At the recent SMC3 Jump Start conference, Mark Albrecht, vice president of artificial intelligence and enterprise strategy at C.H. Robinson, shared insights on how AI is revolutionizing logistics operations. He emphasized a shift from traditional software-driven workflows to a new realm of agentic AI.

Albrecht described how the logistics industry has long depended on computer-aided processes, with software digitizing tasks while humans made the decisions. However, this paradigm is changing due to general-purpose AI advancements. He likens this transition to the transformative effects of the internal combustion engine and electric motor, stating that the AI evolution demands significant adjustments in business operations.

Agentic AI alters this dynamic, allowing systems to independently reason, determine next steps, and execute tasks. As organizations embrace AI, they are advised to target high-impact problems that promise clear returns on investment and to capture the rationale behind decisions, enriching data for future AI reasoning.

C.H. Robinson has already witnessed tangible benefits from implementing agentic AI. For instance, the company simplified digital integration, allowing customers to engage via standard email for quotes, which AI then processes into structured data, streamlining operations and helping customers avoid costly shipping delays. This innovation also enhances durability, as agentic AI can interpret new document formats and maintain high automation rates, soaring beyond 90%.

Albrecht advocates for strategic, disciplined AI investments, urging logistics companies to stabilize their digital operating models before scaling. This approach ensures organizations are equipped to lead the evolving logistics landscape, recognizing AI not merely as a technology upgrade, but as a fundamental shift in workflow and efficiency.

Poloplast has transformed its manufacturing processes and forecasting capabilities by adopting Microsoft Dynamics 365 Supply Chain Management and Finance, supplemented by Power Platform tools. Previously, Poloplast operated on an outdated AS/400 ERP system that hindered efficiency and visibility. By implementing a modern integrated solution, the company has drastically improved demand planning, budgeting, and operational insights.

The integration of Dynamics 365 has enabled Poloplast to achieve faster, more precise forecasting and budgeting, with results generated just one minute after data entry. This swift responsiveness allows department heads to easily adjust plans as needed, using real-time data to enhance decision-making. One key application is the demand planning feature, which employs forecasting algorithms to provide accessible, no-code analysis, leading to improved accuracy in sales predictions.

Additionally, Poloplast enhanced its budgeting process with Business Performance Planning in Dynamics 365 Finance. This modern solution connects financial data to operational planning, making it easier to create and modify budgets aligned with operational goals. Teams can now visualize item-level details in their budgets, facilitating more informed planning cycles.

Poloplast also embraces automation through Power Automate, significantly reducing data processing times and streamlining workflows—data warehouse creation time has dropped from 9-10 hours to just 5-6 hours. The company has implemented AI-driven agents using Microsoft Copilot Studio for tasks like automating supplier communications and summarizing corporate information.

Through these advancements, Poloplast not only anticipates enhanced operational efficiency but also positions itself for future innovations in finance and production automation, fully leveraging AI capabilities to drive continued excellence.

Samsung’s AI Factory Transforms Supply Chain Automation into Data Layer Coordination
BY PYMNTS | MARCH 4, 2026

The future of automation is shifting toward AI coordination, enabling intelligent systems to oversee robots, workers, and logistics in real-time. Supply chains are evolving into integrated ecosystems utilizing robotics, sensors, computer vision, and data platforms to enhance operational efficiency.

By integrating AI-driven data, companies can significantly improve resilience, allowing for predictive maintenance, real-time adjustments, and better management of supply chain disruptions. The traditional approach to warehouse automation has evolved since the launch of Automated Storage and Retrieval Systems (ASRS) in the 1950s. Currently, the focus is not merely on deploying hardware like robotic arms and autonomous forklifts, but on optimizing the orchestration of these elements through sophisticated data management.

Samsung Electronics is leading this transition by committing to “AI-Driven Factories” by 2030. According to YoungSoo Lee, the goal is to develop autonomous environments where AI makes informed decisions in real-time, enhancing operational efficiency and adaptability in the face of demand fluctuations and supply chain challenges.

At the core of Samsung’s initiative are digital twin simulations and AI agents, which enhance production quality, logistics coordination, and predictive maintenance. These AI systems analyze data streams from factory operations to fine-tune workflows and anticipate potential disruptions. Furthermore, automation leaders, including Amazon, are capitalizing on computer vision and data analytics to monitor and optimize warehouse activities continuously.

The focus is shifting toward integrating systems across facilities and transportation networks, enhancing real-time visibility and agility within supply chains. This transformation offers crucial advantages such as predictive maintenance, allowing for advanced equipment failure warnings and dynamic workflow adjustments, thereby fortifying supply chain resilience amidst market uncertainties.

Allan Dow, EVP/General Manager of Aptean Supply Chain, proposes a decision-centric model to rejuvenate broken supply chains. He draws a parallel to Steve Jobs' iconic 1997 vision that rejected antiquated technology and embraced innovation. Today's global supply chains face unprecedented disruptions, and a recent survey found that such disruptions are now the norm, costing companies significant revenue—up to 8% annually.

Despite investing over $200 billion in supply chain technology over the past two decades, many organizations still operate reactively. Traditional solutions are centered on static planning and historical data, leaving businesses ill-equipped to respond to the fluid global market.

A decision-centric approach transforms this landscape by placing decision-making at the core of supply chain operations. This model emphasizes three key applications of artificial intelligence (AI) that yield tangible benefits:

1. Centralizing Data: By unifying data on a single platform, organizations eliminate silos, enabling AI to deliver comprehensive insights that inform better decision-making.

2. Intelligent Responses: Leveraging clean, centralized data, businesses can promptly diagnose issues and take informed action, supported by generative AI that allows for data exploration and machine learning that assists with complex decisions.

3. Predictive Sales and Operations Planning: AI-driven insights anticipate market shifts and customer behavior, allowing companies to turn potential disruptions into opportunities for growth.

By adopting this AI-first, decision-centric model, supply chains can evolve from reactive networks to adaptive engines. This shift not only enhances responsiveness but also equips professionals with the tools needed to strategically navigate disruptions, ensuring that they meet current challenges with agility and precision. This evolution calls for a revolution in how supply chains function, empowering teams to inhibit fragility and adapt proactively to an ever-changing landscape.