AI Integration in Control Rooms: Addressing Knowledge Gaps in Operations

Atlas Prediction Control has conducted polls among engineers and control room staff to assess the integration of AI in plant operations. The findings reveal that while 71% of respondents utilize AI for productivity tasks, its application in critical areas like predictive maintenance remains absent. The majority of knowledge resides with experienced personnel rather than documented systems, highlighting the need for AI solutions tailored to specific plant environments.

The significance of these findings lies in the recognition that operational knowledge is often informal and distributed among staff, rather than centralized in written procedures. This indicates a gap in the deployment of AI technologies, which are currently not aligned with the practical realities of plant operations. Users express concerns about data security and the starting point for AI implementation, suggesting that the challenge is more about deployment than skepticism towards AI's value.

Looking ahead, the focus should be on developing on-premises AI agents that can operate within existing plant infrastructure, ensuring proprietary data remains secure. These agents should be designed to provide specific, actionable insights based on targeted queries, contrasting with generic cloud-based models. No further timeline was disclosed at the time of publication.