Ruslan Shamalov: AI in logistics is no longer just a calculator, It is the nervous system of the entire supply chain

The crisis management and digital transformation expert shares how the integration of artificial intelligence is reshaping logistics, what steps make digitalization safer, and why companies need a culture where discussing mistakes is not only acceptable but essential.

On July 23, the White House launched a sweeping AI Action Plan, issuing three executive orders aimed at supporting the adoption of artificial intelligence and automation in U.S. logistics and manufacturing companies. These measures promise to accelerate order processing and drive new regulations, but they also introduce new risks for global supply chains.

We spoke with Ruslan Shamalov, a crisis management and operations expert, about the nature of those risks and how to minimize them. Ruslan is the developer of an intelligent application for freight analysis and route optimization and has been invited as a judge for the Glonary Awards for Achievement.

With over 15 years of experience in international logistics, he specializes in managing global supply chains, driving digital transformation, and building scalable businesses. In this interview, he discusses operational risks in logistics, explains the concept of high-frequency planning, and shares how to unlock the full potential of AI.

Ruslan, you’ve been managing logistics operations for many years and have successfully led automation projects across multiple countries. Today, artificial intelligence is being actively adopted in critical sectors like transportation. As AI takes on a greater role in decision-making, what risks or vulnerabilities should companies be aware of?

Yes, besides the obvious advantages of implementing artificial intelligence and high-frequency planning systems, this also carries certain risks that need to be prevented and minimized. The first and main concern is technological risks. This involves the continuous functioning of all software and hardware components.

As practice shows, even the slightest error in calculations or delay in telemetry transmission instantly becomes a serious problem for the entire logistics operations chain. In conditions where decisions are made every minute or even seconds, such failures can cause a so-called “chain reaction effect,” leading to massive delays and SLA violations.

Could you clarify the term “high-frequency planning”? What does it mean in the context of logistics? How does it work, and would you say it has already become an industry trend?

High-frequency planning, also known as dynamic planning, is an approach in which routes and key logistics indicators are recalculated continuously, every few minutes or even seconds. The system constantly analyzes real-time data such as traffic congestion, accidents, or technical issues, and suggests a new, more efficient route accordingly.

Unlike static planning, where a driver receives a predefined route and follows it from start to finish, the dynamic model allows the system to adapt to real-time changes. This is especially valuable in freight logistics, as it helps optimize resources and significantly reduce operational costs.

At your current company, VS CENTRAL INC, you restructured the fuel accounting system and launched the development of an AI-based application for freight, route, and fuel price analytics.

As a result, the company’s operational efficiency increased by approximately 30% compared to competitors. In your view, is AI in logistics used solely for calculations, or does it serve broader functions?

Certainly not. AI in logistics is no longer just a calculator, it is the nervous system of the entire supply chain. One of the most important areas is predictive and prescriptive analytics. Think of it as a kind of “weather forecast” for logistics: based on historical patterns and real-time data, the system builds potential scenarios and flags issues before they become visible.

This enables companies to make decisions proactively — not reactively or in a state of crisis. Another promising tool is AI-powered alert systems, which act as early warning centers. They detect even the slightest deviations, whether it’s an accident, localized congestion, or a warehouse delay, and immediately notify decision-makers. This helps prevent the domino effect, where a single disruption can cascade across the entire supply chain.

In your academic work on operational risks in logistics, you emphasize the importance of fostering a “safe-to-fail” culture within companies. What does this principle mean in practice?

While automation and AI significantly optimize logistics processes, the human factor remains a major source of errors, according to industry statistics. That’s why it’s far more effective when an issue is identified and addressed early rather than when it escalates into a serious disruption. This is the essence of a “safe-to-fail” culture: employees are not afraid to speak up when they make a mistake, and the company responds quickly. As a result, larger consequences can often be avoided.

The real danger lies in silence when someone withholds information, and the error surfaces at a critical moment. MBA and crisis management textbooks often cite the Challenger shuttle disaster as an example: engineers were aware of the risks but failed to raise them in time, resulting in tragedy. The same principle applies in logistics: if people aren’t encouraged to report problems openly, even the most advanced systems can fail.

You’ve recently been a jury member at the international Glonary Awards for Achievement. In your opinion, what changes could significantly impact the development of the logistics industry in the near future?

If we’re talking about something fundamentally new, I would highlight the integration of digital twins with artificial intelligence systems. Previously, digital twins were used more as a modeling tool a virtual copy of a warehouse, transport, or entire logistics network. But now, thanks to AI, they’re becoming “living organisms” that don’t just show the real-time picture, but actually propose optimal solutions themselves. Imagine having a digital copy of your entire supply chain: from warehouse to last mile.

It receives data from IoT sensors, analyzes it using predictive analytics, and in real time forecasts where congestion will occur, which road section might become a bottleneck, and immediately adjusts routes. I think in the near future, exactly these types of systems will start changing the market.

They will unite cloud technologies, stress testing, and analytics into a single ecosystem. And the most interesting part—not only market giants will be able to use them, but medium-sized companies as well, because technologies are becoming increasingly accessible.