The Ultimate Guide to AI in Trucking: Empowering Your Fleet for Success

We’ve all heard the buzz about AI in trucking. But let’s be honest: Some promises can feel a little far-fetched. The term AI has been floating around since Dartmouth coined it in 1956, but so far only the big players have been able to afford implementation. Where’s the payoff for a small-to-medium-sized trucking company?

To stay competitive, most smaller trucking companies rely primarily on physical assets including best-performing trucks, well-located warehouses, and experienced people. However, as the workforce ages and infrastructure changes, truckers must find ways to eternalize that expertise and act agilely in increasingly more complex logistics networks. 

The good news is that we’re finally at a tipping point. Thanks to more efficient chip manufacturing and cloud data centers, computing power and data storage are affordable, making AI tools accessible. Route planning tools can operate from real-time traffic data, and vehicle telematics can help truckers optimize fuel consumption without buying brand-new fleets.

Experts predict a surge of AI in trucking over the coming decade. The key is understanding trucking AI applications beyond the hype and the tangible benefits they bring.

Let’s examine the biggest challenges trucking companies face today, AI’s benefits and use cases in trucking, and how to get started.

The Biggest Challenges and How AI in Trucking Can Really Help 🤝

Navigating Economic Fluctuations

Logistics has always been subject to economic cycles; when GDP is low, so is demand, and hikes in living costs, inflation, and interest rates impact business flow. More recently, the pandemic and supply chain disruptions, such as droughts in Panama or limited passage via main ocean routes, have made it difficult for trucking companies to predict demand proactively, but they don’t make it impossible.

With advanced demand forecasting, trucking companies can factor in historical, recent, and real-time disruptions to help build realistic projections. AI algorithms, such as machine learning and deep learning, are stepping in to help analyze historical data, telematics, and external trends, and today, tools are available that make this process 95+% accurate.

In times of low demand, truckers can use these quieter spells to optimize their operations. They can pay closer attention to fuel consumption, drive times, and route planning to identify areas for improvement and enhance efficiency. Advanced logistics planning tools can help here, too.

Optimizing Fuel Efficiency

Did you know that the Environmental Protection Agency (EPA) has found that frequent, aggressive driving can significantly reduce fuel economy? Data on speed, acceleration, braking, and idling provide valuable insights into fuel usage. For this reason, tracking these behaviors and introducing personalized driver coaching is crucial to the bottom line.

Trucking companies can develop practical training and recognition programs to encourage fuel-efficient practices by monitoring driver behavior and identifying optimal driving conditions. Sharing this data directly with drivers helps them understand the impact of their actions on fuel consumption and track their progress over time.

Additionally, avoiding areas with regular traffic congestion can save time and fuel. By analyzing GPS and real-time vehicle tracking data, trucking companies can use route optimization software to immediately re-route trucks and select more efficient ways for future trips.

Attracting Top Drivers

A career in truck driving has a poor reputation — it’s physically demanding, keeps you away from your family, and the size and weight of the vehicles can instill fear in new drivers. Human error significantly contributes to road accidents, accounting for nearly 95% of incidents. However, regulations like the mandatory implementation of advanced driver assistance systems (ADAS) and automatic emergency braking (AEB) systems are increasingly making the market safer and more attractive.

Computer vision, combined with sensors like radar and lidar found in the latest ADAS and AEB systems, can help attract top drivers as it opens doors to higher safety, driving efficiency, and work quality. It can accurately identify and track vehicles, pedestrians, and other road users in its radius. It can also communicate with the truck to ensure the driver is alert and even make the executive decision to break or slow down in unsafe situations.

Trucking companies can integrate AI analytics with visual and vehicle data to build driver coaching tools that offer new drivers a sense of personal development. This data also enables the creation of high-definition maps and precise location tracking, which logistics planners can use to optimize routes and improve journey planning to ensure everyone has enough time with their families.

Truck drivers don’t get enough recognition. By rewarding them for their driving excellence, and using the latest tools to make their experience on the roads safe and enjoyable, the industry will improve its reputation as the much-needed and respected role it has in our society.

Embracing Energy Innovation

HDVs are responsible for more than a quarter of the EU’s road transport greenhouse gas (GHG) emissions, and manufacturers must comply with targets for fleet-wide average CO2 emissions starting from 2025. 

As of Q1 2024, 14% of fleets operated electric vehicles (EVs) across various regions and fleet sizes, with a substantial increase expected over the next five years. Beyond its positive environmental impact, these trucks bring smart features that benefit logistics companies.

EVs often have set routes of known distances, vehicles that routinely overnight in the same location, and built-in telematics — data sources that fuel many of today’s advanced logistics software. The tools that measure battery performance can also enable remote truck monitoring, providing alerts for maintenance needs or potential issues and promoting proactive vehicle care. In addition, it allows for analyzing driving habits, which can be instrumental in improving energy efficiency and reducing truck wear and tear.

Moreover, by 2030, EVs will likely be cheaper than their diesel counterparts across four out of five listed truck categories.

Prioritizing Parking Solutions

Logistics planners have a lot on their plates. Increased regulatory compliance, navigating local, national, and international laws for customs and trade, and more complex intermodal supply chains require them to file a lot of paperwork. The industry has grown so quickly that even city planners are catching up in building infrastructure like parking spots to accommodate more trucks, and planners need help managing routes to ensure drivers always have a service station with plenty of available space.

Several forms of AI in Trucking can help with this scenario:

• Better internal planning tools can reduce logistics planners’ administrative load. Generative AI, for example, is increasingly stepping in to help automate quote, supplier contract, and invoice generation, allowing logistics planners to prioritize strategic decision-making.
• Advanced route planning software can help logistics planners meet driver priorities. For instance, if multiple stops are a priority for a particular driver, logistics planners can add this filter to their planning tool to find the best route.
• Digital twins-powered scenario planning enables logistics planners to create digital simulations of real-world situations. They can create virtual replicas of roads, parking lots, and expected traffic at set times of day, helping to review the optimal routes, including service stations with plenty of amenities — like truck parking — to meet their drivers’ needs.

As logistics technology advances and data capture on truck status, location, global traffic, regulations, and disruptions becomes increasingly automated in real time, trucking companies can more confidently rely on these tools to maximize their operations. They can accurately predict demand and plan accordingly to ensure all deliveries are met, and drivers are offered healthy schedules. They can implement driver coaching and reward programs to give their team the recognition they deserve, and they can save on fuel consumption with drivers’ expert fuel-saving techniques and advanced route planning. 

AI in Trucking: Benefits and Use Cases 🤖

Now that we have reviewed the biggest challenges impacting the trucking industry, let’s move to understand AI in trucking applications beyond the hype and the actual impact that they bring.

Seeing Around the Corner: Computer Vision for Enhanced Driver Awareness (and more)

No one wants Big Brother breathing down their neck and watching every move, but what if you could put eyes on the back of your own head instead? 

Imagine having a virtual co-pilot constantly watching out for you, helping you improve your ability to navigate turns, manage blind spots, and operate in different weather conditions. 

Cameras mounted throughout the vehicle, paired with AI algorithms, offer truckers a new power: computer vision. It can analyze driver behavior in real time and provide immediate feedback through audio alerts or on-screen notifications.

As well as supporting drivers on the go, evaluating historical driver behavior data can help truckers identify patterns and areas where specific additional training is needed. This allows for targeted coaching, preventing accidents, and keeping drivers (and everyone on the road) safe. 

Plus, as autonomous driving matures, this same trucking AI will be crucial for its safe implementation — so getting familiar with it now only sets you ahead.

Predicting the Future, One Mile at a Time: ML & Predictive Analytics

We can’t control the market, but we can prepare for it. Machine Learning (ML) and predictive analytics are a crystal ball for truckers. These tools help to predict maintenance needs before breakdowns occur, forecast operational costs, and even estimate spot rates. 

With access to vehicle data — such as engine speed, RPM, oil pressure, coolant temperature, and fuel consumption; brake pad wear, pressure, and temperature; tire inflation — and GPS data, logistics planning teams can reveal abnormal fuel consumption patterns, indicating potential route inefficiencies or mechanical problems.

Load weight and type, past repair records, part replacements, and driver logs of unusual noises, handling, or vibrations are crucial parameters for algorithms to pinpoint the cause of unusual vehicle performance. But don’t forget the impact weather and traffic conditions have on trucks’ handling and fuel.

While it might sound like a lot, you don’t need a team of data scientists or a mountain of cash to get started; the right data and algorithms are readily available for truckers to leverage. All you need is the blueprint, and you can make informed decisions, optimize routes, and maximize profits.

Big Data, Big Wins for AI in Trucking

So you’ve decided what information you need from your trucks and drivers. Once collected, the Internet of Things (IoT) sends it to the cloud and the connected big data analytics tools. This data goldmine lets you measure key performance indicators (KPIs) for both vehicles and drivers.

IoT sensors on the engine can collect the vehicle and fuel data you need, GPS systems help you track location, measure distance traveled, and calculate fuel efficiency, and computer vision provides driver behavior information. When you cross-analyze these datasets with AI for trucking, the real benefits stream in. If effectively deployed, big data analytics can fuel the most strategic business decisions and put logistics teams ahead of the game. Learn about three key areas where teams can maximize big data analytics in logistics to boost profits.

“AI takes a large amount of data, more information than any person can be expected to manage, and turns it into an actionable tool,” says Jean-Sebastien Bouchard, chief product officer and co-founder of Isaac Instruments. You can identify fuel-inefficient or toll-avoiding routes, forecast demand, offer dynamic pricing, and carefully plan journeys to build maintenance stops into your delivery schedule. Capturing this high-quality big data and processing it with big data tools — such as demand forecasting, maintenance, or pricing analytics — is the best way to control your costs and profitability.

Automated Planning: Dispatching on Autopilot

Forget endless route planning and dispatch headaches. AI-powered dispatching tools use real-time traffic data, weather conditions, and driver availability to automatically assign the best routes and loads.

Ben Wiesen, president of freight management software provider Carrier Logistics Inc., noted that “Complex decisions are now made by software systems that are available 24/7, which is a good thing for an industry that literally does work around the clock. Operations planning, which was previously exclusively done by dispatchers, has transitioned to optimization largely performed by computers.”

Dynamic route optimization constantly tweaks journeys based on unexpected events, saving time and fuel. Trucking companies can move away from previous geographical rules and fixed-route solutions to irregular-route delivery fleets without causing any delays in the network’s freight movement. It can consider specialty equipment, appointment time windows, predicted traffic, and commercial vehicle routing to determine which deliveries should be loaded on each truck and in what order. 

Plus, load-to-capacity matching ensures you find the perfect truck for your cargo, maximizing your earning potential. Logistics planners can use AI to evaluate a multitude of parameters, from perishables, delicate items, heavy goods, and dangerous loads to vehicle height, tires, load packaging, and CO2 emissions. The route itself must also be adjusted based on the truck’s features when it comes to long detours, low tunnels, and rough terrain.

Simulating Success: What-if Scenario Planning & Benchmarking

Before you invest in a new client, vehicle, or trade lane, wouldn’t it be great to see how it plays out? 

What-if scenario simulation allows you to test different strategies in a virtual world, predicting outcomes and identifying potential roadblocks. You can compare clients, vehicles, and trade lanes to see what generates the highest return on investment (ROI). 

Everything from demand and stock to transport, capacity, labor, and communication have a ripple effect across the supply chain. Digital twin technology creates virtual representations of processes to assess options, risks, and likely consequences before future incidents occur. 

Using different demand growth projections and considering various network parameters, DB Schenker Bulgaria used Transmetrics’ AI-powered algorithms to measure the efficiency and resilience of numerous network setups. By comparing the scenarios, the project teams found the best way to adjust the network infrastructure and achieve the most optimal linehaul plan.

It’s not just the optimal network infrastructure you can simulate either; you can also assess the ultimate driving techniques. Driver and staff benchmarking helps you identify top performers and develop training programs to elevate everyone’s game.

From Emails to Contracts, Generative AI Does the Paperwork

Let’s face it: paperwork takes up too much valuable time. Generative AI and Large Language Models (LLMs) can automate repetitive tasks like producing quotes based on email inquiries, drafting contracts, and managing specific back-office operations. 

Take inventory management, for example. Natural language processing (NLP) could boost your performance by analyzing supplier communications, contracts, and other textual data to extract relevant information about product availability, lead time changes, or any disruptions that may affect inventory levels. It can then suggest adjustments to safety stock levels or prepare invoices for customers. This frees your team to focus on what matters most — running the business and building strong client relationships.

Nevertheless, human involvement is essential for tasks such as data preprocessing, providing contextual understanding, and making decisions in ambiguous situations, contributing to the system’s overall robustness.

AI isn’t just for the big guys anymore. It’s a powerful tool that can give your company a significant edge in the competitive trucking market. By embracing AI, you can become more efficient, safer, and profitable. In order to succeed, your organization needs to focus on building a proper AI in trucking roadmap. Check out the visual below.

How to Get Started with AI for Trucking?

If you want to ensure that your business is on the right track to begin adopting AI, there is a simple roadmap to follow. You should start by identifying and benchmarking which key pain points, such as inefficient route planning, reactive maintenance, and data overload, affect you most. Then, craft a data collection strategy to smoothly integrate changes into existing workflows. Here are four key steps to implement AI in trucking:

• Identify and prioritize pain points: What is your cost per mile? Your on-time delivery rate? And how do these compare to the industry average? Which bottlenecks are causing the biggest delays, financial strain, or staff turnover? Analyze records like fuel consumption logs, maintenance reports, driver logs, delivery times, and driver feedback, and prioritize areas for improvement.
• Match pain points to specific AI solutions: Consider tools for route optimization, AI-powered load-matching, driver performance analysis, and data integration.
• Craft a data collection strategy: What data do you already have? Think about GPS, engine information, fuel usage, driver records, and maintenance logs. Where are the gaps? For example, does your GPS track location, speed, and stops? Or do you need a different way to measure idling? Are you collecting any data manually? Could you automate this? Is it easy or difficult to do? Identify necessary data sources, address data gaps, and ensure data security. 
• Navigate workflows to integrate AI smoothly: Processes changes will only work when those using them day-to-day are on board. What are the benefits to them? Communicate these with your team, address concerns, and gradually implement AI solutions, maintaining this transparency throughout the integration process.

By following these steps, trucking companies can leverage AI to overcome their biggest challenges, improve operations, and remain competitive in the industry.

Explore FleetMetrics and see how our software can take your trucking business to the next level. Or, get in touch with our logistics AI experts, and we’ll walk you through the blueprint to seamless operations.