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The report published in April highlights the accelerated shift from schedule-based to condition-based maintenance, driven by advances in AI and machine learning. AI systems now flag early signs of issues, reducing the need to wait for breakdowns.

Predictive maintenance offers a clear return on investment by minimizing downtime, reducing repair costs, and extending asset life.

“This not only safeguards critical assets but also ensures operational continuity in high-stakes industries,” the report states.

The Power Duo: Cloud and Edge

Cloud-based and edge technologies play a crucial role in this transformation, according to the report. Edge devices process real-time data on-site, while the cloud handles broader analytics across multiple sites, even globally. This hybrid approach is vital for industries in remote or bandwidth-limited regions like mining, offshore energy, and rail transport.

According to the report, the Americas lead in AI adoption, driven by significant investments in smart infrastructure and digital transformation. Manufacturing and logistics sectors in this region are particularly advanced in integrating AI. Europe, the Middle East, and Africa are following closely, with stricter emissions and safety regulations driving the adoption of AI-powered maintenance. “Environmental considerations are pushing sustainable, efficiency-oriented practices,” the report notes.

Asia-Pacific, particularly China, India, and South Korea, is also experiencing rapid growth due to industrialization and strong government support. High IoT adoption makes the region ideal for implementing AI-based solutions.

Startups Pushing Innovation

While tech giants like IBM, ABB, and Siemens dominate in the industry, startups are contributing to innovation. “Agile companies are reshaping the competitive landscape,” says the report, citing firms like Clarifai, Craftworks GmbH, and Nanoprecise. Canadian startup Nanoprecise uses vibration sensors and AI to monitor wear in machinery, making predictive maintenance more accessible for smaller manufacturers.

What’s Next?

Industry leaders must adopt a layered strategy that combines technological and operational initiatives, the report suggests. First, investing in integrated AI systems that aggregate and analyse data from various sources is key. Embracing cloud and edge AI will improve predictive capabilities and mitigate risks.

Cybersecurity should be a top priority, as the convergence of IoT and AI technologies creates new vulnerabilities, the report continues. Ensuring these systems are secure is as critical as ensuring the accuracy of predictive algorithms. Partnering with technology providers that offer comprehensive security solutions will be essential.

Additionally, workforce training and upskilling in AI and machine learning will enable teams to stay agile and adapt to technological changes. Collaborations with tech vendors, academia, and industry experts will keep companies at the forefront of innovation. Regular evaluations of predictive maintenance systems will improve maintenance outcomes and uncover new business opportunities.

The key takeaway from the report? The report makes it clear that predictive maintenance is no longer optional. It has become a strategic tool for performance, resilience, and cost control. Those who adopt it now—CIOs, plant managers, and maintenance teams—will not just save money; they will set new standards for reliability in the age of AI.

Source: AI-Based Predictive Maintenance Market Report 2025–2030: A Projected US$1.69 Billion Landscape – Businesses Must Invest in Cloud and Edge Technologies for Future Success, Research and Markets, April 2, 2025.

Text: Nina Garlo Photo: Shutterstock