The “series A” round was led by Andreessen Horowitz (a16z), with participation from Y Combinator and strategic industry investors, including RyderVentures. It follows an earlier, previously undisclosed, pre-seed round raised 1.5 years ago, that was backed by Array Ventures and other angel investors.
“Our mission is to redefine the economics of the freight industry by harnessing the power of agentic AI,ˮ Pablo Palafox, HappyRobotʼs co-founder and CEO, said in a release. “This funding will enable us to accelerate product development, expand and support our customer base, and ultimately transform how logistics businesses operate.ˮ
According to the firm, its conversational AI platform uses agentic AI—a term for systems that can autonomously make decisions and take actions to achieve specific goals—to simplify logistics operations. HappyRobot says its tech can automate tasks like inbound and outbound calls, carrier negotiations, and data capture, thus enabling brokers to enhance efficiency and capacity, improve margins, and free up human agents to focus on higher-value activities.
“Today, the logistics industry underpinning our global economy is stretched,” Anish Acharya, general partner at a16z, said. “As a key part of the ecosystem, even small to midsize freight brokers can make and receive hundreds, if not thousands, of calls per day – and hiring for this job is increasingly difficult. By providing customers with autonomous decision making, HappyRobotʼs agentic AI platform helps these brokers operate more reliably and efficiently.ˮ
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.
Supply chain projects involving generative artificial intelligence (GenAI) are gaining steam, and many early movers are already putting the technology to work serving clients. Logistics service providers are among those pushing forward with tools that are helping to improve the customer experience, applying GenAI to freight shipping tasks—such as quoting, taking orders, and booking appointments—and to sales-development activities.
Streamlining work and creating more efficient processes are at the heart of those advances.
“[GenAI] allows us, as humans, to be more efficient—to work faster,” explains Eric Walters, vice president of analytics and performance management at contract logistics specialist DHL Supply Chain, which introduced a suite of GenAI tools late last year. “For example, you could ask an associate to read through a document and summarize it for you, [but] AI does it faster. Data management is another example. Similar to how a human could comb through data and fill in the gaps—GenAI can do this for us too.”
Traditional AI and GenAI top today’s list of supply chain investment priorities, according to a 2024 survey by research and advisory firm Gartner, which polled more than 400 supply chain leaders responsible for their organization’s digital supply chain strategies. Twenty percent cited traditional AI, including machine learning, as a top priority, and 17% said GenAI projects are getting the most attention these days.
AUTOMATING THE FREIGHT CYCLE
Logistics industry projects are in line with what’s happening with GenAI investments in the broader world, according to the technology research and advisory firm Information Services Group (ISG). In a study released in November, ISG found that that most projects are focused on text-based applications because of their relatively simple interfaces, rapid return on investment, and usefulness.
“Companies have been especially aggressive in implementing chatbots powered by large language models (LLMs), which can provide personalized assistance, customer support, and automated communication on a massive scale,” ISG said in its report on the study.
Large companies with lots of data and a staff of software developers are tackling those projects in house, building LLMs—a type of AI algorithm that can process and understand language or text—that “learn” from all that data and then apply what they’ve learned to solving problems. Freight broker and third-party logistics service provider (3PL) C.H. Robinson is a case in point. The 3PL has launched several homegrown GenAI tools over the past year that can analyze unstructured data, such as emailed text, to speed and streamline the freight shipment cycle.
“We still have customers who want to do things manually—for example, via email,” explains Megan Orth, director of digital connectivity for C.H. Robinson. “GenAI allows us to meet the customer where they are. They get the same [level of customer service] they would get if they were digitally enabled.
“This is different from what other companies [have done]. Some started with chatbots, for example. We took a different approach, [focusing on ways to reduce] the manual tasks and touches in our system.”
C.H. Robinson began with quoting, building an LLM to identify quote-request emails and using GenAI to read the email and respond. The technology eliminates the time-consuming steps of having an employee open and read the email, swivel to another screen, type in the quote request, and then swivel back to send an email response with the price.
“We focused on that swivel—going from one system to another and using the GenAI to read, grab, and go,” Orth explains. “Then we started applying it to the remainder of the shipment cycle.”
C.H. Robinson is now applying GenAI to more complex tasks such as accepting loads, setting appointments for pickup and delivery, and checking on loads in transit. The technology has allowed the 3PL to whittle down the time it takes to complete those tasks from hours to seconds. Before GenAI, it could take an employee as much as four hours to manage and complete a load tender, for example; that task has been reduced to 90 seconds.
The extra time allows employees to focus on more value-added work, like managing exceptions.
“You still have a human in the loop. We still have to monitor and look for exceptions,” Orth says, explaining that C.H. Robinson has designed its workflows to identify issues the GenAI can’t adequately address. “GenAI is not completely hands off the wheel. You still have to build your workflow so that if something doesn’t go right, that’s what our managers work on. [What’s changed is that those managers] are now going to be doing more strategic work.”
Orth credits C.H. Robinson’s software developers for much of the success of the GenAI program.
“We’re fortunate that we have a lot of talented developers who rolled up their sleeves and started learning this,” Orth says, adding that the company also works closely with Microsoft on its GenAI projects.
IMPROVING CUSTOMER SERVICE
DHL Supply Chain is taking a somewhat different approach in its venture into GenAI. The 3PL is deploying a suite of AI applications that are enhancing the company’s data management and analytics capabilities—which, in turn, allows it to provide more value, improve the customer experience, and cut the time it takes to deliver logistics solutions to clients. DHL partnered with Boston Consulting Group to develop the solutions.
The first application is a data cleansing tool that DHL’s in-house design engineers—who develop logistics solutions for shippers—use to clean, sort, and analyze data submitted by potential customers. The tool helps those designers build better transportation routes, as one example.
“This GenAI tool does all that data management for the engineers and allows them to fast forward to the design phase—so [they can] respond to the customer quicker and, at times, [with a] more thorough proposal for their evaluation,” Walters explains.
A second GenAI application supports DHL’s sales team with proposal development. The AI analyzes and manages preliminary RFQ (request for quote) data and fills in the gaps by gathering additional data online, speeding the research and development process and allowing sales to devote more time to specific customer challenges and produce customized solutions.
As Walters explains: “This GenAI tool that business development uses can read through an entire RFQ [and then] go out on the internet and find things like the annual shareholder report for that company …. That helps us identify the key items that are important to that customer and put forward a well-thought-out [proposal] on how we can improve [their] strategy.”
Walters says both tools are part of the company’s broader mission to apply robotics and automation on a large scale.
“Our vision is to deploy strong robotic solutions and [offer] best-in-class data management and data availability,” he explains. “GenAI is the marrying of those two. It’s a robotic solution that’s available to the masses at DHL … Being able to bring GenAI to the fingertips of thousands of associates and increase their efficiency—that allows us to do more to provide value to our customers.”
UNLEASHING OPPORTUNITY
Orth and Walters agree that GenAI is here to stay in supply chain, largely on account of its ability to streamline operations without pressuring customers to change their ways.
“We saw the ‘unlock’ with the unstructured data, which is a big opportunity within the supply chain,” Orth says. “People like to [stick with] their [own] processes; they don’t like to change their ways. [With GenAI], we’re able to unlock those opportunities.”
As some see it, AI is a technology that has finally found its footing in logistics and supply chain, and will only grow from here.
“What’s interesting to me is that AI is not new. It’s really been around for decades,” Walters says, pointing to the Roomba vacuum cleaner and virtual assistants like Siri and Alexa as examples of the technology in action. “AI is just getting the [media attention] now, and we’re seeing it more in the professional work environment.
“I think there is a broad desire to adopt AI and a strong appetite for it.”
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.