Autonomous Asset Management

Boost production uptime and margins with AI agents that orchestrate assets, maintenance, and supply chains.
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Streamline asset management with AI

Optimize performance, cost, and operational risk across assets while improving workforce efficiency, safety, and compliance with AI assistants and agents that enhance decision-making and automate lifecycle processes.

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Autonomous Asset Management

Orchestrate the full asset management lifecycle from acquisition to decommission by combining AI, knowledge graphs, end-to-end automation, asset orchestration, maintenance activities, health and safety, and connected supply chains to optimize financial returns and reduce operational risk.

Reduce unplanned downtime with AI agents that speed plan-to-repair.

Predict failures early and automate plan-to-repair to cut outages and restore production faster.

Lower maintenance costs with AI-optimized asset strategy and planning

Balance downtime, labor, and parts costs to recommend the lowest-cost maintenance plan.

Balance risk, cost, and reliability with a data-driven asset strategy

Analyze criticality, safety and cost tradeoffs to recommend the optimal maintenance strategy per asset.

Speed up work order and schedule creation with agents that streamline work

Auto-create work plans from real-time asset health, with accurate time estimates and fewer manual steps.

Improve technician efficiency with guided field maintenance

Deliver in-context work instructions, permits, and documents to reduce delays and rework.

Asset management assistants that work for you

Set direction across end-to-end industry processes and your assistants will coordinate agents to execute routine work across apps, data, and workflows.

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Use business goals and outcomes to influence asset management strategy and execution directly.​

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$25–35

M

reduction in asset downtime*

$10–20

M

reduction in maintenance personnel cost*

$2–5

M

reduction in planning and scheduling cost*

See what customers are saying

We’re convinced that AI-driven troubleshooting can help us further increase availability across our offshore wind fleet. Together with SAP, we’re introducing agentic AI features into our maintenance management solutions for site teams and technicians. We are exploring the potential of AI to improve access to the right knowledge, enhance work order quality, and simplify the capture and reuse of lessons learned across our sites, with the ambition to increase the efficiency of our troubleshooting activities.

Dr. Thomas Michel, COO RWE Offshore Wind

Industries that use Autonomous Asset Management

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FAQ

Autonomous Asset Management incorporates agentic AI to orchestrate the full asset management lifecycle—from acquisition to decommissioning—across assets, maintenance, health and safety, and connected supply chains. Powered by AI assistants and agents, it helps predict failures early, automate plan-to-repair workflows, optimize maintenance strategy and scheduling, and guide technicians with contextual instructions. As part of SAP’s enterprise asset management software portfolio, it helps organizations improve uptime, workforce efficiency, compliance, and operational risk management.

Autonomous Asset Management applies AI agents to help maintenance teams shift from reactive firefighting to increasingly autonomous maintenance execution.

By continuously analyzing asset health signals, Internet of Things (IoT) sensor data, maintenance history, risk, criticality, labor availability, parts, and cost tradeoffs, AI agents can:

  • Detect anomalies and emerging risks early and explain likely failure modes.

  • Balance uptime, safety and compliance by recommending and adapting maintenance strategies (condition‑based, risk‑based, and reliability‑centered).

  • Convert alerts and insights into prioritized, fully scoped work packages with parts, skills, tools, and permits.

  • Improve scheduling and technician readiness by aligning work with labor, materials, access constraints, and production plans.

  • Reduce repeat work by guiding technicians with contextual checklists, procedures, and AI troubleshooting.

  • Learn from outcomes, continuously improving strategy, planning parameters, and data quality.

Asset-intensive industries benefit most from Autonomous Asset Management—especially utilities, oil and gas, energy, industrial manufacturing, mill products, mining, transportation, and other organizations operating complex, distributed equipment networks where uptime, safety, compliance, and cost control are mission critical.

Industries with high-value, failure-prone, or safety-critical equipment benefit most from predictive maintenance AI. This includes utilities and energy companies managing grids, plants, turbines, and field assets; oil and gas operators maintaining upstream, midstream, and refinery equipment; mining, mill products, and industrial manufacturers running continuous production assets; and transportation organizations managing fleets, rail, port, and logistics infrastructure. In these environments, AI agents can analyze asset condition, sensor signals, maintenance history, risk, and operational context to identify emerging failure patterns before they disrupt operations. The value is strongest where unplanned downtime can create cascading costs—lost production, delayed service, repeat technician visits, safety exposure, or compliance risk.

Autonomous Asset Management helps asset-intensive organizations orchestrate the full asset lifecycle through use cases, such as:

  • Executive guidance: Balance risk, cost, and reliability with a data-driven asset strategy.

  • Asset information management: Reduce asset data onboarding and integration costs, and increase asset lifecycle data quality.

  • Asset health analysis and strategy: Optimize asset strategies based on financial impact, risk, safety, and regulatory requirements.

  • Maintenance planning: Optimize maintenance plans, verify required skills and certifications, and ensure the right parts and equipment are available.

  • Asset logistics: Ensure the right resources, materials, and equipment are available by coordinating inventory, transportation, and rentals.

  • Maintenance execution: Manage safety permits, lockout, and tag-out, deliver contextual instructions, and get real-time decision support.

Users interact with AI agents as part of enterprise asset management through natural-language, role-based experiences embedded in their everyday workflows. Maintenance planners, reliability engineers, field technicians, supervisors, and operations leaders can ask questions, review recommendations, and act on AI-generated insights directly in SAP applications. For example, an agent can surface emerging asset risks, explain why a failure is likely, recommend a maintenance strategy, create or prioritize a work package, support scheduling decisions, or guide a technician through inspection and repair steps. Users remain in control by reviewing, approving, and refining agent recommendations, while the agents reduce manual analysis and help connect decisions across assets, work orders, parts, people, safety, and cost.

*SAP Global Value Advisory, 2026.

Disclaimer: Value estimates are projections based on industry benchmarks and are not guaranteed outcomes.

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