It’s November, which means the holiday season has started — and with it, an influx of online orders.

Most consumers who have ordered something online have experienced delays, failed delivery attempts or worse — lost packages. Frankly, tracking numbers and text message updates aren’t cutting it. Could artificial intelligence finally provide a solution to this problem?

Packages Are Going Missing and It’s a Problem

A package is supposed to arrive today. The person who placed the order watches their front door like a hawk after their app’s tracker changes from “shipping” to “out for delivery.” The estimated delivery time comes and goes — and nothing gets dropped off. Their phone pings with a “Sorry we missed you” message. What happened?

Situations like these are common. Whether the seller reschedules, a delivery driver skips part of their route to save time or a porch pirate swoops in at the last second, parcels go missing all the time. In 2023, almost 40% of people in the United States had at least one package stolen. This problem is challenging to remedy simply because the delivery network is massive.

Accurately tracking missing orders is possible, but it is a tall order with today’s technical limitations. Companies must monitor warehouse workers, drivers and vehicles. Not to mention, they must handle fraud cases where customers claim to have received nothing when, in reality, they did. Even in ideal circumstances, accidents happen — a small envelope could fall behind the sorting machine or get stuck under a larger box.

Even when shipments eventually reach the right address, they stay in the delivery network too long. According to the United States Postal Service (USPS), 17.3% of first-class mail was not delivered on time in the second quarter of the fiscal year 2024, up 3.2 percentage points from the previous quarter. Rates for marketing mail and periodicals also worsened.

The number of parcels going missing in the reverse logistics process is often overlooked. Around 30% of all online orders are sent back, compared to just 9% in brick-and-mortar stores. A disappearing parcel is particularly frustrating here, as the person attempting the return may be denied a refund or end up on the hook for the full price of the item.

Why Use AI to Track People’s Missing Packages?

AI is a powerful technology. A single advanced algorithm can process massive datasets, make data-driven decisions and understand language, regardless of whether it is a chatbot, generative algorithm or deep learning model. Training determines its purpose and behavior, so it can be utilized in virtually any use case.

Versatility is useful when analyzing data since processing centers can produce billions of drop-off images or location data points in short periods. There is even more to go through if they use Internet of Things (IoT) tools or telematics systems in their fleets. AI can respond to prompts with voice, image, text or audio output, streamlining information retrieval and summary.

Only a technology like AI can handle all the data created by the delivery network’s massive, interconnected system of sellers, fleets, customers and warehouses. Its rapid analysis isn’t its only boon — it also has automation capabilities. An advanced model can operate around the clock without stopping for breaks, food or sleep.

Is AI Already Being Used to Track Packages?

Several delivery companies are already using AI to track shipments in transit. For instance, the United Parcel Service uses an AI-enabled supply chain. It uses historical and real-time data to calculate the fastest, safest way to boost the on-time delivery rate. Its algorithm can also anticipate disruptions and provide a comprehensive view of the fleet’s network.

If other firms deploy AI at the edge — at the network’s boundary where data is created — they wouldn’t have to build data centers to process or analyze the massive amount of logistics information they receive. On top of using fewer computing resources, they’d enable real-time results with little to no delay, improving output accuracy.

In a different approach, USPS uses an AI-enabled edge computing program powered by NVIDIA technology. Its model analyzes billions of images that the processing center generates. Before, finding a missing order took a team of up to 10 people multiple days. With this platform, a single individual can track one down in around two hours.

4 Ways AI Can Track Stolen and Lost Packages

There are several ways AI can help people track their missing parcels.

1. Provide Real-Time Updates

Even with a tracking number, updates are often too vague, inaccurate or outdated to be helpful. According to one survey, nine in 10 people want to be able to track their order, with 47% wanting to know exactly where their item is at all times. AI can fulfill this desire, providing real-time updates as shipments travel.

2. Calculate Delivery Estimate

Since as many as 20% of deliveries fail on the first attempt, reschedules are common. While most people wait a few extra days, some don’t receive their items for weeks or months. With AI, they can see how long it will take for a driver to reattempt delivery. The algorithm can factor in variables like weather, consumer demand, route closures and delays.

3. Analyze the Drop-Off Photos

An AI could leverage image recognition technology to analyze millions of drop-off photos. It can flag the delivery as incorrect or suspicious if it notices any discrepancies. If there are enough clues in the photo’s visual details or metadata, the model could help workers track down the missing parcel.

4. Answer Peoples’ Questions

Globally, 46% of online shoppers expect to receive their orders within two to three days. However, delays, reschedules and cancellations are common. When these situations arise, an AI-powered chatbot can answer consumers’ questions to help them understand what happened. Depending on its design, it could discuss the item’s location or estimate delivery dates.

How Integration Could Enhance AI’s Capabilities

While AI is powerful on its own, companies could see significant improvements after integration. For example, embedding a machine learning model into a surveillance system or camera doorbell could help recipients identify porch pirates or misbehaving delivery drivers. This way, they could potentially narrow down the location of their missing item.

An AI-powered computer vision system — an AI-powered device that can interpret visual information — could do something similar, monitoring production lines and automatic sorting machines for misplaced parcels. If something falls off of a belt, gets stuck under another container or slips between a crack in the machine, it could alert a human worker.

Integrations can even help in transit. Combining AI and IoT sensors enables real-time updates on a product’s condition and location. Recipients could receive alerts if their item is tampered with, dropped off at the wrong location or stolen. The algorithm would filter out false positives and concisely explain what happened.

Could AI Prevent Packages From Getting Lost?

While finding missing parcels is an excellent use of a machine learning model, stopping this problem at the source is a more effective solution. Companies could prevent products from getting lost in the first place by leveraging chatbots for communication. The higher the success rate for the first delivery attempt, the less likely the item will disappear from the system.

Decision-makers could also optimize routes to reduce workers’ stress. Demand has far outpaced the workforce’s capabilities — experts predict e-commerce sales will reach over $8 trillion in sales by 2027, a 237% increase in one decade — which puts pressure on drivers to haul more boxes and complete routes faster.

Companies increasingly hire freelance and gig workers to compensate for their labor shortage. These workers are often paid by the number of packages they deliver, incentivizing them to race through their routes. Using AI to optimize their trip saves them time, lowering the chance they will make mistakes in transit.

The Bottom Line of Using AI to Track Orders

Although AI is not a miracle technology, it is one of the best solutions on the market for this problem. Processing centers and delivery drivers need something that can process information rapidly, output responses in natural language and interact with multiple people simultaneously.