SOURCE: Kay Sever | June 25, 2026
After being on an AI learning curve for the past several months, I have decided it’s time to write about my observations, insights and possible ways to selectively apply AI within the optimization framework to create a better result. Therefore, our topic for 2026 will be the intersecting points of Optimization and AI as they relate to productive assets, the organization and the management system. We are told that AI will “know all”, so we expect to find it helpful no matter the question asked or problem to be solved. The more you can learn about AI models (LLMs: large language models) and their strengths and weaknesses, the clearer your vision will be about how to incorporate an AI model into your workflow.
Last month I listed some questions that executives and management teams can ask about performance, ROI shortfalls for projects, budget goals and organizational effectiveness. The answers to these questions could help shape the scope of phases of an AI implementation. This month we will focus on data.
Remember this… AI’s fuel is data. Without data, AI cannot return useful responses or perform analyses of any kind. For data to be uploaded into LLM databases, AI has to have access to it. LLMs (Grok, ChatGPT, Claude, etc.) get their data from websites with the highest visitor counts, books in digital form (especially those published after 1990), digital periodicals and professional publications. Some industry or professional association publications are restricted from being “scraped” (i.e., copied in full to an AI database), which means AI will not have all the available information about some topics. Why is this important to know?
Example: You have decided to upgrade your flotation process and are researching the latest flotation technologies. You have given your plant engineers access to an LLM for research purposes. Information about the latest flotation technology was blocked from being “scraped”, so the LLM’s responses will be missing the information you were looking for. You won’t know what’s missing. Employees may need to manually research industry websites to find what they were looking for.
Managing Expectations about LLM Capabilities
With data availability in mind, let’s talk about choosing an LLM for internal use by employees. The LLM will have to be given access to your production data files for analysis if you want to analyze production trends. NOTE: If there is a problem you’d like an LLM to work on, you must have data for the problem. This sounds like something that does not need to be said… however, people that are not LLM developers tend to ASSUME AI knows things that it does not. No Data = No AI Knowledge!
Example: You would like to use AI to analyze recurring problems in your production process. You give an LLM access to your actual production data and ask it to identify process interruptions for the past year. You want your team to know what to focus on first, second and third, so you ask the LLM to rank the three biggest interruptions and calculate a dollar value for the losses.
Production Data that is probably in the file given to the LLM:
- Actual Runtime by Productive Unit (Shovel, Truck, Crusher, etc.)
- Maintenance Downtime Events by Reason
- Operating Delays by Reason
- Actual TPH when Running
Production Data that is probably not in the actual data file given to the LLM:
- Budget data for some of the above items.
- “Best Possible”/Optimum Values for runtime, downtime, delays, TPH (not the same as budget, are set and approved by management)
- Value of Production Losses (calculated off book)
What can the LLM analyze and calculate?
- With actual production data only, an LLM (or your people) can identify production variations by reason.
- The production losses linked to these variations cannot be calculated unless “best possible”/optimum targets are known and given to the LLM. The true financial loss is determined by the difference between actual and “best”/optimum.
- These financial losses are NOT reported anywhere because the general ledger and financial statements only include actual revenue and cost and budgeted revenue and cost (the plan for actual). No data for what is possible to achieve is in the general ledger.
Kay Sever is an Expert on Achieving “Best Possible” Results. Kay helps executive and management teams tap their hidden profit potential and reach their optimization goals. Kay has developed a LIVESTREAM management training/coaching system for Optimization Management called MiningOpportunity – NO TRAVEL REQUIRED. See MiningOpportunity.com for her contact information and training information.
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- About Us
Kay has worked side by side with corporate and production sites in a management/leadership/consulting role for 35+ years. She helps management teams improve performance, profit, culture and change, but does it in a way that connects people and the corporate culture to their hidden potential. Kay helps companies move “beyond improvement” to a state of “sustained optimization”. With her guidance and the MiningOpportunity system, management teams can measure the losses caused by weaknesses in their current culture, shift to a Loss Reduction Culture to reduce the losses, and “manage” the gains from the new culture as a second income stream.
