Improving Asset Reliability with AI

How a Global Industrial Operator Transformed Facility Maintenance

Executive Summary

A global industrial operator struggled with fragmented maintenance data, reactive repairs, and limited visibility into asset health across facilities. Andersen Consulting’s AI & Advanced Analytics practice delivered an integrated Facility Maintenance System that unified asset, maintenance, and IoT data to enable predictive, insight-driven maintenance decisions. Built on Palantir Foundry and AIP, the solution was implemented in eight weeks and embedded directly into technician and planner workflows. The client realized improved uptime, faster maintenance response, and a scalable foundation for AI-enabled facility operations, reinforcing Andersen Consulting’s role as a trusted execution advisor.

Introduction: Context and Challenge

Across the Industry

Industrial operators face increasing pressure to maximize asset availability while controlling maintenance costs in environments defined by aging equipment and tighter operational tolerances. Legacy maintenance systems rarely integrate sensor data, work orders, and parts information, limiting predictive capability and forcing reactive maintenance.

The Client’s Challenge

The client operated complex facilities with hundreds of critical assets, yet maintenance planning relied on manual logs, disconnected systems, and static inspection schedules. Equipment failures routinely caused unplanned downtime, with mean time between failure varying significantly by site and asset class. Maintenance teams lacked a single view of asset condition, maintenance history, and parts availability, resulting in delayed repairs and excess spare inventory. Existing ERP and CMMS tools captured transactions but did not support predictive analytics or real-time decision-making. Without modernization, the organization faced escalating downtime risk, rising maintenance costs, and inconsistent maintenance quality across facilities.

Solution: AI-Powered Facility Maintenance Built on Palantir Foundry and AIP

Approach Overview

Andersen Consulting deployed a cross-functional team combining facility operations, maintenance engineering, and AI specialists. The engagement focused on enabling better maintenance decisions at the point of work, not just reporting. The solution allowed planners and technicians to move from reactive response to proactive, predictive maintenance execution.

Data Integration and Foundation

Palantir Foundry served as the digital backbone, consolidating asset master data, maintenance logs, inspection records, parts inventories, and IoT sensor streams into a unified ontology. Core objects included Asset, Facility, WorkOrder, Inspection, SensorStream, Part, and BOMItem, each standardized through transformation pipelines. Foundry continuously ingested sensor readings and maintenance updates, enforcing data quality, freshness monitoring, and role-based access controls. This created a governed, real-time view of asset health and maintenance activity across all facilities.

AI and Insight Generation

Andersen Consulting implemented predictive maintenance models using historical work orders and live sensor data to forecast failure risk and recommend targeted inspections or part replacements. In Palantir AIP, technicians and planners accessed these insights through natural-language prompts and guided actions. For example, when vibration and temperature readings crossed defined thresholds, the system flagged elevated failure risk, recommended an inspection window, and automatically generated a prioritized work order with required parts. All recommendations were explainable, auditable, and aligned to safety and compliance requirements.

User Experience and Workflow Integration

The Facility Maintenance System was delivered through a web and mobile Workshop application designed around daily maintenance workflows. Planners accessed Asset 360 views combining condition data, maintenance history, and upcoming inspections on one screen. Technicians used mobile access to review recent work, request parts, and receive AI-guided recommendations on-site. Insights were delivered in real time, enabling faster, more confident maintenance actions without disrupting existing processes.

Rapid Implementation

The solution was delivered in eight weeks using a structured, sprint-based approach. Early phases focused on data integration and ontology design, followed by application build, model development, and pilot user acceptance testing. Andersen Consulting worked closely with client maintenance leaders to validate KPIs, train users, and establish a production support model. The controlled rollout ensured readiness for scale while delivering immediate value.

Results and Impact

The implementation shifted maintenance operations from reactive firefighting to proactive, data-driven execution.

• Improved Asset Uptime: Predictive insights drove a double-digit reduction in unplanned downtime for critical equipment during the pilot phase.
• Faster Maintenance Response: Mean maintenance response time improved materially as work orders were triggered earlier and prioritized accurately.
• Extended Asset Life: Targeted inspections and timely part replacements increased mean time between failure across high-risk assets.
• Reduced Maintenance Cost: More precise maintenance planning reduced unnecessary inspections and avoided costly emergency repairs, delivering measurable cost avoidance.
• Higher Technician Productivity: Technicians spent less time searching for information, increasing wrench time and consistency of execution.
• Stronger Operational Governance: Centralized maintenance history and asset data improved audit readiness and standardization across facilities.
  Collectively, these outcomes elevated maintenance from a cost center to a strategic reliability function.

A New Standard for Asset Reliability

The partnership established a new benchmark for how the client manages and maintains critical facilities. Human expertise combined with AI-driven insight to improve reliability, safety, and confidence at every level of the organization.

“We now see problems before they disrupt operations,” said the company’s Head of Facilities and Engineering. “This platform has fundamentally changed how we maintain our assets.”

Looking Ahead

The client is expanding the system across additional facilities and integrating advanced use cases, including energy optimization and lifecycle cost modeling. Future phases will deepen predictive accuracy and support scenario planning for capital investments. These steps position the organization to operate with a fully AI-enabled maintenance and reliability model.