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An AI Case Study: Improving Asset Data while Setting the Foundation for More Efficient Plant Operations
Generation plants that have been repowered or operated across multiple technology eras accumulate a common problem: the information needed to plan, operate, troubleshoot, and maintain the facility exists, but it is scattered across legacy drawings, CMMS records, historian tags, work orders, and tribal knowledge that no single query can reach.
This session presents a Cleco pilot deploying AI semantic search at a repowered facility where fragmented asset data was slowing work package preparation, troubleshooting, and spare parts identification. The tool connects planners, operators, and maintenance personnel to drawings, procedures, manuals, and historical corrective actions from a single query, while simultaneously surfacing data gaps, conflicting nomenclature, and incomplete equipment relationships that drive targeted remediation.
