AI in trucking

AI in Trucking: 4 Incredible Ways It Will Affect Transportation

The world is abuzz with AI (artificial intelligence) and rightfully so. In just one short year this technology has transformed from creating weird and janky videos of Will Smith eating spaghetti, to now producing detailed and rich scenes that can easily be mistaken for Hollywood productions. But when it comes to AI in trucking however, not as much is being spoken about.

A gif showing the evolution of generative AI. An example of how fast the technology is evolving and how AI in trucking will take advantage of these advancements.

Left: An early version of generative AI used to make a scary-looking Will Smith. Right: OpenAI’s Sora model released 1 year later to make rich, detailed scenes from just a text prompt.

If this level of improvement can happen in just 1 year, imagine where the technology will be in 10, or even just 5 years time.

But aside from being able to create images and videos from thin air, AI has already proven to have some valuable real-world use cases. It’ll eventually become so powerful and so prevalent, it’ll intersect every aspect of our lives. And transportation is one of the countless industries that stands to gain massively from AI.

In this post we’re going to make 4 predictions into how we think ourselves, and the rest of the transportation industry will change as AI in trucking becomes more and more prevalent.

 

1. Route Optimization

In order to improve efficiency and reduce costs, many shippers spend lots of time planning out the best routes their vehicles can take. Traditionally, planning efficient routes involves juggling factors like traffic, weather, load distribution and delivery schedules. This one area is ripe for transformation by AI. AI can supercharge this process by analyzing massive datasets in real-time.

We predict that one day, you’ll be able to talk to your AI assistant in your own voice, telling it the different loads and destinations you have as well as all the different factors which need to be considered. The AI will then instantly produce a thorough and detailed route plan, perfectly optimized for all scenarios and eventualities. Almost seems too good to be true, right?

This really is not hard-to-believe. In fact, you can already use Google’s new Gemini model to do a simple route pan based off a simple text prompt.

 

An example prompt we asked Google Gemini for an FTL route:

Google's Gemini model being used as a helpful AI in trucking for route optimization.

The route output Google Gemini produces. This map can be clicked, taking you directly to Google Maps with the route loaded in:

AI in trucking: using Google Gemini for Route optimization

Want to learn more about route optimization? We recently wrote a great piece that dives deep into everything you need to know about route optimization.

Check it out here, once you’ve finished reading this.

 

2. Admin & Paperwork

Our 2nd prediction is one that we’re already beginning to test out ourselves at PEI. We believe that, just like all technology, AI in trucking has the potential to massively speed up workflows, especially in respect to the more tedious, admin-side of work.

Using OpenAI’s GPT-4 model, we’ve begun analysing new business inquiry generated through our website. This allows us to establish quickly what information is missing and what we need to ask the customer in order to provide a comprehensive quotes for our services.

This does not replace our team. Far from it. It instead enables them to respond even quicker and provide an even higher level of service to our customers.

The way it works is, once an inquiry comes through, our AI assistant analyses the message, creates a short summary of what the customer needs and what questions our team needs to ask in order to provide a comprehensive quote. While it’s still early and we have much more testing to do with it, we think we might eventually be able to have a support agent embedded throughout a website that operates 24/7 365 days per year. It might eventually know everything our team knows and can help field questions, prepare shipping plans and draft quotes – all on its own.

 

Some other example use cases include:

  1. Extracting relevant information from bills of lading, invoices, and customs forms.
  2. Verifying documentation for accuracy and completeness.
  3. Matching shipping documents to internal records for streamlined billing and payment processing.

 

3. Self-driving & autonomy

Perhaps the most sci-fi prediction and one many people might think of when they hear AI in trucking: autonomous trucks.

While fully self-driving trucks navigating our complex roadways may seem far-fetched, the technology is progressing at an incredible pace, and the potential benefits are undeniable.

Imagine a future where trucks can tirelessly transport freight across the country without the need for driver breaks, maximizing efficiency and reducing delivery times. AI-powered systems can continuously monitor road conditions and traffic, making real-time adjustments far faster than any human could react. This has the potential to significantly improve road safety and decrease accident rates.

However, the path to full autonomy is not without its hurdles. Ensuring the systems are reliable enough to handle the vast range of unexpected events on open roads is a major challenge. Additionally, there are significant regulatory considerations to address before fleets of self-driving trucks can fully take over our highways.

 

4. Fraud Prevention

According to the Wall Street Journal, up to $700M annually is lost every year due to double brokerage.

Double brokering itself is not inherently bad – it can connect shippers with extra capacity from carriers and provide flexibility in a dynamic market. However, this practice becomes a target for fraud because it introduces an extra, unverified party into the transaction. Fraudsters exploit the trust involved in double brokering by impersonating legitimate brokers, collecting payment from the shipper, and then disappearing without ever hiring a carrier to deliver the goods. This leaves the shipper out of pocket and the real carrier unpaid, ultimately harming the entire shipping ecosystem.

As highlighted by the Commercial Carrier Journal, one major way AI can help is by identifying fraudulent documents. AI models can be trained to analyze invoices, bills of lading, and other paperwork, detecting subtle patterns that might escape human detection. Additionally, by analyzing massive datasets of transactions, AI can find anomalies and flag suspicious activity for further investigation. Perhaps most excitingly, AI has the potential to predict fraud risk, helping companies understand which shipments may be more vulnerable and allowing them to proactively protect themselves against losses.

Conclusion

AI’s meteoric rise is transforming industries across the board, and transportation is no exception.

While the full impact of AI in trucking may still be years away, the potential benefits are undeniable. From route optimization to administrative automation, self-driving fleets, and advanced fraud prevention, AI is poised to revolutionize the industry.

Of course, it’s important to acknowledge that AI development isn’t without its challenges. “Hallucination” – where AI confidently produces incorrect or misleading information – remains a risk that must be addressed. While it will likely become less of a problem as the technology matures, it’s still important to have a critical eye to ensure accuracy and reliability before relying on them wholesale.

Despite these challenges, the future of AI in trucking is incredibly exciting. The best way for companies to prepare is to start experimenting with AI now. Test these tools, see how they can integrate with current processes, and explore how they might shape the way you work. Even small advancements today can lay the foundation for significant transformation in the years to come.

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