When it comes to AI adoption in supply chain planning, there’s no single path every organization should follow. The journey toward greater autonomy is non-linear, and teams can start wherever AI can deliver the most meaningful value and gradually expand from there.
In this video from our recent webinar, Navigating the Era of Agentic AI: Scaling from Insight to Action, Matt Hoffman explores the different types of supply chain decisions that can be supported by AI and agentic AI, from straightforward operational decisions to more complex and chaotic situations where uncertainty is a real challenge.
In the webinar, our team explored how organizations can move beyond AI experimentation and apply AI to real-world planning and decision-making.
Matt highlights four decision types on the journey to autonomy that illustrate how AI can support planning across different levels of complexity:
- Simple decisions: Straightforward, repeatable decisions that can be increasingly automated. Agentic AI can monitor conditions, take action, learn from outcomes, and adapt without relying on rigid, hard-coded rules.
- Complicated decisions: AI can help balance competing priorities such as service levels, revenue, profitability, cash flow, and inventory investment. Agentic AI can evaluate tradeoffs and identify actions aligned with broader business goals.
- Complex decisions: When decisions involve large datasets, uncertainty, and non-linear relationships, AI can uncover patterns and relationships that may be difficult to identify manually. By augmenting planners with intelligent recommendations, analysis, and scenario modeling, AI helps teams make faster, more informed decisions while keeping people in control.
- Chaotic decisions: During major disruptions and amidst increasing uncertainty, AI helps teams explore scenarios, understand downstream impacts, compare risks and opportunities, and align stakeholders around the tradeoffs.
Companies don’t have to progress through these decision types in a fixed or linear sequence. You can start anywhere on the spectrum, depending on your business priorities, data readiness, and where AI can create the greatest value. You might begin by augmenting a single decision today and gradually move toward greater autonomy as your models, processes, and teams mature.
The Atlas Planning Platform supports this journey with AI-powered capabilities across forecasting and demand planning, inventory, replenishment, scenario planning, and more. Built on more than 30 years of supply chain expertise, Atlas combines machine learning, explainable AI, and agentic AI to help you move from insight to action at a pace that makes sense for your business.
Watch the full webinar: Navigating the Era of Agentic AI: Scaling from Insight to Action.
- Full Transcript
Matt Hoffman: Starting with simple decision types and problems that are easily understood as you work your way down through the slide and the range of decisions and automation approaches, to more chaotic demand or supply environments.
A good example of this, from a simple decision, maybe something like replenishment to a store or picking a warehouse that could be more autonomous, while chaotic decisions have less certainty, and may include more strategic decisions – like moving manufacturing sites or uncertainty around tariffs or prices. But the key here is that the journey is not linear.
You can start anywhere on the spectrum and with any of the decision types here. And then you can also even start, by augmenting a specific decision and then mature more towards autonomy.
