By Christopher A. Boone, Ph.D., Associate Professor, Mississippi State University; Karl B. Manrodt, Ph.D., Professor, Georgia College and State University; M. Douglas Voss, Ph.D., Professor and Scott E. Bennett Arkansas Highway Commission Endowed Chair, University of Central Arkansas; Joseph Tillman, Manager Education Programs, SMC3
Ronald Reagan popularized the Russian proverb doveryai, no proveryai—“trust, but verify”—when describing the relationship between the United States and the Soviet Union during the 1980s. The proverb assumes that trust exists, but verification is required for important matters.
That idea fits today’s logistics environment. Shippers must trust carriers to move their freight. Carriers must trust shippers and brokers to provide accurate information and pay as promised. Managers must trust transportation systems, visibility platforms, and business partners to support decisions involving service, cost, safety, and customers. Increasingly, they are also being asked to trust AI-enabled technologies and outputs.
Last year’s Annual Study identified a gap between knowing and doing. Logistics leaders understood the disruptive forces reshaping the industry, but many organizations had not moved from awareness to meaningful execution.
This year’s findings suggest that the gap is narrowing, particularly in AI adoption, employee usage, organizational support, and training. However, moving into execution has exposed a new gap: Organizations are adopting AI faster than they’re building the confidence, accountability, and verification needed to support it.
This examination of our 35th Annual Study of Logistics and Transportation Trends explores that tension—and the results suggest that trust is becoming more selective, consequential, and evidence-dependent.
The trust-but-verify era
Respondents divided into three groups when asked how overall trust among trading partners in the logistics and transportation industry compares with five years ago. Thirty-nine percent said trust had deteriorated, another 39% said it was about the same, and 22% said it had improved. Those perceiving a deterioration in trust outnumbered those perceiving an improvement by nearly two to one.
A more revealing result may be where respondents place their trust. Asset-based carriers and shippers/customers received the highest ratings, with approximately 53% expressing high or very high trust in each. Freight forwarders and technology vendors occupied the middle, at 33% and 31%, respectively. Only 23% expressed high or very high trust in third-party logistics providers, and 16% said the same about freight brokers.
The survey was conducted shortly after the May 2026 U.S. Supreme Court Montgomery v. Caribe Transport II ruling, which held that certain state negligent-selection claims against freight brokers are not preempted by federal law. The survey did not ask whether this ruling affected respondents’ trust in freight brokers or other transportation partners.
However, its timing provides important context. The decision unsettled longstanding assumptions about risk and liability among trading partners. Brokers now face greater pressure to demonstrate effective carrier selection and monitoring procedures while shippers may require verification that those processes are working.
The survey can’t tell us whether the ruling influenced respondents’ trust ratings. However, taken together, the findings suggest that the industry is entering a “trust, but verify” era that increasingly depends upon documented, defensible verification.
The methods companies use to verify the identity and legitimacy of carriers, brokers, or service providers remain largely traditional despite changing risk dynamics. Contractual requirements, such as insurance certificates and letters of authority, were the most frequently reported method, followed by manual verification processes, including callbacks and document reviews, as well as checks of FMCSA or other regulatory databases. AI-assisted fraud detection tools were the least frequently reported verification method.
AI adoption
Fraud detection tools notwithstanding, AI adoption accelerated across nearly every measure tracked in the study. The share of organizations that were the most passive adopters (traditionalists, gatekeepers, and observers) fell from 64% in 2025 to 30% in 2026, while the share of organizations with a more active approach (optimizers and pioneers) increased from 16% to 43%. Explorers also increased, indicating that more organizations have at least begun to determine how and where AI may fit into their operations.
Employee use is also increasing as more organizations roll out access and provide guidance, support, and training on AI use. In 2025, only 16% reported using AI with their manager’s or organization’s knowledge and approval compared with 47% in 2026. Overall use, with or without formal approval, increased from 45% to 65%.
Organizations are also providing more support. Formal or informal training and guidance increased from 16% to 47%. The percentage actively encouraging or allowing employee use increased from 39% to 59%, while those with no formal position declined from 55% to 32%.
Together, these differences suggest the industry may have reached an AI inflection point and is beginning to move from awareness to more widespread adoption. However, adoption alone does not indicate complete trust and confidence in AI. While respondents appear increasingly comfortable using AI and likely have organizational permission and support to do so, their trust in its outputs is more measured.
Fifty-five percent reported moderate trust in AI-generated outputs and recommendations, compared with 38% reporting low or no trust and only 7% reporting high or very high trust. AI also ranked last among the seven data and information sources evaluated in the study.
The results suggest that logistics professionals have some trust in AI: enough to use it, but not enough to rely on it without review. In that sense, AI may be the clearest example of the industry’s emerging “trust-but-verify” era. Whether moderate trust develops into stronger confidence will depend on experience, transparency, demonstrated accuracy, and the controls organizations put around its use.
Threats to trust
Concerns about fraud and cyberattacks represent additional threats to trust. Phishing and cyberattacks targeting logistics operations generated the greatest concern, with 62% very or extremely concerned.
AI-generated or fabricated documents ranked next at 53%, followed by double brokering at 47%, carrier identity fraud at 45%, fictitious pickups or phantom loads at 43%, and invoice or billing fraud at 42%.
The results suggest a threat environment in which cyberattacks and fabricated documents join more familiar freight-fraud risks. While AI makes information easier to produce, it also makes trust harder to earn. The same technology that helps organizations analyze information and make decisions faster can also be used to create increasingly convincing fraudulent documents, identities, and communications.
Organizations are responding with a range of risk mitigation strategies. The most frequently reported strategy was employee training in fraud awareness and data verification. Organizations also implemented or upgraded fraud-detection or carrier-vetting tools and added human review requirements for AI-assisted decisions. Others established formal policies for verifying AI-generated content.
Trust and the workforce
Hiring processes are one place where the trust gap is already apparent. Thirty-four percent said AI-generated resumes, cover letters, and applications had reduced their confidence in evaluating candidates for logistics and transportation positions. Only 7% said their organization had effectively adapted hiring processes to account for the new AI environment.
This is particularly important for an industry still struggling to attract talent. The industry’s image problem remained the leading attraction challenge in 2026, accounting for 35.6% of reported challenge selections, nearly unchanged from 2025. Demanding work ranked second. Difficulty finding qualified candidates and limited career paths followed.
The result adds a new dimension to a recurring concern in the Annual Study. Prior studies emphasized industry perceptions, development opportunities, training, credentials, and career paths. Those needs have not disappeared. AI now complicates the first step in the talent process by making application materials less reliable. Structured interviews, work samples, internships, and verified credentials may become more important as employers try to distinguish presentation from performance.
Window for action
The trust and AI findings are emerging at a time when the operating environment is improving. When comparing their performance with competitors, average ratings increased across all five performance measures compared with 2025, though the size of the change varied.
Customer satisfaction increased from 3.93 to 4.03, competitive position from 3.56 to 3.65, revenue growth from 3.54 to 3.62, return on assets from 3.50 to 3.56, and profitability from 3.53 to 3.54.
Survey respondents also continued to rank economic uncertainty, workforce shortages, and government policies among the forces expected to have the greatest impact over the next three to five years. AI remained first. Transportation capacity and infrastructure constraints moved from seventh to fifth, indicating respondents’ concern that recent capacity constraints may continue unabated.
Respondents expressed more confidence in their ability to navigate those forces. The percentage of respondents who described their organization as very or somewhat prepared increased from 63% to 77%. However, only 15% said they were very well-prepared with clear strategies and resources, while 62% said they were somewhat prepared but still had gaps.
Making trust operational
The survey results point to a management challenge that extends beyond deciding whom or what to trust. The more important question is how organizations determine when trust is sufficient, when verification is required, and the tools that can be used to reduce trust-related risks. The following actions can help managers turn “trust but verify” from a familiar expression into an operating practice.
Align verification with the consequences of failure: Not every decision requires the same level of scrutiny. A routine planning recommendation does not carry the same risk as changing a carrier’s payment information or releasing a high-value shipment. Managers should identify the decisions with the greatest operational, financial, safety, or legal consequences and establish commensurate verification requirements. Firms can’t verify everything but must have a handle on the essential information.
Turn partner trust into a managed process: The findings should not be interpreted as a reason to distrust brokers, third-party logistics providers, or other intermediaries. However, they do suggest that dynamic logistics risks require implementation of processes to build trusting relationships with reputable partners. Partner onboarding should establish what must be verified, who owns the verification, how frequently information is reviewed, and what conditions trigger additional scrutiny. Trust can remain relational, but there should be visible, verifiable, and current evidence supporting it.
Match AI oversight to the risk of the decision: Organizations have moved quickly from AI awareness to adoption, but confidence in AI output has not kept pace. Managers should classify AI uses by the consequences of an incorrect answer. Low-risk uses, such as summarizing a meeting or producing an initial draft, may require little or no review. Recommendations affecting inventory, routing, capacity, pricing, hiring, safety, or customer commitments, as well as those with regulatory or legal implications, should receive a more detailed analysis.
Measure whether AI and verification controls are working: Training and written policies are necessary, but they do not demonstrate effectiveness. Useful measures include how frequently employees override AI recommendations, what types of errors are discovered, which sources are used to validate important outputs, how long verification takes, and what losses, service failures, or compliance violations the controls prevent.
Managers should also assign responsibility for periodically confirming that AI-enabled processes and verification practices remain consistent with applicable laws, regulations, contractual obligations, and internal policies. These measures can help managers determine where AI is improving decisions, where additional safeguards are required, and where controls create delay without meaningful benefit.
Hire and develop employees who know how to question an answer. As qualifications change faster than employers can define them, resumes and traditional interviews provide limited evidence of capability. Critical thinking and problem-solving become essential when professionals must evaluate incomplete or conflicting information, question an AI-generated recommendation, and decide what to verify before acting.
Managers can reduce uncertainty through hiring candidates with internship experience, using scenario-based interviews, and verifying credentials. Candidates for positions involving AI-assisted decisions should be asked to evaluate conflicting information, identify warning signs, and explain what they would verify before acting. Judgment may be as important as technical proficiency in a trust-but-verify environment.
Use the current window to strengthen controls before the next disruption. Performance and preparedness improvements provide organizations with a window of opportunity to act before a major fraud event, cyberattack, or AI-related failure. Managers should assign ownership of critical verification processes, test whether those controls work, and track whether they reduce risk without creating unnecessary delays. The number of documents reviewed and approvals added is less meaningful than prevented losses, faster exception resolution, reduced identity failures, and better decisions.
Bottom line
Trust remains essential to logistics and transportation. No supply chain can operate if every transaction begins from suspicion. But trust can no longer depend solely on history, reputation, or confidence in a system’s output.
The organizations best prepared for the next operating environment will be those that know what they trust, why they trust it, and when the evidence requires them to verify it.
