• AI can support last-mile delivery by optimizing truck routes and predicting errors before they occur.
• AI-enhanced predictive analytics can also help to prevent package theft.
• This article is part of "How AI Is Changing Everything: Supply Chain," a series on innovations in logistics.

In the last mile, the part of the supply chain that involves transporting goods from a warehouse to a consumer's home, many things could go wrong. 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.

"You're dealing with humans and the real world and trucks and traffic," said Fred Cook, the cofounder and chief technology officer of last-mile delivery company Veho.

In an area long dominated by carriers like UPS, FedEx, and the US Postal Service, Veho and many other software providers are looking to solve the challenges that pervade this notoriously complex and expensive part of the supply chain. They're using AI to design more efficient delivery routes, improve accuracy and the customer experience, and predict errors before they might happen.

Erik Mattson, a partner in consulting firm AlixPartners' Manufacturing and Operations practice, sees "a big opportunity for AI to help this industry catch up to other industries."

E-commerce sales continue to grow, reaching new highs of $300 billion in the last two quarters. This makes the last mile busier than ever and ripe for a technology disruption. A 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.

AI from the road to the front door

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, according to the Capgemini Research Institute.

One of the earlier applications of routing technology in the last mile was a machine-learning application that UPS launched in 2013 called ORION, or On-Road Integrated Optimization and Navigation. Four years ago, the parcel company rolled out an upgrade to ORION, which shortened routes by an average of two to four miles per driver and rerouted drivers based on changing conditions.

"Historic technologies would be static and run the night before," Mattson said. If orders changed or construction started, the tech wouldn't account for those changes.

Today's AI models, on the other hand, adjust in real time.

"Compared to pre-AI methods that relied on static routing rules or dispatcher intuition, our platform now responds dynamically to real-world conditions at scale," said Andrew Leone, the CEO and cofounder of Dispatch, a last-mile delivery platform.

Dispatch uses AI to plan routes based on factors such as traffic, delivery windows, estimated time per stop, and driver capacity. More efficient routes can lower fuel costs, improve density, and enable more deliveries in a day, increasing revenue for providers.

Amazon has been at the forefront of bringing AI into its last mile, said Jett McCandless, the founder and CEO of project44, a supply chain software platform. Last month, Amazon announced an initiative called Wellspring, which uses generative 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. In a test this past fall, the tech identified parking spots at 4 million home addresses.

Veho uses AI for quality assurance on its deliveries. In an ideal world, Cook said, an employee dedicated to quality assurance tasks would look at the geocode of where a parcel was left, examine the delivery photo, gather feedback from the driver, and determine if anything should change for future deliveries.

"It's totally infeasible to do that on millions of deliveries. But those are the types of use cases that we see, in the very near term, that AI is ideal for," Cook said.

Delivery data also allows last-mile providers to keep consumers informed. Deliveright, a last-mile delivery service, saw customer service calls drop by 80% due to real-time tracking and more accurate ETAs, according to Doug Ladden, Deliveright's CEO.

Veho said that its large language model, which it created in-house, answers 60% of customer and driver questions and has cut average response times from 2.5 minutes to 15 seconds.

Predicting and preventing package mishaps

Veho uses AI to pinpoint commonalities among mishaps that occurred during the logistics process, like the same warehouse associate handling multiple packages that resulted in errors, or a trucking company in the middle mile that had damaged items.

The company forecasts the likelihood of issues for specific routes or deliveries. Then it makes 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.

"We've taken that a step further now to where we're trying to predict defects," Cook said.

Swiped packages are a big issue in last-mile deliveries, with 58 million parcels stolen from doorsteps last year amounting to $16 billion in losses, according to a USPS watchdog report.

UPS created an AI-based software, DeliveryDefense, that analyzes historic factors such as loss frequency and delivery attempts. The AI then spots areas that could be targets for porch pirates in the future.

McCandless said AI can predict high-risk areas and times of day, allowing companies to plan delivery schedules and routes accordingly to minimize the chance a package might be stolen.

"AI could play a key role in identifying patterns, helping to prevent theft before it happens," McCandless said.

• Nonprofits like Education Above All are using AI to address global inequities.
• AI initiatives align with the UN Sustainable Development Goals to promote peace and prosperity.
• This article is part of "How AI Is Changing Everything," a series on AI adoption across industries.

As millions of young people worldwide increasingly rely on AI chatbots to acquire knowledge as part of their learning — and even complete assignments for them — one organization is concerned that those in developing countries without access to the tech could be put at an unfair disadvantage.

And it's using the very technology it believes is causing this problem to fix it.

Education Above All, a nonprofit based in Qatar, believes that because most of the world's popular AI chatbots are created in Silicon Valley, they aren't equipped to understand the linguistic and ethnic nuances of non-English-speaking countries, creating education inequities on a global scale. But its team sees AI as a way to tackle this problem.

In January 2025, the charity teamed up with MIT, Harvard, and the United Nations Development Programme to introduce a free and open-source AI literacy program called Digi-Wise. Delivered in partnership with educators in the developing world, it encourages children to spot AI-fueled misinformation, use AI tools responsibly in the classroom, and even develop their own AI tools from scratch.

As part of this, the charity has developed its own generative AI chatbot called Ferby. It allows users to access and personalize educational resources from the Internet-Free Education Resource Bank, an online library containing hundreds of free and open-source learning materials.

Education Above All said it's already being used by over 5 million Indian children to access "project-based learning" in partnership with Indian nonprofit Mantra4Change. More recently, Education Above All has embedded Ferby into edtech platform SwiftChat, which is used by 124 million students and teachers across India.

"Ferby curates, customizes, and creates learning materials to fit local realities, so a teacher in rural Malawi can run the right science experiment as easily as a teacher in downtown Doha," said Aishwarya Shetty, an education specialist at Education Above All. "By marrying offline ingenuity with AI convenience, we make learning local, low-resource, and always within reach, yet at scale."

Education Above All is among a group of organizations using AI to tackle global inequality and work toward realizing the United Nations Sustainable Development Goals. Created in 2015, the UN SDGs comprise 17 social, economic, and environmental targets that serve as guidelines for nations, businesses, and individuals to follow to help achieve a more peaceful and prosperous world. Education Above All's projects fall under SDG 4: inclusive and equitable education.

A global effort

A range of other organizations are using AI to augment and enhance their education programming.

Tech To The Rescue, a global nonprofit that connects charities with pro-bono software development teams to meet their goals, is another organization using AI in support of the UN SDGs. Last year, it launched a three-year AI-for-good accelerator program to help NGOs meet the various UN SDGs using AI.

One organization to benefit from the program is Mercy Corps, a humanitarian group that works across over 40 countries to tackle crises like poverty, the climate crisis, natural disasters, and violence. Through the accelerator, it created an AI strategy tool that helps first responders predict disasters and coordinate resources. The World Institute on Disability AI also participated in the accelerator program, creating a resource-matching system that helps organizations allocate support to people with disabilities in hours rather than weeks.

Similarly, the International Telecommunication Union — the United Nations' digital technology agency, and one of its oldest arms — is supporting organizations using technology to achieve the UN SDGs through its AI for Good Innovation Factory startup competition. For example, an Indian applicant — a startup called Bioniks — has enabled a teenager to reclaim the ability to do simple tasks like writing and getting dressed through the use of AI-powered prosthetics.

Challenges to consider

While AI may prove to be a powerful tool for achieving the UN SDGs, it comes with notable risks. Again, as AI models are largely developed by American tech giants in an industry already constrained by gender and racial inequality, unconscious bias is a major flaw of AI systems.

To address this, Shetty said layered prompts for non-English users, human review of underlying AI datasets, and the creation of indigenous chatbots are paramount to achieving Education Above All's goals.

AI models are also power-intensive, making them largely inaccessible to the populations of developing countries. That's why Shetty urges AI companies to provide their solutions via less tech-heavy methods, like SMS, and to offer offline features so users can still access AI resources when their internet connections drop. Open-source, free-of-charge subscriptions can help, too, she added.

AI as a source for good

Challenges aside, Shetty is confident that AI can be a force for good over the next few years, particularly around education. She told BI, "We are truly energized by how the global education community is leveraging AI in education: WhatsApp-based math tutors reaching off-grid learners; algorithms that optimize teacher deployment in shortage areas; personalized content engines that democratize education; chatbots that offer psychosocial support in crisis zones and more."

But Shetty is clear that AI should augment, rather than displace, human educators. And she said the technology should only be used if it can solve challenges faced by humans and add genuine value.

"Simply put," she said, "let machines handle the scale, let humans handle the soul, with or without AI tools."

• Quick-service and fast-food restaurants typically collect data on customers' purchasing behaviors.
• With the help of AI, they can now leverage their data to better manage inventory and operations.
• This article is part of "How AI Is Changing Everything: Supply Chain," a series on innovations in logistics.

Fast-food chain Juici Patties, which operates more than 70 locations in Florida, New York, and Jamaica, started on the island nation as a family kitchen in 1978. When the chain expanded into the US last year, it experienced stockouts.

Executives knew they needed a different strategy — one with advanced technology to scale their business, manage franchises, and sell thousands of patties each day, Stuart Levy, the company's chief technology officer, told Business Insider.

Today, Juici Patties uses AI's predictive and proactive features to prevent disruptions before they occur.

"AI is helping to keep our distribution centers stocked with enough of our branded packaging to meet demand," Levy said.

Indeed, AI technology is making its way into quick-service and fast-casual restaurant operations. AI can use data to form predictions about customer orders, then generate insights for leaders on how to manage inventory and operations.

Domino's Pizza and Microsoft teamed up to create a generative-AI assistant that saves managers time on inventory management and ingredient ordering. Starbucks also inked a deal with Microsoft to use genAI in its product development. And Yum Brands, the parent company of KFC, Taco Bell, and others, partnered with Nvidia on AI for internal tasks such as labor management and analytics processing.

For many quick-service restaurants, "their entire brand is built on speed and efficiency," said Spencer Michiel, the restaurant technology advisor at Back of House, a resource for restaurant tech solutions. "If there's anything that can help them with speed, efficiency, and lower cost, they're going to jump all over it."

Data-rich restaurants layer on AI

Restaurants are "extremely data-rich," Michiel said, which makes them well-suited to adopt AI. Major fast-food chains already have standard operating procedures to purchase based on demand, but AI takes that to the next level with forecasting abilities that more accurately predict demand and inform supply.

With AI's forecasting capabilities, restaurants can predict what customers might order and use this data to buy ingredients, a notoriously tricky part of restaurant supply chain management.

"The biggest thing that restaurants do badly is purchase," said Stephen Zagor, a consultant focused on restaurants and food businesses and an adjunct assistant professor of business at Columbia Business School.

AI draws from quick-service restaurants' internal point-of-sale data, such as sales trends and which products customers tend to buy at the same time. Then, an AI algorithm combines this data with external factors like the weather or local events.

"The beauty of AI is it's taking forecasted demand and turning that into a reaction all the way through the supply chain," Zagor said.

For example, AI can deliver granular data by location. For a restaurant right off an interstate, AI could predict that travel will slow down on certain days. Seeing that prediction, restaurant managers could decide to drop their inventory levels and purchase fewer items, Zagor said.

He named McDonald's as one quick-service restaurant that uses AI to maximize everything from its point-of-sale to its supply chain. The fast-food giant has partnered with Google Cloud and IBM on various AI solutions.

When it comes to data and AI, the level of standardization across major chains puts them at an advantage over smaller franchises and independent restaurants.

A mom-and-pop restaurant may not have "the time, the bandwidth, the skills, the knowledge" to gather data and create an action plan, Michiel said. Subscribing to software can cost hundreds of dollars each month, presenting financial barriers to small businesses. Any new back-of-house or supply chain software would need to integrate with existing point-of-sale systems. If done incorrectly, the result could be data loss or lag, "and it's going to be frustrating," Michiel said.

Serving up efficiency and financial gains

AI's predictive power can also help minimize waste in restaurant supply chains. If a restaurant orders too much, it could have to discard unused or expired food. This could require the business to increase meal costs to compensate for the loss, according to Michiel.

"Food waste is just a killer," Michiel said. "Over-ordering is straight loss. There's no way you're going to recover that cost."

Controlling costs is especially critical for fast-food chains, which order at scale and sell low-priced products. Making just 5 cents more on an item, or making 5 cents fewer, "is a big deal," Zagor said.

AI can also promote cost savings by flagging if a particular ingredient swap could result in higher profits without sacrificing taste or quality. The technology "smooths out" a restaurant's ability to purchase inventory while still keeping customer satisfaction top of mind, Zagor said.

"You can get good profit, and the customer is going to be happy," Zagor said. "It's win-win."

Levy said Juici Patties' AI implementation into its point-of-sale system and supply chain was time-consuming, involved some growing pains, and sparked fears about replacing the workforce with AI. He acknowledged that "AI isn't flawless."

Now that the technology is in place, though, Juici Patties has seen a boost in operational efficiency, Levy said. In one instance, the AI revealed that customers wanted to purchase food earlier in the day, before Juici Patties locations were open.

"We were missing potential sales during earlier hours of the day," Levy said. The restaurant chain acted upon that information and adjusted its opening times. The result: "a consistent increase in daily sales," Levy said.

• Retailers like Target and Walmart use AI to prevent stockouts and manage inventory.
• AI systems can predict demand to help improve inventory accuracy and availability.
• This article is part of "How AI Is Changing Everything: Supply Chain," a series on innovations in logistics.

The adage "too much, too little, just right" isn't just for Goldilocks and her porridge. Balance is also critical in inventory management, the part of the supply chain responsible for analyzing what consumers will buy and making sure products are in stock at the right place and the right time.

Excess inventory can lead to markdowns or expired goods, but too little product can lead to shortages that impact a retailer's brand image, customer satisfaction, and bottom line.

To prevent inventory from running out, big-box retailers such as Target, The Home Depot, and Walmart are using AI to predict when product amounts could dwindle. As a result, Target's inventory availability has improved every year for the last four years, Prat Vemana, the executive vice president and chief information and product officer at Target, told Business Insider.

AI can help retailers proactively adjust stock before disruption strikes rather than reacting to changing conditions, said Ajoy Krishnamoorthy, the CEO of inventory-management platform Cin7. That's especially critical today, Krishnamoorthy added, when factors like consumer behavior, inflation, and trade policy constantly impact supply chains.

"AI thrives in this environment," Krishnamoorthy said.

From old methods to AI

Traditionally, companies procure inventory, manage logistics, and analyze consumer behavior in silos, said Vidya Mani, an associate professor of business administration focused on technology and operations management at the University of Virginia. Teams do individual research, then come together to develop a strategy and execute it.

"We no longer have that time," Mani said. "By the time you finish doing it, the world will have changed on you."

Before Target started using AI to predict stockouts, the retailer relied on software-based applications, which didn't react or adapt to real-world changes as quickly as AI systems, according to Vemana. In fact, Target said in a blog post that it previously failed to catch half of its products that were out of stock because the technology they used thought that inventory existed when it actually didn't.

Target changed how it managed inventory in 2023 with the introduction of Inventory Ledger, an internal tech system that tracks inventory changes across stores and uses AI to predict when products might be out of stock "even before it's obvious to team members or systems," Vemana said.

Today, Target uses AI-driven inventory management for more than 40% of its product assortment, which is more than double when it started two years ago, Vemana said.

With Inventory Ledger, algorithms pull in data like supply lead times, transportation costs, current inventory, and consumer demand. Some models are more accurate for frequently purchased categories, and others are better suited to discretionary purchases or clearance items, so Target uses both kinds. There are also models that detect items that are in the store but in the wrong aisle or shelf, Vemana said.

Target has a demand forecasting tool that "makes billions of predictions each week about how many units of each item we'll need in stores and online," Vemana said. Together, all of these technologies guide employees' decisions about when, where, and how to reorder products and replenish stock.

"Combining traditional software with AI helps us make smarter, faster decisions about inventory management and keep our stores stocked more consistently," Vemana said.

One of Cin7's customers, ABC School Supplies, is also using AI to access real-time sales data, potential stock shortages, and supplier lead times, so it can "reorder proactively and avoid costly gaps in supply," Krishnamoorthy said.

The AI-driven inventory management marks a big change from what ABC School Supplies did in the past. It used to copy and paste website orders into its system, make a pick-and-pack list on paper, walk that physical list over to the warehouse, and manually update inventory, Krishnamoorthy said

The Home Depot is also taking an AI-based approach to inventory. In 2023, the retailer rolled out a machine-learning-powered app called Sidekick, which guides store workers to restock shelves and find products on overhead shelves, among other features.

"It helps make sure products are on shelves for our customers, and it manages our on-hand accuracy, which feeds to our replenishment and selling platforms," a spokesperson for The Home Depot said.

AI's predictive power for inventory planning

Krishnamoorthy said that in retail, "AI is exploding" as businesses move away from static planning to dynamic forecasts that anticipate demand and prevent stockouts.

AI allows businesses to get more granular with their inventory data, avoiding stockouts at particular store locations or during peak times. Mani gave the example of cosmetics and how different stores will have varying product needs based on consumer demographics.

"AI can figure out those patterns of baskets that are bought frequently in these different clusters," Mani said. "You don't need to feed it that contextual information."

Target is also working on technology that predicts which colors and sizes of seasonal items will sell, so it can stock those items in specific stores "to meet local demand," Vemana said.

At Walmart, AI-based inventory management systems use algorithms to make sure stores in warmer states have the right amount of pool toys and colder states stock enough sweaters, according to a press release on the company's website. If a particular item isn't selling on the East Coast but it's flying off shelves in the Midwest, algorithms flag that pattern so Walmart can reposition its inventory.

As retailers continue to develop and deploy AI tools, Mani predicts the technology's use cases will advance over the next decade.

Mani said that in two to three years, AI will likely flag stockouts without a person needing to walk into a store to confirm. Five years out, AI could automatically reorder inventory when algorithms detect that stock is running low. And in 10 years, AI would understand how macroeconomic events (like inflation) will change future consumer purchase behavior patterns and adjust inventory plans accordingly.

"It will feed it into your rulemaking rather than reacting to the situation," Mani said. "You'll be living in a different world."

• Albert Invent is a digital platform that uses AI-driven analysis to assist chemists with research.
• The platform integrates data from electronic lab notebooks to speed up product development.
• This article is part of "How AI Is Changing Everything: Supply Chain," a series on innovations in logistics.

Cosmetic chemistry, or the science of making beauty products, is a complex process that requires understanding how ingredients interact with each other and with the skin.

With so many variables to consider — safety, shelf lifespan, texture, and appearance — the process of blending ingredients for face creams, eye shadows, lipsticks, and other cosmetics can be time-consuming for chemists, who typically conduct independent research to figure out which compounds and minerals can work together to create a safe, effective, and sellable product.

Albert Invent, based in Oakland, California, seeks to simplify this process for chemists with its digital platform called Albert.

The company's CEO, Nick Talken, said Albert enables chemists to research and develop safe, high-performing products without the need to refer to the notebooks and spreadsheets where they typically store data. Since Albert integrates data that's already been stored in electronic lab notebooks and laboratory information management systems, chemists can come up with test-worthy formulations in less time.

How AI can help chemists develop safe and effective cosmetics

Albert is trained on more than 15 million molecular structures, Talken said. When chemists — from companies like the adhesive and cleaning supplies manufacturer Henkel, the Teflon-maker Chemours, and the chemical manufacturing company Nouryon — use the platform, they can look up which permutations of molecules will work best to achieve a specific goal.

The platform was designed to capture the kind of information that chemists typically track in notebooks or on spreadsheets, such as the materials and substances they might use, their compositions, and processing steps.

When a chemist asks Albert for input on which other substances work well with a particular ingredient, the system offers feedback on possible substance combinations and predicts the physical, toxicological, and visual properties of new compounds before they are synthesized in a lab. This AI-driven analysis gives formulators the opportunity to determine whether a concoction is safe and effective to produce, or whether they should scrap the idea, in minutes.

Albert Invent partnered with Nouryon, which owns a collection of formulation strategies for the personal care industry (think cosmetics, hair care, and skincare products) that have been cleared as effective and safe. The result: a digital platform for developing new cosmetics formulations, called BeautyCreations.

Instead of employing the traditional product-development methods of trial and error and real-time experimentation — methods that can typically take anywhere from four to six weeks — Nouryon's chemists can use BeautyCreations to look through the company's existing formulations for hair and skincare products and filter for results that match their desired safety standards and marketing claims, all while adhering to stringent development timelines.

David Freidinger, the vice president of personal care and pharma at Nouryon, said this technology has enabled the company's chemists to develop new products from almost anywhere in the world. It's also improved the speed and quality of Nouryon's internal product development, as the company can look at BeautyCreations data to better understand market trends and prioritize development initiatives accordingly.

An AI tool for chemistry beyond cosmetics

Arthur Tisi, a former CTO and chief information officer who advises private equity and portfolio companies on digital technology strategies, said that the molecular AI technology behind Albert could be of use to other data-heavy industries in the future.

"The ability to 'digitalize' our technical expertise and make it available to customers 24/7 enables accelerated scaling and efficiency in customer support," Tisi wrote in a recent email to BI. He added that tools like Albert are powerful because they offer both product-formula data and consumer insights.

Tisi said that in the future, the value of molecular AI will go beyond its speed benefits. He said that this technology has the potential to uncover certain chemical formulations that scientists might miss.

Freidinger said industries that use reams of empirical data to create products or deliver services could benefit from AI tools like Albert to improve speed and quality.

"The same technology that speeds up skincare development can revolutionize personalized medicine, where rapidly identifying the perfect molecular combinations could mean delivering custom-targeted therapies for individual patients, potentially turning fatal diagnoses into manageable conditions," Freidinger said.

Meanwhile, Talken said that Albert has the potential to be used for inventing new polymers and batteries.

• Hospitals face frequent disruptions in inventory for supplies like IV fluids and medications.
• Mayo Clinic, Cleveland Clinic, and Rush University Medical Center leaders talk about managing that with AI.
• This article is part of "How AI Is Changing Everything: Supply Chain," a series on innovations in logistics.

When Hurricane Helene hit North Carolina in late September, it caused more than $59 billion in damages.

Among those businesses damaged was one of the US's main manufacturers of IV fluids, used to rehydrate patients and give them medicine. The resultant shortage forced hospitals to conserve and reduce their use of IV fluids, which led to canceled surgeries and treatment delays.

Such disruptions to hospital inventory have long been hard to predict and difficult for hospitals to navigate. At the same time, keeping too much of a given item on hand is wasteful. In 2019, hospitals spent about $25.7 billion on supplies that they didn't need, the consulting firm Navigant found in a study of over 2,100 hospitals — about $12.1 million for an average hospital.

To reduce waste, while ensuring providers have the medical supplies they need, some leading hospital systems are using automation, predictive analytics, and other forms of artificial intelligence to manage inventory.

Business Insider asked supply chain managers from three systems — the Cleveland Clinic, the Mayo Clinic, and Rush University Medical Center — about how they use machine learning, generative AI, sensors, and robotics to anticipate shortages and help with contracting and ordering.

The following has been edited for length and clarity.

Business Insider: What are some of the most effective and innovative ways that you're making use of AI?

Joe Dudas, Mayo Clinic's division chair of supply chain strategy: We've deployed autonomous delivery and robotic warehouse fulfilment — robots that pick orders.

We're advancing our algorithms for auto-replenishment to be even more accurate. We're also using AI to explore savings opportunities and understand the sustainability of those opportunities over the length of an agreement, based, for example, on demand.

We're doing advanced analytics in high-spend categories — we're just getting a lot smarter about what's happening with a little bit more precision. Even our expense management, we're looking at profit and loss, and supply expense, to understand what's happening from a budgetary perspective. Based on the present and what's happened in the past, we can look forward with some degree of accuracy.

Geoff Gates, Cleveland Clinic's senior director of supply chain management: In some of our tools, instead of having someone click lots of buttons and type data into 20 or more fields, for example, we have been able to automate that process with AI, which saves employees 20 minutes every time. Those are the tasks that are the biggest benefit from a pure efficiency standpoint — they let people focus on other things.

We also use AI for document recognition and have been using it to manage invoices through our ERP inventory-management system for the last four years. If a medical-supply rep has a bill sheet that needs to get processed — to create a purchase order — the rep submits it, and our tool automatically creates a requisition.

With distribution, our goal is to create a better view of what we have within our health system and the hospital. Our goal with key suppliers is to be able to see which supplies they have in their warehouses and to predict disruptions. For instance, if we can see that a supplier doesn't have a shipment coming in, the system would alert us that we'll have a problem in two weeks.

Jeremy Strong, Rush University Medical Center's vice president of supply chain: For inventory management, we have weighted bin systems in all heavy-volume areas. When a nurse takes something out and puts something back in, we know it.

Once we implemented that, we could start to be proactive. We have a system that includes our distributor's data about inventory coming into their distribution center. They can see where our utilization patterns are changing. Then AI reviews all that. A back-order dashboard creates alerts when automatic supply-refill levels across the system are low, inventory is low at the distribution center, or shipments from manufacturers are taking longer than anticipated. We can anticipate that we're going to run out in a week from now or going to have a back-order problem.

We also use it in contract management. When a contract is loaded in, AI will send it to the category manager with a summary and potential clauses to review. It can also automatically send contracts to the cybersecurity team for approval. If it has patient information, it sends it to the risk lawyers. If it has indemnification, it sends it to our regular lawyers.

What are some of the advantages of automation that your system has realized?

Dudas: Our automation gives us agility. We can see things sooner and adjust faster because of our technology but also because of our talent.

Somebody asked me the other day, "Where are you advancing?" I said, "We're not advancing. We're keeping up with all of the curveballs we get thrown on a day-to-day basis."

Gates: At this point, the tools have touched almost everyone in the supply chain. Even a specific process that only impacts one or two people who were doing those tasks allows us to be more efficient and accurate.

Strong: The goal was to move from being reactive and putting out fires to being more predictive, to prevent fires from happening, see things ahead of time, and be more efficient.

We've also sped up contract review. We cut the time it takes to review them in half and more than doubled the number of reviews each contract gets.

What advice do you have for other companies interested in implementing AI to streamline inventory?

Dudas: Recognize that you can't do everything yourself. Even as big as our organization is, it's not big enough. Scale is your friend in the supply chain.

Gates: Some things weren't necessarily the biggest opportunities to start with, but they were low-risk processes that gave us the skillset needed to leverage AI.

We're most focused on finding the right solution for the problem rather than forcing a solution.

Strong: The best tasks or processes to tackle are ones that are repetitive or require pulling and summarizing data from multiple digital sources. Tackle these, and you can gain efficiencies, improve productivity, and be proactive.