The federal Transportation Security Administration (TSA) yesterday proposed a rule that would mandate some surface transportation owners and operators, including those running pipelines and railroads, to meet certain cyber risk management and reporting requirements.
The new rule would require:
Owner/operators of pipelines and/or railroads that have a higher cybersecurity risk profiles to establish and maintain a comprehensive cyber risk management program;
Owner/operators that are currently required to report significant physical security concerns to TSA to also report cybersecurity incidents to the Cybersecurity and Infrastructure Security Agency; and
Higher-risk pipeline owner/operators to designate a physical security coordinator and report significant physical security concerns to TSA.
"TSA has collaborated closely with its industry partners to increase the cybersecurity resilience of the nation's critical transportation infrastructure," TSA Administrator David Pekoske said in a release. "The requirements in the proposed rule seek to build on this collaborative effort and further strengthen the cybersecurity posture of surface transportation stakeholders. We look forward to industry and public input on this proposed regulation."
The notice came a week after a White House representative warned the trucking freight industry that the People’s Republic of China (PRC) has remained the most active and persistent cyber threat to the U.S. government, private sector, and critical infrastructure networks. The briefing came from a member of the administration’s Office of the National Cyber Director, in an address to attendees at the National Motor Freight Traffic Association (NMFTA)’s Cybersecurity Conference.
“In January, the National Cyber Director testified in front of Congress along with colleagues from CISA, NSA, and the FBI about this threat from the PRC, dubbed Volt Typhoon,” speaker Stephen Viña said in his remarks. “Volt Typhoon conducted cyber operations focused not on financial gain, espionage, or state secrets but on developing deep access to our critical infrastructure. This includes the energy sector transportation systems, among many others. A prolonged interruption to these critical services could disrupt our ability to mobilize in the event of a national emergency or conflict and can create panic among our citizens. Ultimately, if trucking stops, America stops.”
Mid-marketorganizations are confident that adopting AI applications can deliver up to fourfold returns within 12 months, but first they have to get over obstacles like gaps in workforce readiness, data governance, and tech infrastructure, according to a study from Seattle consulting firm Avanade.
The report found that 85% of businesses are expressing concern over losing competitive ground without rapid AI adoption, and 53% of them expect to increase their budgets for gen AI projects by up to 25%. But despite that enthusiasm, nearly half are stuck at business case (48%) or proof of concept (44%) stage.
The results come from “Avanade Trendlines: AI Value Report 2025,” which includes two surveys conducted by the market research firms McGuire Research Services and Vanson Bourne. Conducted in in August and September 2024, they encompass responses from a total of 4,100 IT decision makers and senior business decision makers across Australia, Brazil, France, Germany, Italy, Japan, Netherlands, Spain, UK, and U.S.
Additional results showed that 76% of respondents state that poor data quality and governance inhibits their AI progress. To overcome that, companies are stepping up investments in that area, with 44% planning to implement new data platforms and 41% setting governance standards. And to support the scaling of AI, budgets will focus on data and analytics (27%), automation (17%), and security and cyber resilience (15%).
"Mid-market leaders are at a defining moment with AI—where investments must not only boost efficiency but ignite future innovation and sustainable growth," Rodrigo Caserta, CEO of Avanade, said in a release. "The tension between cost-cutting and growth ambitions shows the AI value equation is still being worked out. Productivity with AI isn't just about doing things faster; it's about reimagining work itself. People are central to this shift, requiring workforce alignment, clear communication, and new training. Leaders must rethink how they support collaboration, measure productivity, and ultimately, assess the true value AI brings to their organizations."
Editor's note:This article was revised on November 13 to correct the site of Avanade's headquarters; it is located in Seattle.
Third party logistics provider (3PL) C.H. Robinson has applied generative AI tools to automate various steps across the entire lifecycle of a freight shipment, the Minnesota company said last week.
C.H. Robinson said it created AI-based technology that reads incoming email then replicates tasks a person would do, including giving customers a price quote, accepting a load, setting appointments for pickup and delivery, and checking on the load in transit. The company has used the approach to automate more than 10,000 of those routine transactions per day, allowing shippers who use email to get the same speed-to-market and cost savings as customers who use C.H. Robinson’s online platform.
After starting with price quotes, the company said it has applied generative AI to increasingly complex tasks. “We announced in May that we’d been using our new tech for emailed price requests. Within a few short months, we created new models to automate more shipping steps and have already implemented them at scale,” Arun Rajan, the company’s Chief Strategy and Innovation Officer, said in a release. “This a major efficiency breakthrough for the industry and for supply chains around the world. When you think about retailers that need hundreds of different products on their shelves or automakers that rely on just-in-time delivery for the 30,000 different parts in a car, saving hours and minutes on every shipment matters.”
The technology also saves time, cutting the task for a person to take care of an emailed load tender from as much as four hours to 90 seconds, according to Mark Albrecht, the company’s Vice President for Artificial Intelligence.
“Once a person got to the email in their inbox, it still took an average of seven minutes to manually enter all the shipment details into our system – and that’s for a single load,” Albrecht said. “If the email tendered us 20 loads, a person would be stuck manually entering the information one load at a time. With generative AI, we can process all 20 loads simultaneously in the same 90 seconds. That’s an enormous time savings, especially when you consider we’ve scaled this to thousands of shipment orders per day just since June.”
Investing in artificial intelligence (AI) is a top priority for supply chain leaders as they develop their organization’s technology roadmap, according to data from research and consulting firm Gartner.
AI—including machine learning—and Generative AI (GenAI) ranked as the top two priorities for digital supply chain investments globally among more than 400 supply chain leaders surveyed earlier this year. But key differences apply regionally and by job responsibility, according to the research.
Twenty percent of the survey’s respondents said they are prioritizing investments in traditional AI—which analyzes data, identifies patterns, and makes predictions. Virtual assistants like Siri and Alexa are common examples. Slightly less (17%) said they are prioritizing investments in GenAI, which takes the process a step further by learning patterns and using them to generate text, images, and so forth. OpenAI’s ChatGPT is the most common example.
Despite that overall focus, AI lagged as a priority in Western Europe, where connected industry objectives remain paramount, according to Gartner. The survey also found that business-led roles are much less enthusiastic than their IT counterparts when it comes to prioritizing the technology.
“While enthusiasm for both traditional AI and GenAI remain high on an absolute level within supply chain, the prioritization varies greatly between different roles, geographies, and industries,” Michael Dominy, VP analyst in Gartner’s Supply Chain practice, said in a statement announcing the survey results. “European respondents were more likely to prioritize technologies that align with Industry 4.0 objectives, such as smart manufacturing. In addition to region differences, certain industries prioritize specific use cases, such as robotics or machine learning, which are currently viewed as more pragmatic investments than GenAI.”
The survey also found that:
Twenty-six percent of North American respondents identified AI, including machine learning, as their top priority, compared to 14% of Western Europeans.
Fourteen percent of Western European respondents identified robots in manufacturing as their top choice compared to just 1% of North American respondents.
Geographical variances generally correlated with industry-specific priorities; regions with a higher proportion of manufacturing respondents were less likely to select AI or GenAI as a top digital priority.
Digging deeper into job responsibilities, just 12% of respondents with business-focused roles indicated GenAI as a top priority, compared to 28% of IT roles. The data may indicate that GenAI use cases are perceived as less tangible and directly tied to core supply chain processes, according to Gartner.
“Business-led roles are traditionally more comfortable with prioritizing established technologies, and the survey data suggests that these business-led roles still question whether GenAI can deliver an adequate return on investment,” said Dominy. “However, multiple industries including retail, industrial manufacturers and high-tech manufacturers have already made GenAI their top investment priority.”
Read the news today and there is likely to be an article on how generative artificial intelligence (GenAI) is going to change the world. One story will praise the transformative potential of GenAI, the next will shout doom and gloom. Neither is 100% right and neither is 100% wrong.
The mixed perceived potential of GenAI can be seen in responses to the “2023 Gartner Supply Chain Technology User Wants and Needs Survey.” We asked respondents to rate how important they currently saw certain emerging technologies like GenAI and how transformative they felt these technologies were going to be over the next decade. GenAI was an interesting case study. Respondents saw it as one of the most potentially transformative emerging technologies over the long term. But, at this time, it was rated as one of the least currently important technologies.
At this point, many organizations are struggling to prove and realize value from their GenAI investments and pilot projects. Accordingly, Gartner research predicts that at least 30% of GenAI projects will be abandoned after proof of concept by the end of 2025.
The survey findings highlight that before they invest heavily in a technology, companies must honestly assess who they are and what their risk tolerance is. Different supply chain organizations view the value and importance of technology differently, and they should align their investment strategies with their unique identity. Every supply chain and logistics organization has its own culture, and this culture has a significant influence on how leaders must approach and manage their technology investment decisions.
What’s your tech adoption profile?
Distribution of supply chain technology adoption profiles
"2023 Gartner Supply Chain Technology User Wants and Needs Survey"
As part of gaining an understanding of what types of technology they should invest in, supply chain leaders must honestly and candidly assess their organization's risk tolerance. This is because risk tolerance has a notable impact on which technology investments are right for an organization, at what time, and for what reasons.
Gartner research has identified five adoption profiles:
Cautious: These organizations wait until technologies are fully mature and several iterations of the technology have been released.
Conservative: These organizations adopt mainstream technologies once they are proven and lower risk solutions are available.
Adventurer: These organizations prefer to adopt adolescent but maturing technologies with manageable risk.
Pioneer: These organizations may adopt emerging technologies in the early stages of commercialization when there are successful deployments by industry leaders.
Maverick: These organizations favor adopting embryonic, emerging, and often unproven technologies that are relatively new and riskier.
The figure above shows the distribution of adoption profiles among respondents to the “Wants and Needs” survey. As can be seen in the chart, there is a notable disparity among companies in how they approach purchasing and adopting technology.
If buying decisions are not aligned with the organization’s adoption behavior, then investments may be made at the wrong time or for the wrong reasons—or both—and organizations will fail to meet their intended goals and objectives. For example, risk-intolerant companies, like cautious or conservative adopters, should be very cautious of investing in technologies like GenAI today, while risk-exploiting companies (those that are willing to put capital at risk for big payoffs) should take the plunge.
Taking a closer look
In addition to their application profile, there are three other dimensions that supply chain leaders should take into account when deciding what technology they should invest in and when and how these technologies should be implemented: planning, control, and pace of change.
Planning identifies the organization’s approach to creating and adhering to a strategic technology plan. There are three general profile attributes:
Strict—The majority of technology decisions will be driven by and to fit to the plan.
Accommodating—The strategic plan is typically adjusted only under certain situations, such as changes in the business climate or the urgency of responding to new, unanticipated events.
Flexible—The plan is less of a definitive driver for technology decisions, and users have more autonomy to pursue other opportunities where they make business sense.
Control identifies the locus of power for driving the technology agenda, with the following profile attributes:
IT-led—The IT organization and a strong chief information officer drive the technology agenda.
Collaborative—Business and IT work together to determine the technology path.
Business-led—Leaders from the business largely guide the agenda.
Pace of change explores the organization’s receptivity to new ideas and technology approaches, building upon the technology adoption approaches outlined previously with the following profile attributes:
Measured—The enterprise prefers to observe the impact of events and new technology before acting.
Responsive—The enterprise looks for technology to help it react and respond to external events.
Dynamic—The enterprise aggressively pursues new technologies and reacts quickly to external events in pursuit of competitive advantage.
These categories are more indicative of an organization’s buying propensity in terms of when, who, and how they make technology decisions than traditional factors such as company size, geography, or even industry.
These considerations also go a step beyond traditional technology maturity models. Maturity models are a good first step for gaining an understanding of “who are you,” “what you do,” and “how you perform” compared to other companies. However, the three dimensions above help you unlock another level of insight—the “why you do what you do” and the way you do it. This provides leaders with a deeper level of understanding of organizational behavioral traits, much like psychographic targeting in consumer marketing.
These insights can help leaders gain perspective on how cultural differences influence an organization’s supply chain technology adoption behaviors. Thus, they can use the information to determine which technology strategies and investment plans will be most effective with their organization’s unique temperament.
Do you have five minutes to help shape the future of retail by sharing your insights and experience? The MIT Omnichannel Supply Chain Lab has officially opened its 2024 Omnichannel Survey, and researchers want to hear from you.
This year’s survey is investigating the impact of e-commerce on the different tiers of the supply chains, the most common challenges faced when defining and implementing an omnichannel strategy, and the role of artificial intelligence and tech-related innovations supporting it.
What did we learn in 2023?
Last year’s survey unveiled some game-changing trends in omnichannel retailing, particularly around the explosive growth of artificial intelligence (AI). Here are some highlights:
AI is taking over: 70% of retailers are now leveraging AI in their operations, with a 90% adopting it within just the last three years. It’s clear: AI is no longer a luxury; it’s a necessity.
Where's AI making waves? Retailers are using AI for everything from demand forecasting (65% adoption) to pricing optimization (48%) and inventory management (43%). These tools are revolutionizing how businesses operate.
Investing in the future: Looking ahead, 83% of retailers plan to ramp up their AI investments. This commitment signals a new era for retail, driven by technology and innovation.
Be part of the change
As the 2024 survey kicks off, your voice matters more than ever. By participating, you’ll contribute to vital research that will help define the future of omnichannel retailing and technology integration. Don’t miss out on this opportunity to make your mark. Click here to take the survey.