AI-Assisted Maintenance: What Changes, and Does Everything Change?

The short answer is that AI does not replace maintenance expertise; it helps the roles of technician, engineer or document controller, for instance, evolve by accessing relevant established, tried and tested guidance to support their tasks.

Maintenance professionals have long aimed to transition from reactive firefighting toward preventive and predictive approaches. As an accessible, intuitive tool, AI can accelerate that shift by finding patterns humans may miss, especially when data comes from multiple sources: condition monitoring, process data, work orders, spare parts history, and operator notes, for instance.

“The promise for now and in the future is simple: fewer surprises, increased efficiency in work planning, and empowered workers with easy access to right documents and data. Organizations have a clearer view of risk and where any uncertainties will come from,” said Risto Vuopala, Global Business Driver for Industrial Digital Software, ABB’s Process Industries division.

Predictive maintenance answers a familiar question: When is something likely to fail? Prescriptive maintenance goes a step further: What should we do about it, when should we do it, and what is the expected impact on risk, cost, and availability? “AI supports both, but only when it has access to reliable, contextual data,” Vuopala notes.

In day-to-day work, the most important change is not that machines suddenly “maintain themselves”. It is that maintenance teams spend less time searching for signals in noise and more time on decisions: diagnosing root causes, validating recommendations, prioritizing work based on production impact, and improving reliability practices.

“Maintenance becomes more proactive and data-driven, while need for the craft remains the same,” Vuopala said.

Expertise still matters. Machines still wear out over time, environments still vary, and failures still have physical causes, but the ability to anticipate and coordinate improves. Modern AI-assisted tools can support the unexperienced worker to perform as skilled workers.

One of the biggest misconceptions is that AI is primarily a technology project. In reality, it is often a data and operating model project. If data is inconsistent, supporting data is outdated or irrelevant, and sensor data is not trusted, AI will simply scale the same uncertainty.

“If the foundations are not in place, AI does not create clarity, it multiplies ambiguity,” Vuopala said.

Organizations that succeed typically do the basics well: they have clear asset hierarchy and criticality classification, consistent failure modes and coding practices, disciplined work order routines, and reliable instrumentation. AI can help clean and structure data, but it cannot compensate for missing context.

“Maintenance knowledge, such as what happened, why it happened, and what was done, still needs to be captured,” Vuopala adds.

Even a strong model is useless if people do not trust it. For maintenance teams, trust is built when AI outputs are understandable enough to be validated, consistent over time, linked to real operational outcomes, and integrated into everyday workflows, planning routines and daily meetings.

AI hallucinations instantly destroy trust. Therefore, it is important that AI uses only factory and machinery specific data and documentation, work and maintenance instructions and other up-to-date available and relevant documentation. “Explainability and clear workflows empower the worker and turn analytics into action,” Vuopala said.

Combatting the skills shift: In many established industrialized countries, ageing workforces and retirements are an issue. The loss of tacit knowledge poses a real threat to cost-efficient operations.

Younger generations have, however, grown up with smart devices. With AI-supported workflow creation, including easy-to-use enhancement and commenting features, both user groups can be served with clear work instructions. At the same time, critical tribal knowledge can be captured and used to continuously improve workflows and site-specific process instructions.

“The goal is not to turn technicians into data scientists, but to make AI a practical tool that supports expert judgement, work safety and doing things right the first time,” Vuopala said.

As maintenance becomes more connected, cybersecurity and governance move from background topics to frontline concerns. OT environments have long lifecycles, and introducing new analytics layers must not compromise safety or uptime. Clear governance answers practical questions: who owns the data and who can use it, how models are updated and validated, what happens when recommendations conflict with local experience, and how decisions are documented for continuous improvement.

So, does everything change? No. The fundamentals of maintenance remain: asset knowledge, disciplined execution and continuous improvement. What changes is the speed of situational analysis, the quality of decisions, and the capability to act quickly on the factory floor, when AI is implemented with the right foundations.

“AI provides a tool to empower your workforce with capability you build on data quality, reliability practices and cross-functional collaboration,” Vuopala concludes.

According to Vuopala, ABB is currently implementing its new AI-assisted maintenance tools, Industrial Knowledge Vault, at its global customer base. For example, ABB is working with a European mining company on procedure dispatches for their mine hoist inspections and work tasks. Another example case is a major Indian battery materials company taking these new tools into use.

Text: Mia Heiskanen