How Is AI Shaping Demand Forecasting and Supply Chain Planning? Your Top Questions, Answered

Have you ever used AI to help you shop?

In my experience, it goes like this. I find a car model I am interested in and ask AI which of the nine trim levels match my core preferences. Right away, different research paths emerge. Is the matte paint finish only available in one trim? Does the premium package force the low-profile tires that will give me a bumpier ride? And is it impossible to get a heated steering wheel and sunroof at the same time?

For all the answers AI brings, it raises even more questions.

Professionally, you can feel the shift happening right now. We are moving away from traditional forecasting and toward real-time demand sensing and adaptive supply chain planning, but what does it take to get there? How can you make sure your organization gets the answers needed to prepare for AI adoption?

I get many questions on this topic, and as you will soon find out, asking one seemingly simple question leads to many more as you scratch beneath the surface. Let us break them down.

Is my organization ready for AI-powered demand applications?

This isn’t what they want to hear, but it’s the truth: most organizations are not as ready for AI as they think they are. Having the right data infrastructure in place is the number one bottleneck to AI adoption, and that is why it has to be addressed first before a company gets ahead of itself.

It’s ok not to be ready. It means you have the opportunity to make the right moves and lay a strong foundation for AI adoption. As you do so, ask yourself deeper questions around these four critical dimensions of readiness:

• Data Integration & Centralization: Can I track data online, offline, and on mobile? Do I have a centralized data platform or cloud warehouse, or are my data systems disconnected? Does transaction data reach my forecasting systems in weeks, or in hours?
• Data Quality & Governance: How complete and accurate is the data in my organization? Do I have formal data governance and compliance controls in place and clearly documented? Can I trace a bad sourcing outcome to specific data points?
• The People Problem: Do I have the right people in place to run and monitor any new application? Am I prepared to hire dedicated data scientists and engineers?
• Executive Sponsorship Gap: Do I have someone who can make this a priority at the executive level? Will they be committed to championing the project before, during, and after?

Getting to a comfortable place with these questions can take time, but they are the path to leveraging AI’s full potential and making your investment count.

Read this article in full here.