corrective task with an upcoming preventive maintenance or adjust schedules to preempt another cooling issue

Planners can also get help estimating job durations or parts needs by asking things like, “How long did the last motor replacement at Unit A take and what parts were used?” The AI can find that historical work order and summarize: “The last replacement took 6 hours by two technicians and used Part XYZ (1 unit) and gasket ABC (2 units).” This helps improve the accuracy of new work orders, allocate the right resources, and avoid the dreaded scenario of scheduling a job only to have it revised later due to missing parts or undervalued time.

Final thoughts

Maintenance and reliability teams have long been challenged to “do more with less” – more work orders with fewer experienced hands, more uptime with aging assets, more data with less time to analyse it. AI-powered chatbots like AskMai are proving to be a timely solution to these challenges. They act as tireless assistants, available 24/7 to answer questions, provide guidance, and crunch data into actionable insights. By giving technicians and planners instant access to the information they need, these tools are fundamentally improving work execution in asset-intensive industries. The results speak for themselves: faster fixes, fewer errors, and more standardized processes across the board.