AI in Asset Lifecycle Management: Turning Insight into Daily Action
ROUNDTABLE
How can asset-intensive organizations move beyond isolated AI pilots to embed intelligence into everyday asset management decisions—without compromising reliability, workforce trust, or long-term strategy?
Asset-intensive organizations are under growing pressure to improve uptime, control costs, and extend asset life—all while managing workforce constraints and increasingly complex operations. AI is often positioned as the solution, yet many teams remain stuck in experimentation mode. While pilots may show promise, inconsistent data, fragmented systems, and unclear ownership frequently prevent AI from delivering sustained operational value. Leaders are now grappling with a critical question, where does AI truly enhance asset performance today, and where does it introduce unnecessary complexity or risk?
Moving from potential to performance requires a stronger foundation. AI-driven asset management depends on reliable asset, work, and condition data flowing consistently across planning, maintenance, and reliability workflows. This means addressing long-standing issues around data quality, integration between legacy and modern platforms, and governance models that support trust in AI-generated insights. Without this groundwork, AI struggles to move beyond advisory outputs and into daily decision-making that teams rely on in the field and control room.
Join industry peers and IBM to explore how AI can be operationalized across the asset lifecycle to support faster, more confident decisions in planning, maintenance, and reliability. Grounded in real-world operational experience, the discussion will focus on what is working today and what is stalling at pilot stage.
Event host
Scott Campbell
About IBM
IBM is a leading provider of global hybrid cloud and AI, and consulting expertise. We help clients in more than 175 countries capitalize on insights from their data, streamline business processes, reduce costs and gain the competitive edge in their industries. More than 4,000 government and corporate entities in critical infrastructure areas such as financial services, telecommunications and healthcare rely on IBM’s hybrid cloud platform and Red Hat OpenShift to effect their digital transformations quickly, efficiently and securely. IBM’s breakthrough innovations in AI, quantum computing, industry-specific cloud solutions and consulting deliver open and flexible options to our clients. All of this is backed by IBM’s legendary commitment to trust, transparency, responsibility, inclusivity and service. Visit www.ibm.com for more information
Discussion topics
AI value realization across planning, maintenance, and reliability, separating proven use cases from over-promised outcomes and identifying where measurable impact is being achieved today.
The data foundation required to support scalable AI in asset management, including data quality, system integration, workflow consistency, and governance across Computerized Maintenance Management Software (CMMS), Enterprise Asset Management (EAM), and operational systems.
The operational and organizational shift required to scale AI across the asset lifecycle, from evolving workforce roles to platform capabilities that support execution, adoption, and enterprise alignment.
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