Tag: AI

Guest Post: How to Leverage AI in your OM Teaching

Dr. Jonathan Jackson is Associate Professor of SCM at Providence College.

Preparing for an upcoming semester is a challenging task for educators in normal circumstances, but infinitely harder when facing the largest disruption in education since the internet. The introduction of generative artificial intelligence (AI) and large language models (LLMs) like ChatGPT have caused us to rethink an entire course.

While AI will inevitably cause challenges, it also provides great opportunity to improve higher education. Here are 4 opportunities to make your teaching easier and more impactful using AI:

1. We have all been in the situation where no matter how we try to explain a challenging topic (e.g., the bullwhip effect), it still isn’t “clicking” for our students. This is where AI can help. By prompting an LLM like ChatGPT with a topic (say, bullwhip) and an audience  (business students), it can help generate alternative explanations at varying levels of complexity to help improve student understanding.

2. Relevant and interesting examples help keep students engaged, but coming up with good examples is no easy task. Similar to generating alternative explanations, AI can provide various examples to help students connect a topic (e.g., inventory management) to things they see in their day-to-day lives.

3. While your Heizer/Render/Munson OM text has an amazing set of questions that can be auto-graded through MyLab, sometimes we need additional questions for a particular topic or class session. For example, before introducing SPC in Supplement 6, we may want to devise a low-stakes quiz to refresh students on topics from their previous statistics course such as central limit theorem and Normal distribution. AI can not only develop the questions, but also provide an answer key and explanations.

4.  Short feedback mechanisms like “1-minute paper” and “muddiest point” are excellent tools to help gauge student understanding and gaps in knowledge. Unfortunately, they can be cumbersome for us to administer on the back end. Specifically, AI can help in summarizing the responses from the 25+ students into important points (understanding) and areas of confusion. By inputting all student responses into the LLM, it can provide  prevalent key points and areas of confusion in a matter of seconds.

Much of the discussion around AI and education comes in an adversarial tone, but these opportunities are framed around leveraging this new technology for the betterment of our courses (and to save us a little time!).

Note: Generative AI and LLMs are not foolproof and should be carefully vetted using your content expertise.

OM in the News: What Is a “Digital Twin”?

A digital twin is a virtual representation of an object or system that spans its lifecycle, is updated from real-time data, and uses simulation, machine learning and reasoning to help decision-making.

NASA tested an early iteration of a digital twin in response to the Apollo 13 disaster in 1970, using training simulators to match the conditions on the crippled spacecraft and test potential strategies for bringing the astronauts home safely. Today’s digital twins are much more advanced, writes The Wall Street Journal ( March 20, 2023). Not only do they pull in real-time data, but also use AI to capture insights and make predictions, such as identifying potential problems before they happen. The technology also can eliminate the need for physical prototyping of products such as automobiles, and offer a way to test different configurations for spaces such as warehouses and stores, potentially saving time and money.

Companies in every industry are looking at the technology to help them improve processes, reduce costs, conserve resources, boost employee safety and productivity: 17% said they have or plan to deploy digital twins.

San Francisco Airport’s digital twin of its Terminal 2.

For example, the massive San Francisco Airport relies on a digital twin to keep the facility running smoothly. It is a 3-dimensional virtual replica of the airport that is continuously updated with data gathered from embedded sensors throughout the airport. If the maintenance team were to receive a request to change door locks, for example, it could consult the digital twin to find the locations of all the doors that need service.

Another growing area is construction. Modern buildings are already layered with sensors and data-gathering systems that building operators can combine in a digital twin to help them improve a structure’s efficiency, sustainability and security. Building managers can use digital twins to keep track of systems—such as EV charging, smart glass that darkens to reduce energy costs and even soap dispensers with built-in sensors that know when it’s time for a refill—all in one place.

Other complicated systems might benefit from connected digital twins, too. A collection of twins representing everything from stadiums to freeways to public parks has the potential to change the way governments build cities and provide services. Cities might use the technology to create more efficient trash-pickup schedules and routes, for example, or to change traffic patterns when there is a spike in additional people getting on the road from, say, a stadium event.

Classroom discussion questions:

  1. How might a digital twin be used at your university?
  2. Why are twins so useful?

Teaching Tip: Glossary of Supply Chain Terms

The Financial Times  (Nov. 22, 2022) has just issued a report called How Technology Can Help Redraw the Supply Chain Map . In it, the newspaper provides this useful glossary of current SCM terms for your students to keep handy.

Internet of things (IoT) The IoT consists of sensors that make goods “smart”. These can both send information and communicate with each other. The IoT is used in the supply chain for tracking and monitoring. (See p. 451 in your Heizer/Render/Munson text).

Blockchain Blockchain is also known as distributed ledger technology. It allows for the digital recording of transactions and tracking assets in a business network. It introduces trust where this is scarce. The verifiability of transactions can help to reduce fraud. (See p. 451 and 591).

Artificial intelligence (AI) and data analytics These involve statistics at a huge scale processed at a blistering speed. They can help with warehousing and inventory, improving sourcing relationships and predicting demand AI and machine learning. (See p. 823-831).

Machine Learning (ML) is a facet of AI that applies an algorithm to data. It then taps into previous experience and then accomplishes tasks without human involvement. The algorithms can, for instance, make predictions, form personalized recommendations and recognize images in photos. Examples of ML with which you may be familiar include TikTok recommendations, photo portrait recognition and sentence completion.

Robots and automation This covers the physical side of distribution centers and includes optimizing storage, moving stock and picking and packing. It is increasingly sophisticated. (See p. 277, 292, 371, 490).

3D printing This involves the creation of three-dimensional objects by a machine that uses a computer model. It applies layers of substrate (plastics, liquids or powders) to create physical goods. It allows for the making and replication of extremely complex shapes that cannot be constructed by hand. (See p. 170).

OM in the News: Exciting New Trends in ERP

Enterprise resource planning (ERP) solutions, our topic in Chapter 14, have grown from back-end processes like accounting, HR, purchasing and supply chain management to front-office functions such as sales force automation, marketing automation and ecommerce. Businesses rely on ERP to not only cut costs by eliminating redundant processes and automating mundane tasks but also to provide employees access to critical information to make better decisions, faster.

As companies increasingly rely on ERP, these systems continue to evolve to incorporate new technologies and support a broader range of functions. Oracle NetSuite identifies several major trends this year.

  1. Cloud ERP Historically, many organizations used on-premises ERP applications. But businesses are adopting cloud ERP to take advantage of lower costs and the ability to easily add users and functions to accommodate business growth.
  1. Two-Tier ERP Historically, many companies tried to deploy a single ERP system for both the headquarters and all regional offices and subsidiaries. Two-tier ERP is a strategy that enables organizations to leverage their investment in existing ERP systems at the corporate level (tier 1), while subsidiaries and divisions operate using a different ERP solution (tier 2), which is often cloud-based.
  1. Digital Transformation Digital transformation refers to integrating digital technology into all business functions to improve daily operations. The integration of ERP with IoT devices and the adoption of AI and advanced analytics are part of this change.
  1. Other Technology Integrated With ERP Companies are integrating their business applications with other new technologies to improve core processes. For example, retailers use warehouse management systems that collect data from mobile scanners and smart conveyers to track the movement of goods within the warehouse.  We also see a greater connection between social media and ERP.
  1. Personalization Historically, ERP platforms with complex languages were difficult to customize to the specialized needs of each business. But organizations can now take advantage of what are called “low-code” platforms.
  1. AI-Powered Insights and Improvements Artificial intelligence and machine learning capabilities embedded into ERP systems work behind the scenes to help meet increased demand for personalization and improve a broad range of business processes.
  1. Predictive Analytics The hunger for AI-infused ERP highlights organizations’ increasing desire to mine their operational and customer data for new and relevant insights that will increase the top and bottom lines. (See Module G of your text).

Classroom discussion questions:

  1. Why have ERP systems seen these major changes?
  2. What is the advantage of a 2-tier ERP?

 

OM in the News and Video Tip: FedEx Recruits Robots

Robotic arms manufactured by Yaskawa America

Sue, Randall, Colin and Bobby are 4 of the most reliable FedEx workers in Memphis. Each clocks 8 hours a day, sorting 1,300 packages an hour. They almost never take breaks, as they are actually 260-pound industrial robot arms.

They work only about half as fast as skilled humans, but they are quickly becoming an important part of the chain that keeps packages flowing. These robots, getting both “eyes” and “brains” that allow them to sense and respond, typify an important and growing trend in automation, writes The Wall Street Journal (Aug. 8-9, 2020). They have cameras which perceive visible light and sensors to perceive depth, and their “brains” are built with machine-learning AI. This gives them a level of adaptability not before seen. (There is an excellent 6 min. video that opens this WSJ article).

But the robots are not about to steal all the jobs in these industries. For now, they’re mostly filling vacancies created by surging demand. The explosion of e-commerce means an explosion in the volume of packages shipped to homes. Some 87 billion parcels were shipped worldwide in 2018—that’s 40 a year to every person in the U.S.—and this volume will more than double by 2025.

The need for social distancing within warehouses means robots can play a role in helping workers do their jobs without being directly adjacent to one another. And logistics companies are still finding it hard to hire people fast enough. (FedEx’s air hub in Memphis currently has 500 job openings).  FedEx estimates one human could tend up to 8 robots.

The overwhelming majority of industrial robot arms in the world are still the “dumb” kind: They repeat the same action over and over again—for example welding the same parts together repeatedly on an auto production line. The holy grail of picking technology—a robot that can handle the same variety as a human—will remain out of reach for a long time, in the same way we have yet to create an autonomous vehicle that can handle the same variety of road situations a human can.

Classroom discussion questions:

  1. Why is it so hard for robots to replace warehouse workers?
  2. Why are firms like FedEx more driven to automate?

OM in the News: How Applications, Automation, Analytics and AI Transform OM

Digital transformation, writes the INFORMS magazine Analytics (June 2020), is leveraging modern technology and innovation so that an organization can help its people achieve maximum capability and the company processes can run optimally. Digital transformation also helps a business focus on its greatest means of success: its customers. Its main technology drivers come from the “Straight A’s”: applications, automation, analytics and AI. Technology, which we discuss in Chapter 7, is a great enabler for organizational productivity, creativity, efficiency and improved profits.

Applications: Ideal business applications help organizations manage business processes and enhance productivity. There are cloud-based business application platforms that provide solutions for end-to-end business processes right from strategy development, product development, work management, project management, field services, customer services, and operations.

Automation: Automating business processes to remove manual, redundant tasks, which can free staff from repetitive, time-consuming work items.

Analytics: Using actionable analytics, organizations can access relevant data and relationships to take immediate action on business initiatives to achieve stronger outcomes.

AI:  With AI, companies can literally transform business processes into intelligent systems that will help identify patterns, gain deeper insights from data, and leverage data science to improve fact-based decision-making.

Classroom discussion questions:

  1. What is data analytics, and why is it an important OM tool? (Hint: see Module G in your Heizer/Render/Munson text)
  2. Why is automation so important to U.S. supply chains?

OM in the News: Retailers , Artificial Intelligence, and No Cashiers

Sam’s Club’s AI-powered cashierless shopping.

U.S. retailers large and small are pressing ahead with testing the use of AI to track what products shoppers pick up and to automatically bill their accounts when they walk out the door, eliminating the need for checkout lines. The concept got a push from Amazon Go stores, which Amazon launched in early 2018; there are now 15 stores, A new study found that 28% of retailers are testing cashierless systems

Sam’s Club is offering AI-powered cashierless shopping this month at a 32,000-square-foot store in Dallas, reports The Wall Street Journal (Aug. 13, 2019). Currently, customers shop at the store by scanning barcodes on the products, an older cashierless-checkout technology. With the AI system is in place, customers use their smartphone cameras to scan the product itself. The cloud-based system, which uses computer vision and machine learning, recognizes products by matching them to a database of stored images. This is different from Amazon Go, where cameras installed in the stores do the work of scanning the products.

Not every type of store is suited for cashierless technology. Walmart tried out a cashierless system based on scanning barcodes for about six months in more than 100 stores but discontinued it in 2018. The technology proved impractical for pricing produce and other items that had to be taken to a cashier to be weighed, causing delays.  Theft is also a concern. Manual scanning operates on an honor system and some customers don’t scan every item, often requiring stores to validate purchases.

Still, the potential benefits include speed and convenience. Even small companies are testing the waters.

Classroom discussion questions:

  1. Why is this an important OM issue?
  2. How does this AI approach differ from self-checkout?

OM in the News: Using Machine Learning to Keep the Beer Flowing

Anheuser-Busch uses this sensor to pick up ultrasonic sounds coming off conveyor belt and motors.

The world’s largest beer maker is using low-cost sensors and machine learning to predict when motors at a Colorado brewery might malfunction, reports The Wall Street Journal (Jan. 24, 2019).  The Anheuser-Busch plant was the first among the company’s 350 beer facilities to test whether wireless sensors that can detect ultrasonic sounds—beyond the grasp of the human ear—can be analyzed to predict when machines need maintenance. “You can start hearing days in advance that something will go wrong, and you’ll know within hours when it’ll fail. It’s really, for us, very practical,” said the VP.

The installation at the brewery cost just $20,000. Since the system was deployed, it has predicted pending equipment failures and prevented unscheduled production-line halts, and more than $200,000 in product loss. (The Colorado plant employs 580 people and ships 225 truckloads of Budweiser, Bud Light and other beer brands each day).

Sensors have been used for predictive maintenance in the past, but they were unable to transmit information in real time. Advances in processing data at the edge of the network, referred to as edge computing, enable companies to collect and analyze real-time sensor data from machines. Machine learning refers to the subset of AI that allows computers to act “intelligently” without being explicitly programmed. Algorithms can increase the accuracy of predictions based on large amounts of historical and real-time sensor data.

Organizations that own wind turbines or jet engines are expected to save about $1 trillion a year as a result of predictive maintenance techniques. Sound-based predictive maintenance is becoming more important for companies, as there has been a wave of retirements among workers who were tasked with listening to machines to identify potential breakdowns. The price of internet-of-things sensors is expected to fall to 26 cents on average by 2024, from 46 cents in 2018.

Classroom discussion questions:

  1. What is predictive maintenance?
  2. How does this differ from “breakdown maintenance?”

OM in the News: A Primer on Predictive Maintenance

“Nearly everyone in manufacturing, from equipment manufacturers to processing plants, commonly face the challenge of keeping their fleet, machinery, and other assets working efficiently, while also reducing the cost of maintenance and time-sensitive repairs,” writes Industry Week (Dec. 6, 2018).  So it is crucial to identify the cause of potential faults or failures before they have an opportunity to occur. Emerging technologies such as the Industrial Internet of Things, data analytics, and cloud data storage are enabling more vehicles, industrial equipment, and assembly robots to send condition-based data to a centralized server, making fault detection easier, more practical, and more direct. By proactively identifying potential issues, companies can deploy their maintenance services more effectively and improve equipment up-time.

Using AI to identify anomalous behavior, the information derived from the equipment sensors can be turned into meaningful and actionable insights for proactive maintenance of assets, thereby preventing incidents that result in asset downtime or accidents. Known as predictive maintenance (a topic we have added to Chapter 17 in our new edition, due out Jan. 1st), this added intelligence enables organizations to forecast when or if functional equipment will fail so that its maintenance and repair can be scheduled before the failure occurs. As industrial customers become increasingly aware of the growing maintenance costs and downtime caused by the unexpected machinery failures, predictive maintenance solutions are gaining even more traction.

Predictive maintenance is also a step ahead of preventive maintenance. As maintenance work is scheduled at preset intervals, maintenance technicians are informed of the likelihood of parts and components failing during the next work cycle and can take action to minimize downtime. In addition to the advantages of controlling repair costs, avoiding warranty costs for failure recovery, reducing unplanned downtime and eliminating the causes of failure, predictive maintenance employs non-intrusive testing techniques to evaluate and compute asset performance trends.

Classroom discussion questions:

  1. How do preventive maintenance and predictive maintenance differ?
  2. What technologies are allowing predictive maintenance to spread?

OM in the News: Maybe Robots Are Not Taking Over

“In the race between humans and robots, humans are often winning,” writes The Wall Street Journal (Nov. 3, 2018). Companies like Airstream, the maker of retro-cool, high-end trailers, find it more efficient to use a workforce of people, rather than make sizable investments in automation that risks being wasted if the economy slows.

Airstream’s factory in Ohio is racing to fill a backlog of orders that spans well into next year. It takes 8 workers climbing through an Airstream to bolt a hulking aluminum shell to a steel chassis, and snake fluid lines and wires through walls. To finish the shiny, silver capsule off, workers will need to install 3,000 rivets by hand. There’s not a robot in sight. They may speed production, but the machines require a substantial investment that risks being wasted if the economy slumps.

We’ll continue to see innovation in fields that are traditionally dependent on people. For example, grocery stores, which often struggle to find people to work for lower wages, are experimenting with robots. It could take decades, though, for these initiatives to meaningfully alter the employment picture. Robotics spending is forecast to equal $90 billion in 2018, a considerable increase compared to prior years, but it is only a sliver of the nearly $3 trillion committed to capital investment. An MIT prof adds: “There is a big debate on whether robots are really delivering on the productivity benefits they might promise.”

Companies appear to be trying to “optimize how they use people” rather than install more machines. For example, Ford spent nearly $1 billion converting the factory to go from making small cars to producing pickup trucks. Much of that went toward new tooling for stamping out body parts, but relatively little went toward adding automation. Artificial intelligence is now integrated into the final inspection lines to boost quality. But skilled workers are needed to interact with the AI tools.

Classroom discussion questions:

  1. Why does Airstream prefer humans over robots?
  2. Where are robots making big impacts in American business?

OM in the News: How Robots Will Change Retail Forever

This Amazon distribution center in Baltimore can fulfill a million orders in a day. It may not need humans for long.

What if your company could store and deliver goods as easily as data? Amazon, Walmart and others are using AI and robotics to transform everything from appliance shopping to grocery delivery. “Welcome to the physical cloud,” writes The Wall Street Journal (Oct. 15, 2018).

Take, as an example, Amazon’s one-million-square-foot distribution center in Baltimore. Its scaffolding and seemingly endless conveyor belts disappear at a vanishing point within the building. The machine is a dazzling combination of chutes, ladders, rollers and 11 miles’ worth of conveyor belts. Customers’ orders move from shelving into bins and from bins into boxes as they travel via the machine straight into delivery vans, passing by stationary workers at various points along the way. Humans are rarely required to move around here. It’s much faster, and cheaper, to have stuff brought to them.

This is where robots come in. Kiva robots can carry up to 750 pounds of goods in their 40-odd cubbies. After a customer places an order, a robot carrying the desired item scoots over to a worker, who reads on a screen what item to pick and what cubby it’s located in, scans a bar code and places the item in a bright-yellow bin that travels by conveyor belt to a packing station. AI suggests an appropriate box size; a worker places the item in the box, which a robot tapes shut and, after applying a shipping label, sends on its way. Humans are needed mostly for grasping and placing, tasks that robots haven’t mastered yet.

Amazon’s robots signal a sea change in how the things we buy will be aggregated, stored and delivered. The company requires 1 minute of human labor to get a package onto a truck, but that number is headed to zero. Autonomous warehouses will merge with autonomous manufacturing and delivery to form a fully automated supply chain.

Classroom discussion questions:

  1. How does this latest Amazon facility differ from the one we describe in the Global Company Profile that opens Chapter 12?
  2. How is AI being used in this warehouse?

 

OM in the News: From Reindeer to Robots, Automation Set to Deliver This Holiday

Warehouse robots created by GreyOrange resemble shelf-moving systems developed by Kiva Systems, now part of Amazon, but add AI to the technology

Never mind the reindeer and elves. This year, robots are helping deliver the holidays. Gap is using automated arms and AI to sort the retailer’s clothing orders. Walmart is testing robots that roam store aisles to check inventory and tell workers where to find goods. And logistics providers are sending mobile step-stools mounted with shelves through fulfillment centers to help pull online orders.

With the busy holiday peak looming, retailers and logistics companies are ramping up automation as surging demand for labor outstrips the number of available workers, reports The Wall Street Journal (Oct. 4, 2018). Much of the technology is being used in distribution operations, where workers are increasingly working alongside machines built to keep goods moving at a rapid pace. The use of robotics and other automation technology in industrial operations is growing, although the vast majority of warehouse work remains largely manual. About 16% of organizations across several industries including warehousing are now using commercial service robots, and 21% have them in pilot programs. Online fulfillment centers—where companies like Amazon pick, pack and ship consumer orders—require 2-3 times as many workers as traditional warehouses.

XPO said this week it is deploying 5,000 autonomous mobile units from GreyOrange at logistics sites across North America and Europe. The robots, which resemble Roomba autonomous vacuum cleaners, sync up with XPO’s warehouse-management software to help workers fulfill up to 48 orders at a time. The robots more than doubled the speed at which orders are processed and help the company keep better tabs on inventory. Logistics-industry interest in robotics is spreading as the technology gets cheaper and easier to adopt. Collaborative robots for example, can work safely alongside humans and be added quickly to existing sites without disrupting operations.

Classroom discussion questions:

  1. How are robots being used in retailers and warehouses?
  2. Why will robots not replace most warehouse workers?

OM in the News: Vision-Automation Technology is Taking over the Factory Floor

 

Humans overseeing the toppings at a German frozen-pizza plant, a task now within the reach of technology.

Robots that see underpin the future of self-driving cars, humanoid robots and autonomous drones, writes The Wall Street Journal (Sept. 14, 2018). Now, food manufacturers are combining advances in laser vision with artificial-intelligence software so that automated arms can carry out more-complex tasks, such as slicing chicken cutlets precisely or inspecting toppings on machine-made pizzas.

Being able to see is a major frontier in robotics and automation—crossing it is key to autonomous vehicles that can navigate obstacles, humanoid robots that can more closely integrate with humans and drones that can fly more safely. Companies world-wide are investing in computer vision-based technology.  Intel recently bought Mobileye for $15 billion, in part for the Israeli company’s vision-based driver-assistance technology.

Food manufacturers have been early adopters of new technologies from canning to bread slicers, and vision automation has been used for years for tasks such as reading bar codes and sorting packaged products. Leaders are finding the technology valuable because robot eyes outpace the human eye at certain tasks. Now technical improvements, tougher materials and declining prices mean Tyson can integrate vision technology in its new $300 million chicken-processing plant. The technology helps optimize the use of each part of the bird.

Advances so far allow vision technology to ensure frozen pizzas have the correct toppings. Other applications include the ultrasonic slicing of cheese, cutting bread rolls with water jets and picking pancakes off a production line. Car makers, historically the biggest user of vision technology, are using it for emergency braking and scanning road signs; logistics companies deploy it to more quickly identify packages, and consumer electronics companies to position liquid-crystal display screens more precisely than is possible with the naked eye.

Classroom discussion questions:

1. Why are vision systems becoming an important OM tool?

2. Will driver-assistance technology really eliminate the need for drivers? Why? When?

 

Good OM Reading: AI + Blockchain

This new book (www.mkpress.com) alerts readers to the impending collision of the two largest foundational technologies for the coming decades: Artificial Intelligence and Blockchain.

AI has had a long history of hype and excitement about how we can externalize our human skills. Blockchain is the newer technology that is motivated largely by a change in control of cryptocurrencies and inventory.  AI, claim the authors, seeks to displace individuals while blockchain seeks to displace a controlling team of individuals. AI will continue to disrupt business in many ways, leading to job loss and rendering irrelevant many human cognitive skills. Blockchain too is challenging and will continue to change the position of trust of central authorities, whether in the government or in big business.

Both these technologies, however, have enormous potential to make positive changes in the world of operations management. AI, rightly engineered and deployed, has the potential to become humanity’s servant, freeing up humans. Blockchain, responsibly governed and deployed, has the potential to democratize society, by eliminating friction in the world’s transactions and eliminating middlemen, and by facilitating a more equitably distributed internet of value. The most efficient and effective ways for this to happen is through a partnership between these two powerful technologies, where blockchain delivers trusted and immutable information for AI, and AI delivers cognition and automation to business processes.

OM in the News: Seven Jobs Robots Will Create

People are needed to oversee the work of machines to make sure they’re doing their jobs properly.

As machines get smarter, will millions of people will be left obsolete and jobless? Yes, jobs will be lost, and many people will be forced to learn new skills. But “AI opens up opportunities for many new jobs to be created—some that we can’t even envision now,” writes The Wall Street Journal (April 30, 2018). McKinsey predicts that artificial intelligence and automation could add 20-50 million jobs globally by 2030. Here is a look at 7 of them:

AI Builders There will be a greater need for people who can develop the underlying systems that make AI work. Other fields will need people with knowledge of how to integrate their work with AI.

Customer-Robot Liaisons  Companies that make AI applications use “customer success managers” to help ease clients into working with the systems, answering complaints and making adjustments. This is currently among the most sought-after jobs on ZipRecruiter.

Robot Managers  Even though AI can be amazingly smart at some jobs, its judgment can be very limited compared with a person’s. Hence the job someone who oversees the work of machines to make sure they’re doing their jobs properly, and intervenes if the AI asks for help in a tricky situation.

Data Labelers For AI to properly understand the world, it needs humans to explain what things are—meaning, the data that the AI absorbs need to be labeled. That could mean identifying objects in images.

Drone-Performance Artists Drones are  starting to work their way into the arts, where they act as dynamic light installations and flying props. And there is a growing need for artists who can customize those drones.

AI Lab Scientists Experts are needed to teach AIs about the life sciences or chemistry so that computers can surface novel ideas. Technicians, who test the results that AI comes up with to see which are valid and which aren’t, are also needed to help make machines smarter.

Safety and Test Drivers Most self-driving vehicles aren’t fully capable of working on their own just yet—and that means opportunities for people who help the vehicles do their jobs safely.

Classroom discussion questions:

1.Which of these jobs will fall under the purview of operations managers?

2. What kind of jobs will be lost in the AI revolution?