Agentic AI: business transformation for the modern supply chain

The global supply chain, once a model of "just-in-time" efficiency, has become dangerously brittle. Today’s environment of "permacrisis" marked by constant geopolitical, climatic, and economic disruptions has rendered the old playbook obsolete. The traditional model, reliant on human managers reacting to lagging data, is overwhelmed. The sheer volume and velocity of modern data have surpassed human cognitive limits. The challenge is no longer just moving goods, but deciding where, when, and how to move them at the speed of the modern world.

This breakdown is driven by a compounding crisis of two distinct shocks, either a Direct shock, or an indirect shock. With a direct shock, there is unprecedented volatility, and external disruptions are no longer rare events. Geopolitical conflicts can sever trade routes overnight, while climate events disable ports and infrastructure. On the demand side, consumer behaviour has become radically unpredictable, with signal variance increasing by over 300% in some sectors. This kind of volatility creates an environment where minor changes in consumer demand amplify catastrophically up the supply chain, leading to simultaneous shortages and overstocks. If there is an indirect shock, internally, supply chains are crippled by a patchwork of siloed digital systems. Data is fragmented, inconsistent, and, most critically, latent. A planner’s view is a "rear-view mirror," showing a reality that is already hours or days out of date. This makes a holistic, real-time view impossible and forces teams to rely on intuition and tools that are inadequate in the current environment. The result is a system perpetually in a state of reactive firefighting, forcing companies into a false choice between holding inventory in costly "just-in-case" scenarios or accepting the high risk of stock-outs and lost sales. A new architecture is needed, one that is not just more efficient, but inherently intelligent, adaptive, and autonomous.

This article was written by Mario van den Broek ([email protected]) and Marius Ungureanu ([email protected]). Mario and Marius are consultants with RSM Netherlands Business Consulting with a focus on AI & Supply Chain.  

The agentic revolution: A new class of digital intelligence

The solution to this crisis is Agentic AI. This technology represents a shift from passive analytical tools to active, autonomous digital entities capable of perception, reasoning, and execution. It marks the critical evolution from systems that provide decision support to systems that execute decisions themselves. The journey of enterprise technology can be seen in three phases:

• Robotic Process Automation (RPA): Systems that can "do" by mimicking repetitive, rule-based human tasks.
• Machine Learning (ML) & Analytics: Systems that can "think" by analyzing historical data to provide forecasts and insights. They are passive and require a human to act.
• Agentic AI: Systems that can "think, plan, and do." An AI agent is a software program that perceives its environment, reasons about a complex goal, formulates a multi-step plan, and executes that plan using digital tools. This is true autonomy.

Re-architecting the value chain: agentic AI in action

An AI agent operates in a continuous loop enabled by three core capabilities:

1. Perception: Ingesting and synthesizing massive volumes of structured (ERP data) and unstructured (news, weather, social media) data in real time.
2. Reasoning & Planning: Using Large Language Models (LLMs) to break down a high-level goal (e.g., "ensure 98% on-time delivery while minimizing cost") into a logical sequence of executable steps.
3. Action & Tool Use: The key differentiator. An agent can interact with other software via APIs to execute its plan—placing a purchase order in SAP, booking a shipment on a carrier's platform, or adjusting safety stock parameters.

Deploying specialized AI agents to core supply chain functions transforms them from reactive, manual processes into proactive, continuously optimized operations. In procurement, for example, today's reality often involves static supplier relationships, infrequent risk reviews, and a slow, manual scramble to find alternatives when a disruption occurs a process that can take weeks or months.

The agentic future fundamentally changes this. A dedicated "Procurement Agent" continuously scans global data to monitor supplier health, geopolitical risk, and compliance in real time. Upon detecting a risk indicator, it can proactively identify and qualify alternative suppliers. This agent can be empowered to autonomously execute a spot buy from a pre-approved secondary supplier or even conduct dynamic, multi-variable negotiations with other digital agents to optimize for price, lead time, and carbon footprint. This shifts procurement from a slow, manual function to an intelligent and resilient one. 

The future of the autonomous ecosystem

The ultimate potential of Agentic AI is unlocked when agents operate across corporate boundaries, dissolving information friction and forming a decentralized "supply web." This ecosystem functions like a highly sophisticated, real-time digital marketplace.  

An agent from one company can securely broadcast a need (e.g., "10,000 components delivered in 72 hours"), and agents from suppliers and logistics providers can instantly analyze the request and submit multi-faceted, autonomous bids. The originating agent evaluates all possible combinations in milliseconds and executes binding contracts to procure and ship the goods. This creates a "self-healing" supply web. If a major supplier is knocked offline, the network reacts instantly. The agents of affected companies automatically re-broadcast their needs to the marketplace, and the system dynamically re-routes demand and capacity around the disruption. The "wound" is healed in hours, not months, creating a truly resilient global commerce system.

Navigating the transition: Risk mitigation & strategic imperatives

The transition to an autonomous supply chain is fraught with technical, operational, and ethical risks. Successful adoption requires a sober assessment of these challenges and robust governance frameworks.

1. Technical and Integration Hurdles:
   • Risk: Most companies suffer from siloed legacy systems and poor data quality. The "garbage in, garbage out" principle is amplified in an autonomous system.
   • Mitigation: The first investment must be in a unified data foundation a "digital twin" of the supply chain that serves as a single source of truth. An API-driven architecture is essential for interoperability.
2. Operational and Security Risks:
   • Risk: The "black box" problem, where an agent's reasoning is opaque; the potential for algorithmic collusion between competing agents; and the entire ecosystem becoming a target for adversarial attacks.
   • Mitigation: A "human-on-the-loop" governance model is essential, where humans set strategic goals and review high-risk decisions. Investments in explainable AI are needed to ensure transparency. Robust AI security protocols, analogous to firewalls, must be developed to identify and neutralize rogue agents.
3. Ethical and Workforce Implications:
   • Risk: Widespread job displacement in transactional roles. Agents programmed to optimize for cost might make ethically undesirable decisions in a crisis without proper guidance.
   • Mitigation: Proactive and substantial investment in workforce transformation, reskilling employees for higher-value roles in AI governance, data science, and strategy. An "AI Ethics Board" must be established to define and embed ethical principles into the agents' operational parameters.

Forward thinking: strategic imperatives for the autonomous enterprise

Imperative 1: Architect an Agent-Ready Data Infrastructure The journey begins not with AI software but with a radical commitment to fixing the enterprise data foundation. Creating a clean, accessible, real-time "digital twin" of the value chain is the prerequisite for any successful AI implementation.

Imperative 2: Cultivate Human-Agent Collaboration as a Core Competency The organizational mindset must shift from "managing processes" to "governing autonomous systems." This requires cultivating new roles like AI trainers, AI ethicists, and high-level strategists who design the goals and risk tolerances for the agent ecosystem. This is a cultural transformation, not just a training initiative.

Imperative 3: Launch a Portfolio of Pilot Programs to De-Risk Adoption A "big bang" implementation is a recipe for failure. Leaders should adopt a disciplined, portfolio-based approach, launching focused pilot programs on high-value, contained use cases. This allows the organization to test the technology, demonstrate ROI, and build the internal knowledge and confidence needed for a broader rollout.

RSM is a thought leader in the field of Strategy and Supply Chain consulting. We provide frequent insights through training and the sharing of thought leadership, based on our detailed knowledge of industry developments and practical applications gained from working with our customers. To discuss a practical roadmap for adopting Agentic AI and navigating the transition to a resilient, autonomous supply chain, please contact one of our consultants.