Over the past year, the conversation around artificial intelligence (AI) in power generation has shifted from experimentation to execution. Utilities are no longer debating whether AI belongs in operations. Boards, regulators and executives are now asking how quickly it can be scaled, how reliably it can be deployed, and what measurable value it can deliver in operations, maintenance and workforce productivity.
Organizations that own and operate power generation assets understand the importance of asset data. It is foundational to operations and maintenance, embedded in day-to-day workflows and historically difficult to manage at scale. Yet despite decades of investment in documentation, engineering standards and enterprise asset management (EAM) systems, many organizations remain constrained by the same limitation: operational decisions are still slower, more reactive and more manual than they should be.
The importance of asset data has not changed. What has changed is what organizations can build on top of it. Advances in AI and related emergent technologies are fundamentally altering how asset information can be accessed, interpreted and applied. These technologies enable organizations to move beyond recordkeeping toward automation, intelligence, and, ultimately more reliable operations.
This progression can be viewed as an asset intelligence pyramid. At the base is asset data. Above the base are layers of insight, accessibility and automation that progressively improve how assets are maintained and operated. At the top are optimized outcomes, including faster decisions, lower costs and higher reliability, enabled by systems that not only inform work but also help execute it.
Asset Data as the Foundation of Operations and Maintenance
Asset data has always underpinned effective operations and maintenance, but for most organizations it has remained locked within legacy practices and formats. Engineering drawings, manuals and historical records contain deep technical value, yet much of that value is trapped within the unstructured documents. Populating and maintaining EAM systems has required sustained manual effort, and even then, data completeness and accuracy remain difficult to preserve.
AI addresses this challenge. By extracting equipment data, attributes and relationships directly from existing documentation, organizations can rapidly establish a more complete and consistent representation of their asset portfolio. The task is no longer to create new data from scratch, but to unlock and structure information that already exists. This approach eliminates the traditional trade-off between speed and accuracy.
Asset records can be built and refreshed faster, with greater consistency and far less burden on engineering and maintenance teams. While this improved foundation is not an end state, it is the prerequisite for more mature operational outcomes.
From Records to Operational Insight
Once asset data is reliable, EAM systems begin to function as more than static repositories. Capabilities that already exist within these platforms — criticality analysis, preventive maintenance optimization, spare parts planning and reliability analytics — suddenly have the quality inputs required to deliver meaningful results.
Maintenance strategies can align with actual risk rather than generalized assumptions. Inventory decisions can reflect real equipment configurations and failure consequences. Planning and scheduling improve as scopes become clearer and uncertainty narrows. Individually, these improvements may appear incremental. Collectively, they produce compounding operational benefits.
The shift from tracking assets to actively improving how they are maintained delivers tangible results, including reduced downtime, lower maintenance spend and improved availability. More importantly, it advances organizational maturity. Decisions become proactive rather than reactive, informed rather than experiential and less dependent on institutional memory that may be difficult to retain.
Putting Information in Motion
The next level of maturity emerges when asset data becomes broadly accessible and actionable across roles and systems. Advances in AI-driven data processing now allow asset information to be vectorized, which in turn makes it searchable, contextualized and usable in ways that were previously not possible.
This capability enables tools such as semantic search to reshape daily workflows across teams. Semantic search is an AI-driven approach to information retrieval that interprets the intent and contextual meaning behind a user’s question instead of relying solely on exact keyword matches. By understanding how terms, concepts and relationships are used within asset data, semantic search returns information that aligns with what the user is trying to accomplish.
Operators, planners and technicians no longer need to know where information resides or how it is labeled. Instead, they can ask questions in natural language and receive immediate, relevant answers derived from drawings, manuals, work history and asset records.
The operational implications are significant. Troubleshooting accelerates. Planning becomes more precise. Knowledge is distributed across experience levels rather than concentrated in a few individuals. Time once spent searching for information is redirected toward execution and analysis. Technical leaders are no longer constrained by system architecture. The right information is delivered to users when and where it is needed.
Automation, Agents and Adaptive Operations
At the top of the asset intelligence pyramid are systems that not only inform decisions but help execute them. With structured, complete and searchable asset data in place, AI agents can begin to act within defined guardrails.
Work orders can be generated and populated automatically based on inspection results, condition indicators or operational events. Asset records can be updated continuously through automated or human-in-the-loop workflows. Data quality can be monitored and corrected in real time rather than through periodic cleanup initiatives.
Organizations begin to operate differently at this stage. Processes accelerate. Variability decreases. Reliability improves — not because teams are working harder, but because systems are working smarter and more consistently.
Importantly, this progress does not occur in a single leap. Organizations climb the pyramid step by step. What has changed is that emergent technologies dramatically compress that climb, lowering barriers to participation, shortening timelines, and allowing value to be realized earlier and more predictably than in the past.
Improved, Faster and More Reliable
The promise of AI in asset management is not technology for its own sake. It is the ability to move away from manual, reactive workflows toward operations that are faster, more accurate and more reliable by design.
Asset data remains foundational, but it is no longer the destination. When paired with aligned tools and modern workflows, AI becomes the launch point for a new operating model, one in which information flows freely, systems act intelligently and reliability is embedded into everyday processes.