Aqil is a logistics enthusiast and entrepreneur, known for his expertise in supply chain management. Proficient in marketing communications, CRM, and sales strategy, he’s driven by a passion for optimizing operations and fostering client relationships.

In recent times, Artificial Intelligence (AI) has surged to the forefront of technological discussion, particularly in the context of supply chain management. However, despite the buzz, the reality within our industry is that AI's integration is more nascent than one might expect. It's not just in its infancy; it's at a stage so preliminary that calling it a 'baby stage' might be overly generous.

Our industry has historically been a late adopter of new technologies, from the internet to blockchain, from analytics to generic digitization and automation. AI, despite its current hype, is no exception. As a proponent of technology and AI, I argue that, to leverage AI effectively in supply chains, we must first step back, assess our readiness, and take specific prerequisite actions.

The excitement around AI often focuses on the technology itself rather than its practical application to solve genuine business problems. This approach overlooks a crucial fact: the real value lies not in the algorithms but in how we use data to achieve specific goals and outcomes. In essence, data trumps algorithms, fancy algorithms may seem appealing, but improving and effectively utilizing data yields the best results.

For AI to be truly transformative in supply chain management, we must focus on mobilizing supply chain and enterprise data to create actionable AI cases that deliver real value. This endeavor is challenging, as many companies struggle to embed analytics at scale. This struggle is not due to imperfect analytical executions or flawed machine learning models but rather due to gaps in other essential factors for success.

One significant hurdle is the preparation of training data for AI models. The existing form of enterprise data was likely not collected with AI in mind, making data preparation a daunting task. Moreover, in many supply

chain contexts, data collection is not fully digitized, further complicating this process.

To overcome these challenges, it is imperative to invest in data preparation. Setting clear goals and targets is crucial, but it's equally important to have a consistent pipeline of training data. Without it, even the most advanced algorithms cannot function effectively.

Educating our leaders on data literacy is a pivotal step towards realizing AI's potential in supply chain management. Understanding the importance of data, how to prepare it for AI applications and recognizing the genuine problems AI can solve is essential for moving beyond the hype and towards deriving real value from AI technology.

Educating our leaders on data literacy is a pivotal step towards realizing AI's potential in supply chain management.

One of the largest challenges in modern supply chain management is the multi-stakeholder model. The supply chain relies on data fed and collected by a diverse set of participants, including suppliers, manufacturers, shipping lines, airlines, third-party logistics providers (3PLs), carriers, consumers, distributors, brokers, and retailers. This diversity is one of the primary reasons for slow adoption of new technologies within the industry. The presence of stakeholders from such a wide array of domains and capabilities complicates the process of standardizing and governing data. Currently, there is a significant lack of standards to regulate the duplication and governance of data across the supply chain, creating an environment ripe for inefficiency and error. Addressing this issue requires a concerted effort to develop and implement industry-wide data standards, a task that will not only facilitate the adoption of AI but also streamline operations and enhance collaboration across the supply chain.

In conclusion, while AI holds tremendous promise for revolutionizing supply chain management, realizing this potential requires a fundamental shift in focus. Instead of being captivated by the allure of new technology, we must prioritize data preparation, education on data literacy, and a clear understanding of the problems AI can solve. By addressing the prerequisites of data standardization and governance, in addition to preparing training data and enhancing data literacy, we can pave the way for AI to move from a nascent stage to a powerful tool in enhancing supply chain efficiency and effectiveness.