AI in logistics and transportation: Use cases

Artificial intelligence (AI) has emerged as a transformative technology that has disrupted several industries—and transportation & logistics is no exception.

As one of the product managers here at Dialpad, this is an industry I’ve definitely seen a big increase in as far as AI adoption goes. And considering AI has helped businesses improve logistics costs by 15%, inventory levels by 35%, and service levels by 65% (according to McKinsey), it makes sense.

In this blog post, we’ll explore the role of AI in the transportation & logistics sector and provide some real-world examples of AI use cases.

Why transportation & logistics companies are turning to AI

I’ll let the numbers talk here.

I mentioned in the intro that McKinsey has found some impressive ROI numbers on AI for logistics. Another report by McKinsey estimates that logistics companies will generate $1.3 to $2 trillion per year for the next 20 years in economic value by adopting AI.

What’s more: 50% of global enterprises will invest in real-time transportation visibility solutions in 2023 (Gartner). And 82% of transportation & logistics companies believe that AI and machine learning (ML) will be important to their business in the next three years (American Transportation Research Institute).

It’s no wonder global players like FedEx, UPS, and DHL have already invested heavily in AI.

AI use cases in transportation & logistics

Below I’ve provided some real-world examples of use cases for AI in transportation & logistics.

Use case #1: Note-taking and call summaries

AI can improve overall productivity at your organization by taking care of note-taking during customer calls. Real-time transcription eliminates the need for agents to take notes during calls, so they can focus on the call itself. It also provides a written record of the conversation that they can refer back to for more contextualized follow-up interactions.

Here’s what that looks like with Dialpad Ai Meetings, for example:

After the call, Dialpad Ai will send the call summary with a searchable AI-generated transcript, the recording, and key highlights to attendees, which again automates some of the administrative follow-up work that would normally be required:

Oh—and an AI-generated recap of the whole conversation, automatically summarized into a few succinct sentences:

Use case #2: Live sentiment analysis

AI can help you understand how customers or fleet teams are feeling and provide personalized recommendations. It does this through live sentiment analysis, which means it can instantly flag calls with negative sentiment to the supervisor, giving you a chance to listen in or barge the call to help turn the experience around.

Here’s what that looks like with Dialpad Ai:

Use case #3: Real-time dispatcher / agent empowerment

AI tools can give agents instant access to insights into their customers’ needs and helpful information so they can tailor their responses and provide more effective solutions faster.

Dialpad’s Ai Agent Assist, for example, automatically searches all your connected knowledge sources (even unstructured ones like PDFs and past customer conversations) to find the information an agent needs, and pull it right onto the agent’s screen as they’re talking to the customer:

Use case #4: Chatbots / conversational AI

According to PWC, 73% of consumers consider customer experience to be an important factor in their purchasing decisions. AI-powered chatbots and virtual assistants can help enable 24/7 customer support on your website and digital channels by handling customer inquiries, providing instant support, and offering personalized recommendations.

By using natural language processing and machine learning, these AI systems can understand and respond to customer queries in real-time, providing a seamless and efficient communication channel. This helps reduce response times, improve customer service, and ultimately increase customer satisfaction. (Not to mention the cost-savings from a staffing perspective.)

It’s not that hard to set up these flows either—you don’t need to know how to code, and it could just take a few minutes. In fact, here’s how conversational AI looks in Dialpad Ai Contact Center. It’s just a drag-and-drop builder, and anyone could do it:

Use case #5: Demand forecasting

One of the key areas where AI is making a significant impact in logistics is demand forecasting. With the help of AI algorithms, logistics companies can analyze historical data to predict future demand for their services. This allows them to optimize their operations, reduce inventory costs, and ensure timely delivery of goods.

And we’re only just getting started. As AI continues to collect more and more data, it becomes more and more accurate. Soon, you’ll be able to use it to make strategic decisions about your logistics, like where to build a new warehouse or which delivery routes to take.

Use case #6: Route optimization

Another area where AI is making a significant impact in logistics is route optimization. AI algorithms can analyze data on traffic patterns, road conditions, and delivery schedules to optimize the most efficient route for delivery drivers. This not only reduces fuel costs and carbon emissions but also ensures timely delivery of goods.

Use case #7: Warehouse automation

AI is also being used to optimize warehouse operations. Logistics companies can analyze data on inventory levels, order volumes, and delivery schedules to optimize warehouse operations.

Use case #8: Predictive maintenance

Finally, AI is also being used to predict equipment failures and prevent downtime in the logistics sector. Logistics companies can analyze data on equipment performance, maintenance records, and environmental conditions to predict when equipment is likely to fail. This allows them to perform preventive maintenance before equipment failure occurs, reducing downtime.

The future of AI in transportation & logistics

AI is transforming the transportation & logistics sector by optimizing operations, reducing costs, and improving both customer and employee satisfaction. As AI technology continues to evolve, we can expect to see more innovative AI use cases for this fast-growing sector in the future.