The significance of predictive maintenance in enhancing productivity within factories cannot be overstated. The widespread adoption of predictive maintenance solutions is rapidly gaining traction across both large enterprises and small to medium-sized enterprises (SMEs). This surge in adoption can be attributed to a myriad of advantages, such as diminished downtime, prolonged equipment lifespan, heightened plant safety, optimized maintenance schedules, diminished maintenance costs, and an enhanced yield rate.

The escalating adoption of emerging technologies is playing a pivotal role in harnessing valuable insights for the AI-based predictive maintenance market. Organizations are increasingly leveraging cutting-edge technologies to enhance the capabilities of predictive maintenance systems. This trend encompasses the integration of advanced tools such as AI, ML, and the Internet of Things (IoT) to gather, process, and analyse vast amounts of data.

The services segment is expected to be led by the deployment and installation services segment. This dominance is driven by the increasing need for predictive maintenance services, particularly in industries such as automotive, transportation, energy, utilities, and aerospace and defense.

On the basis of organization size, the SMEs segment is expected to witness significant growth during the forecast period due to the rising number of SMEs globally. Large enterprises dominate the market share in AI-based predictive maintenance. This is driven by their increasing preference for optimizing and automating operational maintenance processes through the adoption of predictive maintenance solutions. In large enterprises, the high costs associated with downtime and assets have led to a significant increase in demand for predictive maintenance solutions globally.

The fluid analysis segment exhibited the highest CAGR during the forecast period owing to its ability to monitor liquid contamination and reduce uncertainty, risk, and reactive work for a maintenance department. Fluid analysis is instrumental in optimizing asset performance, reducing downtime, and maximizing the efficiency and reliability of industrial equipment. The integration of fluid analysis data into AI algorithms empowers organizations to make proactive, data-driven decisions that lead to more effective maintenance strategies.

Europe is expected to secure the second-leading position, showing remarkable CAGR growth due to increased organizational investments and growing awareness of the technology’s competitive advantages. Germany has the greatest market share in Europe for predictive maintenance, due to its increasing manufacturing facility development to meet the expanding demand, a factor that tends to increase the growth of predictive maintenance in the country.

The AI-based predictive maintenance market is characterized by the presence of various small and big players. The major market players ABB, AWS, C3.AI, Dingo, General Electric, Honeywell, IBM, Microsoft, PTC, SAP. In a competitive environment, players are swiftly embracing advanced technologies to enhance AI-based predictive maintenance operations and bolster their competitive stance through strategic initiatives, including mergers, acquisitions, and the introduction of new products.