AI’s Steady Maturation in Supply Chains
While industry analysts, reporters, and technologists continue to debate the value of AI, I decided to conduct a five-year retrospective, a healthy look back to identify where AI has genuinely made a difference in supply chain management. The results reveal a transformation that extends far beyond hype—AI has evolved from an obscure tool for managing materials and inventories into a strategic imperative for manufacturing and industrial enterprises.
Companies that have adopted AI in their internal (procurement and maintenance) processes have realized substantial benefits, including significant cost savings, improved service levels, reduced downtime, and optimized working capital. These documented advantages have proven essential for navigating an increasingly uncertain global landscape marked by shifting regulations, evolving tariffs, and the strategic pivot toward nearshoring manufacturing—all while addressing ongoing challenges in the availability of transport, highly volatile material pricing, labor market shifts, and region-specific legislative changes.This AI transformation has fundamentally repositioned supply chain data and traditional ERP systems as the epicenter of e-business evolution. As procurement and MRO operations have become more intelligent and responsive, organizations have unlocked millions in savings, and I’ve witnessed it firsthand.
Today, the questions clients ask have evolved accordingly, reflecting their deeper understanding and elevated expectations, shifting from hoping for unknown benefits to demanding substantial ROI. And that’s all a part of the journey.
2020: Early Adoption of AI During a Crisis
Before the pandemic, “supply chain” and “AI” rarely appeared in mainstream business headlines. The global shutdown changed that overnight, elevating both terms to prominence as enterprises scrambled to adapt to the new reality. Companies began partnering with startups to address immediate challenges, such as identifying duplicate materials, reducing risk across their supply chains, all to secure the necessary supply of materials to maximize production for their business..
While machine learning models had previously analyzed historical data to predict demand patterns and optimize inventory levels, new AI processes emerged that synchronized inventories with real-time data across various ERP, EAM, and P2P systems. Early adopters discovered substantial savings by identifying excess inventory, locating misplaced critical parts, and reducing facility downtime costs. These pioneers also achieved meaningful reductions in logistics expenses.
2021: Building Resilience Through Scale
The ongoing pandemic has prompted companies to look beyond traditional supply chain solutions, seeking AI-powered tools to manage their vast amounts of data and deliver resilience and agility. Naturally, interest has accelerated rapidly, with new applications emerging for real-time tracking, predictive maintenance, and scenario planning. These factors are at the very essence of scaling operations efficiently to achieve hard-dollar returns.
However, most companies were unprepared for such a transformation. As consultancy McKinsey noted at the time, “New technology solutions could be transformative—but only if executives properly prepare their organizations.”
Organizations began partnering with foundational AI and data innovators, offering cloud-based machine learning platforms that can analyze inventory levels, vendor performance, safety stock requirements, duplicate SKUs, and usage history. Real-time monitoring and analysis enabled companies to identify parts and products held in inventory, potentially saving millions in revenue risk.
2022: Connecting Disparate Data Streams
By 2022, AI adoption in supply chains had gained significant momentum. Enterprises looked to nearshoring and onshoring as proactive supply chain strategies, leveraging AI for strategic inventory and supply optimization. As big data proliferated, companies sought solutions to harmonize information from separate systems for optimal supply chain performance.
Machine learning significantly improved demand forecasting accuracy, while enterprises using AI-powered predictive maintenance achieved measurable reductions in asset downtime.
However, in this rush to adopt AI-powered solutions, many businesses saw a disconnect between the effort and intent for success from the realized value for the business stakeholders, which hurt trust in AI solutions.
2023: The Generative AI Revolution
The introduction of Generative AI marked a watershed moment for operational applications. GenAI promised—and delivered—cost savings and enhanced user experiences in supply chain planning and logistics. AI expanded into supply chain finance, detecting fraud, assessing creditworthiness, and streamlining supplier onboarding processes.
Enterprises sought partners with AI-native, purpose-built solutions, like harmonizing MRO data across global supply chains, processing movement and procurement data for millions of SKUs to generate actionable insights. These solutions enabled companies to reduce lead times, decrease working capital requirements, and improve uptime by ensuring the availability of supply for critical parts.
2024: Strategic Necessity Achieved
2024 marked the inflection point at which AI became integral to supply chain resilience. Organizations integrated AI for enhanced traceability and visibility, while deep learning and machine learning employed prescriptive analytics to optimize the balance between costs and risks.
Manufacturers began implementing fuzzy logic search capabilities to locate parts across facilities and deployed AI-driven criticality assignments for more informed stocking decisions. AI systems have matured in their ability to handle unstructured data and deliver real-time insights with increasing reliability.
The 2025 Landscape: Beyond Automation
Just a few years ago, enterprises primarily sought AI to automate existing processes. Today’s demands are even more sophisticated. We’re seeing requests for real-time data integration across call center operations, database inventory systems, and broader enterprise infrastructure. To address government tariffs, companies utilize AI to identify immediate demand requirements, as well as to forecast demand for 3-, 6-, and 12-month periods.Looking ahead, agentic AI is establishing its presence in supply chains. Industry analysts at EY forecast that more organizations will deploy agentic AI in their supply chains over the next 12-18 months. Agentic AI-driven “Robot” helpers will be available around the clock to guide and optimize.
A Proven Path Forward
Over the past half-decade, AI has fundamentally transformed inventory management, asset optimization, and MRO operations. As we progress through 2025, AI systems will continue to lead supply chain maturation efforts.
While skepticism about AI’s capabilities persists across many industries, manufacturing and industrial sectors have proven AI’s value in understanding and managing complex data networks. This technology enables data harmonization, cost reduction, and resilience enhancement for asset-intensive industries.
AI has established itself as the proven approach for managing supply chain data. Companies that align with AI’s momentum will continue reaping substantial value from these investments. And we’re just getting started.
Paul Noble,
Founder & CSO of Verusen