AI for Logistics & Transport Companies: Getting Started

AI is here. And for logistics and transport companies, it’s a game-changer.

It has the potential to streamline operations across the board - from internal workflows like invoicing to customer-facing tasks like resolving disputes.

But with so many possibilities, where do you even begin?

To find out, I spoke with Joe Kayser, founder of SynapseVue.

Joe’s been in the logistics and tech space for over 30 years, holding roles from CFO to tech consultant. He’s now leveraging his expertise to help businesses in transportation and logistics implement AI-driven solutions that optimize operations, improve cash flow, and enhance customer satisfaction.

Whether you’re exploring AI for the first time or looking to level up, Joe’s insights are gold. 

Let’s dive in. 👇️ 

Table of Contents

1. Opportunities

2. Sample Use Case - Problem and Solution

3. Sample Use Case - Managing AI Systems and Data

4. Sample Use Case - ROI and Training

5. What’s Next

Connect with Joe on LinkedIn and SynapseVue on their website.

This conversation has been edited for length and clarity. If you're short on time, skip to the TL;DR section for the key takeaways.

1. Opportunities

To begin, what are the key opportunities for transport and logistics companies in adopting AI, specifically in operations? Can you share a list of workflows where AI can have a significant impact?

JK: It’s an exciting time for transportation and logistics, especially with the rise of generative AI. Over the past few years, we’ve seen small but impactful changes, and now in 2025, we’re starting to realize the next steps.

On the logistics side, one major area is customer service. Workflows like quoting shipments, booking shipments, and tracking and tracing have traditionally involved human intervention. Now, generative AI can handle pointed questions and provide accurate answers without human input. This eliminates delays that used to occur with email-based human responses.

Beyond customer service, AI is being integrated into back-office functions like accounts receivable and payable, addressing communication bottlenecks across the service loop. On the vision side, AI is making significant strides in driver safety. Cameras in cabs can now detect driver fatigue or other concerning behaviors in real-time, allowing companies to intervene proactively and prevent accidents. This is no longer just about gathering data; it’s about taking action when needed.

Before we dive into other topics, let’s dig deeper into the communication and driver safety workflows. How else can AI assist logistics businesses on the strategic or business side, like demand forecasting?

JK: AI is transforming demand forecasting by making it more accessible. In the past, effective forecasting required massive datasets and large budgets to build predictive models. 

Now, modern AI tools—especially those leveraging generative AI—can synthesize smaller data sets and extract context from communications to make accurate predictions.

For example, AI can help businesses forecast demand, manage inventory more effectively, and even predict potential disruptions based on patterns. This applies across areas like driver safety as well, where AI can anticipate risks based on real-time data.

Based on your experience, are most companies ready to implement AI, or do they need to organize their data first?

JK: That’s a great question. It really depends on the use case. If we’re talking about predicting the next port strike, that’s outside AI’s current scope. But for day-to-day workflows like responding to customer queries, resolving disputes, or negotiating, most companies already have the data they need.

This data often resides in their TMS, email communications, or other internal systems. These existing data points can be leveraged to implement generative AI effectively. 

So yes, the low-hanging fruit for AI adoption is definitely in communication and operational workflows that already have structured data.

Is communication automation the best starting point for companies looking to implement AI?

JK: Yes, communication is often the easiest entry point. Low-hanging fruit includes simple chatbots that can be trained on company-specific information. For example, a chatbot could answer basic questions about service locations or hours of operation.

Many companies already have rudimentary chatbots, but these often just pass customer emails to a human for response. By training the chatbot on standard operating procedures (SOPs) and internal documentation, businesses can enable it to handle more complex inquiries. 

This also helps with onboarding and training new employees, as the chatbot becomes a reliable source of information for standard processes.

2. Sample Use Case - Problem and Solution

Can you share a recent use case that illustrates the AI implementation process? What challenges did the company face, and how did you resolve them?

JK: Sure. We recently worked with a company facing cash flow challenges due to slow dispute resolution for past-due invoices. Their bottleneck was on the receivables side, specifically resolving disputes related to charges like chassis rental dates and assessorial approvals.

After a thorough discovery process, we mapped out their workflows and built an automation system. Using data from their TMS, we implemented AI to read customer emails, understand the context of disputes, and respond with accurate information. 

For example, if a customer questioned the number of chassis days billed, the AI would check pickup and return dates, calculate the correct charges, and generate a response.

This automation drastically reduced response times and allowed 24/7 operations. While some tasks still required human oversight, the system handled most queries efficiently, improving both cash flow and customer satisfaction.

Can you share a recent use case that illustrates the AI implementation process? How did the process unfold from discovery to implementation, and what challenges did you address?

JK: Absolutely. Let me walk you through a recent example. 

The use case started with a discovery process, which is essential to identify the company’s pain points and communication bottlenecks. Historically, automation projects have focused solely on pain points, but with AI, it’s equally important to consider where communication breaks down and where repetitive processes occur.

For this particular customer, the bottleneck was in their accounts receivable department, specifically related to cash flow. As a former CFO, I deeply understand how critical this pain point can be. After a deeper dive, we identified the main issue: resolving disputes for past-due invoices. Their staff struggled to keep up with these disputes, often involving chassis rental dates or accessorial approvals.

We mapped out the entire workflow and implemented an automation system that utilized AI to streamline responses. For example, when a customer questioned the number of chassis days billed, the AI could read the email, understand the context, pull the relevant data from the TMS, and generate an accurate response.

This system drastically reduced response times—what used to take hours now takes minutes, even seconds. Emails received at 6:00 p.m. can have a response sent by 6:01 p.m. This speed, coupled with 24/7 availability, significantly alleviated the workload for an understaffed team.

What tools and technologies did you use to build the solution? How did you ensure data security and efficiency?

JK: The first step was to integrate their TMS to pull data on outstanding invoices and trigger automated email notifications. We used tools within the Azure stack to ensure data security and scalability. Our team of programmers built custom functions for optimal efficiency.

On a simpler level, many of these tools are available within Microsoft 365, allowing smaller-scale automations. For this use case, we set up triggers to send templated emails for past-due invoices. These templates didn’t initially require AI, but as we progressed, we layered AI for more complex tasks like reading and responding to customer emails.

We used OpenAI’s large language models (LLMs) via secure enterprise APIs to ensure data security and compliance. Depending on the use case, we also utilized other AI models, such as Anthropic’s Claude or Google’s AI, ensuring the right tool fit the context.

3. Sample Use Case - Managing AI Systems and Data

How did the AI handle customer email classifications? What workflows were built around these classifications?

JK: We used a retrieval-augmented generation (RAG) approach to enhance the AI with company-specific data. The AI classified emails into three categories:

1. Payment Confirmations – Simple acknowledgments of payments went into an automated workflow that updated the collection system.

2. Invoice Disputes – These required the AI to read the context of the dispute (e.g., chassis rental dates) and retrieve the relevant data from the TMS. For example, if a customer claimed an incorrect chassis charge, the AI would check the pickup and return dates and respond with accurate calculations.

3. Frustration Detection – The AI flagged emails with signs of customer frustration (e.g., caps lock, exclamation marks, or repeated complaints). These emails were routed to a human for immediate handling.

Each workflow was tailored to maximize efficiency while ensuring accuracy.

How was data prepared for AI processing? What role did automation play in supporting the AI?

JK: Data preparation involved pulling and cleaning information from the TMS to ensure accuracy. While 30-40% of the workflow relied on AI, the rest was built on traditional automation methods honed over the past 25 years.

The AI’s role was to interpret data, while automation ensured that the information was properly structured and securely passed between systems. This hybrid approach balanced the strengths of AI and conventional automation, enabling the AI to provide accurate, context-aware responses.

What about hallucinations in AI? How do you handle those in workflows where accuracy is critical?

JK: Hallucinations happen when AI has too much information to process or lacks the right guardrails. For instance, in our use case, we had 4–6 different AI agents working on distinct parts of the process. 

Each agent had specific tasks, and we used a retrieval-augmented generation (RAG) approach to inject precise company information.

To minimize hallucinations, we break down workflows and keep prompts clear and concise. It’s essential to put the AI “in a box”—defining what it can and can’t do. 

For example, when quoting rates, we rely on traditional if-then statements for data accuracy. The AI only formulates the customer-facing response, ensuring the data is always correct.

Can you elaborate on how guardrails help ensure AI accuracy?

JK: Guardrails are critical in workflows where you need 100% accuracy. Take our quoting use case, for example. 

The AI gathers data like pickup and delivery locations, but the actual rate lookup relies on deterministic logic (if-then statements). This ensures the quoted rate is always correct.

Once the data is validated, the AI generates a human-like response. By blending traditional automation with AI, we can prevent errors while maintaining the speed and efficiency AI offers. This “check the checker” system ensures consistent and accurate outputs.

4. Sample Use Case - ROI and Training

What was the impact of the solution on the customer’s operations and ROI?

JK: The solution had a significant impact. By automating these workflows, the customer saw immediate improvements in response times and staff productivity. 

Employees shifted from being overwhelmed by routine tasks to supervising AI and handling higher-level responsibilities.

In terms of ROI, the customer is on track to achieve a 10x return on the cost of the subscription model within the first year. As their operations grow and the AI system handles increasing volumes, their ROI will only improve.

What kind of training was required on the people side after implementing the AI solution? How did you engage the accounts receivable team and ensure the process shift went smoothly?

JK: This is where the people side is critical. 

Building the process involves engaging the people who will use it. We started with proof of concepts to help the team visualize how AI could alleviate pain points in their workflow. Keeping them in the loop was essential.

The training wasn’t so much about teaching them to use AI as it was about helping them understand what the future workflow would look like and their role in it. Naturally, there was skepticism and mistrust—questions like, “Is this going to replace my job?” always come up. So, before letting AI respond to emails directly, we had the team interact with the AI. This helped us train the AI based on their feedback and adjust responses.

Once workflows were built, we didn’t turn on the AI’s direct responses immediately. Instead, AI-generated drafts were sent to a live person for review. This approach allowed employees to gain confidence in the system. Over time, as they saw the AI performing accurately, the process became automated, and employees could step back and take on supervisory roles. We’re still in the early stages, but I’m confident we’ll see strong results across multiple use cases.

How do employees typically react to AI taking over parts of their roles? Do they feel replaced, or do they see it as an opportunity to level up?

JK: It’s always a challenge. 

People naturally fear that AI might replace their jobs, which is why leadership needs to play a key role in communicating the vision. The message has to be clear: this isn’t about replacing jobs but about helping employees level up. For example, someone in collections might feel threatened, but with proper guidance, they can shift to more strategic roles, supervising and managing the AI.

From my 30 years of experience implementing technology, failed implementations rarely happen because of the tech itself—it’s usually a people and process issue. Vision, communication, and participation are the keys to success.

How can companies think about ROI when adopting AI? What areas typically yield the most savings?

JK: The ROI comes primarily from time savings, labor efficiency, and faster response times. For instance, faster billing improves cash flow, while quicker quoting can help secure more business. In global logistics, quotes that used to take days can now be generated in 15 minutes with AI.

The key is to identify pain points, measure their business impact, and then find the right technologies to address them. 

The ROI calculation depends on understanding the cost of implementing the solution and the value it brings, whether it’s time saved, increased revenue, or improved customer satisfaction.

Do off-the-shelf AI tools work for these use cases, or do most require customization?

JK: It depends. Simple use cases, like pushing data to a chatbot, can work with off-the-shelf tools. However, more complex workflows, especially those involving multiple systems like TMS, usually require customization.

While I see a future where modular AI solutions can handle these tasks with minimal coding, most current use cases still involve a mix of AI and traditional integration techniques. Each workflow is unique, so the challenge is to engineer a tailored solution that fits the company’s specific needs.

5. About SynapseVue and What’s Next

What’s your advice for companies starting their AI journey? Is there a specific approach they should take?

JK: My first piece of advice is to step back and assess where your organization stands. While many companies are still in the “what” phase—figuring out what they want to do with AI—it’s critical to move beyond that and build a strategic roadmap.

This doesn’t have to be a long-term, complex plan. It’s about understanding your current capabilities and identifying areas where you can start small and scale up. Importantly, don’t rely solely on external consultants to define your strategy. 

Start by tapping into your internal talent—there’s likely someone on your team with AI experience, even if it’s at a basic level.

Adopting AI should follow a crawl-walk-run approach. Start with simpler applications like chatbots, test them, and gradually expand their scope. Think of AI adoption like raising a child—you’ll guide it step by step, learning as you go, and aligning your people, processes, and technology along the way.

Can you tell me more about SynapseVue and what sets it apart?

JK: SynapseVue was founded by myself and my co-founder, both of us veterans in the industry. We bring decades of experience in business operations, accounting, technology, and analytics. Our goal is to take a consultative approach to implementing AI solutions, focusing on more complex workflows where we can deliver real value.

Every company has unique workflows that define how they operate, and our job is to understand those nuances and show how AI can enhance them. We don’t just implement solutions; we partner with our clients to ensure success.

What’s your take on the future of AI in logistics?

JK: We’re in a hyper-automation phase where AI will significantly streamline business processes. The headlines may focus on futuristic AI developments, but the real impact will come from automating simple to complex workflows that drive business value. 

Logistics companies operate on tight margins, and AI offers a way to do more with less—something every company in this space strives for.

TL;DR - Key Takeaways for Supply Chain Pros

1. Identify Pain Points and Bottlenecks: Start by mapping out operational inefficiencies and communication bottlenecks. AI is most effective when applied to repetitive, high-volume tasks like customer service queries, invoicing, or dispute resolution.

2. Leverage Low-Hanging Fruit: Begin with simpler applications like chatbots to automate FAQs, service inquiries, or tracking updates. These tools can be trained on company-specific SOPs to provide immediate value with minimal setup.

3. Adopt a Crawl-Walk-Run Approach: Implement AI incrementally. Start small by testing AI on low-risk tasks, monitor performance, and then expand into more complex workflows like demand forecasting or dynamic pricing.

4. Use Existing Data: Most companies already have valuable data in systems like TMS, CRM, or internal communications. Leverage this data to train AI models and optimize workflows without the need for extensive new data collection.

5. Align and Train Your Team: Successful AI implementation requires alignment across leadership and operational teams. Clearly communicate the goals and benefits of AI, involve employees in the process, and provide hands-on training to help them adapt to new workflows.

6. Invest in People and Processes: AI adoption requires a cultural shift. Engage employees early in the process, address concerns about job security, and train them to supervise and work alongside AI tools effectively.

7. Ensure Data Security and Accuracy: Use secure platforms and establish strict data governance. Employ guardrails and if-then logic to validate AI outputs, especially for workflows requiring 100% accuracy.

8. Focus on Speed and Efficiency: AI reduces response times significantly, enabling 24/7 operations. For example, tasks like quoting shipments or resolving disputes can be completed in minutes, improving customer satisfaction and cash flow.

9. Quantify ROI Early: Measure AI’s impact in terms of time saved, increased productivity, and faster decision-making. Start with clear benchmarks and track results to demonstrate ROI.

10. Don’t Overlook Internal Expertise: Before outsourcing, tap into your internal team. Employees with even basic AI experience can help identify opportunities and guide implementation.

11. Partner with the Right Experts: For complex workflows, collaborate with experienced AI solution providers who understand both the technology and your industry’s unique challenges.

12. Prepare for Hyper-Automation: The next few years will see rapid automation across logistics. Stay informed about advancements and be ready to integrate modular AI solutions into your operations.