The groundswell of excitement we have seen with recent developments around generative artificial intelligence has created a level of expectation that supply chain leaders will begin to use AI and other advanced technologies to deliver new levels of efficiency and growth for their organizations.
This development may also have shone an unwelcome spotlight into the supply chain planning world, where many have yet to build proficiency in the requisite building blocks to take full advantage of both next level AI solutions coming down the pike, as well as established planning tools that already have AI capabilities embedded within them. The good news is that supply chain planning is ripe for experimentation with a variety of AI and machine learning use cases today, and small steps can add up to big results — if you start now.
Even before the most recent wave of excitement around ChatGPT and generative AI, supply chain leaders ranked data-centric AI and ML as their top digital priority among technologies that they are piloting or deploying, according to respondents in Gartner’s Digital Business Impact on Supply Chain Survey. The survey was conducted from March to April 2022.
The intent to implement is clearly there, but when it comes to where to begin, supply chain leaders face a dual challenge. They must strengthen their existing data management practices to effectively leverage AI and ML technologies, while also identifying the right opportunities to deploy these solutions across their planning processes.
For leaders grappling with these challenges, now is the time to act. Supply chain organizations that do not implement AI and ML supported planning processes will be at a competitive disadvantage, struggling to derive insight into market dynamics that can support related agile decision-making.