How TMS Providers Use AI Bots to Enhance Your Business

As the transportation industry evolves, the integration of artificial intelligence (AI) has become a pivotal element in creating smarter business operations. Companies are increasingly recognizing the potential of AI to streamline processes, enhance decision-making, and ultimately improve profitability. But how can these technologies be effectively harnessed to address specific business challenges? Let's explore the transformative impact of AI on transportation management systems (TMS) and how companies can navigate this complex landscape.

Identifying the Right Problems to Solve with AI

Before diving headfirst into AI adoption, it’s crucial to identify specific problems that the technology can address effectively. Bill Cain, director of product management at Trimble Transportation, emphasizes the importance of pinpointing issues that keep leadership awake at night. This targeted approach helps ensure that AI is not just a trendy addition, but a solution to real challenges.

Some common issues that fleets face, which AI can help solve, include:

• Reducing empty miles to optimize routes and save costs.
• Minimizing the time spent on order entry, allowing staff to focus on more strategic tasks.
• Decreasing distractions for drivers caused by excessive communication from dispatch.
• Eliminating unprofitable loads that waste capacity and resources.

Hans Galland, CEO of TMS BeyondTrucks, categorizes potential AI use cases into three main areas: automation, decision-making, and risk reduction. However, he cautions against rushing into AI adoption without a clear plan. Galland shares the story of a food distributor that aimed to save $10 million by optimizing routing but failed because of operational practices that undermined the technology's potential benefits. This highlights the significance of addressing foundational issues before implementing advanced solutions.

The Distinction Between Automation and AI

As the landscape of technology evolves, particularly with the rise of AI, understanding the difference between simple automation and true AI is essential for trucking companies. In the early days of electronic logging devices (ELDs), many vendors emerged, promising revolutionary changes, yet many have since disappeared. A similar trend is being observed with AI providers today.

Companies must grasp the distinction between native AI incorporated within a TMS and external plug-in tools. While add-ons may solve specific problems, managing a TMS with multiple plug-ins can complicate operations. As Justin Sky, co-founder and CEO of Rose Rocket, puts it, “An AI can only be as effective as the data model that underpins it.”

When evaluating TMS vendors, consider asking:

• Which large language model (LLM) is the product built upon?
• Does the AI make autonomous decisions or merely automate existing workflows?
• How seamlessly is AI integrated into the overall user experience?

For instance, Trimble’s TMWSuite TMS uses an AI agent in its Order Module feature to assess tender submissions, allowing fleets to evaluate freight profitability quickly and make informed decisions.

Leveraging AI for Enhanced Decision-Making

AI's integration into transportation management systems is not just about automation; it significantly enhances decision-making processes, benefiting all areas of a trucking company. One critical area where AI can make a substantial impact is in assisting drivers. An AI-enabled TMS can reduce unnecessary communication from dispatch, allowing drivers to focus on their routes and safety.

Moreover, AI can proactively guide drivers away from hazards, such as dangerous weather conditions. For example, a forthcoming product from Trimble will provide real-time alerts about adverse weather and suggest alternative routes. This capability not only enhances safety but also optimizes operational efficiency:

• Alerts drivers to hazardous weather conditions.
• Provides rerouting options based on real-time data.
• Predicts how weather events may affect travel time and speed.

Building Trust in AI Technologies

For the successful implementation of AI, it’s vital for everyone in an organization—from executives to drivers—to trust the technology's decision-making capabilities. Sky describes the AI adoption process as a three-stage journey: in the loop, on the loop, and out of the loop.

The first phase involves active participation and scrutiny of AI decisions. As confidence in the technology grows, companies transition to monitoring its performance before eventually trusting it to operate independently. Sky warns against vendors who suggest immediate disengagement from the decision-making process, as this could indicate a lack of transparency or reliability in the AI system.

It’s also crucial to recognize that AI tools should not be treated as quick fixes for existing problems. Galland highlights that vendors often imply that acquiring an AI tool will automatically resolve underlying issues. In reality, this can lead to “automating trouble into more trouble” if the root causes are not addressed first.

Future Prospects of AI in Transportation Management

The future of AI in transportation management looks promising, with advancements that could allow fleets to create customized TMS solutions that evolve alongside their business needs without requiring extensive coding knowledge. For example, as a fleet begins handling more less-than-truckload (LTL) freight, the TMS could adapt to accommodate these changes, eliminating the need for multiple systems or software replacements.

Amid discussions about AI’s impact on jobs, many experts, including Trimble’s Cain, encourage a more optimistic perspective. He believes that rather than leading to job losses, AI will create opportunities for workers to upskill and engage in more technology-driven roles. This shift will enable employees to focus on data analysis and decision-making, ultimately improving their work experience and opening doors to new career paths.

As companies embrace AI, they should recognize it not just as a buzzword or trend, but as a powerful tool that can lead to smarter, more efficient operations. To further explore the intersection of AI and transportation management, watch this insightful video on the subject:

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