What is an AI Worker?

An AI Worker is an autonomous AI agent that works like a real teammate: it has a personality, specialized skills and memory, runs tasks end to end and integrates with your tools (Slack, Teams, Gmail, Notion…).

How many AI Workers are available?

15 specialized AI Workers are available, covering marketing, development, design, data, human resources, finance and customer support.

Can I try it for free?

Yes, a free trial is included with no commitment. You can add or remove AI Workers at any time and cancel whenever you want.

Yes. Your data is encrypted end to end, hosted in isolated environments, and never used to train models. Workers Delos is GDPR-compliant and SOC 2-certified.

Maintenance Predictive par IA

Par Delos Intelligence — 2026-08-21

Learn how AI-driven predictive maintenance and autonomous field service dispatch cut downtime by 45% and boost first-time fix rates.

The Industrial Downtime Crisis

Unplanned equipment failures cost industrial manufacturers an average of 50 billion dollars annually in lost production, emergency maintenance, and supply chain disruptions. A single unplanned outage at an automotive assembly plant costs an estimated 22,000 dollars per minute. Despite decades of investment in SCADA systems and ERP platforms, most industrial maintenance programs remain time-based rather than condition-based.

!Asset Intelligence Lifecycle

From Reactive to Predictive: The AI Transformation

AI-powered predictive maintenance and field service management replace reactive break-fix cycles with a closed-loop intelligence system.

Continuous Asset Health Monitoring

IoT sensors capture vibration, temperature, pressure, acoustic emissions, and power consumption at millisecond intervals. Edge AI nodes preprocess this telemetry locally, filtering signal noise and transmitting only anomalous patterns to cloud-based ML models. Physics-informed neural networks model the degradation trajectory of each asset class, learning failure precursors specific to equipment age, operating environment, and maintenance history.

Remaining Useful Life Prediction

When degradation patterns cross early warning thresholds, AI models calculate the Remaining Useful Life (RUL) of critical components with 94% accuracy. Maintenance windows are automatically scheduled during planned production pauses, eliminating the emergency repair premium that adds 300-500% to unplanned maintenance costs.

Autonomous Technician Dispatch and Route Optimization

AI dispatch engines match field service tickets to the optimal technician based on real-time proximity, skill certification, and parts availability. Predictive parts procurement ensures spare components are pre-positioned at field depots before failures occur, boosting first-time fix rates from 60% to 94% and eliminating costly return visits.

Digital Twin Diagnostics

For complex multi-component assets, AI workers instantiate digital twin simulations that model the interdependency of mechanical, electrical, and hydraulic subsystems. When a sensor reading indicates a potential bearing failure, the digital twin simulates the cascading impact on adjacent components, allowing maintenance teams to address root causes rather than symptoms.

!Operational Impact

Quantified Business Impact

|---|---|---|---|

| First-time fix rate | 60% | 94% | +34pp |

Implementation Roadmap

Phase 1: Deploy IoT sensors on critical asset classes. Establish SCADA and historian data pipelines.

Phase 2: Train anomaly detection models. Activate RUL prediction for high-criticality equipment.

Phase 3: Launch autonomous dispatch integration. Deploy digital twin diagnostics for complex assets.

This article was written with AI assistance.