Generative AI (GenAI) is being deployed by 72% of supply chain organizations, but most are experiencing just middling results for productivity and ROI, according to a survey by Gartner, Inc.
That’s because productivity gains from the use of GenAI for individual, desk-based workers are not translating to greater team-level productivity. Additionally, the deployment of GenAI tools is increasing anxiety among many employees, providing a dampening effect on their productivity, Gartner found.
To solve those problems, chief supply chain officers (CSCOs) deploying GenAI need to shift from a sole focus on efficiency to a strategy that incorporates full organizational productivity. This strategy must better incorporate frontline workers, assuage growing employee anxieties from the use of GenAI tools, and focus on use-cases that promote creativity and innovation, rather than only on saving time.
"Early GenAI deployments within supply chain reveal a productivity paradox," Sam Berndt, Senior Director in Gartner’s Supply Chain practice, said in the report. "While its use has enhanced individual productivity for desk-based roles, these gains are not cascading through the rest of the function and are actually making the overall working environment worse for many employees. CSCOs need to retool their deployment strategies to address these negative outcomes.”
As part of the research, Gartner surveyed 265 global respondents in August 2024 to assess the impact of GenAI in supply chain organizations. In addition to the survey, Gartner conducted 75 qualitative interviews with supply chain leaders to gain deeper insights into the deployment and impact of GenAI on productivity, ROI, and employee experience, focusing on both desk-based and frontline workers.
Gartner’s data showed an increase in productivity from GenAI for desk-based workers, with GenAI tools saving 4.11 hours of time weekly for these employees. The time saved also correlated to increased output and higher quality work. However, these gains decreased when assessing team-level productivity. The amount of time saved declined to 1.5 hours per team member weekly, and there was no correlation to either improved output or higher quality of work.
Additional negative organizational impacts of GenAI deployments include:
Frontline workers have failed to make similar productivity gains as their desk-based counterparts, despite recording a similar amount of time savings from the use of GenAI tools.
Employees report higher levels of anxiety as they are exposed to a growing number of GenAI tools at work, with the average supply chain employee now utilizing 3.6 GenAI tools on average.
Higher anxiety among employees correlates to lower levels of overall productivity.
“In their pursuit of efficiency and time savings, CSCOs may be inadvertently creating a productivity ‘doom loop,’ whereby they continuously pilot new GenAI tools, increasing employee anxiety, which leads to lower levels of productivity,” said Berndt. “Rather than introducing even more GenAI tools into the work environment, CSCOs need to reexamine their overall strategy.”
According to Gartner, three ways to better boost organizational productivity through GenAI are: find creativity-based GenAI use cases to unlock benefits beyond mere time savings; train employees how to make use of the time they are saving from the use GenAI tools; and shift the focus from measuring automation to measuring innovation.
Oh, you work in logistics, too? Then you’ve probably met my friends Truedi, Lumi, and Roger.
No, you haven’t swapped business cards with those guys or eaten appetizers together at a trade-show social hour. But the chances are good that you’ve had conversations with them. That’s because they’re the online chatbots “employed” by three companies operating in the supply chain arena—TrueCommerce,Blue Yonder, and Truckstop. And there’s more where they came from. A number of other logistics-focused companies—like ChargePoint,Packsize,FedEx, and Inspectorio—have also jumped in the game.
While chatbots are actually highly technical applications, most of us know them as the small text boxes that pop up whenever you visit a company’s home page, eagerly asking questions like:
“I’m Truedi, the virtual assistant for TrueCommerce. Can I help you find what you need?”
“Hey! Want to connect with a rep from our team now?”
“Hi there. Can I ask you a quick question?”
Chatbots have proved particularly popular among retailers—an October survey by artificial intelligence (AI) specialist NLX found that a full 92% of U.S. merchants planned to have generative AI (GenAI) chatbots in place for the holiday shopping season. The companies said they planned to use those bots for both consumer-facing applications—like conversation-based product recommendations and customer service automation—and for employee-facing applications like automating business processes in buying and merchandising.
But how smart are these chatbots really? It varies. At the high end of the scale, there’s “Rufus,” Amazon’s GenAI-powered shopping assistant. Amazon says millions of consumers have used Rufus over the past year, asking it questions either by typing or speaking. The tool then searches Amazon’s product listings, customer reviews, and community Q&A forums to come up with answers. The bot can also compare different products, make product recommendations based on the weather where a consumer lives, and provide info on the latest fashion trends, according to the retailer.
Another top-shelf chatbot is “Manhattan Active Maven,” a GenAI-powered tool from supply chain software developer Manhattan Associates that was recently adopted by the Army and Air Force Exchange Service. The Exchange Service, which is the 54th-largest retailer in the U.S., is using Maven to answer inquiries from customers—largely U.S. soldiers, airmen, and their families—including requests for information related to order status, order changes, shipping, and returns.
However, not all chatbots are that sophisticated, and not all are equipped with AI, according to IBM. The earliest generation—known as “FAQ chatbots”—are only clever enough to recognize certain keywords in a list of known questions and then respond with preprogrammed answers. In contrast, modern chatbots increasingly use conversational AI techniques such as natural language processing to “understand” users’ questions, IBM said. It added that the next generation of chatbots with GenAI capabilities will be able to grasp and respond to increasingly complex queries and even adapt to a user’s style of conversation.
Given their wide range of capabilities, it’s not always easy to know just how “smart” the chatbot you’re talking to is. But come to think of it, maybe that’s also true of the live workers we come in contact with each day. Depending on who picks up the phone, you might find yourself speaking with an intern who’s still learning the ropes or a seasoned professional who can handle most any challenge. Either way, the best way to interact with our new chatbot colleagues is probably to take the same approach you would with their human counterparts: Start out simple, and be respectful; you never know what you’ll learn.
Netstock included the upgrades in AI Pack, a series of capabilities within the firm’s Predictor Inventory Advisor platform, saying they will unlock supply chain agility and enable SMBs to optimize inventory management with advanced intelligence.
The new tools come as SMBs are navigating an ever-increasing storm of supply chain challenges, even as many of those small companies are still relying on manual processes that limit their visibility and adaptability, the company said.
Despite those challenges, AI adoption among SMBs remains slow. Netstock’s recent Benchmark Report revealed that concerns about data integrity and inconsistent answers are key barriers to AI adoption in logistics, with only 23% of the SMBs surveyed having invested in AI.
Netstock says its new AI Pack is designed to help SMBs overcome these hurdles.
“Many SMBs are still relying on outdated tools like spreadsheets and phone calls to manage their inventory. Dashboards have helped by visualizing the right data, but for lean teams, the sheer volume of information can quickly lead to overload. Even with all the data in front of them, it’s tough to know what to do next,” Barry Kukkuk, CTO at Netstock, said in a release.
“Our latest AI capabilities change that by removing the guesswork and delivering clear, actionable recommendations. This makes decision-making easier, allowing businesses to focus on building stronger supplier relationships and driving strategic growth, rather than getting bogged down in the details of inventory management,” Kukkuk said.
Artificial intelligence (AI) and data science were hot business topics in 2024 and will remain on the front burner in 2025, according to recent research published in AI in Action, a series of technology-focused columns in the MIT Sloan Management Review.
In Five Trends in AI and Data Science for 2025, researchers Tom Davenport and Randy Bean outline ways in which AI and our data-driven culture will continue to shape the business landscape in the coming year. The information comes from a range of recent AI-focused research projects, including the 2025 AI & Data Leadership Executive Benchmark Survey, an annual survey of data, analytics, and AI executives conducted by Bean’s educational firm, Data & AI Leadership Exchange.
The five trends range from the promise of agentic AI to the struggle over which C-suite role should oversee data and AI responsibilities. At a glance, they reveal that:
Leaders will grapple with both the promise and hype around agentic AI. Agentic AI—which handles tasks independently—is on the rise, in the form of generative AI bots that can perform some content-creation tasks. But the authors say it will be a while before such tools can handle major tasks—like make a travel reservation or conduct a banking transaction.
The time has come to measure results from generative AI experiments. The authors say very few companies are carefully measuring productivity gains from AI projects—particularly when it comes to figuring out what their knowledge-based workers are doing with the freed-up time those projects provide. Doing so is vital to profiting from AI investments.
The reality about data-driven culture sets in. The authors found that 92% of survey respondents feel that cultural and change management challenges are the primary barriers to becoming data- and AI-driven—indicating that the shift to AI is about much more than just the technology.
Unstructured data is important again. The ability to apply Generative AI tools to manage unstructured data—such as text, images, and video—is putting a renewed focus on getting all that data into shape, which takes a whole lot of human effort. As the authors explain “organizations need to pick the best examples of each document type, tag or graph the content, and get it loaded into the system.” And many companies simply aren’t there yet.
Who should run data and AI? Expect continued struggle. Should these roles be concentrated on the business or tech side of the organization? Opinions differ, and as the roles themselves continue to evolve, the authors say companies should expect to continue to wrestle with responsibilities and reporting structures.
The deal will add the Google DeepMind robotics team’s AI expertise to Austin, Texas-based Apptronik’s robotics platform, allowing the units to handle a wider range of tasks in real-world settings like factories and warehouses.
The Texas firm joins other providers of two-legged robots such as the Oregon company Agility Robotics, which is currently testing its humanoid units with the large German automotive and industrial parts supplier Schaeffler AG, as well as with GXO. GXO is also running trials of a third type of humanoid bot made by New York-based Reflex Robotics. And another provider of humanoid robots, the Canadian firm Sanctuary AI, this year landed funding from the consulting firm Accenture.
“We’re building a future where humanoid robots address urgent global challenges,” Jeff Cardenas, CEO and co-founder of Apptronik, said in a release. “By combining Apptronik’s cutting-edge robotics platform with the Google DeepMind robotics team’s unparalleled AI expertise, we’re creating intelligent, versatile and safe robots that will transform industries and improve lives. United by a shared commitment to excellence, our two companies are poised to redefine the future of humanoid robotics.”
When it comes to logistics technology, the pace of innovation has never been faster. In recent years, the market has been inundated by waves of cool new tech tools, all promising to help users enhance their operations and cope with today’s myriad supply chain challenges.
But that ever-expanding array of offerings can make it difficult to separate the wheat from the chaff—technology that’s the real deal versus technology that’s just “vaporware,” meaning products that don’t live up to their hype and may even still be in the conceptual stage.
One way to cut through the confusion is to check out the entries for the “3 V’s of Supply Chain Innovation Awards,” an annual competition held by the Council of Supply Chain Management Professionals (CSCMP). This competition, which is hosted by DC Velocity’s sister publication, Supply Chain Xchange, and supply chain visionary and 3 V’s framework creator Art Mesher, recognizes companies that have parlayed the 3 V’s—“embracing variability, harnessing visibility, and competing with velocity”—into business success and advanced the practice of supply chain management. Awards are presented in two categories: the “Business Innovation Award,” which recognizes more established businesses, and the “Best Overall Innovative Startup/Early Stage Award,” which recognizes newer companies.
The judging for this year’s competition—the second annual contest—took place at CSCMP’s EDGE Supply Chain Conference & Exhibition in September, where the three finalists for each award presented their innovations via a fast-paced “elevator pitch.” (To watch a video of the presentations, visit the Supply Chain Xchange website.)
What follows is a brief look at the six companies that made the competition’s final round and the latest updates on their achievements:
Arkestro: This San Francisco-based firm offers a predictive procurement orchestration solution that uses machine learning (ML) and behavioral science to revolutionize sourcing, eliminating the need for outdated manual tools like pivot tables and for labor-intensive negotiations. Instead, procurement teams can process quotes and secure optimal supplier agreements at a speed and accuracy that would be impossible to achieve manually, the firm says.
The company recently joined the Amazon Web Services (AWS) Partner Network (APN), which it says will help it reach its goal of elevating procurement from a cost center to a strategic growth engine.
AutoScheduler.AI: This Austin, Texas-based company offers a predictive warehouse optimization platform that integrates with a user’s existing warehouse management system (WMS) and “accelerates” its ability to resolve problems like dock schedule conflicts, inefficient workforce allocation, poor on-time/in-full (OTIF) performance, and excessive intra-campus moves.
“We’re here to make the warehouse sexy,” the firm says on its website. “With our deep background in building machine learning solutions, everything delivered by the AutoScheduler team is designed to provide value by learning your challenges, environment, and best practices.” Privately funded up until this summer, the company recently secured venture capital funding that it will use to accelerate its growth and enhance its technologies.
Davinci Micro Fulfillment: Located in Bound Brook, New Jersey, Davinci operates a “microfulfillment as a service” platform that helps users expedite inventory turnover while reducing operating expenses by leveraging what it calls the “4 Ps of global distribution”—product, placement, price, and promotion. The firm operates a network of microfulfillment centers across the U.S., offering services that include front-end merchandising and network optimization.
Within the past year, the company raised seed funding to help enhance its technology capabilities.
Flying Ship: Headquartered in Leesburg, Virginia, Flying Ship has designed an unmanned, low-flying “ground-effect maritime craft” that moves freight over the ocean in coastal regions. Although the Flying Ship looks like a small aircraft or large drone, it is classified as a maritime vessel because it does not leave the air cushion over the waves, similar to a hovercraft.
The first-generation models are 30 feet long, electrically powered, and semi-autonomous. They can dock at existing marinas, beaches, and boat ramps to deliver goods, providing service that the company describes as faster than boats and cheaper than air. The firm says the next-generation models will be fully autonomous.
Flying Ship, which was honored with the Best Overall Startup Award in this year’s 3 V’s competition, is currently preparing to fly demo missions with the Air Force Research Laboratory (AFRL).
Perfect Planner: Based in Alpharetta, Georgia, Perfect Planner operates a cloud-based platform that’s designed to streamline the material planning and replenishment process. The technology collects, organizes, and analyzes data from a business’s material requirements planning (MRP) system to create daily “to-do lists” for material planners/buyers, with the “to-dos” ranked in order of criticality. The solution also uses advanced analytics to “understand” and address inventory shortages and surpluses.
Perfect Planner was honored with the Business Innovation Award in this year’s 3 V’s competition.
ProvisionAi: Located in Franklin, Tennessee, ProvisionAi has developed load optimization software that helps consumer packaged goods (CPG) companies move their freight with fewer trucks, thereby cutting their transportation costs. The firm says its flagship offering is an automatic order optimization (AutoO2) system that bolts onto a company’s existing enterprise resource planning (ERP) or WMS platform and guides larger orders through execution, ensuring that what is planned is actually loaded on the truck. The firm’s CEO and founder, Tom Moore, was recognized as a 2024 Rainmaker by this magazine.