Why autonomous orchestration is the next frontier in supply chain management

In an era defined by geopolitical instability, tariffs, inflation, climate pressures, and rapid technological change, traditional supply chains – built on rigid, linear models – are ill-equipped to meet the flexibility and responsiveness that today’s fast-paced markets demand.

Business leaders face mounting challenges as disruptions expose the fragility of global supply networks.

To thrive in this environment, enterprises must evolve from reactive management to proactive orchestration, powered by artificial intelligence.

A digitalized supply chain provides the foundation for applying artificial intelligence (AI). While digitalization increases visibility and resilience – it focuses heavily on fixing foundational data challenges, such as data completeness and correctness and using this data for critical business decision-making.

Leading organizations are going further, by leveraging AI to automate decisions entirely, which enables the orchestration of supply chains by integrating applications, data and new automation technologies into truly agile operations.

According to a 2024 IDC Supply Chain Survey, advanced analytics and AI are the top technology investment priorities for supply chain leaders over the next three years.

Applications trained on industry-specific business data, such as AI agents, can detect threats, analyse their potential impact, recommend mitigation strategies, and even execute responses before disruptions occur. This proactive capability pushes supply chain operations beyond basic digitalization, empowering teams to make faster, smarter decisions.

The foundation of supply chain orchestration is high-quality, contextualized business data. Yet many organizations are hindered by data silos, making it increasingly complex to ‘stitch together’ data across supply chain domains, such as procurement, logistics, manufacturing or planning.

Additionally, some data that is critical for orchestration automation is unstructured or not readily available information – like shipment delays, the status of contracted manufactured goods or major supply base disruptions, resulting in blocked automation and insight.

By applying knowledge graphs, this data can be connected across supply chain domains or silos to form the foundation of a digital twin of the entire supply chain, enabling end-to-end visibility and more intelligent orchestration. AI tools enable enterprises to ingest and interpret this data, helping teams reduce time to recovery during disruption and substantially improving decision-making.

However, supply chain executives are increasingly concerned about the readiness and quality of their data. A PwC survey found that 37% of operations and supply chain leaders cite data availability and quality among their top three challenges to scaling AI effectively.

The reality is stark: inconsistent, outdated, or unreliable data severely limits AI’s effectiveness, while legacy systems create additional integration hurdles. With regulations like the EU AI Act adding compliance requirements, organizations must prioritize making their data AI-ready.

It is critical to have a clear strategy for collecting external data, such as supplier data e.g. advanced shipping notice or shipment statuses as well as unstructured supply chain risk and disruption data like port congestion or natural disasters.

Recognizing the value of data acquisition and collaboration through business-to-business (B2B) enterprise networks enables access to critical information, strengthens partnerships with trading partners, and enhances cross-enterprise process efficiency.

Success also requires integrating intra-company planning and execution data with procurement, financial, customer and product data across traditional silos to create a unified foundation for intelligent decision-making.

This convergence enables both generative and agentic AI deployment, creating truly adaptive decision-making. Delivered through cloud-based supply chain solutions enriched with predictive algorithms and real-time analytics, these capabilities give companies the agility to manage complexity, support flexible production and recover faster from disruptions.

AI agents are poised to redefine supply chain operations. These task-driven tools interpret complex planning results, prioritize risks and generate mitigation scenarios, analyse impact to demand and inventory levels and optimize supply strategies. The result: reactive, rigid processes become proactive, continuous operations.

Research from Economist Impact reveals that approximately 40% of companies already use agentic AI, while another third are experimenting with specific applications like inventory or route optimization.

In maintenance and field service dispatch, AI agents can autonomously schedule and optimize service and maintenance orders, enhancing responsiveness and customer satisfaction. On the shop floor, they can proactively detect and resolve disruptions such as machine failures and reschedule tasks in real time to maintain productivity. In supply chain planning, agents can generate scenario simulations and trigger the execution of specific tasks and actions.

By handling routine monitoring and surfacing contextually relevant insights, AI agents free up teams to focus on strategic initiatives rather than daily firefighting. For business leaders, this translates to faster decision-making, improved operational visibility and more accurate demand forecasting.

Supply chain’s evolution is moving toward intelligent, self-regulating systems – what we call autonomous orchestration.

Modern orchestration platforms bring together traditional operational data sources (inventory levels, track-and-trace information), risk signals and supplier data (supplier health, geopolitical events, financial risk) and unstructured inputs (emails, meeting notes, market news) to create a comprehensive, real-time view of the supply chain. This connected intelligence strengthens compliance, improves responsiveness (time to recovery) and enables better strategic planning.

Today, these platforms are still in their infancy – fragmented, with limited automation. But advances in AI and agent-based systems are set to massively accelerate their maturity.

Gartner projects that by 2030, half of all supply chain management solutions will employ agentic AI to autonomously execute decisions. Early progress is already visible: from real-time emissions monitoring and automated dispute resolution to ERP-driven equipment creation and embedded quality checks.

For business leaders, the trajectory is clear: supply chains will increasingly see autonomous orchestration, reducing human intervention to exceptions and strategic disruptions. The result is a more resilient, adaptive and efficient network – one that empowers executives to focus on growth and innovation rather than daily firefighting.

The transition to autonomous orchestration is already under way. Supply chain executives must lead the charge by investing in data quality, data acquisition strategies through business networks, embracing AI agents and integrating intelligent orchestration tools across their operations.

Those who act decisively will not only mitigate risks and improve agility but also discover new opportunities for innovation and sustainable growth in an increasingly unpredictable business landscape.