As the 21st century progresses, the trucking industry is in need of innovative solutions. According to a recent study from Gartner, by 2026, 30% of enterprises are expected to have automated more than half of their network activities, an increase from less than 10% in mid-2023. The report emphasizes how automating decision-making is front and center for those in operations and infrastructure. 

Following are four common legacy challenges, and the opportunities they present for automating the trucking industry.

Manual Processes

The challenge: These can add significantly to process inefficiency, including:

• Time. Tasks like data entry, route planning, scheduling, and document management done manually take longer.
• Human error. Manual entry and paperwork are prone incorrect information and missing data.
• Scalability and labor cost. As trucking companies grow, manual handling requires more staff and resources. Workers performing daily, repetitive manual activities often experience fatigue, develop biases, and make “good enough for now” decisions. This can result in errors that spread throughout the supply chain. In addition, the human mind can’t evaluate and balance multiple goals, make sequential decisions, and plan comprehensively for the future.

As trucking companies evaluate their workflows and resources, they can often quickly identify several tasks that can be automated, while reassigning human operators to areas where they can have the most significant impact — those that require creativity, strategy, and personal touch, such as customer and driver relationships.

The opportunity: Artificial decision intelligence shines in areas where human capabilities fall short, such as swiftly evaluating thousands of potential driver and load assignment choices. This technology achieves a level of optimization and efficiency that was once out of reach. By automating these processes, workers are freed from monotonous, repetitive duties. This redistribution of human effort enhances workforce value, focusing employee contributions on high-impact activities that directly support customer satisfaction and drive business growth.

Siloed Views

The challenge: Traditional trucking operations are often divided based on geography, with asset and brokerage divisions operating independently. Each region is managed by its own set of planners, dispatchers, driver managers, and customer service representatives.

Visibility issues arise when loads or available drivers are just within a given area’s borders. Because each planner is focused on their area, they have no visibility into what’s best for the entire network. This narrow focus can result in inefficiencies such as underutilized vehicles, increased deadhead miles, and missed load opportunities.

The opportunity: Artificial decision intelligence serves as a powerful tool for breaking down operational silos between teams that are separated by geography or function. Local expertise and regional knowledge are converted into valuable data for an intelligent decision-making system. This shift allows artificial decision intelligence to provide informed, data-driven recommendations that optimize strategic planning and day-to-day operations throughout the network.

Frustrated Drivers

The challenge: Working in isolated silos narrows focus and leads to frustration for drivers. They frequently follow plans devised by coordinators with a limited perspective, concentrating only on the needs and resources within their specific regional areas. This restricted approach can result in instructions that make sense locally, but are counterproductive from a broader network view.

The disconnect between regional planning and overall network efficiency can force drivers to take suboptimal routes. They might feel that their time and skills aren’t being utilized effectively. Over time, this can contribute to higher stress levels, decreased job satisfaction, and potentially higher driver turnover rates.

The opportunity. Artificial decision intelligence in trucking operations plays a key role in reducing driver frustrations. When drivers feel satisfied, they become true partners in delivering exceptional service.

Decisions Without Data

The challenge; Even if those making the decisions are intelligent and experienced, they can only make the best and most informed decisions with the correct data and insights. Without a dependable forecast shaped by current data (including existing unknowns) and an analysis of past trends and events, decision-makers often navigate without clear direction, relying heavily on prior experience, intuition, and subjective judgment.

Human insight is valuable, but inherently limited and prone to bias. Decision-makers do their best to predict outcomes and make reasonable decisions, but this approach is akin to guessing. Success hinges more on luck than calculated strategy. Although intuition and experience can sometimes yield good outcomes, they cannot match the reliability and accuracy that data-driven decision-making consistently provides.

The opportunity: Artificial decision intelligence revolutionizes resource optimization for trucking companies, enabling them to fully leverage their workforce, drivers, equipment, and capital. The technology can help trucking organizations to analyze extensive data sets, forecast trends and identify the most efficient use of their resources.  

The trucking industry is at a pivotal moment, where adapting to new technologies is no longer optional but necessary for long-term success. By embracing artificial decision intelligence, trucking companies can automate repetitive tasks, enhance network-wide visibility, improve driver satisfaction, and enable data-driven decision-making. This evolution streamlines operations, increases profitability, and positions companies to thrive in an increasingly competitive and complex supply chain landscape. The path forward is clear: integrating intelligent solutions that maximize resources and empower human efforts to focus on strategic, value-added activities.

Erica Frank is vice president of marketing with Optimal Dynamics.