Since the success of the AlexNet model in machine learning, major technology companies like Alphabet have been actively integrating deep neural networks into their products. In 2013, Google launched projects focused on improving image search, object recognition, and other services that leverage deep learning.

Since then, the results of implementing AI have gradually transformed the services we use daily. From route planning with traffic considerations to using ChatGPT for everyday tasks, AI is now a part of our lives. It is no surprise that AI implementation has become a strategic priority for many companies across various economic sectors, including logistics.

The logistics industry is a complex ecosystem that includes carriers, forwarders, and government regulators such as customs authorities. The success of interactions between these participants directly impacts the efficiency of the entire supply chain. While there are local successes in AI adoption by individual companies, these are not sufficient for a significant leap forward at the industry level.

Problems and challenges

– High entry costs

The challenge lies not in the technology itself but in adapting it to the specifics of logistics. Currently, there is insufficient experience in AI adaptation within this industry, and pioneering companies bear significant costs.

– Conservatism among supply chain management entities

For example, customs authorities, one of the key government regulators in the import and export of goods, are highly conservative. Their processes are legislatively regulated, and implementing AI requires significant political effort. As of now, there are no prominent examples of active AI integration within U.S. government institutions, including customs.

– Availability of big data

The effectiveness of AI is directly dependent on the volume and quality of data, which presents a significant challenge in logistics. Most companies lack the necessary expertise to collect and analyse such data.

– Lack of standards and regulation

The logistics industry currently lacks clear standards and guidelines for AI applications in the supply chain sector, making implementation difficult and increasing risks.

The need for a cautious approach

Implementing AI in logistics requires a cautious and well-considered approach. History shows examples of companies that collapsed after succumbing to the hype surrounding new technologies. The dot-com crisis, which led to the bankruptcy of numerous companies due to overconfidence in internet technologies, is a vivid example. Yet, the temptation remains for many companies to become the next Amazon or eBay in the industry, as these companies survived the crisis and became some of the largest by market capitalisation after successfully integrating internet technologies.

However, the cost of errors in the pursuit of AI implementation could be fatal for a company. This is why many industry participants prefer to wait until the technologies reach a certain level of maturity and the industry reaches a consensus on standards and practices.

Transforming the logistics industry with AI is not just a matter of technical implementation; it also requires creating the right conditions for all supply chain participants. 

Only through coordinated efforts can the industry achieve the significant leap needed to drive transformative change.

The post The role of AI and industry challenges appeared first on Air Cargo Week.