As a Subject Matter Expert (SME) who played a key role in steering our large-scale digital platform modernization project, I’ve gained significant insight into how organizations can strategically leverage Generative AI (GenAI) and Large Language Models (LLMs) within complex, real-world operational environments. Our focus was identifying areas where simple AI applications could deliver immediate, demonstrable business value and efficiency, forming a robust foundation for future, higher-value automation initiatives.
The Strategic Imperative for LLM Adoption
When introducing advanced technologies like LLMs into an existing, complex operational technology (OT) ecosystem, the initial approach must be disciplined. We prioritized solutions that could deliver maximum learning opportunities quickly and were viable enough to demonstrate value within a short program increment.
We leveraged a specific framework to evaluate potential AI problems, ensuring we weren’t just pursuing “shiny objects”. We focused on tasks characterized by high data volume, the potential for automation of repetitive work, and those requiring complex pattern recognition. Crucially, LLMs proved highly suitable for tasks involving processing human language and generating content across various formats, which became our low-hanging fruit for initial implementation. If a task required deep human judgment or if insufficient training data was available, we considered AI to be a suboptimal solution.
Identifying and Solving High-Value Operational Problems
I categorized potential applications based on their complexity and expected return on investment (ROI). Our goal was efficiency and risk reduction, delivered incrementally and iteratively.
1. Enhancing Knowledge Accessibility and Reducing Support Workload
One of the most immediate pain points identified was the difficulty technical personnel faced accessing and synthesizing critical institutional and technical documentation.
- Automated Summarization and Reporting: Creating executive summaries from extensive reports and technical specifications was previously a time-consuming, manual effort. We leveraged LLMs to automate this process, allowing technical staff faster access to crucial knowledge and providing time savings for report summary creation. This falls squarely into the “Generate content across various formats” problem type.
- Intelligent Knowledge Retrieval: A significant source of support tickets stemmed from users struggling to find relevant information within fragmented internal and external documentation. By implementing an AI-driven search and document interaction layer (a smart self-service solution), we aimed to reduce manual search time and enhance knowledge accessibility, thereby lowering the overall support workload and ticket volume.
- Work Instruction Generation: For specialists drafting maintenance or operation plans, the manual search through complex operation and maintenance (O&M) manuals for specific, step-by-step instructions was tedious. The proposed LLM solution extracts the precise relevant content automatically, reducing search time and enhancing accuracy in execution.
2. Automating Complex Workflows and Project Delivery
Beyond document management, we identified opportunities to streamline traditionally high-effort engineering phases:
- Signal Mapping and System Configuration: During the onboarding phase for new operational sites or expansion projects, manual signal mapping and configuration takes considerable time. An AI-powered solution for mapping and auto-configuration was identified as a key high-complexity, high-ROI initiative designed to accelerate project configuration and deliver substantial cost reduction.
- Ticketing Automation: Streamlining the core support structure was critical. We found that integrating an AI layer could auto-create and update support tickets through a chatbot interface, drastically improving workflow efficiency and reducing the administrative overhead associated with manual ticket handling.
3. Advancing Operational Decision Support
For real-time control room staff, decision support is paramount. LLMs offer a path to move beyond simple data logging to contextualized decision-making support:
- Alarm Interpretation and Handling: Operators are often inundated with multiple alarms that require manual interpretation and correlation. An AI-based solution was proposed to perform alarm correlation analysis and suggest appropriate responses, leading to faster resolution times and reduced downtime. This directly addresses the need to “Detect anomalies and unusual patterns”.
- Codifying Institutional Knowledge: A long-term ambition is to centralize and codify the collective engineering knowledge and expertise held by experienced personnel. Leveraging AI on top of operational data allows for the extraction and structuring of this knowledge, making it available to less-experienced staff. This essentially transforms tacit knowledge into an accessible asset, such as automatically generating complex technical documentation like Failure Mode and Effects Analysis (FMEA).
Embracing an Iterative Development Path
My experience showed that defining clear, measurable value is essential before technical implementation. We deliberately started small with document-centric solutions, as these tasks showed existing success in comparable enterprise tools and represented the simplest path to demonstrating measurable efficiency gains.
Our ongoing strategy ensures that domain Subject Matter Experts and business stakeholders define the what and the why—the desired value and context—while the technical teams focus on the how and the architectural fit. This iterative approach ensures that our digital platform evolves effectively, maximizing the return on investment in GenAI capabilities.
If your organization is looking to integrate these capabilities, the first step is always to conduct a thorough problem definition and suitability check against business drivers and available data.
I can help your organization find ways to leverage the power of AI/LLM into your products and processes by navigating the technical, strategic, and financial constraints unique to your industry.