Juiced unveils another lower-cost moto-styled electric bike: Scrambler Core
Transportationvia Electrek
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Juiced Bikes is expanding its recently relaunched Scrambler lineup with a new, more affordable model known as the Juiced Scrambler Core. The new bike keeps much of the moto-inspired styling and performance of the existing Scrambler Hardtail, but trims back several components to knock several hundred dollars off the price. more…
Juiced Bikes is expanding its recently relaunched Scrambler lineup with a new, more affordable model known as the Juiced Scrambler Core. The new bike keeps much of the moto-inspired styling and performance of the existing Scrambler Hardtail, but trims back several components to knock several hundred dollars off the price.
One in six supply chain, logistics, distribution and wholesale professionals said their company experienced an incident in the past year in which supplier or customer information was exposed through an artificial intelligence tool, according to a new industry survey. The findings come as businesses increasingly give AI systems access to sensitive commercial information, raising questions about how the technology handles confidential data belonging to customers and suppliers. The survey was conducted by InstaLILY, which develops AI agents for distribution, and was fielded through Pollfish. Launched Oct. 9, 2026, it collected 250 responses from managers and executives at companies using AI in their daily operations. It found that 78% of respondents said their company’s AI tools could access customer pricing and margins, while 67% said the tools had access to supplier pricing and rebate information. InstaLILY defined an incident as a situation in which supplier or customer data was exposed, shared or accessed incorrectly through an AI tool. The results reflect respondents’ reported knowledge and experiences, rather than independently verified data exposures. Also, the report did not assess the severity of the incidents, meaning the findings do not distinguish between minor internal mistakes and more serious incidents. window.googletag = window.googletag || {cmd: []}; googletag.cmd.push(function() { var gptSlot = googletag.defineSlot('/21776187881/FW-Responsive-Main_Content-Slot1', [[300, 100], [320, 50], [728, 90], [468, 60]], 'div-gpt-ad-1709668545404-0').defineSizeMapping(gptSizeMaps.banner1).addService(googletag.pubads()); googletag.pubads().enableSingleRequest(); googletag.pubads().collapseEmptyDivs(); googletag.enableServices(); setInterval(function() { if (!document.hidden) { googletag.pubads().refresh([gptSlot]); } }, 60000); }); googletag.cmd.push(function() { googletag.display('div-gpt-ad-1709668545404-0'); }); The survey also found that 63% of respondents said their company had been asked by a supplier or customer how it protects their information. For 30%, those inquiries had influenced a decision involving a software provider, vendor or AI tool. “Speed means very little if a faster answer costs you a relationship that took years to build,” said Adam Isenberg, head of distribution at InstaLILY. “AI working in distribution should know that one customer’s contract price never belongs on another customer’s quote. Buyers should make vendors prove that before they sign.” Despite the reported incidents, confidence in existing AI safeguards remains high. Eighty-seven percent of respondents said they were very or somewhat confident their AI tools kept supplier and customer information appropriately separated. However, 63% acknowledged some overlap between supplier and customer data across their company’s platforms, including AI tools. The survey reflects broader concerns about AI security, going beyond just the logistics industry. IBM’s 2025 Cost of a Data Breach report found that 13% of surveyed organizations had experienced a breach involving AI models or applications. Among those organizations, 97% reported lacking proper AI access controls. The report also found that breaches involving unauthorized AI tools, commonly called shadow AI, added an average of $670,000 to breach costs. InstaLILY found that the most commonly cited obstacle to separating supplier and customer information was that existing AI tools were not designed with that requirement in mind, which was cited by 22% of respondents. Cost and a lack of technical expertise followed at 21% and 20%, respectively. Nine in 10 respondents said their employers would be more likely to select an AI tool specifically designed for distribution and supply chain operations if it offered stronger controls over supplier and customer data. Meanwhile, 92% said data separation was a high or moderate priority for their company over the next year. The report recommends testing AI systems against routine business scenarios, including whether a tool can prepare a customer quote without exposing confidential supplier pricing or contract terms. It also recommends establishing when human approval is necessary before AI systems act on information. window.googletag = window.googletag || {cmd: []}; googletag.cmd.push(function() { var gptSlot = googletag.defineSlot('/21776187881/fw-responsive-main_content-slot3', [[728, 90], [468, 60], [320, 50], [300, 100]], 'div-gpt-ad-1665767553440-0').defineSizeMapping(gptSizeMaps.banner1).addService(googletag.pubads()); googletag.pubads().enableSingleRequest(); googletag.pubads().collapseEmptyDivs(); googletag.enableServices(); setInterval(function() { if (!document.hidden) { googletag.pubads().refresh([gptSlot]); } }, 60000); }); googletag.cmd.push(function() { googletag.display('div-gpt-ad-1665767553440-0'); }); Why this matters AI tools are gaining access to sensitive commercial information, including customer pricing, supplier contracts and rebate data. For logistics providers, distributors and wholesalers, a data exposure could damage relationships with trading partners and influence which technology vendors companies choose. The post 1 in 6 supply chain professionals report AI-related data exposure, survey finds appeared first on FreightWaves.
Most transportation management systems were built to answer a simple question: What’s the cheapest way to move this order? What they’ve been slower to answer is whether that plan will actually work once a driver, a dock door and a delivery appointment enter the picture. Then add in multiple dock doors, several delivery appointments, hours of service regulations and more, and there is a lot of complexity for the average planner to work through. That gap is where FreightWise is placing its bet with Kuebix AI, an AI-backed TMS for small and mid-market shippers. Announced Oct. 20, the platform becomes the single system for every FreightWise and Kuebix customer, covering LTL, truckload and parcel in one place. FreightWise CEO Chris Cochran sat down with FreightWaves at the F3: Future of Freight Festival in Chattanooga, a day after demoing the platform to attendees. His pitch was direct about who the product is for. “We’re focused 100% on the shipper,” he said, contrasting Kuebix AI with the many technology providers at the event that build for brokers. One platform, one roadmap The launch is partly a consolidation story. FreightWise acquired Kuebix in 2023 and has since operated two separate systems: FreightWise TMS and the Kuebix TMS platform. According to the company’s announcement, every customer will now work on the same technology, supported by one team and guided by one product roadmap. The practical upside, FreightWise says, is that features built in Kuebix AI reach every user faster. Cochran described the starting point as a legacy platform with a loyal user base. FreightWise already ran a modern, native application for its managed transportation business, which gave the team a head start. They brought the features Kuebix users knew into that platform, and the result became Kuebix AI. “Over the past year, our team has been rebuilding Kuebix with the same functionality Kuebix users have known for almost two decades, while innovating on the infrastructure, aligning AI with core features, and ensuring the platform has real staying power,” Cochran said in the announcement. Thousands of users are already using Kuebix AI, and upgrades onto Kuebix AI will continue for SaaS only customers in the coming year. Replacing the black box on multi-stop routes The feature Cochran emphasized most is multi-stop truckload planning, which is where he sees the biggest gap between how TMS platforms work today and how planners need them to work. In a traditional system, he explained, the multi-stop route is planned and then handed to a planner, who often discovers that it will miss delivery appointments. The planner can’t see why the system built the route that way and usually has no alternatives to choose from. Cochran said he sees that as a legacy of optimization logic written years ago. “AI disrupts that in general because AI doesn’t need those algorithms,” he said. “It’s smart on its own.” In Kuebix AI, the planner sees several route options on one screen, along with where each one is weak. One option might add miles to hit a delivery appointment. Another might cut miles to save cost but force a change to the appointment. The planner drags over the one that works and acts from there, whether that means resetting a customer’s expectations or flagging a retailer shipment where fees or damages could follow. The point, Cochran said, is to keep planners inside the TMS. “Instead of them going away from the TMS when they have a bad route or a false positive, it allows them to do it right there.” The underlying pieces highlight AI-driven service time builds delivery plans around hours of service, wait time, loading and unloading, and facility hours. Multi-stop AI routing then uses that data to flag whether a sequence of stops is feasible and recommends the optimal route. FreightWise says the goal is for the delivery dates shippers give their customers to hold up. Integration without the IT queue Integration is the other pain point Kuebix AI solves. A TMS is rarely the system of record for orders, Cochran noted, and in the SMB and small enterprise segments, IT resources are scarce and the systems feeding data are often old. Few people can work on them, and they may not support current JSON calls. Kuebix Connect, a no-code middleware layer, is FreightWise’s answer. Cochran said the aim is to put control in the hands of the business user. That person matches fields between a WMS, ERP or other source system and the TMS visually, and maps the logic. Then AI within Kuebix Connect will monitor the connection on an ongoing basis. “As the daily feeds come through, sometimes there might be bad data, or failures in feeds,” Cochran said, usually from something small like an incorrect character that keeps orders from coming into the system. “The AI embedded within Kuebix Connect will monitor that, will fix it and help you diagnose the source of that” so that freight doesn’t sit in the warehouse waiting on a data fix. IT still has a role, he added. They’ll need to provide credentials and test for security, but the company isn’t relying on IT to make manual calls every time something breaks. Dashboards that explain themselves The third piece is Kuebix Intelligence, which the company describes as AI-backed dashboards for spend by location, carrier and mode that explain the drivers behind the trends, not just the numbers. Cochran tied this to end-to-end design not only of Kuebix AI, but FreightWise’s solutions as whole. Because rating, routing, freight audit and payment all live in one system, rather than having settlement data sit with a separate audit provider, he argued that the data feeding the AI is better. “If your KPI is off, the AI tells you why it’s off, where did it go wrong,” he said, rather than a CFO waiting on a logistics analyst for an explanation. The release adds a related capability. AI-powered freight audit exception resolution analyzes quoted rates against final invoiced amounts, identifies discrepancies, quantifies the financial impact and recommends carrier dispute strategies. Why FreightWise waited Cochran was candid that FreightWise wasn’t first to the AI conversation, and that it was a deliberate choice. By his own estimate, the company moved six to eight months later than he would have liked. But the management team was wary of a trend they saw across the industry. “We were kind of uncomfortable with this trend that people were putting AI for the sake of AI in applications,” he said, pointing to chatbots running on incomplete data and no context. For some of the early adopters, users would engage, but they were not longstanding value-driven AI solutions that solved real problems. “We waited until we could make it operational,” Cochran said. That philosophy shows up in the company’s term for its approach: operationally executable AI, meaning AI that solves real pain points. FreightWise CTO Chris Haarmeyer echoed the point in the announcement: “We didn’t set out to add AI for the sake of it.” The team started with problems customers and internal users raised, like delivery dates that ignore hours of service or scheduled appointments. The company also addressed data security up front. “Customer data stays protected, it is never used to train third-party models, and every AI feature is opt-in,” Haarmeyer said. Where Cochran draws the line Cochran’s view of AI in logistics is enthusiastic but bounded. He named two concerns. The first is data quality. “You’re not ready for AI if you don’t have good data,” he said, warning that AI can hallucinate and reach false conclusions when it’s fed bad inputs. Shippers without a clean, single source of record, he argued, will be at a disadvantage to peers who have one. The second is customer relationships. “AI is not here to manage your customer relationships,” Cochran said. He doesn’t want customers calling FreightWise support or an account manager and ending up talking to a bot. When a customer is in a pinch, he wants them talking to a real person. He also isn’t positioning AI as a cure-all. Someone still has to sell the order, load the truck and deliver the goods, he noted. But he was firm that shippers who skip it risk watching their cost to serve drift out of control. What to watch For shippers, the proof will come in execution. The release says additional AI-backed functionality will roll out in 2027 and beyond, and the upgrade of legacy Kuebix users to Kuebix AI is actively underway. The priority continues to be ensuring new features are solving real business problems, easy integrating for shippers with their existing technology with AI to flag any errors, and prioritizing data to inform better decisions. Learn more about Kuebix AI The post Inside Kuebix AI: A Shipper TMS Rebuilt Around Executable AI appeared first on FreightWaves.
New EV prices edged up in October, but buyers still paid less than they did a year ago. The average transaction price for a new EV in the US was $56,220, up 1% from September and down 2.7% from October 2025, according to Kelley Blue Book, a Cox Automotive brand. more…
