AI in the Chain

Context

Agentic AI is the term used to describe AI systems that can act autonomously on behalf of a user or business. Instead of simply answering questions or providing data, an agentic model can interpret an objective, break it into tasks, call the necessary tools and complete the job with little human involvement. In supply chains this concept is already taking hold. For example, logistics provider CH Robinson announced in 2024 that its generative AI agents have executed more than three million transportation tasks across quoting, booking and track‑and‑trace workflows. These agents automatically provide price quotes to customers, process orders and communicate with carriers, reducing the time it takes to arrange shipments from hours to seconds and enabling a 30 % productivity increase from 2023–2024. TruckingDive reported that after CH Robinson added less‑than‑truckload (LTL) quoting to the agent, customers’ loads are accepted in under 90 seconds and AI‑generated quotes grew 30 % per month. Such examples demonstrate that autonomous agents are not just theoretical; they are already streamlining routine logistics work and freeing human planners to focus on exceptions and higher‑value tasks.

Why It Matters

Supply chains are under pressure to become faster, more resilient and more cost‑effective. Traditional automation through robotic process automation (RPA) or scripted bots speeds up repetitive tasks but cannot adjust to changing conditions or coordinate across systems. Agentic AI, by contrast, can react dynamically to new information and orchestrate multiple systems. The result is greater responsiveness and a level of efficiency that manual processes cannot match. According to a 2025 survey by the Hackett Group, around half of supply‑chain executives are piloting generative‑AI solutions for planning, procurement and logistics, but adoption remains uneven. Economic uncertainty and data quality issues hinder broader deployments. Nevertheless, the potential to reduce cycle times, decrease manual workload and improve decision quality make agentic AI a compelling frontier for supply‑chain leadership.

Immediate Impacts and Challenges

The most obvious impact of agentic AI is speed. CH Robinson’s case shows that quoting and booking tasks that previously took hours can be completed in seconds. Beyond speed, productivity gains are substantial; the company reports a 30 % jump in worker productivity since introducing generative‑AI agents. Similar improvements are expected in inventory management, procurement negotiations and transport planning as agents handle routine communications and calculations. However, challenges remain. Many supply‑chain systems are siloed, making it difficult for agents to access the data they need. There is also a skills gap: building and supervising agents requires a mix of domain expertise and machine‑learning knowledge. Finally, governance and safety are critical: AI systems must be monitored to prevent erroneous bookings or unintended consequences.

Traditional Approaches and Their Limitations

Before agentic AI, businesses relied on a mix of spreadsheets, legacy enterprise resource planning (ERP) systems and static rules to manage quotes, dispatch instructions and inventory. While RPA bots automated some processes, they could only follow predefined steps and would often break when data formats changed. These tools also lacked the ability to learn from new data or predict outcomes. For example, quoting tools often failed to consider real‑time capacity constraints or lane disruptions. Human coordinators still needed to review shipments, communicate with carriers and update customers. As demand volatility and trade complexity increased, these manual approaches became unsustainable.

AI‑Enabled Solutions

Agentic AI introduces several hands‑on solutions that supply‑chain professionals can adopt today:

1. Intelligent quoting and booking agents – Use generative‑AI platforms integrated with your transport management system (TMS) to provide instant price quotes, evaluate capacity and book shipments automatically. CH Robinson’s agents, built with large‑language‑model technology, show how even complex LTL shipments can be quoted in seconds. To start, identify high‑volume, standardised lanes and collaborate with a vendor or internal data science team to train an agent on historical pricing and booking rules.
2. Proactive shipment management – Agents can continuously monitor real‑time status updates and weather or traffic feeds to adjust routes and notify stakeholders. Instead of waiting for a driver to report a delay, an agent can reassign a carrier or re‑sequence deliveries to maintain on‑time performance.
3. Automated procurement negotiation – Generative‑AI agents can handle routine RFQ (request for quotation) exchanges with suppliers, propose rates based on market indexes and company thresholds, and flag exceptions for human review. This reduces the negotiation workload and provides consistent messaging.
4. Inventory balancing and replenishment – Agents can analyse inventory levels across distribution centres, forecast demand, and automatically initiate transfers or purchase orders. By learning from past patterns and current signals, agents help avoid both stockouts and overstock situations.
5. Digital‑twin integration – Pairing agents with a digital twin of your supply chain allows you to run continuous simulations. For example, if an agent identifies a potential disruption, it can query the twin to simulate alternative scenarios and choose the optimal response.

Step‑by‑Step Implementation Guide

1. Start with a clear use case – Choose a process that is repetitive, data‑rich and impacts customer service (such as quoting, booking or inventory transfers). Document the current workflow and identify key data sources.
2. Prepare your data – Agents rely on accurate, up‑to‑date information. Consolidate data from your TMS, ERP and planning tools into a secure location. Cleanse and standardise the data to ensure uniform formats and naming conventions.
3. Select a platform – Decide whether to build an agent internally using open‑source frameworks such as LangGraph or CrewAI, or work with a software provider that offers pre‑built agents. Evaluate options based on integration ease, security features and cost.
4. Train the agent – Provide the model with historical transaction data, pricing rules and exceptions. Work with domain experts to define boundaries: what decisions the agent can make autonomously and what requires escalation.
5. Pilot and monitor – Launch the agent on a limited set of orders or shipments. Monitor its actions and gather feedback from planners. Measure improvements in cycle time, accuracy and user satisfaction. Adjust the agent’s parameters as needed.
6. Scale incrementally – Once the pilot delivers positive results, expand the agent to additional lanes, suppliers or product categories. Gradually increase the level of autonomy as confidence grows.
7. Govern and audit – Establish oversight to ensure the agent behaves as intended. Maintain logs of decisions and implement regular audits. Provide a mechanism for users to override or correct the agent when necessary.

Conclusion

Agentic AI is transitioning from a buzzword to a practical toolset that is already transforming supply‑chain operations. By automating millions of routine tasks, companies like CH Robinson have proven that generative‑AI agents can deliver substantial productivity gains, faster quoting and booking, and better customer experiences. While challenges such as data readiness, skills gaps and governance remain, these can be addressed by starting with targeted use cases, investing in data integration and adopting a human‑in‑the‑loop approach. Supply‑chain leaders who embrace agentic AI today will be better positioned to respond to volatility, cut costs and empower their teams to focus on strategic value creation.

References

1. C.H. Robinson press release highlighting over three million generative‑AI‑automated tasks and a 30 % productivity increase from 2023–2024.
2. TruckingDive article on CH Robinson’s LTL quoting agent reporting a 30 % monthly jump in AI‑generated quotes and sub‑90‑second load acceptance times.
3. Hackett Group supply‑chain study noting that roughly half of supply‑chain leaders are piloting generative AI solutions but have yet to scale them widely.
4. Logistics Viewpoints / FourKites summit coverage emphasising the need for connected systems, automation of routine tasks and digital‑twin‑enabled optimisation.
5. McKinsey insights on digital twins and generative AI benefits, including a 20 % improvement in delivery promise and 10 % reduction in labour costs from digital‑twin adoption.
6. SupplyChainBrain commentary on the limitations of fully autonomous supply chains, highlighting the importance of human oversight and data quality.