In our recent webinar, Turning AI into Results: Practical Applications Across Supply Chain Planning, Zac Nemitz, Global Product Strategy at John Galt Solutions, explores how artificial intelligence (AI) drives value in supply chain planning across different layers of maturity – from reactive to predictive and, ultimately, to autonomous and the emerging world of agentic AI.
For many organizations, AI adoption follows a natural maturity curve. At early stages, companies focus on capabilities to react to events after they occur. More advanced steps include predictive capabilities and using historical and real-time data to anticipate changes and guide better decisions. Agentic AI represents the next stage: intelligent systems that not only analyze and predict but can also take action autonomously within defined parameters.
The Atlas Planning Platform from John Galt Solutions is designed to support organizations at every stage of this AI maturity journey. From foundational analytics to predictive modeling and agentic capabilities, Atlas enables companies to leverage AI in practical ways that deliver measurable business value. As planning teams advance, the platform provides the intelligence and automation needed to scale those capabilities across the organization.
Agentic AI builds on this foundation by introducing intelligent agents that assist with both planning and execution. These agents can help identify potential issues, recommend the best course of action, and in some cases autonomously complete routine tasks, keeping humans in the loop.
Watch the webinar on demand here to learn more about the different AI functionality, examples of applications, and the value of their outcomes.
Just for You:
- Full Transcript
Zac Nemitz: AI functionality comes in quite a variety of flavors. So, again, you've probably heard all of the different buzzwords flying around. This slide, you know, we're trying to just anchor you all around these different applications of AI and sort of different buckets that they may fall in.
As you'll see on the top, we have reactive AI; we'll talk a little bit about predictive AI, and then we'll eventually get to more autonomous AI. And then we're highlighting what these concepts actually are; what is the value that is available through these concepts? What sort of maturity should you consider when approaching these different flavors of AI? And then just what are some examples to maybe get the wheels turning a bit.
Overall, the combination of these things, in conjunction with your planning processes, is really, really, you know, where we're seeing value in improving the outputs of the plan, as well as responding to any disruptions to the plan.
So, I'll go through reactive AI first. As the name implies, this is where I'm trying to respond to any sort of disruption in near real time or instantly. You know, I'm really gaining a lot of value and potential competitive advantage, because I'm able to react to these disruptions as fast as I possibly can. This will help me mitigate things like fill issues or lost sales. Maturity is going to vary.
As you see with the examples, we'll talk about demand sensing. That's much more approachable than, say, real-time logistics rerouting, both in the same realm of reactive AI. Predictive, again, I'm just trying to get more ahead now of what could potentially happen. Things like scenario assessment and probabilistic outputs are really helping me drive value and alignment across the organization. A little bit more from a medium maturity standpoint, but if you think about simple things like just demand forecasting at different levels, that's a form of predictive AI. We'll talk again about MEIO and inventory optimization later, but even assessing risk for suppliers, I'm helping get up front of potential problems that could arise.
And then on the far right, autonomous, this is sort of the more end state, and you're, you're essentially using AI under some sort of strict governance and explicit instructions to automate some tasks, and this kind of supports what I was mentioning earlier on the Doomsday article of, you know, I can still leverage AI to augment these things, and then I can focus my time on more value-add, as opposed to just, again, those more transactional, tactical steps that, you know, maybe bog me down, as opposed to going in and out and analyzing something that could have, you know, a large impact across the organization.