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The Top Challenges that Occur with AI (Artificial Intelligence) in Logistics

AI in LogisticsThe integration of artificial intelligence solutions in logistics appears to be approaching – sooner than many would like. Although the concept of AI is appealing to shippers, carriers, warehouse providers and others within the supply chain, there are several challenges that will occur with AI in Logistics that needs to be carefully considered.

Noted below are a few of the top challenges the logistics industry will face as AI becomes a reality.

The Cost of Integration

There is no escaping the fact that AI technology is expensive. While the cost of AI systems can be incredibly expensive, the real challenge is integration due to one simple fact – they are all customized. An AI system is not like most computer-based software programs or hardware. It is made up of multiple, independent systems that must be integrated and installed together in order to be effective. This includes, but is not limited to:

The hardware: AI systems are usually cloud-based, due to the expansive bandwidth needed to power the system. However, there is specialized hardware that many AI providers utilize to access the Ai. As such, the cost of AI-specific hardware will be a huge initial investment for many supply chain partners.
The scalability factor: Most AI and cloud-based systems are quite scalable, which is actually one of the best features for those in logistics. However, the problem is that some AI systems require a higher level of initial start-up users/systems in order to be impactful. Since all AI systems are unique, this is something that supply chain partners will have to discuss in depth with their AI service providers.
The cost of training: Like any other new technology solution, training is another reality. This will cost companies money, time, and an initial reduction in business efficiency. Logistics partners will need to work with the AI provider to create a training solution that is impactful – yet affordable during the integration phase.

The Operational Costs of AI

An AI-operated machine has an exceptional network of individual processers, relays, and other components. Each of these parts requires replacement from time-to-time to maintain operational integrity. The problem is that these parts can be rather expensive. Parts like computer chips are made from incredibly rare materials – like Selenium. AI machines require constant updates, which also includes replacing internal batteries which are also expensive.

They also consume a tremendous amount of energy to operate correctly. A company will typically utilize AI machinery to replace human workers, and also increase the operational time – since they don’t require brakes, have work regulations, or labor laws to follow. This increases the cost of utility bills – which directly impacts the overhead expenses of keeping them running.

Fewer Human Jobs

With more automation comes the inevitable reality of reducing the workforce. Unfortunately, the victim of this improved efficiency are the people who currently occupy these positions. When jobs are eliminated due to the integration of AI solutions, the company needs to either find new positions for their employees to take on or release them all together.

While it’s a reality of operating a business, companies in the logistics industry will have to consider this challenge before making the investment into AI replacement systems.

The Expansion of AI in Logistics

Finally, arguably the biggest challenge facing the logistics industry is are the ethical considerations. As discussed directly above, a major victim of AI integration are people, who depend on their logistics-based jobs to feed their families, pay their bills, and live a good quality of life. While several people are quite experienced – the fact is as automation takes over, fewer jobs across the board will be available.

Another important question to ask is – how far is far enough with AI? Right now, transportation and automotive manufacturers are investing hundreds of millions of dollars on the development and testing of autonomous operational equipment. From self-driving trucks, airplanes, cargo ships, and more, it seems every time you turn your head, a new autonomous vehicle is debuted. The ethical question is the safety consideration of AI in transportation. There have already been fatal accidents involving semi-autonomous vehicles, and the biggest question is whether the technology failed to think quick enough to avoid the accident.

Regardless of the advantages AI might offer a logistics partner, these challenges will need to be factored and considered seriously – to ensure a smooth, safe, and affordable transition.

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