Artificial intelligence (AI) is rapidly becoming part of everyday trucking operations. From route optimization and predictive maintenance to customer service and cybersecurity, AI-enabled tools are appearing across nearly every transportation technology platform.
But while AI adoption is accelerating, governance often isn’t keeping pace.
For trucking executives, the challenge isn’t simply deciding where to use AI. It’s ensuring those tools are deployed securely, responsibly, and in ways that support business objectives. Without clear governance, organizations risk exposing sensitive data, creating cybersecurity vulnerabilities, and investing in solutions that deliver little measurable value.
The most successful companies are discovering that AI governance must come before AI deployment.
Start With the Business Problem, Not the Technology
One of the biggest mistakes organizations make is adopting AI because it’s available rather than because it’s needed.
AI vendors are flooding the market with compelling demonstrations and promises of efficiency gains. Yet transportation leaders should begin with a simpler question. As discussed during the National Motor Freight Traffic Association, Inc.® (NMFTA)®‘s Governing AI in Trucking—A Practical Framework for Secure Deployment webinar, leaders should resist the temptation to adopt AI simply because it’s available. As ArcBest Vice President of Strategic Products and Analytics, Erica Brigance, noted:
“The question isn’t, ‘How can we use AI?’ The question is, ‘What problem are we trying to solve?’”
Whether the goal is improving dispatch efficiency, reducing maintenance costs, enhancing customer service, or strengthening cybersecurity, the business outcome should drive the technology decision—not the other way around.
This approach helps organizations avoid investing in tools that may be impressive but ultimately fail to deliver meaningful operational value. It also creates a foundation for governance by clarifying where AI belongs within the business and where it does not.
Why Governance Comes First
Before deploying AI, leadership teams need to establish clear guardrails around its use.
According to Brigance, organizations should first determine where they are comfortable using AI and where they are not. Those decisions influence everything from security controls and employee training to vendor selection and operational oversight.
Governance is ultimately about accountability. Every AI initiative should have a defined owner, clear objectives, and established processes for monitoring outcomes. Without executive alignment, organizations often end up with inconsistent AI adoption, overlapping tools, and unmanaged risk.
This becomes especially important as AI capabilities become embedded in software that many organizations already use. In many cases, companies may have more AI-enabled tools in their environment than they realize.
Data Governance Is the Foundation of Responsible AI
For trucking companies, data is one of their most valuable assets. That makes data governance a critical component of responsible AI in transportation.
Organizations must understand what data AI systems can access, where that data is stored, and whether it could be used to train external models. These questions are particularly important when working with third-party vendors.
Transportation companies routinely manage proprietary information related to customers, freight, pricing, and operations. Sharing that information without appropriate controls can create both security and competitive risks.
Data quality is equally important. AI systems rely on historical information to generate future recommendations. If the underlying data is incomplete, outdated, or biased, the resulting outputs may be unreliable. AI is not a magic solution that transforms poor-quality information into good decisions.
AI Vendor Risk Requires Greater Scrutiny
Most trucking companies are not building AI models from scratch. Instead, they are consuming AI through software vendors and technology partners.
That makes vendor evaluation one of the most important components of an AI governance framework.
Before adopting any AI-enabled solution, organizations should understand how customer data is stored, who can access it, and whether it is isolated from other customers’ information. They should also ask whether their data will be used to train future AI models.
These conversations should happen early in the procurement process—not after a contract has been signed.
As AI becomes a standard feature across transportation technology platforms, vendor due diligence will increasingly become a core element of trucking technology risk management.
Human Oversight Still Matters
Despite advances in AI, human judgment remains essential.
Traditional software systems are deterministic—they follow predefined rules. AI systems are probabilistic, meaning they generate outputs based on statistical likelihood rather than certainty.
That distinction matters.
For low-risk applications, organizations may be comfortable allowing AI to operate with limited oversight. For higher-risk activities involving customer commitments, operational decisions, safety concerns, or financial impacts, human review remains critical.
Many transportation companies are finding the greatest value in using AI as a decision-support tool rather than a decision-maker. AI can help employees process information faster, identify patterns, and surface recommendations, while experienced professionals provide context, validation, and accountability.
The goal isn’t replacing people. It’s helping them make better decisions.
“If 85% accuracy isn’t an acceptable outcome, then I need a person to verify it.”
— Erica Brigance, ArcBest
The Growing Challenge of Shadow AI
One of the fastest-growing risks facing organizations today is shadow AI—the use of unapproved AI tools by employees.
Attempting to ban AI altogether is rarely effective. Employees will often find their own solutions if approved tools do not meet their needs. As NMFTA’s Director of Cybersecurity, Ben Wilkens, shared in the recent webinar, “if you’re encountering shadow AI, it’s typically a symptom of a need not being met in the organization.”
Instead, organizations should focus on providing secure, approved alternatives while clearly communicating expectations around acceptable use. Employee education plays a major role in AI governance. Workers need to understand both the capabilities and limitations of these tools, along with their responsibility for reviewing and validating AI-generated outputs.
As Brigance noted, if an employee sends an AI-generated email without reviewing it, responsibility still belongs to the employee—not the AI.
Preparing for the Future of AI in Trucking
The pace of AI innovation shows no signs of slowing. Emerging capabilities, both positive and negative, such as computer vision, predictive analytics, deepfakes, and AI-powered cyberattacks are already reshaping the transportation landscape.
That makes governance increasingly important.
Organizations do not need to predict exactly how AI will evolve over the next five years. They do need a framework that allows them to evaluate new technologies consistently, manage risk effectively, and make informed decisions about adoption.
The companies that succeed with AI won’t necessarily be those that move the fastest. They’ll be the ones that move deliberately; balancing innovation with accountability, security, and business value.
Key Takeaways
- Start with the business problem before evaluating AI solutions.
- Establish governance and accountability before deployment.
- Treat data governance as a foundational AI risk management practice.
- Conduct rigorous AI vendor risk assessments before sharing sensitive information.
- Maintain human oversight for high-impact decisions and critical business processes.
Conclusion
AI is poised to become as fundamental to business operations as email, spreadsheets, and enterprise software. For trucking companies, the opportunity is significant—but so is the responsibility.
By implementing a structured AI governance framework, transportation leaders can confidently adopt new technologies while protecting their data, customers, and operations. The future belongs not to the organizations that adopt AI the fastest, but to those that govern it the best.
Download the NMFTA Cybersecurity AI Governance Framework for Trucking and use the implementation checklist to assess your organization’s AI readiness, vendor risk, governance policies, and security controls.
