Some of the most important supply chain decisions are also the hardest to quantify. When the path forward isn’t obvious, teams can fall back on subjective analysis, familiar approaches, or gut instinct, making it easy for human bias and decision fallacies to derail your strategy.
Agentic AI changes the way supply chain teams navigate these complex decisions. Harnessing the power of agentic AI, the Atlas Planning Platform helps supply chain teams quickly analyze trade-offs across likely outcomes and evaluate different strategies and tactics before committing to a course of action. Rather than simply presenting another recommendation, Atlas helps teams understand the potential upside, risks, and implications of each option.
In this clip from our webinar, Navigating the Era of Agentic AI: Scaling from Insight to Action, Matt Hoffman explains how this is particularly valuable when uncertainty is high and there is no single right answer. Galt AI, John Galt Solutions’ agentic system, also helps teams move beyond common decision-making biases, such as the sunk-cost fallacy or misunderstanding probabilities. Teams can evaluate decisions based on what the data and potential outcomes indicate.
Atlas makes agentic AI explainable, which means each recommendation is delivered with context, helping teams understand the how and the why behind a suggested decision. This transparency builds the trust needed to move from AI-generated insight to action, even when uncertainty remains.
With Atlas, your team can access the power of agentic AI to explore complex scenarios, understand trade-offs, challenge assumptions, and make better-informed decisions, to navigate uncertainty and act with confidence.
Watch the full webinar to learn more: Navigating the Era of Agentic AI: Scaling from Insight to Action.
- Full Transcript
Matt Hoffman: Agentic really shines in chaotic situations. So these are decisions that can be difficult to quantify, and decisions are often made then from subjective analysis or gut feel.
Here, agentic AI analyzes trade-offs across likely outcomes, allowing you to compare different strategies and tactics, and then at the end provides insights into upside exposure.
A big element here where AI really shines also reducing human decision bias. So, instead of doing what we've always done, and moving towards better decisions by moving away from typical decision fallacy. So, things like sunk costs and misunderstanding probabilities.
Finally, agentic here helps align the business faster around these different decisions because it provides the explainability in the context – the how and the why behind the recommended decisions, which then provides the trust necessary to accept that recommendation, especially with so much uncertainty underneath it.
