Forrester: Managing supply chain volatility with agentic AI

Source Summary

<p>Supply chain management has induced headaches at enterprises struggling with geopolitical challenges, sustainability mandates, and the sheer volume and velocity of decisions they must take to win, serve and retain customers. While not yet realised in the wild, <a href="https://www.techtarget.com/searchenterpriseai/definition/AI-agents">artificial intelligence (AI) agents</a> offer the prospect of boosting supply chain resilience and sustainability.</p> <p>Most agentic deployments are still being established, but <a href="https://www.techtarget.com/searcherp/tip/Use-cases-for-machine-learning-in-the-supply-chain">agents in the supply chain</a> will help supply chain analysts scale their insights. These analysts monitor the balance between anticipated demand and expected supply by stock-keeping unit (SKU) and fulfilment location for each period in the planning horizon.</p> <p>Enterprises that offer more product options to serve long-tail customers, value propositions like direct to consumer, or products as a service risk overwhelming analysts, desperately rebalancing stock across proliferating SKU/location combinations – not to mention generating frequent scenario analyses for executives to assess risk. Enterprises that fail to seize the opportunity to deploy agents for scaling supply chain insights risk stockouts that fail customers and overstocks that fail shareholders.</p> <p>Agentic AI also helps to reduce the burden of mundane logistics tasks. Supply chain resilience depends on a combination of capacity or inventory buffers and sourcing and transportation options. But the exercise of options is often surprisingly labour-intensive.</p> <p>According to Logistics Management’s <em>DispatchTrack</em> benchmark survey, a typical dispatcher in an enterprise transportation office makes 120 to 200 calls in an eight-hour shift. We’ve all heard stories about the scramble for alternate carriers and routes during recent crises, such as the port congestion on the US West Coast or the Ever Given ship stuck in the Suez Canal. Fortunately, there are already live examples of agents finding and negotiating rates with carriers. </p> <p>Another benefit is that agentic AI can manage the avalanche of <a href="https://www.techtarget.com/pharmalifesciences/tip/Improving-pharma-supply-chain-visibility-with-AI-technology">paperwork across the supply chain</a>. The flow of documents should match the flow of goods in every supply chain: for example, purchase orders, purchase order acknowledgements, advanced shipping notices and bills of lading.</p> <p>There are also customer expectations about supply chain carbon footprint transparency, together with multi-tier supply chain visibility mandates, such as the <a href="https://www.techtarget.com/sustainability/tip/Build-a-comprehensive-supply-chain-traceability-checklist">EU’s Supply Chain Due Diligence Law</a>. These add to the mountain of documents that supply chain professionals must parse and match to file the correct customs declarations, remit the correct tariffs, and authorise carrier or supplier invoices.</p> <p>Missing data elements or mismatched data inevitably lead to delays in customs clearance or invoice settlement, so agents that can reduce discrepancies are essential. For instance, global shipping giant Maersk uses AI agents to streamline documentation and supplier interactions, while Schneider Electric uses AI agents for document validation and sustainability tracking. </p> <div class="extra-info"> <div class="extra-info-inner"> <h3 class="splash-heading">Read more about supply chain management</h3> <ul class="default-list"> <li>SAP introduces <a href="https://www.computerweekly.com/blog/CW-Developer-Network/SAP-introduces-Autonomous-Supply-Chain-Management">Autonomous Supply Chain Management</a>: At SAP Sapphire 2026, SAP presented what it thinks autonomy in the supply chain actually looks like when translated into production systems.</li> <li>Top 8 KPIs for <a href="https://www.techtarget.com/searcherp/tip/3PL-KPIs-that-can-help-you-evaluate-success">3PL organisations</a>: Supply chain leaders can use 3PL key performance indicators to evaluate vendor performance, manage costs and ensure logistics partners meet service-level and customer experience expectations.</li> </ul> </div> </div> <section class="section main-article-chapter" data-menu-title="A foundation for agentic AI success"> <h2 class="section-title"><i class="icon" data-icon="1"></i>A foundation for agentic AI success</h2> <p>Beyond these benefits, agentic AI offers the prospect of mitigating supply chain instability and managing supply chain complexity by automating some analysis and supplying imbalance remediation interventions. But to seize the opportunity, technology and innovation leaders must lay the policy and planning foundation for agentic AI success and focus on data preparation, protocols and standards for execution success.</p> <p>To set the foundation for their agentic AI journey, Forrester recommends that technology and innovation leaders estimate the business opportunity. Supply chains break down when the volume, velocity and complexity of decisions overwhelm decision-makers. Supply chain decision complexity depends on factors like the combination of SKUs and supply chain nodes, as well as the level of institutional and market uncertainty.</p> <p>Savvy technology and business leaders keep track of growth in SKU/fulfilment channel combinations and layers in the supply chain and regulations – such as supply chain traceability – carefully noting the potential or actual financial impact of rushed or poorly informed decisions.</p> <p>But technology leaders need to acknowledge the frustration of rules-based automation. Forrester notes that enterprises like Siemens have successfully invested in their own operations and those of their clients’ rules-based automation technologies – such as robotic process automation (RPA) – to boost supply chain performance.</p> <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> Traditional cyber security architectures were designed for organisations built around people – agentic AI disrupts this </figure> <i class="icon" data-icon="z"></i> </div> </blockquote> <p>However, enterprise applications governance is adapting to accommodate the changing future of work. Forrester warns that rigid rules-based task automation can fossilise the status quo, jeopardising fluid adaptability to new business opportunities, such as selling through multiple new channels or the servitisation of assets or products.</p> <p>Forrester recommends establishing a framework for agent security and authorisation. Traditional cyber security architectures were designed for organisations built around people – agentic AI disrupts this. To deploy goal-oriented, ephemeral, scalable, dynamic agents, where unpredictable emergent behaviours are incentivised to accomplish objectives, it recommends enterprises adopt Forrester’s <a href="mailto:https://www.forrester.com/technology/aegis-framework/">Aegis (Agentic AI Guardrails for Information Security) framework</a>, which is designed to help chief information security officers (CISOs) secure, govern and manage AI agents and related infrastructure.</p> <p>Authorisation also applies to usage, which means enterprises will need to apply FinOps to agents.</p> <p>Forrester recommends that technology leaders develop a semantic model that embraces all supply chain processes and apps. Agentic systems require high-quality and consistent data to support precise decision-making and accurate actions. Enterprises investing in creating and maintaining a high-quality metadata layer, such as <a href="mailto:https://www.techtarget.com/searchenterpriseai/definition/knowledge-graph-in-ML">knowledge graphs</a>, to help agents understand the data and business context in which they operate, are already seeing more success than those that lag.</p> <p>Building a complete semantic model that connects payments, enterprise resource planning, procurement and other areas of enterprise logistics management to partners in sales, product and customer support is essential for building next-gen, differentiated agentic applications.</p> </section> <section class="section main-article-chapter" data-menu-title="Trusting agentic decisions"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Trusting agentic decisions</h2> <p>Explainability is another area technology leaders need to focus on. Trust is the primary challenge for AI agents, as most supply chain professionals bear the scars of project failures in forecasting and replenishment automation. AI-powered supply chain implementations have a 72% failure rate, so savvy technology and innovation leaders applying any technology to supply chain use cases must invest in explainability and compliance: for example, with regional AI regulations like the <a href="https://www.computerweekly.com/feature/Preparing-for-AI-regulation-The-EU-AI-Act">EU’s AI Act</a>.</p> <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> Forrester urges technology leaders to exercise caution over proliferating agents in an agentic chain </figure> <i class="icon" data-icon="z"></i> </div> </blockquote> <p>Overall, Forrester urges technology leaders to exercise caution over proliferating agents in an agentic chain. Technology and innovation professionals should consider factors like latency and performance in real-time or near-real-time applications; cloud pricing models that discourage long chains; error propagation, which amplifies ambiguity or error in agents; context window limitations that depend on token limits in large language models; and orchestration overhead. Wherever there are multiple agents in a chain, IT leaders must introduce tooling to verify agent outputs, ensuring the feasibility of results that pass to downstream agents.</p> <p>Forrester recommends that organisations establish human-in-the-loop and human-on-the-loop guidelines. This limits the responsiveness of complex supply chains to frequent shocks. However, autonomous agents always require human-in-the-loop and human-on-the-loop resolution for exceptions. For instance, companies such as Costco use supplier onboarding technology – from companies like Osapiens – to orchestrate agents retrieving data that’s vital to comply with regulations, such as the EU Supply Chain Act. But agents always route ambiguous cases to experts for resolution. </p> <p>Agentic systems behave unpredictably, and even competent artificial general intelligence (AGI) requires close supervision. Forrester urges IT leaders to govern decision boundaries between humans and AI, such as by deploying tools like Nvidia NeMo Guardrails.</p> <hr> <p>This article is based on Forrester’s <a href="https://www.forrester.com/report/the-supply-chain-platforms-landscape-q2-2026/RES194241"><em>Agentic AI will scale and accelerate your response to supply chain volatility</em></a> report by Forrester vice-president and principal analyst <a href="https://www.forrester.com/analyst-bio/george-lawrie/BIO709">George Lawrie</a>.</p> </section>