For many supply chain organizations, putting artificial intelligence to work is proving to be a much bigger undertaking than simply adding a new piece of software.
That’s become abundantly clear as companies as diverse as Microsoft, Gallo and Blue Diamond Growers have worked to bring AI and more advanced planning tools into their operations. And although each of these businesses are fundamentally different, their experiences point to many of the same priorities: Clean up the data first, rethink processes before automating them, give employees clear authority over important decisions, and resist the temptation to change everything at once.
Perhaps even more important than any of that though, is that much of the work happens before AI ever even enters the picture.
Companies can have enormous amounts of information without having data that is clean, connected or useful. They can also implement new planning systems while leaving old organizational silos and workflows largely intact. And even a technically sound implementation can struggle if employees aren't prepared to change how they work.
Blue Diamond Growers encountered that firsthand. The agricultural cooperative, which processes and markets almonds across its branded products and ingredients businesses, had separate systems managing demand and supply. It wanted to bring those processes together while gaining more flexibility to model disruptions and changes in demand.
The same gradual approach is just as important for a company when figuring out the authority an AI model should have. For veteran winemaker Gallo, SAP systems already supported areas like integrated business planning, warehousing and production planning. In that context, vice president of supply chain excellence Nitin Murali describes the company's AI work as a “decision improvement journey.”
Across these projects, the most practical AI playbook seems to be one where companies are beginning with the decisions and problems they need to solve, getting their data and planning processes in order, then strategically adding AI where it can make a measurable difference.