April 2026

Companies are pouring millions into AI, and it is tempting to assume that adoption alone creates value.  From an engineering economics perspective, that assumption does not hold. Most of this spending has no proven value on its own. The evaluation is straightforward. Does the investment reduce ongoing operating costs or increase output in a way that drives revenue?  AI itself does not create value. Cash flow changes do.  A tool has no inherent value unless it impacts the economics of the business. If it does not lower costs or increase production, it does not justify the investment.

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We are starting to see this play out in real time. Some organizations have used AI to streamline operations and reduce staffing tied to repetitive work. In those cases, the economics are beginning to make sense. Ongoing costs decline while output is maintained.

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At the same time, many companies are investing heavily in AI without clear changes to their cost structure or production. In those situations, AI is simply an added expense. From an engineering economics standpoint, the outcome is still uncertain, and time will tell which investments truly pay off.  The most common mistake is simple. Companies add AI without removing anything else or ensuring it’s adding to the overall output of the company.

AI only creates value when it changes how work gets done. If it eliminates low value repetition, reduces labor requirements, or allows the same team to produce more, it improves the economics of the operation. If it is layered on top of existing processes without changing them, it increases cost.

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We have been testing this directly in our courses at Mines using an AI teaching assistant called HiTA. The goal is not to replace instructors or remove human judgment. Instead, the AI handles repetitive tasks such as first pass feedback, enforcing grading rubrics, and answering common student questions.  This could allow us to reduce teaching assistant support while maintaining the same level of instructional quality and lowering total instructional support cost.

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The economics are clear. If we reduce teaching assistant support and use AI, total cost goes down and value is created. If we add AI on top of the existing staffing structure, total cost goes up and value is destroyed. It is the same tool, but a completely different economic outcome.

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This highlights a principle that applies far beyond education and is not just about AI. It is about how we evaluate decisions in business. Engineering economics provides a framework that can be applied every day. Whether the question is hiring, automation, or investing in new tools, the objective remains the same. Improve the economics of the operation.

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AI is a tool. Nothing more. The real question is not whether you are using AI; it is whether it is improving your cash flow.  AI itself is not the added value; the change in economics is.  If you want a structured way to evaluate decisions like this, we teach these frameworks in Economic Evaluation and Investment Decision Methods at Colorado School of Mines and in our public courses. Understanding how to measure value is a skill that applies far beyond AI. It applies to every decision you make.