📦 Supply Chain AI: What’s Real vs. Hype? | 5 Insightful Minutes With Eugene Amigud Of Infios

Foreign US now for five insightful minutes is Eugene Amagood, the Chief Innovation Officer at infios.

Eugene is here to help us separate fact from fiction when it comes to harnessing AI for your supply chain.

Okay Eugene, let's start with this.

AI is freaking everywhere.

But as the Chief Innovation officer, how do you think about deploying across the supply chain?

I mean like I get bombarded every day with everything from predictive algorithms to gentic AI.

How should our audience think about separating true innovation from hype?

In this era we think about purposeful innovation to start with.

Right.

Again, there is a lot of AI hype and the way kind of to break through that is I think of different use cases and the right technology, right approach for these use cases.

When we think about AI, there's machine learning and predictive analytics, there are optimizers as well as the latest and greatest gen AI.

And depending on the use case, there's the right kind of tool tool to address it.

So first of all is partner with customers to co develop on specific use cases on specific needs instead of kind of thinking it within the black box.

And number two, many of us already have existing systems deployed and the innovation has to be augmentative.

Meaning that I may create a new module, new component that solves my specific need, drop it in, realize the benefit in maybe a couple weeks to a couple months and move on.

I have no more time to deploy these capabilities over long period of time.

So again, make it with purpose, use case based as well as making it augmentative.

Eugene, the retail industry is in a constant state of disruption right now.

And it seems like staying ahead is more than just reacting to market uncertainty.

It means designing your business operation for adaptability.

How would you say that Infios thinks about innovation not only to navigate this change, but really to predict what's ahead.

With so much supply chain uncertainty and constant changes, right.

The deterministic rules and ability to configure the systems becomes almost impossible.

Right.

Again, during COVID there was a lot of disruption, but over time people thought, well, maybe it will stabilize.

And now we all understand, right, that this uncertainty does not go away.

If anything, it becomes more and more complex.

Right.

And so how do you react to this uncertainty?

So historically you would kind of set up all these different rules configurations in your systems or deploy the entire system to address it.

But now it has to be a lot more dynamic.

It has to be a lot more aligned with the business needs versus the systems.

Right?

So again, if I have a labor strike in my port, how Do I comprehensively address it?

If I have a strategy to get close to my customers, how do I comprehensively address it and how do I react to these uncertainties?

And that's where AI and ML come in, where it can look at historical data, right.

And being able to react based on that historical data and make some predictions on it.

However, historically the planning system used to look at years and years worth of data.

But what we're seeing now from planning perspective, right, look at tariffs that change pretty much daily, or the labor strikes, etc.

Right.

You have to react so much faster.

So now that the scope becomes so much more important to kind of look at the near real time data and to be able to react accordingly.

You know, the one thing you guys always talked about to me was like this idea of a quote unquote brain or a decision engine.

Given what you're describing, does that, does that concept still.

Is that concept still in play here or how should we think about that?

Yeah, realigning the system capabilities to be closer to the kind of functional solutions that business needs is absolutely key.

And the decisioning engine sits at the core of it.

Historically, these decisioning engines used to be within transportation or within order.

Right.

What's the most effective way to fulfill an order?

What's the most cost effective way to ship a load across the country?

Now all of those need to be tied together to be able to react to those disruptions.

And the decisioning engine sits kind of outside and, and makes all this kind of both deterministic as well as AI based decisions.

Looking at all this holistically, which is not really possible in the old world when those decision engines used to be isolated.

Got it.

So.

So if I say that back to you, then you're saying that there's basically there's like going to.

The decision engine still matters, but there's almost a module in and of itself that is going to command and control everything else that's going on.

Is that right?

Exactly.

So this module is the key, Chris, as you mentioned, because if it's an independent module, you can deploy it fast.

You can realize the benefits, the intelligence of each individual system you rely on less.

But now this consolidated decisioning engine can come up with the most comprehensive and kind of cost effective whatever you're optimizing on solutions that you would need.

What's a breakthrough that you see happening on the horizon?

Something that you believe is going to fundamentally redefine commerce in the next three to five years?

Again, there are two parts and I think they're Both aligned.

So business is moving from again, this systematic approach of buying individual systems like omas or front end or payment, et cetera, to kind of solving business needs.

And on the other side from technology is again, as I was mentioning, from this microservice to modules to agentic.

And what gets me excited the most, because I'm in the supply chain execution side, this whole gen AI started as a natural language kind of processing LLMs models, et cetera.

So it was really good, and I would always say it was really good for planning systems because planners interact in that way from execution.

The less human intervention there is, the better your system works.

So historically these LLMs and Genai was not built for supply chain execution.

So I think the most innovation will come from that space.

And now with the new technology around agentic, around this kind of autonomous agents being able to orchestrate in the real time, right?

Not spinning chatgpt.

And you ask me a question, right?

If I'm scanning shipments, I'm scanning them within milliseconds.

If I'm processing orders, I'm processing them within single digit milliseconds.

And so that kind of ability to meet the business needs with this new tag, it's probably what's going to evolve over time in a very exciting and new ways.

Essentially you're saying protect the brain.

Thank you, Eugene.

That was great.

Exactly, Eugene.