Tag: generative AI

OM in the News: The Environmental Cost of Quizzing AI

Every time you ask Google’s Gemini a query, it takes the same amount of energy as watching 9 seconds of TV. So says Google’s new report detailing the energy consumption, emissions and water use of its generative AI that users turn to every day for everything from writing tips to fact checking. A single Gemini text query emits 0.03 grams of carbon dioxide equivalent and consumes about 5 drops of water.

Microsoft plans $80B for data centers as power constraints loom

The tech giant appears to be looking to ease brewing anxieties about AI searches: that frequently using generative AI such as Gemini can be detrimental to the environment.

Global demand for AI is ramping up rapidly, writes The Wall Street Journal (Aug. 21, 2025). Electricity demand from data centers worldwide is set to more than double by 2030 to about 945 terawatt-hours, which is more than Japan’s total electricity consumption. A single AI-focused data center can use as much electricity as a small city of 100,000 and as much water as a large neighborhood. But the largest ones, that haven’t been completed yet, could consume 20 times more as much. It’s a particular problem in the U.S., with data centers making up 1/2 of its electricity demand growth over the next 5 years.

OpenAI Chief Executive Sam Altman, when asked how much energy a ChatGPT query uses, responded “the average query uses about the amount an oven would use in just over one second, and 1/15 of a teaspoon of water.”

The type of query we feed to generative AI also matters, however. Energy demands can be dampened if we can remove some of that back and forth, and make our prompts a little simpler and easier to understand. Shorter, more concise prompts, along with using smaller AI models, can dramatically reduce energy use.

Tech giants are announcing many new clean-energy power agreements to fuel their AI ambitions, including Google, which recently announced new power deals from geothermal to hydropower. It also plans on an advanced nuclear reactor project in Tennessee.

It’s important for tech companies to divulge how frequently their AI is receiving queries. If it’s being used by one person, emissions are lower, but that’s different if it’s billions of people at 30 data centers across the world.

Classroom discussion questions:

  1. Why is the growth of AI searches an OM issue?
  2. How can this growth be contained, or minimized?

Teaching Tip: AI in the OM Classroom– Panic, Possibility, and Pedagogy

The gulf between those working to integrate AI into their teaching and those swearing off its use entirely is growing wider by the month. It’s not just about comfort with technology; it’s about pedagogical identity, ethics, trust, and the role of higher education in a rapidly changing world, reports Faculty Focus (Aug. 13, 2025). 

Some faculty are experimenting with AI-graded orals. Others are defaulting to analog tools like in-class handwritten exams. Still others are choosing not to address AI at all—perhaps hoping it will fade.

AI may or may not upend higher education, but in the meantime, it’s prompting urgent questions: What are we assessing? What do we value? How do we prepare students not just to perform, but to think, reflect, and adapt in a world where generative tools are the norm?

Faculty skepticism toward AI isn’t unfounded (data privacy, environmental electricity toll, murkiness of “scraped” datasets, student creativity loss, voice and bias, etc.). It’s easy to reduce the AI debate in education to one issue: cheating. And yes, generative AI makes it easier than ever to outsource writing, coding, or even lab reports.

But neither is pretending this technology doesn’t exist. AI isn’t just a technological shift; it’s a mirror reflecting what we value in education, labor, and society at large. In today’s classroom, silence or neutrality sends a message.

So the most important place to start is also the simplest: your syllabus. Be specific about when, how, and why students are or are not allowed to use generative tools. If AI is restricted for certain assignments, explain the rationale. If it’s allowed, clarify what constitutes appropriate use—and what crosses the line into misrepresentation. Our goal is to  model critical thinking. When we articulate our stance on AI, we teach students how to approach emerging technologies with intention rather than fear or opportunism. It’s a pedagogical opportunity. It invites students to see learning as more than task completion—and faculty as more than enforcers of boundaries.

Our students don’t need us to have all the answers. They need us to model how to live with the questions. They need to see that thoughtful, ethical, human learning is still possible, especially in a world full of algorithms.

OM in the News: Manufacturing and Early AI Adoption

Manufacturers are betting artificial intelligence (AI) can help address pressing challenges, from supply chain volatility to the shortage of skilled workers. Three-quarters of  manufacturing executives say that adopting emerging technologies such as AI is their top priority in engineering and R&D, says Industry Week (Oct. 4, 2024)

AI is a broad term that encompasses basic data analytics (Module G in our text), machine learning, deep learning, and generative AI. Adopters are using AI to solve key problems in procurement, assembly, maintenance, quality control, and warehouse logistics. Some are deploying generative AI to synthesize huge volumes of unstructured data. Others are experimenting with AI service bots that partner with field technicians, for instance, to recognize more quickly when maintenance is required and to improve the quality of that work.

AI can also report supply chain bottlenecks in real time and predict potential disruptions in advance. In manufacturing it can include: minimizing assembly defects and improving quality control; boosting productivity; and streamlining warehouse management.

For example, one manufacturer adopted AI-based video processing to track manual assembly activities and automate quality checks of those activities,. This reduced failures in the assembly process by 70%, while also cutting down efforts for quality checks by 50%.

Another firm adopted an AI-powered industrial copilot that converts natural language into code and translates old programming languages into natural language, completing both tasks faster and better than human developers. Engineers using this AI solution were 5% more productive.

AI can also help ensure that warehouses operate at top efficiency, carrying items that meet demand and minimizing extra inventory. One company adopted an AI-based inventory management system that helped it minimize overstock while still fulfilling all orders. AI also provides more flexible job production planning so that companies can allocate specific assembly activities to the most relevant assembly expert at a given time to maximize productivity.

As a growing number of companies experiment with and deploy new AI solutions, they are raising the industry bar for productivity and performance. The article suggests that  companies that defer investing will need to run twice as fast to keep pace.

Classroom discussion questions:

  1. Summarize the AI advantages noted in the Industry Week article.
  2. Provide additional examples of potential AI use in manufacturing. In services.