Benefits for Amazon's customers--who include marketplace retailers and logistics services customers, as well as companies who use its Amazon Web Services (AWS) platform and the e-commerce shoppers who buy goods on the website--will include generative AI (Gen AI) solutions that offer real-world value, the company said.
The launch is based on “Amazon Nova,” the company’s new generation of foundation models, the company said in a blog post. Data scientists use foundation models (FMs) to develop machine learning (ML) platforms more quickly than starting from scratch, allowing them to create artificial intelligence applications capable of performing a wide variety of general tasks, since they were trained on a broad spectrum of generalized data, Amazon says.
The new models are integrated with Amazon Bedrock, a managed service that makes FMs from AI companies and Amazon available for use through a single API. Using Amazon Bedrock, customers can experiment with and evaluate Amazon Nova models, as well as other FMs, to determine the best model for an application.
Calling the launch “the next step in our AI journey,” the company says Amazon Nova has the ability to process text, image, and video as prompts, so customers can use Amazon Nova-powered generative AI applications to understand videos, charts, and documents, or to generate videos and other multimedia content.
“Inside Amazon, we have about 1,000 Gen AI applications in motion, and we’ve had a bird’s-eye view of what application builders are still grappling with,” Rohit Prasad, SVP of Amazon Artificial General Intelligence, said in a release. “Our new Amazon Nova models are intended to help with these challenges for internal and external builders, and provide compelling intelligence and content generation while also delivering meaningful progress on latency, cost-effectiveness, customization, information grounding, and agentic capabilities.”
The new Amazon Nova models available in Amazon Bedrock include:
Amazon Nova Micro, a text-only model that delivers the lowest latency responses at very low cost.
Amazon Nova Lite, a very low-cost multimodal model that is lightning fast for processing image, video, and text inputs.
Amazon Nova Pro, a highly capable multimodal model with the best combination of accuracy, speed, and cost for a wide range of tasks.
Amazon Nova Premier, the most capable of Amazon’s multimodal models for complex reasoning tasks and for use as the best teacher for distilling custom models
Amazon Nova Canvas, a state-of-the-art image generation model.
Amazon Nova Reel, a state-of-the-art video generation model that can transform a single image input into a brief video with the prompt: dolly forward.
Having reported on the supply chain world for some 25 years, I've seen technologies come and go. Many were once touted as the best thing since sliced bread but either failed to live up to the hype or else had to simmer a few years before they caught on.
Remember the hoopla surrounding dot-com retail? In the late 1990s, we were told that stores as we knew them would eventually go away, to be totally replaced by online shopping. The ease and convenience of e-commerce made that a reasonable expectation. But in March 2000, the bubble burst, and a host of online retailers closed their virtual doors forever. Of course, online shopping is still very much with us, and its share of total retail sales is growing by the year. Maybe we'll get to that retail seventh heaven someday, but it's taking much longer than originally predicted.
Then there's RFID (radio-frequency identification). These small electronic tags were going to replace barcodes largely because of the vast amount of data they can hold and their capacity to update information.
In 2003, Walmart famously demanded that its top 100 suppliers affix RFID tags to all pallets and cases shipped to its DCs. We figured that if Walmart had gone all in on RFID, the rest of the industry would automatically follow. Well, not so fast. It's true that after years of stutter-step progress, Walmart today is more heavily invested in RFID than ever. But in the rest of the world, the humble barcode is still king.
A more recently hyped technology is blockchain. It was actually conceived back in 1982 but remained just a concept until 2008, when a person (or persons) using the name "Satoshi Nakamoto" created an actual blockchain to serve as the public distributed ledger for cryptocurrency transactions. Blockchain was expected to revolutionize the way supply chain partners do business. But it, too, has been a bit slow to take off, and it's still unclear how the blockchain story will play out.
That brings us to the latest potentially game-changing technology: artificial intelligence (AI). In some ways, AI is really just data analytics on steroids. Supply chains have relied on data analytics for decades—the difference now is the promise of greater accuracy and better simulations. Will it ultimately change everything we do in supply chain management? Maybe. But it may take a while. A November report from workplace tools developer Slack showed that AI adoption rates among U.S. workers had slowed in the last quarter, while a recent analysis of open supply chain jobs by software integration specialist Cleo found that only 2% of open jobs required AI skills.
So is AI just another fad or a truly transformative technology? It appears we'll need a few good use cases before we can make that call.
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.
The company’s Oracle Cloud SCM is part of its Oracle Fusion Cloud Applications Suite, and enables customers to connect supply chain processes and quickly respond to changing demand, supply, and market conditions. In addition, embedded AI now acts as an advisor to help analyze supply chain data, generate content, and augment or automate processes to help improve business operations and create a resilient supply network to outpace change, Oracle says.
The new tech comes in the form of role-based AI agents within Oracle Cloud SCM that are designed to automate routine tasks, deliver personalized insights and recommendations, and allow organizations to focus more time on strategic supply chain initiatives. While past versions of Oracle software already included AI assistants—that could formulate descriptions or write emails—the new AI agents can help users by answering natural language queries about complex rules such as a policy document on returns or claims.
“One of our goals is to not have AI be an esoteric thing that you need special training to use, but just to act as part of the tool, to help you accomplish each task according to the standards of your own particular company,” Srini Rajagopal, Oracle’s vice president of logistics product strategy, said in a briefing.
For example, once a company’s IT office has uploaded the firm’s unique policy documents—on issues such as packaging, deadlines, or transaction requirements—then all the company’s customer service representatives (CSRs) can use the new AI-based advisory agents to type natural-language queries into a text-based chat box to obtain quick answers to complicated questions.
In addition to providing quick business answers to current employees, that approach can also help to train new workers on company policies in a labor market with high turnover rates. Additional use cases could apply to workers in roles such as a shop floor operator or warehouse worker, Rajagopal said.
In another application of the new AI, the updates have added new capabilities to Oracle Transportation Management, Oracle Global Trade Management, and Oracle Order Management.
Applied to the transportation management system (TMS) product, the AI enables “better, faster, smarter” operations through new capabilities such as AI-powered order route predictions, transit time predictions, and a transportation emissions calculator. In the global trade management (GTM) took, the new AI supports a user-configurable platform that can provide trade incentive program processing relief and reporting. And in the order management software, the AI can provide a returns summary, pricing promotions summary, item availability check, and order fulfillment view.
“To successfully navigate an increasingly complex global landscape, supply chain leaders need agile and efficient processes that can help them diversify and strengthen supplier networks, adapt transportation and logistics strategies, and stay ahead of regulatory changes,” Rajagopal said in a release.
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.