Tag: AI

OM in the News: How AI Consumes–and Saves–Energy in Transportation

We all know AI’s dirty secret: It gobbles up a huge amount of electricity—and spits out a large volume of greenhouse gases in the process. But what if using AI can also save energy?

AI has the potential to drastically slash energy demand across a swath of industries and cut down on their carbon emissions. And it may be so effective, writes The Wall Street Journal (Sept. 16, 2025), that it will easily balance out its own power demands and carbon emissions.

In our blog today, we discuss how AI is remaking transportation, planning routes and timetables.

AI-driven route planning has helped major U.S. freight companies cut fuel use in ground vehicles—in some cases by 5% to 10%—by simply lowering the miles they travel. The whole ground-freight industry could cut its emissions by 10% to 15% by using AI-led dynamic route optimization in all vehicles.

Getting stuck in traffic adds up to a lot of pointless emissions. AI-driven route planning has cut fuel use in ground vehicles as much as 10%.

AI can analyze traffic in real time, and is starting to get better at guiding vehicles away from busy areas, reducing the fuel wasted by stop-and-go driving.  (Sitting in traffic adds up to a lot of pointless emissions: Americans wasted 3.3 billion gallons of gasoline and diesel fuel in 2022—over 215,000 barrels a day of petroleum).

 Also, e-tailers cluster deliveries together to save miles traveled. A crucial form of routing goes on behind the scenes. AI-enabled logistics predicts what goods people will be ordering, and where and when. That way, e-tailers can stock their distribution centers according to probable local demand, which means fewer miles spent on deliveries.

Further, marine freight is using AI to calculate the best times for ships to “slow steam”—lower their speed—which can greatly boost efficiency: A 10% drop in speed cuts fuel use by 20%. Improving traffic at ports can also cut down on wasted fuel. Ships burn as much as 7-10 tons a day of fuel while anchored near ports, waiting for congestion to clear. AI-assisted programs help shippers lower the waiting period by timing their arrivals at port efficiently.

The International Energy Agency says the spread of AI in the transportation sector alone could slash 900 million metric tons of carbon emissions by 2035. In comparison, the agency expects emissions from data-center electricity use to rise to 300-500 million metric tons by 2035, up from 180 million metric tons today.

Classroom discussion questions:

  1. How might AI be used in the commercial aviation industry?
  2. How else can AI be of benefit to delivery firms like Amazon?

OM in the News: The AI-Driven Assembly Line

More than a century ago, Ford’s moving assembly line reorchestrated work. Instead of a master mechanic walking to each car to perform complex tasks, the car moved to workers, who each executed a single repeatable action. As a result, work became easier, costs fell and return on investment skyrocketed.

In a similar way, AI will soon sit at the center of a business, smoothly guiding work through every department, with bots sorting and refining tasks before they reach a human, writes The Wall Street Journal (Sept. 11, 2025).

Here are two areas in which this might unfold:

• Onboarding. In a large enterprise, bringing a new hire up to speed requires 10 to 20 hours of effort. Forms, policy explanations and training sessions add up quickly. In an AI-first model, an onboarding orchestrator bot coordinates with AI agents that handle compliance (background checks, payroll setup, training) and equipment (granting access to systems and tools). A virtual assistant answers questions from the new hire.

• Software delivery. Today, new information-technology features—such as launching a new process control program—are specified by operations managers, designed by user-experience teams, coded by developers and tested before use. This cycle can take 80 to 85 business days, with error rates around 10% to 15%.

In an AI-first setting, a digital IT orchestrator bot coordinates AI agents that do everything from product specifications and design to coding and testing, delivering the first version. People then review the newly created features and test their AI-generated code and design. Instead of arriving in 3 months, features are ready in weeks. Time to delivery falls by 60% to 70%, while code quality improves through continuous AI-driven testing.

The common threads of AI-native work are clear: Orchestration moves from humans to AI. Specialized AI bots handle repeatable tasks. Human experts intervene when judgment, negotiation or oversight is required. Results come faster, with lower unit costs and better customer experience.

Henry Ford pioneered a new way of doing business in the 1900s. We have a similar opportunity in 2025. While Ford’s assembly line turned employees into specialists, putting AI at the center of business turns us all into generalists, allowing us to be creative, enlist problem-solving skills and handle ambiguous work.

Classroom discussion questions:

  1. How else might AI be used in factories?
  2. What is an AI “agent”?

OM Podcast #39: AI, Sustainability, Cybersecurity, & Blockchain in Operations

We’re back with another exciting episode of the Heizer Render Munson OM Podcast! Today, Barry Render sits down with Dr. Subodha Kumar, Paul Anderson Distinguished Chair Professor at Temple University and Founding Director of the Center for Business Analytics and Disruptive Technologies.

Barry and Subodha dive into the transformative role of artificial intelligence in operations management, exploring how AI is reshaping sustainability practices, enhancing cybersecurity, and driving innovation in blockchain applications. Subodha shares real-world examples from industries like retail, dairy, and luxury goods, and discusses how AI is helping companies tackle greenwashing and improve supply chain visibility.

They also discuss the evolving threat landscape in cybersecurity, especially in logistics and supply chains, and how AI and IoT are both part of the problem—and the solution. Subodha also shares some powerful advice for students preparing for a future where AI will be central to every workplace.

 

Transcript
A Word document of this podcast will download by clicking the word Transcript above.

Have you subscribed to this podcast on Apple Podcasts? Just go to your Apple Podcasts app, search “Heizer Render Munson OM Podcast,” and subscribe to get all our podcasts on your mobile device as soon as they come out!

Prof. Subodha Kumar
Prof. Barry Render

OM in the News: The Future of Trash Pickup and AI

Americans are among the top producers of trash per capita. Each person in the U.S. disposes of nearly a ton of refuse annually. Simplifying trash day, and diverting the 80% of reusable material that still ends up in landfills, is one key to solving our problems.

Urban planners, the refuse industry and cities across the country are reimagining how we manage and dispose of our waste, reports The Wall Street Journal (Aug. 28, 2025). The New York City and MIT are among those leveraging AI, robotics and electric power to tackle a growing garbage crisis fueled by cheap products and throwaway culture.

Most of Americans don’t recycle regularly, citing the inconvenience and confusion involved in sorting their trash. To help people up their sustainability game, sanitation engineers are promoting a new system: the single-stream model. The operation is simple—residents throw everything into one trash bin. Then, that waste is transported to a remote facility, where AI-powered cameras and robots sort it, diverting items that can be recycled. The goal is to have a system that’s more circular, that can reuse and recycle things more.

AI can also identify items such as electronics that contain hazardous or valuable materials—including copper, silver, gold and rare-earth minerals—and send them on for disassembly and harvesting before they enter the waste stream.

Individual garbage bins or piles of plastic bags aren’t only an all-you-can-eat buffet for rodents—but also malodorous, leaky and inefficient, requiring endless noisy stops from garbage trucks on collection day.

The new NYC shared Empire Garbage Bins.

To solve these problems, cities are moving toward containerization: large, centralized bins shared by a street or neighborhood. One NYC neighborhood  is already piloting a program of such containers, with plans for citywide expansion in the future.

Smart bins could even ping dispatch offices when they are ready for pickup. Large collection vehicles could be used more sparingly, and with fewer stops—thus decreasing noise, pickup time and pollution. In the future, the parameters that we use could be, ‘Is it full? Or is it smelly?’ Then collection on that bin can take place only if the contents meet those conditions.

AI-optimized routing and trash-loading technologies could also help make pickups shorter, less frequent and less disruptive.

Classroom discussion questions:

  1. How could AI be used to help recycle?
  2. What are the major inefficiencies of most garbage collection and recycling systems?

Guest Post: Merging OM Tradition with Digital Innovation

Dr J. Prince Vijai is Assistant Professor of Operations Management at IBS Hyderabad, in India.

The transition from traditional OM to digital operations is not a replacement but an evolution. Digital tools enhance the classical OM framework by adding intelligence, speed and adaptability.

1. Process Optimization and Automation In classical OM, process optimization involved detailed mapping and iterative improvements. With digital operations, AI can now identify inefficiencies, simulate improvements and automate decision-making without human intervention. Siemens has integrated sensors, cloud platforms and AI to create a digital thread across product design, manufacturing and logistics resulting in a 20% reduction in production time and a 30% reduction in energy consumption.

2. Inventory and Supply Chain Management Traditional inventory models rely on forecasts and safety stock assumptions. Digital operations use real-time data from IoT sensors and machine learning to predict demand, monitor inventory levels and automate replenishment. For instance, Walmart uses AI and IoT to streamline its vast supply chain, reducing stockouts and improving shelf availability.

3. Forecasting and Scheduling Operations managers have long used statistical tools for forecasting. Digital operations use advanced analytics and machine learning to provide more accurate, dynamic forecasts. Real-time analytics enables organizations to quickly adapt to market changes, weather disruptions or supply chain breakdowns.

4. Quality Management Traditional quality management emphasizes inspection and control charts. Digital quality management integrates data from machines, sensors and customer feedback for continuous, real-time quality assurance. Predictive maintenance, enabled by digital twins and IoT, reduces downtime and improves asset reliability. For example, GE developed digital twins to monitor the performance of jet engines in real time, enabling predictive maintenance and reducing unexpected failures.

The shift to digital operations is not without challenges. Employees accustomed to traditional processes may resist adopting new technologies. Data from different departments or legacy systems can be siloed, limiting visibility and coordination. Implementing AI, IoT and automation involves significant expenses. And digital operations increase exposure to cyber risks.  

Future trends include:

  • Hyperautomation that combines  AI and machine learning to automate increasingly complex tasks.
  • Cognitive operations that use AI not just to automate but to learn and adapt continuously.
  • Edge computing that enables data processing closer to the source (e.g., in factories or stores) for faster insights.
  • Green operations that leverage digital tools to track carbon footprints and support sustainable practices.

Embracing the synergy between OM and digital operations is a strategic imperative for long-term success.

OM Podcast #38: Editorial Leadership and the Future of Operations Management

Welcome back to the fall semester!

In our latest podcast, Barry Render hosts Dr. Tyson Browning, Professor of Operations at Texas Christian University and former co-editor-in-chief of the Journal of Operations Management.  Barry and Tyson discuss Tyson’s six-year tenure as journal editor of the Journal of Operations Management, the evolving role of AI in research, and the future direction of the operations management field.

 

Transcript

A Word document of this podcast will download by clicking the word

Prof. Barry Render
Dr. Tyson Browning

Transcript above.

Have you subscribed to this podcast on Apple Podcasts? Just open your Apple Podcasts app, search “Heizer Render Munson OM Podcast,” and subscribe to get all our episodes delivered straight to your mobile device.

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: AI, Lean Cultures and Toyota

“Artificial intelligence is going to replace literally half of all white-collar workers in the U.S.,” said Ford’s CEO Jim Farley, in the latest in a succession of executives warning of large-scale job cuts from AI.

Such claims can be pretty convincing—and unsettling.  Large-scale AI-related workforce reductions to date, however, are almost exclusively limited to AI-aligned companies like Meta and Google, writes Industry Week (Aug. 7, 2025).

That said, it’s undeniable that tools like ChatGPT are already having a profound influence on the future of OM work. And the bar keeps raising as AI platform providers release more powerful versions. (ChatGPT currently has around 700 million weekly users).

AI-first companies may be willing to shell out big money for AI “agents” that take the place of human workers. A popular target is workflows that are standard across many companies, such as handling employee queries to accounting or HR. Such work, however, is not as straightforward as it may seem.

Nobel Laureate Daron Acemoglu  predicts that over the next 10 years, only 5% of all tasks currently undertaken by humans will be profitably automated. He calls for a more human-centric approach. “That best possible way is a much more pro-human approach to AI that’s much more targeted at working with human decision-makers”.

Acemoglu’s findings are consistent with what lean leaders have been saying for decades. Uniquely human capabilities are essential to continuous improvement and central to lean’s most important pillar—respect for people.

Toyota’s approach to technology has been to articulate the need to improve the process and then, before evaluating automation solutions, investigate ways of meeting that need by simplifying the process (e.g., removing unnecessary steps). Taking this step avoids the common mistake of automating waste and leads to more effective and durable technology solutions.

A key point here is that continuous improvement is a holistic undertaking that seeks to reduce costs and increase value. This is starkly opposed to the common preoccupation with cost cutting, and the use of AI as primarily a vehicle for reducing headcount. The human skill areas in the left column of the above table, however, are not widely recognized or developed in most organizations, and a culture that supports them takes years to build. Lean organizations, accordingly, place considerable emphasis on developing and nurturing skills such as listening, collaborating, problem solving, following a vision and mentoring.

Classroom discussion questions:

  1. How does Toyota’s approach tie with AI use?
  2. How can Chat GPT impact manufacturing work?

OM in the News: AI and The Last Mile

The final mile—the last leg of the delivery process where goods are transported from a distribution center or store to their ultimate destination—is one of the most critical and cost-sensitive components of the modern supply chain. A package could end up at the wrong address, shipments could be late due to traffic, or a thunderstorm could damage a parcel left out in the rain.

Now AI and machine learning are playing a greater role in predictive analytics, helping companies anticipate delivery issues before they occur and proactively adjust.  AI can design more efficient delivery routes, improve accuracy and the customer experience, and predict errors before they might happen, writes Material Handling & Logistics  (July 22, 2025).

A new McKinsey report found that in the last decade, about $80 billion in venture capital went to logistics startups, with on-demand last-mile delivery platforms getting the greatest share of those funds.

Last-mile routes typically involve multiple stops and individual small packages — rather than one truck delivering pallets to a single warehouse — making this supply chain segment difficult to manage efficiently and expensive for the businesses involved. Last-mile delivery makes up an estimated 41% of all logistics costs in the supply chain.

AI can be  used to plan routes based on factors such as traffic, delivery windows, estimated time per stop, and driver capacity, reports Business Insider (July 15, 2025). More efficient routes can lower fuel costs, improve density, and enable more deliveries in a day, increasing revenue for providers.  Amazon just announced Wellspring, which uses AI to analyze satellite images, apartment building layouts, street imagery, consumer instructions, and photos from past deliveries. It can recommend which parking spot or apartment building entrance a driver should use to drop off a shipment.

AI can also forecast the likelihood of issues for specific routes or deliveries. Then it can make decisions based on the patterns, like moving packages to different facilities or increasing rates on a certain route, so drivers will be incentivized to pick them up earlier in the day. UPS created AI-based DeliveryDefense to analyze historic factors such as loss frequency and delivery attempts. The AI then spots areas that could be targets for porch pirates in the future.

Companies that can balance cost efficiency with delivery accuracy will be best positioned to thrive in today’s environment of volatility and heightened customer expectations.

Classroom discussion questions:

  1. How can AI be used in last-mile delivery?
  2. What are the complicating factors in last-mile deliveries?

OM in the News: Walmart’s AI “Super Agents”

Walmart has developed an AI strategy in the creation of four “super agents” reports The Wall Street Journal (July 24, 2025). Agents refer to artificial intelligence tools that can independently take some action on behalf of a user. One is for customers, one is for employees, one is for engineers, and one is for sellers and suppliers. The super agent for each group will tap the capabilities of a number of behind-the-scenes agents, all in a single unified experience.

“Artificial intelligence is already changing how we work,” said Walmart’s CEO. “Learning and applying what we learn, as we build new tools, is the responsibility and an opportunity for all of us to improve experiences for our customers, members and fellow associates.”

The firm believes it is critical to stay ahead of the technology curve in an area like retail, where the top 10 retailers can change dramatically decade to decade. Its hope is that AI agents will help deliver top-line growth, as they give customers more personalized and enticing shopping experiences, as well as bottom-line savings, where they can help manage supply chains and inventory more efficiently.

Walmart’s situation is unique, with most companies still figuring out how to deploy even one AI-powered agent that can perform a task autonomously or in coordination with humans.

The four super agents are at different stages of development. The customer-facing super agent, Sparky, is already live. Marty, the supplier-facing super agent, launches in Fall and will include functions like checking the analytics on purchases and suggesting and putting into motion advertising campaigns. The employee and engineering super agents are expected in the next year.

Classroom discussion questions:

  1. Explain what an AI “agent” does.
  2. Why does the firm want to be a leader in AI technology and how is it implementing this goal?

Teaching Tip: Teaching OM in an AI Age

We know our students need to think critically in an AI age to be productive and engaged future employees. One solution, writes Faculty Focus (July 9, 2025), to the triple challenge of fostering critical thinking, meaningful learning, and academic integrity is to double down on transparency. We can emphasize the why we want responsible AI use: why we want students to use their own cognitive abilities for some tasks, why using AI could be helpful at times, and why we’ve crafted AI-integrated assignments in the ways we have.

Here are five steps to update assignments in the AI age:

Step 1: Take a critical look at your current syllabus. If AI can easily complete a task (try running your instructions through ChatGPT to find out), maybe it’s no longer a relevant measure of authentic learning. Add new instructional practices (like modelling AI use) and new components of the assignments you update or keep.

Step 2: Consider whether and how students should use AI on the assignments. Students want to know exactly what is appropriate for AI use in your class. A helpful tool for this process is the 5-level AI Assessment Scale (AIAS). The levels range from No AI, AI Planning, AI Collaboration, Full AI, and AI Exploration. Each one identifies and sanctions different ways students can use AI in appropriate and meaningful ways to support their learning.

Step 3: Discuss and model your expectations. Students are not sure what is acceptable in this current moment. What better way to help them feel confident while developing the AI skills they need than modelling what you’re looking for? Take class time or record a video for your online class to teach your students what you expect them to do with AI for each assignment, what not to do, and what you’ll be looking for in their finished product.

Step 4: Ask students to disclose their AI use. One approach is to use the AI Disclosure (AID) framework to document how students used AI, or add an appendix to each assignment, or add comments or footnotes to make transparent what they wrote and what AI wrote. 

Step 5: If you suspect inappropriate use of AI, don’t accuse students of cheating. Instead, have a conversation with them. A primary goal of the AIAS is to facilitate discussions about AI use.

As I pointed out in a recent blog, our author team can help. We have developed AI exercises for each chapter of the new 15th edition.

OM in the News: Amazon Is on the Cusp of Using More Robots Than Humans

The automation of Amazon facilities is approaching a new milestone: There will soon be as many robots as humans. The e-commerce giant, which has spent years automating tasks previously done by humans in its facilities, has deployed more than one million robots in those workplaces, reports The Wall Street Journal (July 1, 2025). That is the most it has ever had and near the count of human workers at the facilities.

Mobile robots reposition package carts

Company warehouses buzz with metallic arms plucking items from shelves and wheeled droids that motor around the floors ferrying the goods for packaging. In other corners, automated systems help sort the items, which other robots assist in packaging for shipment.

One of Amazon’s newer robots, called Vulcan, has a sense of touch that enables it to pick items from numerous shelves. Amazon has taken recent steps to connect its robots to its order-fulfillment processes, so the machines can work in tandem with each other and with humans. Now some 75% of Amazon’s global deliveries are assisted in some way by robotics. The growing automation has helped Amazon improve productivity, while easing pressure on the company to solve problems such as heavy staff turnover at its fulfillment centers.

For some Amazon workers, the increasing automation has meant replacing menial, repetitive work lifting, pulling and sorting with more skilled assignments managing the machines. Amazon has trained more than 700,000 workers across the world for higher-paying jobs in mechatronics and robotics apprenticeships.

The number of packages that Amazon ships itself per employee each year has also steadily increased in the past decade to 3,870 from 175, an indication of the company’s productivity gains.

Amazon is also rolling out artificial intelligence in its warehouses to improve inventory placement, demand forecasting, and the efficiency of its robots. Amazon said it will cut the size of its total workforce in the next several years.

Classroom discussion questions:

  1. Research Amazon’s history of using robotics.
  2. What are the advantages of introducing more robots?

Teaching Tip: The 15th Edition Ties AI Into Your OM Class

Prof. Jon Jackson

Our new 15th edition, just released, brings the topic of artificial intelligence into the course with AI in Action boxes and new material throughout the text. But we have gone a step further through our Instructor’s Resource Manual, a fantastic teaching tool. If you are new at teaching the course, you will find this 400 page guide an invaluable resource. Each chapter now contains an AI in the Classroom section, created by Prof. Jon Jackson at Providence College, providing 15-20 minute exercises. Here is a sampling from 3 early chapters:

Chapter 1
All firms, regardless of industry, can use productivity measures to track process performance. This exercise is designed to explore relevant productivity measures for different industries with the help of an AI-powered chatbot. Student groups can explore the following types of
facilities/firms (each group will pick one):
 Manufacturing facility  Market research firm
 Warehouse facility  Accounting firm
 Retail store  Financial services firm
 Restaurant  HR department
Students can use the following AI prompt structure: ROLE: I am a manager in a [insert facility/firm here]. GOAL: I want to measure productivity (an output divided by an input). REQUEST: generate 5 measures of productivity.
In groups, students can compare AI responses, evaluate the validity of the productivity measures (connecting to Chapter 1 definitions), and identify the best productivity measures to implement.

Chapter 3
A work breakdown structure (WBS) can provide a hierarchical description of a project into more and more detailed components. This activity is designed to practice this process for fictional projects around campus with the help of an AI-powered chatbot. Student groups can explore the following projects (each group will pick one):
 College graduation party  Student art exhibition
 Charity 5K event  Entrepreneur shark tank
 Intramural sports tournament
Students can use the following AI prompt structure: ROLE: I am the project manager for an upcoming [insert event here]. GOAL: I want to create a work breakdown structure to break the project into more manageable components. REQUEST: generate a work breakdown structure with 4 main tasks, each with 2 subtasks. For each subtask, also provide a short description and an estimated duration to complete the subtask.
In groups, students can compare AI responses, identify if any main tasks are missing (or unnecessarily included), and evaluate the accuracy of duration estimates.

Chapter 4
AI-powered chatbots can be helpful to enhance our understanding of confusing topics, but it isn’t guaranteed to provide accurate information. This in-class activity (15-20 minutes) is designed to get us in the habit of being critical of AI output, and if necessary, re-prompting the AI-powered chatbot to give a better answer. Student groups will try to answer the following questions with the help of the AI-powered chatbot:
 When does a 2-period weighted moving average equal the Naïve approach?
 When does the exponential smoothing method equal the Naïve approach?
 When is it best to use MAD vs. MAPE?
In groups, students can critically assess the accuracy of the AI responses (referencing material in Chapter 4) and identify more effective ways to prompt AI-powered chatbots.

For a desk copy of the 15th edition, please click on this link.

OM in the News: A.I. and Computer Programming Productivity

Since at least the industrial revolution, workers have worried that machines would replace them, writes The New York Times (June 8, 2025). But when technology transformed auto-making, meatpacking and even secretarial work, the response typically wasn’t to slash jobs and reduce the number of workers. It was to break them into simpler tasks to be performed over and over at a rapid clip. Small shops of skilled mechanics gave way to hundreds of workers spread across an assembly line. The personal secretary gave way to pools of typists and data-entry clerks.

Workers complained of speed-up, work intensification, and work degradation. Now this appears to be happening with A.I. in one of the fields where it has been most widely adopted: coding.

As A.I. spreads through the labor force, many white-collar workers have expressed concern that it would lead to mass unemployment. But the more immediate downside for software engineers appears to be a change in the quality of their work. It is becoming more routine, less thoughtful and, crucially, much faster pace.

Like assembly lines of old that we discuss in Chapter 1, A.I. can increase productivity. Microsoft found that programmers’ use of an A.I. coding assistant called Copilot, which proposes snippets of code that they can accept or reject, increased output more than 25%. Amazon’s CEO wrote that generative A.I. was yielding big returns for companies that use it for “productivity and cost avoidance.”
Shopify, a company that helps entrepreneurs build e-commerce websites, announced that “A.I. usage is now a baseline expectation” and that the company would “add A.I. usage questions” to performance reviews.

The shift has not been all negative for workers. At Amazon and other companies,  A.I. can relieve employees of tedious tasks and enable them to perform more interesting work. Amazon says it saved “the equivalent of 4,500 developer-years” by using A.I. to do the thankless work of upgrading old software. Many Amazon engineers use an A.I. assistant that suggests lines of code. But the company has more recently rolled out A.I. tools that can generate large portions of a program on its own. One engineer called the tools “scarily good.”

Classroom discussion questions:

  1. How can A.I. transform factory jobs?
  2. Professors’ jobs?

 

 

OM in the News: Locating an AI Data Center Means Huge Power Needs

Meta Platforms just scooped up 2,700 acres of Louisiana farmland for what would be its largest-ever data center, built over flat rice fields in one of the poorest corners of the state.  At 4 million square feet, or 70 football fields, Meta’s data center will cost $10 billion and sit on more acreage than L.S.U. in Baton Rouge, which has more than 34,000 students. CEO Zuckerberg says the site will be used to train future versions of Meta’s open source AI models and be “so large it would cover a significant part of Manhattan.”

Building advanced artificial-intelligence systems will take city-sized amounts of power, which has turbocharged electricity demand projections for the first time this century, reports The Wall Street Journal (March 31, 2025). 

operations management and artificial intelligence and AI and location
Construction at the site of Meta’s new data center in Holly Ridge, La

Tech companies are pressing into unexpected parts of the country, far from traditional data-center markets such as Northern Virginia. They are hunting for huge swaths of flat land with access to natural gas and transmission lines, landing them on the doorstep of oil-and-gas country. To meet the voracious power needs of the project and other growth, Entergy Power intends to spend $3.2 billion to build three natural gas-fired power plants, tapping the state’s vast gas reserves.

In tiny Holly Ridge, La., hundreds of pieces of construction equipment are rolling past, with 5,000 construction workers on the way. Meta will bring money, jobs and local tax revenue. But the project also threatens to burden electricity customers across much of Louisiana with higher costs if demand from the tech giant eventually dries up.

L.S.U. estimates Meta could use 15% of Louisiana’s current electricity generation. That is worrisome to other utility customers largely because of the mismatch between the 40- 50 year lifespan of gas-fired power plants and Entergy’s 15-year deal with Meta.

Meta’s permanent jobs—around 500—are fewer than the thousands that might have accompanied an auto factory. For a region with a median household income of $53,000, the impact will be meaningful, though. Average salaries at Meta are projected at $82,000.

As we discuss in Chapter 8, Location Strategies, states often must offer financial incentives to draw major new employers. To woo Meta, Louisiana approved a sales-tax exemption for data-center equipment and helped procure more land from local farmers.

Classroom discussion questions:

  1. Are the incentives offered Meta unusual or risky?
  2. Why are data centers and their current technologies controversial?