AI – Page 3 – The OM Blog by Heizer, Render, & Munson

OM in the News: AI and the Learning Curve

Scale drives efficiency—for almost a century, industrial planners have relied on this simple principle. In 1936 aeronautical engineer Theodore Wright discovered that costs fell in a predictable way every time production doubled. The more you produce, the cheaper things become, in part because the learning cost per unit declines. This is the topic of Module E in your text.

Artificial intelligence has accelerated this principle, writes The Wall Street Journal (Oct. 23, 2025). It is rewriting Wright’s Law, which assumes that experience follows production: You make mistakes, learn from them and improve. AI makes it possible for experience to come before production. Simulation can happen millions of times before a single box is shipped. Experience scales almost instantly at no real cost. The learning curve doesn’t only steepen. It collapses.

That means knowledge that once took decades of human trial and error can emerge in weeks, days, even hours. In a supply chain, this is a profound shift. Decisions about capacity, warehouse space, routing, technology adoption and risk management can be modeled, tested and optimized in advance. The costs of imprecise planning shrink dramatically.

AI is breaking Wright’s Law because the learning cycle is no longer physical but computational. Models can test, fail and improve millions of times faster than any team of human engineers. Experience can be generated in advance, and at negligible cost.

The implications for logistics are extraordinary. AI agents will negotiate, reroute and optimize flows of goods in real time. Traditional ownership models, fleets, warehouses and even labor could be replaced by dynamic orchestration of perfectly used assets.

This new golden age of logistics will unveil solutions to problems we may not even know exist. Wright’s Law still matters, but perhaps AI has broken it. The challenge will be not building the tools but surviving the pace of their consequences.

Classroom discussion questions:

Why can AI have this impact on learning curves?
Besides logistics, which is mentioned in this article, can AI impact operations?

OM in the News: The AI’s Industry 100-Hour Workweeks

The explosive growth of artificial intelligence has forced leading tech companies to rethink their human resource strategies and job design, reports The Wall Street Journal (Oct. 23, 2025). As the demand for rapid innovation intensifies, organizations like Google, Microsoft, Meta, and Anthropic are relying on small, highly skilled teams to push the boundaries of AI development. These teams often work 80 to 100 hours per week, far exceeding the traditional schedules we discuss in Chapter 10, as they race to keep up with the pace of technological change.

Several researchers compared the circumstances to war. “We’re basically trying to speedrun 20 years of scientific progress in two years,” said one Anthropic scientist. “Extraordinary advances in AI systems are happening every few months. It’s the most interesting scientific question in the world right now.”

This environment has led to a redefinition of job roles and expectations. Rather than adhering to standard 9-to-5 or even the demanding “9-9-6” (9 a.m. to 9 p.m., six days a week) schedules, some AI workers describe “0-0-2” routines—working around the clock with minimal breaks. The pressure is especially acute for those directly involved in developing new AI models, where the unpredictability of research outcomes and the speed of breakthroughs require constant adaptability.

To support these extreme demands, companies are adapting their HR strategies. Some provide weekend meals and ensure continuous staffing, while others appoint rotating “captains” to monitor model outputs and oversee product development. These measures aim to sustain productivity and manage burnout, acknowledging that the traditional boundaries between work and personal life have blurred for many in the field.

Job design in this context emphasizes autonomy, intrinsic motivation, and a sense of mission. Many top AI researchers are driven not just by compensation but by the excitement of discovery and the belief that their work is shaping a pivotal moment in history. This self-motivation reduces the need for formalized overtime requirements, as employees willingly invest extra hours to stay ahead in the competitive landscape.

But this also raises concerns about sustainability and well-being. While some workers have become wealthy from their efforts, most have little time to enjoy their success or maintain relationships outside of work. The model raises questions about long-term retention and the potential need for more balanced, human-centered HR strategies as AI becomes further integrated into mainstream business operations.

Classroom discussion questions:

Your comments on the 100 hour workweek?
Is this a valid human resource strategy?

OM in the News: Amazon’s AI-Robotics Warehouse Revolution

Amazon is rapidly transforming its e-commerce fulfillment operations through a bold integration of artificial intelligence (AI) and robotics, reports The Wall Street Journal (Oct. 23, 2025). The company’s vision is clear: make human workers more efficient while automating repetitive, menial tasks. At the heart of this shift is Amazon’s Shreveport, Louisiana facility, which now boasts ten times as many robots as a typical warehouse. This leap in automation enables packages to move through the system 25% faster, with anticipated cost savings passed on to customers.

Safety and efficiency are top priorities. Robots now handle tasks such as sorting packages, transporting carts, and retrieving out-of-reach items. Amazon is also investing in its workforce, offering apprenticeships to train employees in managing these advanced systems. The company’s latest innovations include Blue Jay, a robot arm designed for sorting in tight spaces, and Eluna, an AI agent that helps managers optimize staffing and avoid bottlenecks. Blue Jay’s rapid development—just over a year, compared to 3 years for previous models—was made possible by generative AI, which allowed for virtual prototyping.

The company aims to deploy Blue Jay robots in urban, space-constrained warehouses, enabling same-day delivery networks that are both faster and more cost-effective.

These advances could save Amazon billions annually. By the end of next year, nearly 40 fulfillment centers will be equipped with robots, with an estimated to $4 billion in yearly cost reductions. This automation trend is expected to reduce the need for both warehouse and white-collar workers. In fact, the average number of workers per facility dropped to around 670 in 2024, the lowest in 16 years.

Amazon is testing augmented-reality glasses

Amazon’s automation push extends beyond warehouses. Augmented-reality glasses are being tested for delivery drivers, helping them identify packages and navigate routes more efficiently.

Amazon’s journey began with its $775 million acquisition of Kiva Systems in 2012. Today, three-quarters of its deliveries involve some form of robotic assistance. The company’s latest announcements—Blue Jay, Eluna, and AR glasses—signal a new era where AI and robotics are supercharging logistics, reshaping the future of retail fulfillment.

Classroom discussion questions:

How does Eluna work?
Why is Amazon trying to eliminate warehouse jobs?

OM in the News: China’s Rare-Earth Escalation Threatens the Global Economy

China’s newest restrictions on rare-earth materials would mark a nearly unprecedented export control that stands to disrupt the global economy and threaten the supply chain for semiconductors, writes The Wall Street Journal (Oct. 10, 2025). Chips are the lifeblood of the economy, powering phones, computers and data centers needed to train artificial-intelligence models. The rule also would affect cars, solar panels and the equipment for making chips and other products, limiting the ability of other countries to support their own industries. China produces roughly 90% of the world’s rare-earth materials.

Global companies that sell goods with certain rare-earth materials sourced from China accounting for 0.1% or more of the product’s value would need permission from Beijing, under the new rule. Tech companies will probably find it extremely difficult to show that their chips, the equipment needed to make them and other components fall below the 0.1% threshold.

“These rare-earth minerals and the ability to refine them are just the basis of modern civilization,” said one industry expert. “It’s an economic equivalent of nuclear war—an intent to destroy the American AI industry,” added a second. The U.S. and other countries are pouring hundreds of billions of dollars into data centers, making AI a key economic engine. China gaining control of the technology would potentially let it catch up in the AI race and upend the world order.

The semiconductor supply chain is vulnerable to actions like China’s because large chip plants require big capital investments from an ecosystem of companies providing specialized equipment, intricate technical processes and final packaging. Companies in the U.S., Taiwan, Japan and the Netherlands all collaborate with one another.

The Trump and Biden administrations have offered subsidies and other policies to aid the process, but domestic capacity generally remains in its infancy. Some analysts said the new rules will fuel new urgency for big tech companies to invest more in these areas.

Classroom discussion questions:

Why are rare earths so important?
Why doesn’t the U.S. produce and process the minerals needed?

OM Podcast #40: AI in the MBA Classroom and Beyond

We’re excited to share another Heizer/Render/Munson OM Podcast episode! Today, Barry Render sits down with David Rosenthal, a recent MBA graduate from the University of Texas at Austin, to explore how artificial intelligence is transforming business education and early career experiences.

David shares insights into AI in the classroom, AI as a central part of his internship, and his entrepreneurial journey using AI to build his app, Fantasy Fusion Sports.

Heizer Render Munson OM Podcast · OM Podcast #40: AI in the MBA Classroom and Beyond

TRANSCRIPT
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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:

How might AI be used in the commercial aviation industry?
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.

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:

How else might AI be used in factories?
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.

Heizer Render Munson OM Podcast · OM Podcast #39: AI, Sustainability, Cybersecurity & Blockchain in Operations

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

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.

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:

How could AI be used to help recycle?
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.

Heizer Render Munson OM Podcast · OM Podcast #38: Editorial Leadership and the Future of Operations Management

Transcript

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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:

How does Toyota’s approach tie with AI use?
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:

How can AI be used in last-mile delivery?
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:

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