AI-Powered Proof-of-Delivery: A Game-Changer for Shippers

In collaboration with Timur Eligulashvili, President of Logistics Remix, this post explores the transformative impact of AI on the Picture POD process.

The introduction of Picture Proof-of-Delivery (POD) by Amazon in 2018 prompted a wave of adoption by other major players in the logistics industry. Companies such as FedEx (2022), UPS (2023), Optima Overnight, and SpeedX (2022) swiftly followed suit. The e-commerce boom, further accelerated by the COVID-19 pandemic, has made such innovations not just beneficial but necessary.

Let’s look into how AI can really power POD: 

AI offers significant advantages in POD processes. Manually screening hundreds of thousands of delivery images is both costly and prone to human error. In contrast, a well-trained AI model can accurately identify good vs poor quality images with very high precision and detect subtle nuances that may elude even trained human inspectors. AI-PODs can automatically verify:

  • Presence and condition of the package
  • Correct delivery address
  • Compliance with specific delivery instructions (like front door delivery) 

Shippers have a lot to gain from integrating AI into their POD systems:

Enhanced customer experience

Picture POD enhances the customer experience by providing visual confirmation of successful package delivery, instilling confidence and trust. This transparency mitigates concerns about lost or damaged items, facilitating smoother resolution processes. 

By offering clear documentation of each delivery, it improves transparency, accountability, and provides overall peace of mind for your customer.

It’s also been quantified by companies like FedEx where CEO Raj Subramaniam noted in a Q4’23 earnings call that the company saw a significant decrease in disputed delivery cases and reduction in call volume in the US after implementing Picture POD. 


Image 1: PODs with over 99% score on AI-POD scale

Reduce failed deliveries, refunds, and re-shipping expenses.

If your customer requested the package be hidden on their front porch, AI-PODs can automatically flag whether the carrier followed the instructions. Following instructions along with sharing concrete evidence of successful package delivery puts the onus on the carrier to take that extra step to ensure accurate delivery and gives the customer piece of mind. Imagine getting a disputed delivery case for a POD like any of the ones below: 

Image 2: Pictures with no parcel in the proof-of-delivery

Staying Ahead of Bad Deliveries, Damaged Packages to enhance customer experience

AI-POD can flag deliveries where the proofs were erroneous, to begin with. Instead of your admin team having to sift through the images and manually verify the quality, they can have an upfront flag for all the orders with potential delivery or POD issues. Additionally, the AI system is configured to recognize and alert staff to packages that appear damaged in delivery images, providing an immediate opportunity for corrective action like in the examples below:

Image 3: PODs that show a damaged package according to’s AI-POD scale

Predict failures and cut costs. 

If you shipped 10,000 orders in the month of February, you’ll likely receive callbacks for the ones with poor quality POD images. You can start estimating potential losses from failed deliveries better and start pushing for even higher-quality deliveries from your carriers. 

Fraud prevention

Picture POD's help reduce fraud by providing concrete evidence of delivery, making it more difficult for fraudulent claims to succeed. With visual confirmation, shippers can verify the authenticity of delivery claims and deter attempts at deceit.

Improve carrier relations 

AI-POD’s help measure against set KPIs and guidelines, and provide faster and more proactive resolutions to issues, in turn helping shippers and carriers focus on building deeper relationships. With AI, shippers can effectively set new benchmarks for carriers, and carriers can efficiently track success of deliveries, ensuring consistent quality in POD documentation.

Applying AI to the Picture POD signals a monumental leap in productivity for last mile delivery providers and shippers. It’s not just a time and cost saver, it's a catalyst for enhancing customer experience.

If you’d like to learn more, please contact: Akash Agarwal,

About Timur: 

Timur Eligulashvili is the Founder and President of Logistics Remix, which helps retailers and 3PLs bring new delivery providers into their network to save on cost, improve delivery performance, and drive mutual success. Timur has 20 years of experience in logistics and a unique perspective from previous roles at Lone Star Overnight, ShippingEasy, uShip, Echo Global Logistics, C.H. Robinson, and Honda Logistics. 


Beans is a pioneering technology firm specializing in AI-powered delivery management enriched by advanced geospatial data analytics. By harnessing the power of data and artificial intelligence, Beans empowers companies to overcome complex delivery challenges and optimize end to end last-mile operations.

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