Autonomous Maintenance: Origin, Evolution, And Why It’s Time for a New Definition

For decades, Autonomous Maintenance has been a fundamental pillar of Total Productive Maintenance (TPM), designed to empower operators to take responsibility for basic equipment care. The approach was revolutionary in its time — reducing dependence on specialized maintenance technicians for routine upkeep and fostering ownership among frontline workers.

However, the original concept of the autonomous maintenance model fails to keep pace with contemporary needs and expectations. The sheer complexity and scale of modern asset operations and maintenance demand a more intelligent, automated, and scalable approach that moves beyond operator-led routines and embraces data-driven techniques, AI-powered workflows, and advanced process automation.

This article explores the origin, evolution, and shortcomings of traditional autonomous maintenance and why it’s time for a new definition — one that reflects today’s digital, tech-driven world of smart buildings and state-of-the-art facilities.

Autonomous Maintenance: Original Concept & Definition

Autonomous maintenance is a maintenance strategy where operators continuously monitor their assets, make adjustments, and perform minor maintenance tasks on their own, rather than assigning a dedicated maintenance technician or team to perform scheduled maintenance and routine repairs. Rooted in Total Productive Maintenance principles, the traditional concept of Autonomous Maintenance revolves around these core aspects:

• Operator-led maintenance – Machine operators take charge of basic maintenance tasks such as cleaning, lubrication, and visual inspections.
• Early detection & prevention – Frontline workers are trained to identify early signs of equipment failure.
• Workforce empowerment – Maintenance ownership is distributed, reducing reliance on specialized technicians.

The goal is to shift from a reactive approach to a proactive and preventive one, fostering collaboration between operators and maintenance personnel while improving ownership of assets, reducing downtime, and increasing equipment efficiency.

Autonomous Maintenance: Brief History & Evolution

1. Origins in Total Productive Maintenance (1970s)

• Autonomous Maintenance (AM) emerged as part of the Total Productive Maintenance (TPM) framework for increasing equipment reliability and efficiency in manufacturing industries, which originated in Japan in the late 1960s and 1970s.
• Nippondenso, a subsidiary of Toyota, was a pioneer in TPM and AM. They formalized the practice to address inefficiencies caused by lack of ownership in equipment upkeep.

2. Adoption in Manufacturing (1980s–1990s)

• The global success of Japanese manufacturing led to the adoption of TPM—and consequently, AM—by industries worldwide, particularly in sectors reliant on heavy machinery.
• AM’s structured process typically involves seven steps, starting with cleaning and inspecting, then gradually building operator knowledge and skills to take on advanced maintenance tasks.

3. Expansion to Other Industries (2000s)

• As TPM principles gained popularity beyond manufacturing, the adoption of AM expanded to industries such as energy, utilities, and facilities management.
• In the built environment, AM was applied to systems like HVAC, elevators, and electrical systems, where regular inspections and cleaning by operators could prevent costly breakdowns.

Autonomous Maintenance 2.0: Why It’s Time for a New Definition

The traditional version of Autonomous Maintenance was built for an era where human-led, operator-driven maintenance was the norm. While it improved equipment reliability and workforce ownership, it no longer meets the demands of modern, complex, and data-driven asset ecosystems. Here’s why:

1. Over-Reliance on Human Effort & Individual Knowledge

• Conventional Autonomous Maintenance requires frontline operators to manually inspect, clean, and detect anomalies, which is time-consuming and prone to human error.
• The model relies on operators knowing when a sophisticated intervention is necessary or when a simple fix can quickly get the asset running again. Operators must become experts in detecting anomalies, understanding asset composition, identifying recurring issues, determining their causes, and implementing the appropriate fixes.  
• With ageing workers, skill gaps, and talent shortages, relying on operators for routine interventions is unsustainable.

2. Limited Scalability & Efficiency in Complex Asset Ecosystems

• Traditional AM was designed for mechanical equipment in manufacturing but struggles with modern assets in highly distributed and diverse environments (large-scale facilities, new energy assets, etc.).
• The growing volume of data and digital twins makes it impossible for human teams to process and act on insights efficiently without AI-driven assistance.
• Manual, operator-driven processes create bottlenecks, and continuous training for a large number of people across thousands of assets makes it hard to conduct a standardized autonomous maintenance strategy and scale across multi-site operations.

3. Reactive Nature vs. Proactive / Predictive Maintenance

• Traditional AM focuses on basic preventive tasks but lacks predictive analytics to anticipate failures before they happen.
• Lack of real-time data-driven insights means subjective decision-making, often leading to reactive maintenance instead of proactive asset management.
• Modern facilities management requires proactive and prescriptive insights — AI-driven monitoring and automated diagnostics filter out one-off issues and increase focus on planning and optimization.

Xempla: Defining A New Era of Autonomous Maintenance

Xempla’s Autonomous Maintenance approach aims to evolve the traditional definition by aligning it with today’s technology-driven and data-centric environment. Here’s an updated definition:

‘A maintenance or asset-intervention process in which most tasks — such as monitoring, diagnostics, and routine inspections (non-intrusive) — are automated or guided by technology, with human involvement focused on oversight and critical decisions. Operators step in only when onsite action / physical intervention is deemed necessary.’

In the context of modern asset operations, this highlights a paradigm shift in how autonomous maintenance is perceived and executed. Here’s what it means:

A Radical Redefinition of Maintenance Operations

Autonomous Maintenance is not just about incremental improvements like productivity gains, better asset reliability, or efficiency. Instead, it represents a fundamental transformation in how maintenance operations are structured and managed, driven by cutting-edge technology.

From Reactive to Transformative Operations

This shift involves moving beyond the traditional mindset of ‘thinking outside the box’ to rethinking the box entirely — rebuilding maintenance systems from the ground up with a focus on automation, AI-driven decision-making, and reimagined workflows. Maintenance will no longer be just a task or function; it will become a strategic process embedded deeply within the organization’s operational fabric.

Impact on Organizational Success

The stakes are high: adopting this new vision for Autonomous Maintenance could determine whether an organization thrives or lags behind. For larger organizations, this could directly affect shareholder value by influencing operational costs, asset longevity, and even market competitiveness.

Conclusion: Embracing Truly Autonomous Maintenance Operations 

Autonomous Maintenance should no longer mean operator-led maintenance. Instead, it must evolve into a technology-enabled, AI-augmented approach where: 

• Monitoring, diagnostics, and routine assessments are automated using AI.
• Human involvement is focused on oversight, validation, and critical decision-making.
• Operators step in only when needed for complex interventions.

In fact today, the shift to a truly autonomous maintenance model is already underway with AI Agents reducing human effort on routine manual tasks, providing real-time data-driven insights for enhanced decision-making, and improving scalability and cost-efficiency across industries with minimal disruption. By shifting from operator-driven to AI-augmented maintenance, organizations can achieve scalability, efficiency, and truly autonomous O&M — where human expertise is maximized for strategic actions rather than repetitive tasks.

So the real question is no longer whether autonomous maintenance should evolve, but how fast organizations can adapt to the new reality.

At Xempla, we’re not just redefining Autonomous Maintenance but making it a practical, efficient, and scalable solution for non-industrial facilities like commercial buildings, hospitals, malls, and data centers, etc. It’s about turning Autonomous Maintenance into a transformative force that redefines how maintenance operations are integrated into your organization’s broader strategy.

We are partners in this transformation, committed to helping you experience the ‘end state’ of fully autonomous, tech-enabled maintenance. Ready for a world of new possibilities? See how you can start your journey.