Agentic AI in Predictive Maintenance Market

Agentic AI in Predictive Maintenance Market Size, Share, Growth Report, 2032

The global agentic AI in predictive maintenance market is valued at USD 0.85 billion in 2025 and is projected to reach USD 6.87 billion by 2032, growing at a CAGR of 35.4% between 2026 and 2032, as industrial operators move beyond dashboards and alerts toward autonomous agents that can diagnose a failing asset, draft the work order, check spare-parts inventory, and schedule a technician without a human starting the process. That shift - from predictive insight to autonomous action - is what is pulling agentic AI in predictive maintenance investment out of pilot budgets and into core reliability strategy across manufacturing, energy, oil and gas, and data center operations.

Top 10 Key Takeaways

• North America holds the largest share of the agentic AI in predictive maintenance market in 2025, supported by early enterprise AI budgets and a mature ecosystem of CMMS and asset performance management vendors.
• Asia Pacific is the fastest-growing region, driven by China's factory automation push, India's manufacturing expansion, and dense robotics installations across Japan and South Korea.
• Manufacturing remains the dominant end-user vertical, with process industries and discrete manufacturing both retrofitting agentic layers onto existing sensor and CMMS investments.
• Oil and gas and energy verticals are emerging as high-value adopters, given the cost of unplanned downtime on offshore and grid-connected assets.
• Fully autonomous, closed-loop agents are a smaller but fast-growing category compared with assistive, human-approved agents, which still dominate near-term deployments.
• Cloud deployment leads overall, but hybrid and edge architectures are gaining ground for latency-sensitive, safety-critical operations.
• Multi-agent orchestration - where specialized agents for diagnostics, root cause analysis, and work order generation collaborate - is the key technology shift defining next-generation platforms.
• Data governance, model explainability, and industrial AI safety expectations are shaping vendor roadmaps as much as prediction accuracy itself.
• IBM, Siemens, Cognite, Augury, Uptake, and GE Vernova are among the companies actively shipping agentic capability into their predictive maintenance and asset performance suites.
• The near-term opportunity lies in retrofitting agentic AI onto brownfield assets that already have sensor data but lack the workflow automation to act on it; the near-term risk is stalling in pilot mode without the data foundation to support autonomous action.
• For industrial operators and technology buyers, the strategic implication is clear: agentic AI in predictive maintenance is shifting from an IT experiment to a reliability and operations decision with direct P&L consequences.

Why Agentic AI in Predictive Maintenance Matters Now

Predictive maintenance has existed in some form for more than a decade, built on vibration sensors, thermal imaging, and machine learning models trained to flag anomalies before they become failures. What has changed is the layer sitting on top of that prediction. Traditional predictive maintenance told a reliability engineer that a bearing was likely to fail within two weeks; it did not check whether a replacement bearing was in stock, did not know which technician had the right certification, and did not generate the work order. Agentic AI closes that gap. Its reasons across the prediction, the maintenance history in the CMMS, the parts inventory in the ERP system, and technician availability, then acts - within guardrails a plant or reliability leader has approved in advance.

That capability matters now because the macro pressures on industrial operations have not eased. Skilled maintenance labor is scarce and aging out of the workforce faster than it is being replaced. Unplanned downtime remains one of the largest controllable cost lines in asset-intensive industries, and margin pressure across manufacturing, energy, and logistics leaves little room for reactive firefighting. At the same time, the underlying technology - large language models capable of reasoning over unstructured maintenance logs, multi-agent frameworks that can coordinate specialized tasks, and industrial data platforms that unify OT and IT data - has matured enough to make autonomous or semi-autonomous action commercially viable rather than experimental.

The broader digital transformation context reinforces this. Enterprises that have already invested in IoT sensor networks, digital twins, and cloud-based asset performance management platforms are finding that agentic AI is the fastest way to extract additional value from data they are already collecting, rather than requiring an entirely new sensor or infrastructure buildout. Sustainability and energy-efficiency mandates add a further push, since agentic maintenance systems that keep motors, compressors, and HVAC systems running at optimal efficiency also reduce energy waste tied to degraded equipment performance. Regulatory and safety expectations, particularly in oil and gas, aerospace, and utilities, are also nudging operators toward systems that can document, in an auditable way, why a maintenance action was recommended or taken - a natural fit for agentic architectures built with explainable reasoning chains.

The pace of enterprise investment highlights both market momentum and implementation constraints. Agentic AI adoption intent within manufacturing has increased approximately fourfold over a relatively short period, while actual deployment continues to lag stated ambitions. This gap between strategic intent and operational execution represents a key market challenge. Enterprises are allocating budgets and conducting pilot programs, but many remain constrained by inadequate data foundations, immature governance frameworks, and limited implementation readiness.

The transition from pilot projects to enterprise-wide platforms will determine competitive positioning. Early adopters that establish scalable operating models are likely to define performance benchmarks for the broader market. For technology buyers and investment decision-makers, the primary challenge is no longer whether to evaluate agentic predictive maintenance. The focus is shifting toward how quickly organizations can develop defensible data, governance, integration, and oversight frameworks required to support responsible deployment at scale.

Market Trends

The transition from predictive insights to prescriptive recommendations and autonomous execution represents the most significant trend shaping the market. Early predictive maintenance solutions primarily identified potential equipment failures. Current-generation platforms, including Cognite Atlas AI and Augury AI Agents, extend these capabilities by generating work orders, adding diagnostic information, and routing tasks to qualified technicians. This development reduces maintenance coordination timelines from several days to a few minutes.

The emergence of multi-agent orchestration is another defining market trend. Leading platforms increasingly deploy specialized agents for anomaly detection, root cause analysis, inventory management, and maintenance scheduling. These agents exchange operational context through a centralized orchestration layer. This architecture reflects the functional structure of reliability teams and enables vendors to enhance individual capabilities without retraining the entire system.

The convergence of agentic AI with digital twin technology is also gaining importance. Integrating a live digital representation of a production line, industrial asset, or offshore platform with an agentic reasoning layer allows recommended actions to be simulated before execution. This capability improves operational safety, strengthens decision confidence, and reduces the limitations associated with black-box predictive models. Agentic copilots are also being embedded directly within existing computerized maintenance management system and enterprise asset management platforms. IBM Maximo and the integration of Siemens Industrial Copilot with Senseye Predictive Maintenance illustrate this trend. Such integrations reduce adoption barriers for organizations seeking to retain their existing asset management infrastructure.

Partnerships between agentic AI platform providers, hyperscalers, and semiconductor companies represent another notable trend. Cognite has integrated NVIDIA NV-Tesseract time-series models into its Atlas AI platform, while Siemens delivers its generative AI-powered maintenance offering through Microsoft Azure. These developments indicate that leading platforms are increasingly combining specialized computing infrastructure, AI models, and industrial data capabilities rather than developing every technology layer internally. This ecosystem-based approach is also improving accessibility for mid-sized manufacturers. Organizations with limited internal data science capabilities can adopt agentic predictive maintenance through pre-integrated and vendor-managed platforms instead of developing customized systems independently.

Market Drivers

Labor scarcity across skilled maintenance and reliability functions is a major market driver. As experienced technicians retire, organizations risk losing institutional knowledge required to diagnose complex equipment behavior. Agentic systems can capture diagnostic reasoning and apply it consistently across assets and facilities. Enterprises increasingly view these platforms as mechanisms for preserving expertise, rather than merely automating repetitive maintenance tasks.

The rising economic impact of unplanned downtime is accelerating adoption across continuous-process industries. Oil and gas, chemicals, food and beverage operations face substantial losses from unexpected production stoppages. In certain environments, a single disruption can exceed the maintenance program’s annual budget. A 2025 economic impact assessment of Augury’s machine health platform indicated approximately 310% returns over three years. The assessment also identified payback periods of less than six months. Such documented outcomes are supporting faster budget approvals for agentic extensions of predictive maintenance platforms.

dvances in generative AI and large language models are strengthening technology adoption. These capabilities allow agentic systems to interpret maintenance logs, technical manuals, and historical work orders. Traditional predictive maintenance platforms have generally relied more heavily on structured sensor information. Natural-language processing extends diagnostic coverage across previously underutilized unstructured maintenance data. Siemens applied this approach by expanding its Industrial Copilot with generative AI-powered maintenance capabilities. The offering builds upon Senseye Predictive Maintenance and provides expert-level diagnostic assistance without specialized data science skills.

Broader enterprise investment in artificial intelligence is also supporting market expansion. Recent adoption assessments indicate a fourfold increase in agentic AI deployment intent within manufacturing. Although current implementation remains below stated adoption plans, enterprise investment momentum remains strong. Regulatory, safety, and operational requirements provide additional impetus across aerospace, energy, and utilities. Organizations increasingly require auditable and explainable maintenance decision trials. Agentic systems with structured reasoning and governance capabilities are well positioned to address these requirements.

Sustainability and energy-efficiency commitments represent another increasingly important market driver. Equipment operating outside optimal parameters generally consumes more energy and produces higher emissions. Common causes include worn bearings, fouled heat exchangers, and misaligned drive systems. Agentic systems can identify operational deterioration earlier and recommend timely corrective actions. Operators are therefore positioning these platforms as reliability and energy-management investments. This alignment with decarbonization objectives strengthens predictive maintenance budgets against competing capital initiatives.

Market Challenges and Restraints

Data readiness remains the most significant challenge limiting agentic AI adoption beyond pilot deployments. Agentic systems depend heavily on accessible, accurate, and contextualized operational data. However, many industrial facilities continue operating fragmented sensor networks and inconsistent asset-tagging structures. Maintenance histories also remain distributed across paper records, spreadsheets, and disconnected enterprise systems. Consequently, infrastructure limitations and poor data quality often restrict scalability more than model capabilities.

Trust and governance represent another major market restraint. Allowing autonomous agents to generate work orders carries considerably lower operational risk. Granting authority to shut down or throttle equipment creates greater safety concerns. Most organizations therefore retain human oversight for decisions affecting production continuity or workforce safety. Although enterprise deployment interest is increasing, relatively few organizations currently possess mature operating models. Responsible adoption requires clear accountability, approval mechanisms, escalation procedures, and explainable decision processes.

Integration complexity further restricts implementation across asset-intensive industries. Predictive maintenance agents must connect with CMMS, ERP, historian, SCADA, and IoT platforms. These systems are frequently supplied by different vendors and deployed across different periods. Establishing a unified operational data environment therefore requires substantial systems-integration capabilities and investment. Limited interoperability across industrial software platforms can extend deployment timelines and increase implementation costs.

Talent availability and organizational change management create additional adoption barriers. Reliability engineers and maintenance technicians require new capabilities for supervising and validating agentic recommendations. Organizations must also address cultural resistance toward transferring operational judgment to autonomous software. These concerns are particularly relevant within shop-floor environments requiring extensive experiential knowledge. Workforce training, transparent communication, and phased deployment are therefore critical for adoption.

Cybersecurity risks are intensifying as agentic systems gain access to operational technology environments. Autonomous execution increases the number of systems, interfaces, and permissions requiring protection. This expanded attack surface creates greater requirements for access controls, monitoring, segmentation, and incident response. Organizations must ensure that agentic capabilities cannot initiate unauthorized or unsafe operational actions.

Measurement difficulties also constrain investment decisions. Many reliability leaders struggle to establish clear and defensible returns on investment. Quantifying avoided downtime attributable to a specific agentic intervention remains particularly challenging. Improvements may also result from broader maintenance programs, workforce actions, or equipment upgrades. Organizations therefore require disciplined baseline measurement, intervention tracking, and post-deployment performance assessment. Without reliable measurement frameworks, securing large-scale investment approval may remain difficult.

Industry and Application Growth

Manufacturing, spanning both discrete and process operations, is where agentic AI in predictive maintenance is growing fastest in absolute deployment volume, simply because manufacturers already carry the largest installed base of rotating equipment, conveyors, and production-critical machinery that benefits from continuous monitoring. Automotive and heavy discrete manufacturers are early adopters given their history with condition monitoring and their appetite for further automating already-instrumented production lines.

Energy and utilities are growing quickly as grid operators and renewable asset owners look for agentic systems to manage geographically distributed assets - wind turbines, substations, transformers - where sending a technician for manual inspection is expensive and slow. Oil and gas are an especially compelling growth vertical: offshore platforms carry enormous downtime costs and safety stakes, and operators such as Aker BP have been early and vocal adopters of agentic reasoning layers for anomaly detection and root cause analysis across wells, heat exchangers, and other critical equipment.

Data centers and IT infrastructure represent an emerging high-growth pocket, as hyperscalers and colocation operators apply agentic monitoring to cooling systems, power distribution, and backup generation, where even brief failures carry outsized financial and reputational consequences. Aerospace and defense, transportation and logistics, and healthcare and medical equipment round out the vertical landscape, each drawn by a common thread - high consequence of failure, high cost of manual diagnostic labor, and existing sensor infrastructure that agentic AI can be layered onto rather than replaced.

Within transportation and logistics, fleet-focused platforms such as Samsara are extending agentic monitoring from individual vehicles to entire distributed fleets, using telematics data to plan usage-based maintenance windows rather than fixed calendar schedules, a shift that matters most for operators running thin margins on long-haul or last-mile networks. In aerospace and defense, the emphasis is on airworthiness-grade auditability, meaning agentic recommendations must be traceable and explainable to regulators as well as technicians, which is shaping a more conservative, assistive-first adoption pattern in that vertical. Healthcare and MedTech equipment providers, meanwhile, are exploring agentic maintenance for high-value imaging and diagnostic equipment, where unplanned downtime directly affects patient care access, making the business case for even modest reliability gains unusually compelling.

Agentic AI in Predictive Maintenance Market, By Component

Platforms and software form the leading component of the market, reflecting the fact that most organizations are licensing an agentic layer or agent-orchestration platform rather than building one internally. Vendors from Cognite to Augury to IBM are packaging their agentic capability as a software or platform add-on to existing industrial data or asset management products, which keeps adoption friction low.

Services, spanning both professional and managed services, are the fastest-growing component, because deploying agentic AI well requires more than buying a license - it requires data integration, model tuning to a specific plant's failure modes, and change management to get maintenance teams comfortable acting on agent recommendations. As more first-generation deployments move from pilot to scale, demand for implementation and managed-service support is rising in step.

Agentic AI in Predictive Maintenance Market, By Autonomy Level

Assistive agents - those that surface a recommendation and require a human to approve any action - lead the market today, reflecting the cautious, trust-building posture most industrial organizations are taking toward autonomous software. This is the level at which most enterprise deployments currently sit, since it lets maintenance teams validate agent judgment before ceding more control.

Fully autonomous, closed-loop agents that can take action without human sign-off are the fastest-growing autonomy tier, even from a small base, as vendors demonstrate reliability in lower-risk, non-safety-critical tasks such as work order generation and inventory reordering. As confidence builds through track record, this tier is expected to expand into a wider set of use cases, particularly in non-safety-critical administrative and scheduling workflows.

Agentic AI in Predictive Maintenance Market, By Deployment Mode

Cloud deployment leads the market, since most agentic AI platforms are delivered as cloud-native software that can pool data across multiple sites and continuously retrain on incoming sensor and maintenance data without local infrastructure investment. Cloud delivery also lets vendors ship new agent capabilities as continuous updates rather than periodic on-premises upgrades.

Hybrid and edge deployment is the fastest-growing deployment mode, driven by latency-sensitive and safety-critical operations - such as offshore platforms, remote grid assets, and factories with unreliable connectivity - where at least part of the agentic reasoning needs to run close to the equipment itself. Expect this hybrid pattern, rather than a pure cloud-versus-edge divide, to define enterprise architecture choices going forward.

Agentic AI in Predictive Maintenance Market, By Technology

Machine learning and predictive analytics remain the technical foundation of the market, since accurate failure prediction is still the starting point every agentic workflow builds upon. These models continue to improve as more operational data flows through them, and they remain the leading technology category by revenue contribution.
Natural language processing and large language models are the fastest-growing technology category, because they are what give agentic systems the ability to read unstructured maintenance logs and technical documentation, communicate recommendations in plain language, and coordinate across multiple specialized agents - capabilities that were largely absent from the prior generation of purely numerical predictive maintenance models.

Agentic AI in Predictive Maintenance Market, By Application

Asset health monitoring and anomaly detection remains the leading application, as it is the entry point for nearly every predictive maintenance deployment and the foundation on which agentic capability is layered. Organizations rarely adopt agentic workflow automation before they have established reliable anomaly detection.
Autonomous work order generation and scheduling is the fastest-growing application, since it is where agentic AI delivers the most visible and easily quantified return - compressing coordination workflows that once took days into a task completed in minutes, freeing reliability engineers to focus on judgment-intensive exceptions rather than routine administrative coordination.

Agentic AI in Predictive Maintenance Market, By End-User/Industry

Manufacturing leads the end-user landscape by a clear margin, given the sheer scale of instrumented rotating and production equipment already in service across discrete and process plants worldwide, and the relative maturity of condition-monitoring practice in the sector.

Oil and gas and energy verticals are the fastest-growing end-user segments, propelled by high downtime costs, remote and hazardous asset locations that make manual inspection expensive, and visible early adopter results - such as Aker BP's deployment of agentic root cause analysis agents that have measurably cut engineering investigation time on offshore assets.

• Manufacturing leads current deployment volume, backed by mature sensor infrastructure and condition-monitoring practice.
• Oil and gas and energy are growing fastest, driven by downtime cost and remote-asset economics.
• Assistive, human-approved agents dominate today; fully autonomous agents are the fastest-growing autonomy tier.
• Cloud remains the leading deployment mode, but hybrid/edge architectures are gaining share in safety-critical settings.
• Natural language processing and large language models are the fastest-growing underlying technology, enabling multi-agent coordination and unstructured-data reasoning.

Regional Analysis

North America

The United States, Canada, and Mexico together make North America the largest regional market for agentic AI in predictive maintenance in 2025, a position built on early and sustained enterprise AI investment, a dense concentration of CMMS, EAM, and industrial AI vendors headquartered in the region, and a manufacturing and energy base that has been digitizing maintenance operations for over a decade. The United States, in particular, benefits from significant hyperscale and industrial demand converging around the same agentic infrastructure, along with reshoring-driven manufacturing investment that is bringing new, digitally native production capacity online. Regulatory clarity around AI use in critical infrastructure, alongside strong venture and enterprise capital flowing into industrial AI vendors such as Uptake and Augury, continues to reinforce the region's leadership. The North American market, valued at USD 0.32 billion in 2025, is projected to reach USD 2.57 billion by 2032, growing at a CAGR of about 35.1%.

Europe

Germany, the United Kingdom, France, Italy, Spain, and the Nordic countries anchor the European market, where a dense industrial manufacturing base - particularly in automotive, machinery, and process industries - combines with a strong regulatory emphasis on safety, data governance, and the EU AI Act's risk-based framework to shape how agentic systems are deployed. German industrial automation leadership, embodied by vendors such as Siemens extending its Industrial Copilot with generative AI-powered maintenance capability, gives the region a strong domestic supply side alongside adopter demand. The Nordics stand out for early sustainability-linked adoption, using agentic maintenance to extend asset life and reduce energy waste in energy-intensive operations, while the UK's financial and industrial services base is driving interest in agentic platforms for infrastructure-heavy sectors such as utilities and transportation. Europe's market, valued at USD 0.21 billion in 2025, is expected to reach USD 1.57 billion by 2032 at a CAGR of roughly 33.7%, the most measured pace among the four regions, reflecting its careful, compliance-conscious adoption posture.

Asia Pacific

China, Japan, India, South Korea, Australia, and Singapore make Asia Pacific the fastest-growing region in this market, underpinned by China's aggressive factory automation and smart manufacturing push, India's expanding manufacturing base under government production-linked incentive programs, and Japan and South Korea's deep bench of precision robotics and electronics manufacturing that is now being layered with agentic monitoring capability. Government-backed digitalization initiatives across the region are accelerating enterprise willingness to adopt cloud-based agentic platforms, while a growing base of domestic industrial software vendors is lowering the cost of entry for mid-sized manufacturers. Singapore and Australia are notable for their roles as regional testbeds for advanced industrial AI, given strong digital infrastructure and government support for AI innovation hubs. Asia Pacific, valued at USD 0.24 billion in 2025, is forecast to reach USD 2.22 billion by 2032, expanding at the region's leading CAGR of approximately 38.4%.

Rest of World

Brazil and the broader Latin American market, the United Arab Emirates and Saudi Arabia in the Middle East, and South Africa in the African market make up the Rest of World region, where adoption is emerging but uneven. Middle Eastern sovereign investment programs tied to national diversification strategies are funding significant digital infrastructure and industrial AI pilots, particularly in oil and gas and utilities, where downtime costs are high and government-backed innovation funds are active. Brazil's agricultural processing and mining sectors are beginning to adopt agentic maintenance layers to manage remote, distributed equipment, while South Africa's mining and energy sectors face similar remote-asset dynamics that make agentic diagnostic capability attractive. The Rest of World market, valued at USD 0.08 billion in 2025, is projected to grow to USD 0.51 billion by 2032 at a CAGR of about 331.3%, a pace shaped heavily by the trajectory of Middle Eastern infrastructure investment.

• North America leads on installed base and enterprise AI maturity; Asia Pacific leads on growth rate.
• Europe's regulatory rigor shapes a more measured, compliance-first adoption curve.
• Middle Eastern sovereign investment is the key swing factor for Rest of World growth.
• Government-backed digitalization programs across China and India are accelerating Asia Pacific enterprise adoption.
• Germany and the United States both combine strong domestic vendor ecosystems with mature adopter demand.

Country-Specific Insights

In the United States, adoption is being pulled forward by a combination of reshoring-driven manufacturing investment, a mature venture-backed industrial AI vendor base, and increasingly proactive regulatory guidance around trustworthy AI use in critical infrastructure. Large industrial conglomerates and mid-market manufacturers alike are piloting agentic layers on top of existing CMMS and IoT investments rather than waiting for a next-generation platform.

In Germany, the government's continued push around Industrie 4.0 principles, combined with the presence of global automation leaders headquartered domestically, gives the country an unusually strong supply-side foundation; adoption is concentrated in automotive, machinery, and chemical processing, where reliability engineering culture is already deeply established.

In China, national smart manufacturing policy and substantial state-backed investment in industrial digitalization are accelerating enterprise adoption of agentic maintenance tools, particularly among large state-owned and export-oriented manufacturers seeking to reduce production disruption.

In India, expanding manufacturing capacity under production-linked incentive schemes is creating a wave of new, digitally native factories that are adopting agentic predictive maintenance from the outset rather than retrofitting it onto legacy infrastructure, giving the country a distinct greenfield adoption pattern.

In the United Arab Emirates and Saudi Arabia, sovereign wealth-backed diversification strategies are funding large-scale industrial AI programs across oil and gas, utilities, and new industrial cities, positioning the Gulf region as an outsized adopter relative to its current market size.

In Japan and South Korea, a long tradition of precision manufacturing and factory-floor discipline gives operators a strong existing base of condition-monitoring practice, and adoption of agentic layers is being driven largely by the need to offset a shrinking skilled technician workforce with software that can retain and apply diagnostic expertise consistently across shifts. In the United Kingdom, adoption is concentrated in utilities, transportation, and financial-services-adjacent infrastructure, where regulatory expectations around operational resilience are pushing operators toward more auditable, explainable maintenance decision-making. In Brazil, agentic maintenance is gaining early traction in agricultural processing, mining, and energy infrastructure, where assets are often geographically remote and the cost of dispatching a specialist technician is high, while in South Africa a similar dynamic is playing out across the mining and utilities sectors, both of which face aging asset bases and constrained technical labor pools.

• The United States combines reshoring momentum with a mature vendor ecosystem.
• Germany's Industrie 4.0 legacy gives it strong domestic supply and adopter demand alike.
• China's state-backed smart manufacturing policy is a primary adoption accelerant.
• India's greenfield manufacturing capacity is adopting agentic maintenance from day one.
• Gulf sovereign investment programs are punching above the region's current market weight.
• Japan, South Korea, Brazil, and South Africa are each using agentic maintenance to offset labor scarcity or remote-asset cost pressure.

Key Company Insights

The competitive landscape spans established industrial automation and enterprise software leaders alongside specialized industrial AI vendors that have built agentic capability natively into their platforms. Companies profiled in this study include IBM, Siemens, GE Vernova, C3.ai, SAP, Cognite, Augury, Uptake, Samsara, PTC, AVEVA, Honeywell, ABB, Rockwell Automation, and SparkCognition.

• IBM
• Siemens
• GE Vernova
• C3.ai
• SAP
• Cognite
• Augury
• Uptake
• Samsara
• PTC
• AVEVA
• Honeywell
• ABB
• Rockwell Automation
• SparkCognition

Strategic activity across this group is intensifying. Cognite has been expanding its Atlas AI agent workbench, releasing specialized agents for work package generation and root cause analysis, and has deepened its industrial AI capability by integrating NVIDIA's NV-Tesseract time-series models to strengthen anomaly detection for customers such as Aker BP. Siemens has extended its Industrial Copilot portfolio with a generative AI-powered maintenance offering built on its Senseye Predictive Maintenance solution, delivered on Microsoft Azure, aimed at giving maintenance teams expert-level diagnostic guidance without deep technical expertise. IBM has continued to invest in its Maximo Application Suite, releasing updated AI service components with enhanced predictive maintenance and anomaly detection capability. Augury has moved from prescriptive alerts to autonomous workflow action with the 2026 launch of its AI Agents capability, which triggers CMMS work order creation directly from a diagnosed fault. Across the board, the strategic emphasis is consistent: partner with hyperscalers for compute and distribution, embed generative and agentic capability into existing platforms rather than launching entirely new standalone products, and demonstrate quantifiable downtime and labor-hour reduction to justify enterprise budget.

Beyond the vendors already discussed, GE Vernova continues to extend agentic and AI-driven capability across its asset performance management and grid software portfolio, aimed largely at energy and utility operators managing large, distributed generation and transmission assets. SAP has been embedding predictive and increasingly agentic maintenance capability into its Asset Performance Management and broader enterprise suite, giving it a natural distribution channel among the large base of manufacturers already running SAP for core operations. PTC and AVEVA, both long-established in industrial software, are extending their IoT and engineering platforms with agentic reasoning layers aimed at connecting design, operations, and maintenance data more tightly together, while Honeywell, ABB, and Rockwell Automation are each layering agentic capability onto their existing automation and control system installed bases, giving them a distribution advantage rooted in decades of on-site equipment relationships. SparkCognition, as a more specialized industrial AI vendor, continues to focus on model-driven anomaly detection and prescriptive maintenance for asset-intensive sectors including energy and aviation.

• Cognite is deepening agent specialization and partnering with NVIDIA to strengthen anomaly detection.
• Siemens is embedding generative AI-powered maintenance directly into its existing Industrial Copilot and Senseye portfolio.
• IBM continues to invest in Maximo as an agentic-ready asset management backbone.
• has extended from prescriptive insight into autonomous workflow action.
• Across vendors, hyperscaler partnerships and native platform integration define current go-to-market strategy.

Recent Developments

• In February 2025, Augury announced a $75 million funding round led by Lightrock, with participation from Insight Partners, Eclipse, Qumra Capital, La Maison Partners, and SE Ventures, maintaining a valuation above $1 billion as it scales its industrial AI and machine health platform.
• In March 2025, Siemens expanded its Industrial Copilot portfolio with a new generative AI-powered maintenance offering built on Senseye Predictive Maintenance, delivered via Microsoft Azure with Entry and Scale packages.
• In September 2025, Aker BP expanded its AI-first partnership with Cognite, deploying Cognite Atlas AI agents for root cause analysis that have cut engineering investigation time by more than 70%.
• In January 2026, IBM released the Maximo Application Suite AI Service Component version 9.2.0, adding enhanced machine learning models and real-time condition intelligence for predictive maintenance.
• In 2026, Augury launched AI Agents, extending its Machine Health platform from prescriptive recommendations to autonomous work order creation and technician assignment inside connected CMMS systems.
• In March 2026, Cognite and NVIDIA operationalized the NV-Tesseract time-series AI model with Aker BP to expand anomaly detection and predictive maintenance across a broader range of offshore assets, including wells and heat exchangers.
• In June 2026, Schneider Electric announced a definitive agreement to acquire Cognite in an all-cash transaction valued at approximately $3.1 billion, with plans to integrate Cognite's Atlas AI platform into its AVEVA industrial software business to strengthen its position in industrial AI and agentic predictive maintenance.

Real-World Use Cases

In September 2025, Aker BP expanded its AI-first strategy in partnership with Cognite, deploying Cognite Atlas AI's root cause analysis agents across its exploration and production operations. The initiative aimed to reduce the engineering time required to investigate equipment anomalies and build a more scalable, AI-ready data foundation for offshore asset monitoring. By automating cause-mapping and evidence-gathering steps that previously required extensive manual engineering effort, Aker BP cut root cause analysis time by more than 70%, and has since extended the underlying platform, with NVIDIA's NV-Tesseract time-series models, to expand anomaly detection across a wider range of critical equipment, including wells and heat exchangers.

Market Segmentation Summary

Taken together, the segmentation structure of the agentic AI in predictive maintenance market reflects an industry still in the early stages of autonomy adoption but moving quickly. On the component axis, platform and software licensing leads, while services are scaling fastest as enterprises need help integrating agentic layers with existing CMMS, ERP, and IoT infrastructure. On the autonomy axis, most deployments remain assistive today, with humans approving agent-recommended actions, even as fully autonomous, closed-loop agents grow quickly from a small base in lower-risk administrative workflows such as work order generation and parts reordering. Deployment preference continues to favor the cloud for its scalability and continuous-update model, though hybrid and edge architectures are gaining ground wherever latency, connectivity, or safety considerations demand local processing. Underlying technology is shifting from pure machine learning-based prediction toward natural language processing and large language model-powered reasoning that allows agents to read unstructured data and coordinate with one another. Within applications, asset health monitoring remains the anchor use case, while autonomous work order generation and scheduling is growing fastest as the clearest, most quantifiable source of return. Across end-user industries, manufacturing leads on volume while oil and gas and energy verticals lead on growth rate, reflecting the outsized cost of downtime and remote-asset economics in those sectors.

• Platforms and software lead by revenue; services are the fastest-growing component as integration needs scale.
• Assistive, human-approved agents dominate deployments today; fully autonomous agents are the fastest-growing autonomy tier.
• Cloud remains the preferred deployment mode; hybrid/edge is gaining share in safety-critical environments.
• Natural language processing and large language models are driving the shift from prediction to autonomous reasoning.
• Manufacturing leads by volume; oil and gas and energy lead by growth rate.

Conclusion and Future Outlook

Through 2032, the agentic AI in predictive maintenance market will be shaped by enterprises’ ability to convert deployment intent into operational execution. A significant proportion of organizations plan to deploy agentic AI within the next two years, although relatively few currently possess the operating models required for effective implementation. As enterprise data foundations strengthen, multi-agent orchestration capabilities advance, and confidence improves through demonstrated outcomes, autonomy levels are likely to progress steadily. Evidence from Aker BP’s root cause analysis improvements and Augury’s documented downtime avoidance illustrates this transition. Adoption will gradually shift from assistive recommendations toward broader autonomous actions operating within clearly defined governance and control frameworks.

Across manufacturing, energy, oil and gas, and data center operations, agentic AI is becoming a strategic infrastructure consideration rather than a discretionary software investment. Organizations developing data foundations and governance frameworks today will be better positioned to realize compounding operational advantages. Early investment can strengthen asset reliability, decision speed, maintenance productivity, and competitive differentiation. Companies delaying adoption until the technology reaches full maturity may face higher integration costs and slower capability development.

During the forecast period, assistive agents requiring human approval will progressively gain greater autonomy. Initial adoption will focus on lower-risk administrative activities, including parts reordering, scheduling, and work-order management. Autonomous capabilities will subsequently expand across broader operational decisions as performance records improve and governance frameworks mature. Automation, digitalization, and sustainability objectives will continue reinforcing one another. Well-maintained assets generally deliver greater reliability, improved efficiency, and lower emissions. Organizations evaluating this market through 2032 should therefore view agentic AI in predictive maintenance as an evolving enterprise capability. Realizing its full strategic value will require sustained investment in data quality, workforce upskilling, systems integration, and governance discipline.

Frequently Asked Questions

How big is the agentic AI in predictive maintenance market?
The global agentic AI in predictive maintenance market was valued at USD 0.85 billion in 2025 and is projected to reach USD 6.87 billion by 2032.

What is the agentic AI in predictive maintenance market growth rate?
The market is projected to grow at a CAGR of approximately 35.4% between 2026 and 2032, making it one of the fastest-growing segments within the broader industrial AI landscape.

Which segment leads the agentic AI in predictive maintenance market?
Platform and software offerings lead by component, manufacturing leads by end-user industry, and assistive, human-approved agents lead by autonomy level, though fully autonomous agents and services are both growing quickly.

Who are the key players in the agentic AI in predictive maintenance market?
Key players include IBM, Siemens, GE Vernova, C3.ai, SAP, Cognite, Augury, Uptake, Samsara, PTC, AVEVA, Honeywell, ABB, Rockwell Automation, and SparkCognition.

What are the factors driving the agentic AI in predictive maintenance market?
Growth is driven by skilled-labor scarcity in maintenance roles, the high cost of unplanned downtime, advances in large language models and multi-agent orchestration, rising enterprise AI investment, and regulatory pressure for auditable, explainable maintenance decisions in high-consequence industries.

Speak With Our Analysts

Agentic AI in predictive maintenance is moving quickly from pilot to platform decision, and the right segment-level intelligence - by industry, autonomy level, and region - can materially change how a reliability or technology roadmap is built. MarketsandMarkets works with operations, technology, and investment leaders to customize the scope of this research to a specific business question; readers evaluating their own agentic maintenance strategy are welcome to speak with our analysts, request a sample of the underlying study, or customize the scope to their industry and region of focus.