SOURCE: Kay Sever | October 29, 2026
In 2026 we have focused on the intersecting points of Optimization and AI as they relate to improving the performance of productive assets, the organization and the management system. As you know, the AI landscape changed almost daily in 2026, making it difficult to keep up with AI’s capabilities. This article summarizes the topics we covered. It can be used as a reference document if your company decides to explore AI’s fit with performance optimization. Following is a brief month-by-month summary of topics covered. You can access these articles in full on the Aggregates and Mining Today website.
January – To set the stage for the content I wanted to cover, I listed seventeen AI topics I wanted to explore in 2026. I split these topics into two groups:
Group 1: February – May: Overview of general topics about AI capabilities
Group 2: June – September: AI applications in optimization work (for management to consider)
Almost all of the 17 topics were discussed at some level in the articles that followed.
February – LLM KPIs: Mine performance is captured in KPI trends. LLM performance has KPIs too… we covered four sets of metrics used to determine “best” LLM at any point in time:
- KPIs that evaluate LLMs as they compete with each other using pre-determined datasets.
- Accuracy KPIs that measure the accuracy of responses compared to available reference data.
- Processing KPIs that measure processing speed and throughput.
- System Capacity KPIs that measure system maximums/constraints.
March – LLM Strengths, High Rate of Change, Limiting Factors: We delved into the details here:
- AI capabilities/strengths vary from LLM to LLM. Understanding these strengths plays a major role in selecting the AI most suited for your needs.
- High rate of change across the AI industry that creates “moving targets”. My analogy: Imagine a world where all car manufactures introduced new features every week. Selecting the best car for you would be difficult… it would feel like you were chasing a moving target. AI development is currently like that. Strengths (and weaknesses) change weekly, making the selection of an LLM that best fits your needs difficult.
- Limiting Factors across the AI industry: Data Access, Power Sources and Chip Availability. Details on each factor are provided.
April – AI Accuracy and Risk: We expanded on the topic of AI accuracy as a factor in deciding to integrate AI into the workplace. I listed questions that management should think about and be able to answer before moving forward. These questions pertain to employees, data sharing/confidentiality, data sources and risk. Feel free to use this article as a management tool for AI integration. To further explore AI accuracy, I asked a popular LLM 4 questions about how accurate it was. I posted its answers in their entirety in this article. You will find the answers fascinating.
May – Data Security and Risk: Protecting your company’s IP (intellectual property) and keeping your operating and financial data secure is high on the priority list for executives and management teams everywhere. This article expands on things to consider sharing data with AI. If employees are going to use AI in their work, the scope of that use and an AI training plan for employees must be defined before AI is given assess to your internal files. Three areas of corporate risk and a “chicken and the egg” scenario are presented for your consideration
June through September: Applying AI in Optimization Work: After spending four months overviewing general AI concepts and management concerns, it was time to talk about how or if AI could be employed to facilitate optimizing a production value stream or an entire mine site or company. Each month we covered different aspects of such an integration… positive and negative. Views shared are mine only and are based on 1) what I have learned about AI capabilities and risks and 2) my hands-on experience training management teams and employees on optimization concepts, metrics and strategies for quantifying and converting millions of dollars of hidden operating/earnings potential to reported profit. Here’s a brief paragraph summarizing the content of each month’s article:
June – Can AI calculate production losses? To answer this question, let’s start with the knowledge that AI’s fuel is data! AI must have the right data to calculate hidden operating potential. I listed actual and budget production data that could be made available to AI, as well as data that must be calculated by people who understand work processes tied to hidden potential. AI cannot estimate your upside potential based on estimates from other operations. Data for potential are mine-specific due to the uniqueness of orebodies, mine plans, equipment employed, operating procedures, recurring problems that affect performance, etc. So, the answer to the question is no.
July – Can AI be used to help identify and quantify problems that prevent optimization? I shared some insights about identifying recurring problems that affect operating and financial performance. Unexpected variation provides a clue that such a problem exists; however, the economic impact of these problems cannot be calculated by AI because each problem has different factors that contribute to its total impact on the bottom line. The difference between achieving budget vs. achieving optimum (best possible) performance is big! Budget assumptions cannot be used to calculate optimums or operating potential. Conclusion: AI does not have access to the information it needs to identify and quantify problems that prevent optimum performance from being achieved.
August – Can AI be used to recommend corrective action for problems that prevent optimization? To answer this question, AI would need access to process knowledge linked to value stream process characteristics, excess capacity and distinctions between value-added costs, fixed costs and excess costs. Because that distinction is not captured in operating stats, the general ledger or the financial system, AI is not capable of suggesting corrective action that would achieve “best” results.
September: Optimization and Your Organization: This month we looked at the “people side” of optimization… employees that care about doing a good job but work in weak organizations that prevent them from doing so. How much does a weak organization cost a company every year? Many executives and management teams wish they knew, but most can’t answer that question because data linked to the losses these weaknesses cause are not captured or reported. It is possible to calculate the financial impact of organizational weaknesses on the bottom line, as well as impacts on corporate culture and management credibility. However, because these dollars are routinely captured or quantified, AI cannot identify the losses or assist management with solving problems that are causing them.
Final Thoughts about Integrating AI into Optimization Work: I have learned a lot from my deep-dive into AI capabilities and limitations. What I have learned will help me manage my expectations (and assumptions) about what AI can and cannot do. I hope this information has been an asset to you also.
TOPIC next month: We are going to shift topics for the remainder of 2026 and into 2027. Starting next month, we will be talking about management’s “Optimization Engine”, how it links to Newton’s laws of physics, and how to operate it so it safely delivers maximum earnings!
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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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.
