“ One of the most powerful capabilities of AI is its ability to perform predictive analytics and give us a projection of future needs based on past data .” streamlining data , it complicates by overwhelming us with data to consider . As we look at how AI can help improve operations in the last-mile , we must also recognize the potential pitfalls it presents us in regard to risk .
Take , for instance , route optimization and dynamic routing — AI can and has already helped improve routing . It allows you to maximize efficiency while minimizing time on the road , adjusting routes in real time based on congestion or weather factors . These are undoubtedly big factors in reducing the likelihood of an incident of auto and cargo claims .
Drivers have come to enjoy the AI-enabled routing software and use it daily . But what happens when the seasoned driver decides to disregard the routing software and take their own route , only to have that result in an accident where they are at fault ? Your drivers have all operated without incident by utilizing AI , but an accident occurred when this driver did not . In this instance , the liability impact of this driver going off script and having an accident will not be lessened by AI , but instead will be exacerbated by AI . Even if this driver is an independent contractor , and you can argue that you are not responsible for their actions , recent claims trends suggest that your company will still be held vicariously liable for the accident . What if this driver has a history of going off script and not listening to the AI routing ? Have you failed to reinforce the need to follow the suggested route for safety reasons ? Have you documented those discussions ? Are you potentially complicating labor law issues by mandating the routing software be utilized and followed ? Any last-mile delivery company using AI routing software must thoroughly consider the risks , heavily document protocols , and act immediately when drivers veer from suggested routes .
One of the most powerful capabilities of AI is its ability to perform predictive analytics and give us a projection of future needs based on past data . In the last-mile delivery sector , it becomes an incredibly valuable tool to assess the “ peak season .” As you look to AI to review past peak seasons or any defined period , the tools will predict what the seasons may look like in the future . Bear in mind that using predictive analytics does not come without risk . Many last-mile providers approach the booms in business by securing additional drivers and / or short-term rental vehicles to bolster their ability to handle the increases .
These short-term solutions come with their own set of risks such as lack of familiarity with a driver or putting a driver in a rental vehicle that is larger than they are used to driving . This increases the risk to the last-mile delivery company to meet the clients ’ needs . In some cases , we have seen these peak times and operational changes completely overwhelm last-mile delivery businesses with unintended incidents and claims , given the lack of control . Growing too much too quickly leads to a lack of proper controls . Approach cautiously , and integrate measures that mitigate the risk whenever possible , including thorough training and vetting of drivers .
28 customized logistics & delivery Magazine I winter 2024