AI in the Chain

Context

Many supply chains were designed for a world of predictable demand, long lead times and siloed information. Today’s world is the opposite: demand swings wildly, disruptions occur regularly and data is scattered across spreadsheets, ERP systems and external partners. According to reports from the logistics technology firm FourKites, companies with disconnected systems experience up to 33 % customer churn and 10‑30 % revenue loss because they cannot respond quickly to delays or make accurate delivery commitments. The Hackett Group’s 2025 supply‑chain survey finds that roughly half of supply‑chain leaders are piloting generative‑AI tools for planning and procurement, but full‑scale deployment is rare and data readiness is a major barrier. Meanwhile, McKinsey research shows that digital twins — dynamic, virtual replicas of the supply chain — can improve delivery promise by 20 %, reduce labour costs by 10 % and boost revenue by 5 % when used alongside AI‑driven automation. Building an AI‑ready supply chain, therefore, requires more than plugging a chatbot into existing tools; it demands an intentional strategy to connect systems, clean data, and train teams.

Why It Matters

An AI‑enabled supply chain is resilient, responsive and efficient. By integrating planning, procurement, inventory and logistics data, companies gain a unified view that allows them to see disruptions in real time and simulate scenarios. This not only improves customer experience and lowers costs but also supports sustainability by reducing waste and emissions. In contrast, companies that maintain fragmented systems will struggle to keep pace with competitors that use AI to anticipate risks and optimise operations. As mid‑sized firms adopt AI‑powered digital twins, larger enterprises risk being outmanoeuvred if they do not modernise their infrastructure.

Impacts and Current Challenges

Disconnected systems create blind spots. Without end‑to‑end visibility, supply‑chain planners must rely on outdated or incomplete data, leading to stockouts, overstock and missed opportunities. Manual analysis and reporting consume significant time, leaving little room for strategic tasks. Hackett Group researchers note that economic uncertainty and the skills gap are the top barriers to scaling AI; many organisations lack people who can interpret AI outputs and integrate them into decision‑making. Data quality is another hurdle: inconsistent part numbers, supplier codes and shipment references confuse machine‑learning models. Finally, governance frameworks are often missing, exposing companies to risks when AI recommendations run counter to policies or regulatory requirements.

Traditional Approaches and Their Limitations

Traditional supply‑chain improvements focus on incremental technology upgrades or bolt‑on solutions. For example, an ERP upgrade may centralise some data, but it rarely captures real‑time telemetry from sensors or partners. Transportation management systems (TMS) optimise carrier selection but often operate independently of planning and procurement processes. Many firms adopt robotic process automation (RPA) to automate specific tasks like invoice matching or order entry, but these bots can break when data formats change and they lack the predictive capabilities of AI. Without an integrated architecture, each tool remains an island, and planners must manually stitch insights together.

AI‑Powered Solutions

To build an AI‑ready supply chain, companies should take a holistic view. Key steps include:

1. Connect your data – Consolidate data from planning, procurement, inventory, logistics and finance systems into a central platform or data lake. Use APIs and integration tools to automate data flows. Normalise and clean the data to ensure consistent units, naming conventions and time stamps. This creates the foundation for any AI application.
2. Adopt a digital twin – Create a living model of your supply chain that mirrors facilities, inventory levels, capacities, lead times and costs. Digital twins allow you to simulate the impact of changes such as demand spikes, factory closures or policy shifts. McKinsey reports that using digital twins has improved delivery promises by 20 % and reduced labour costs by 10 %, which demonstrates their value beyond simple visibility.
3. Automate routine tasks with agents – Deploy generative‑AI agents to handle standardised tasks like generating quotes, booking shipments, placing replenishment orders and drafting procurement contracts. These agents integrate with your TMS and procurement platforms to execute tasks faster and more accurately than manual workflows.
4. Implement real‑time control towers – Build dashboards that combine IoT sensor data, transportation status and external feeds (weather, port congestion, geopolitical risks). AI models can predict delays and propose rerouting options. FourKites highlights that dynamic optimisation of routes and loads reduces dwell time, improves asset utilisation and lessens emissions.
5. Embrace synthetic data and small models – Where real data is limited or sensitive, generate synthetic datasets to train models. Use smaller specialised models for tasks like invoice classification or shipment ETA prediction; these require less computational power and can be deployed at the edge (e.g., in a warehouse) to deliver low‑latency insights.
6. Institute governance and compliance – Define who can access data, what decisions AI is allowed to make and how biases will be monitored. Establish ethics committees and involve legal teams early. Train users to override or question AI recommendations when necessary.
7. Develop talent and culture – Upskill existing employees in data literacy and AI fundamentals. Encourage cross‑functional teams (supply chain, IT, procurement, finance) to collaborate on AI projects. Create an environment where experimentation is encouraged and failures are treated as learning opportunities.

Step‑by‑Step Guide

1. Map your current architecture – Document all systems and data flows, noting gaps and duplication.
2. Prioritise high‑value use cases – Identify pain points that AI can solve, such as late shipments, excess inventory or manual quoting. Quantify the potential value to build a business case.
3. Run a data readiness assessment – Evaluate data quality, availability and integration. Address issues such as missing data, inconsistent units and duplicate records.
4. Design your digital twin – Select a platform (e.g., built in‑house or from vendors) and model your network, facilities and flows. Validate the twin against historical performance to ensure accuracy.
5. Pilot an AI agent – Begin with a narrow process, such as automated quoting or demand forecasting. Set clear success metrics (speed, accuracy, cost reduction) and involve end‑users from the start.
6. Monitor and refine – Use dashboards to monitor agent performance and collect feedback. Adjust data feeds, model parameters and rules as you learn.
7. Scale and continuously improve – Gradually roll out AI capabilities to more products, regions or functions. Refresh models regularly with new data. Continuously train employees and update governance practices.

Conclusion

An AI‑ready supply chain is within reach for organisations willing to invest in data connectivity, digital twins, automation and culture. While the transition requires effort – cleaning data, integrating systems and developing talent – the benefits are substantial: improved service levels, lower costs, greater resilience and the ability to respond in real time. As FourKites warns, companies that remain mired in disconnected spreadsheets risk losing customers and market share. By following the steps outlined here, supply‑chain leaders can lay the foundation for advanced AI applications and position their businesses for success in 2025 and beyond.

References

1. FourKites logistics summit coverage highlighting 33 % churn and 10‑30 % revenue loss from disconnected systems, and emphasising automation and dynamic optimisation.
2. Hackett Group 2025 supply‑chain survey noting that about 50 % of leaders are piloting generative AI but data readiness and skills are major barriers.
3. McKinsey research on digital twins demonstrating a 20 % improvement in delivery promise, 10 % labour reduction and 5 % revenue uplift.
4. BCG and Deloitte insights on digital supply‑chain transformation and the importance of data integration and AI readiness.
5. Logistics Viewpoints articles emphasising the need to connect fragmented systems and adopt AI‑enabled control towers.