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A Gartner analysis conducted last spring on a sample of 500 global supply chain managers clearly highlighted that they were at the top of the list of technologies with the greatest impact and considered most strategic for increasing operational efficiency, optimizing system resilience and enable real-time decision-making processes if there were artificial intelligence. The analysts of the American company have coined a new term to define the contribution of machine learning algorithms to supply chain management, “Actionable AI”, that is, an intelligence characterized by being specifically designed to make decisions by involving humans to validate them and learning from patterns based on past experiences to adapt to present and future circumstances. However, on the occasion of the last Supply Chain Symposium/Xpo EMEA held in June, she pointed out that advanced technologies such as intelligent robotics and AI, despite having the potential to transform the function of supply chains, will hardly be able to raise the historically low levels of labor productivity in this field if the same technologies are not used as an integral part of a broader strategy, which necessarily also looks at the valorization of human capital and the rethinking of organizational structures. In particular, the experts explained, what makes the difference should be the ability to integrate innovative tools and people, through targeted workforce training projects and the development of skills and knowledge.
See you in 2030
Looking again into Gartner’s crystal ball (the Hype Cycle for Artificial Intelligence), the experts from Manhattan Associates, one of the world‘s leading suppliers of technological platforms for the supply chain, have detected how the complexity and specificity of the operating models that regulate this sector will significantly extend the timing of the impact of Gen AI on supply chains. Compared to segments and markets where the technology is expected to become mainstream supply within two to five years, in the supply chain the maturation times reach the limit of ten years. OpenAI models that exploit Gpt-4, in other words, are unlikely to find space and applications in the short term but there is no doubt that they can be considered useful tools, to help automate processes and facilitate communication and collaboration between the various interested parties . There is obviously a series of critical issues linked to the large-scale implementation of generative AI in supply chains, not least those of a legal and ethical nature. Hence the need, suggested by the experts at Boston Consulting Group, to address this issue by working closely with employees, end customers and supply chain partners to develop responsible AI principles, generating trust among these key stakeholders and defining precise lines guide to keep technology under control within the organization and the entire business ecosystem. We are only at the beginning, Manhattan Associates confirm in this regard, in the “proof of concept” phase for the verification and validation of the benefits of this technology, both from the point of view of vendors of solutions for the digitalisation of the supply chain (the Gen AI tools could help configure specific solutions better and faster) both in terms of improving the quality of customer use experiences. We are unlikely to see a pervasive diffusion of these applications in supply chains before 2030 and in the meantime another level of criticality will have to be addressed, the one that refers to the sustainability of Gen AI in terms of greater consumption of energy and computational resources for processing in real time data.
A potential billion-dollar market between no-code services and platforms
The data that Abi Reserach instead made public at the beginning of August is certainly significant, and coincidentally has a timeframe of 2030: revenues calculable at over 60 billion dollars thanks to an annual composite growth rate of 162%. The potential business linked to the applications of generative artificial intelligence in the supply chain is therefore decidedly relevant but, according to experts, will have to leverage the consolidation of paid offering models. No ChatGpt or similar zero-cost tools to manage supply flows and all the actors in the chain, in short, but enterprise-class solutions based on large language models (such as Gpt-3, Claude, LLaMa, Titan and others) appropriately trained and trained. The study (“Generative Ai: Identifying technology supply side revenue opportunities”) analyzes in detail the possible concrete outlets of the technology in different markets, highlighting how many of its monetization opportunities can in fact be distributed along the entire supply chain: let’s talk specifically of advertising models similar to those used by search engines, of creation and management of synthetic datasets but also of open source LLM models developed in a B2B key. Another very important area for Gen AI providers will also be consultancy and training (to overcome the lack of skills of many of the supply chain actors) and the offer of “low/no-code” platforms (which do not provide i.e. writing source code) capable of supporting the development, implementation and optimization of applications based on generative artificial intelligence technologies.