Digital supply chain enters maturity: from visibility to intelligent execution

Digital supply chains enter maturity: from visibility to intelligent execution

After experiencing pandemic shocks, geopolitical turmoil, and demand fluctuations, global supply chains are accelerating toward a new stage of digitalization, automation, and intelligence. The commercial deployment of technologies such as artificial intelligence (AI), machine learning (ML), the Internet of Things (IoT), and large language models (LLMs) means supply chains are no longer merely pursuing "visibility" but evolving toward "decision-making and fast execution." At the same time, customer demands for delivery speed, personalization, and real-time transparency are providing sustained momentum for this transformation.

Key developments: shifting from digital systems to AI-driven operations

After years of digitalization efforts, companies are beginning to ask, "What's next?" Gartner vice president and analyst Balaji Abbabatulla noted that the industry focus has shifted from "digital supply chain transformation" to "deploying AI at scale." However, most companies' AI applications are still in the early stages, with benefits mainly reflected in throughput improvements or process execution consistency, while greater value such as autonomous decision-making and end-to-end collaboration has yet to be realized.

Abbabatulla stressed that the true exponential gains have yet to arrive, and that upgrading the foundational layer is a prerequisite. "Data, processes, decision-making, and the employees who use technology every day all need to be upgraded in tandem." Although many companies have digitized their processes, the underlying data remains highly fragmented, inconsistent, and even locked in siloed systems, which directly limits the value realization of AI tools.

Supply chain impact: data governance and system collaboration remain bottlenecks

Sandeep Saroha, head of GTM for the services industry at Capgemini, said that most enterprises have digitized core transactions such as orders, inventory, and transportation, and have made substantial progress in system interconnection. Many companies are moving from batch processing to faster data exchange to gain a clearer view of their networks.

However, the challenge lies in turning visibility into consistent execution. "The systems are in place, but can they make the right decisions and execute them consistently?" Saroha asked rhetorically. In practice, issues such as inconsistent master data, differences in processes across sites, and information delays remain widespread. These problems not only weaken system responsiveness but also hinder advanced capabilities like AI from being embedded in daily operations.

At the warehouse level, Howard Turner, director of supply chain systems at St. Onge Company, observed that companies are weighing "full-suite systems" against "best-of-breed combinations." The addition of AI has raised the decision bar, but the usability and integration advantages of a single platform remain significant. Turner believes AI agents will become the new focus in warehouse management; they can monitor operations, learn patterns, and make recommendations—such as slotting, dock scheduling, and labor planning—while human supervisors only need to review and approve execution.

Regional and industry perspectives: North America under pressure, global digitalization uneven## Regional and Industry Perspectives: North America Under Pressure, Global Digitization Uneven

According to a McKinsey survey, nearly 80% of U.S. companies experienced some form of supply chain disruption in 2025, compared to just 33% in 2024. Tariffs, rising costs, geopolitical events, and demand shifts were the main drivers. Despite the pressure, companies have not cut digital investments; about 19% of surveyed enterprises have already deployed AI tools at scale, and another 40% are implementing Advanced Planning and Scheduling (APS) systems.

Globally, Asian markets are digitizing faster, benefiting from e-commerce and advanced manufacturing; European companies focus more on resilience and compliance; emerging markets are still in the stage of building infrastructure and foundational systems. A common trend, however, is that demand for supply chain visibility is extending beyond tier-one suppliers to deeper levels.

Future Outlook: GenAI Moves from Assistance to Execution

Currently, generative AI (GenAI) applications still primarily assist human work, such as summarizing anomalies, extracting information from documents, and optimizing partner communications. Saroha noted, "It's not about making the supply chain drive itself, but about helping people work faster." Next, GenAI will take on a more proactive role—systems will not only provide recommendations but also execute automatically once authorized. But Saroha emphasized that this stage requires building "trust": trust in data, trust in decisions, and trust in control and automation.

Turner, meanwhile, is excited about "adaptive process orchestration"—systems that adjust and guide operations based on real-time conditions, such as automatically reallocating warehouse resources during sudden demand spikes or transportation delays. Such capabilities will gradually unlock the deeper efficiency potential of supply chain digitization.

Conclusion

The digital supply chain has moved past the proof-of-concept stage. ERP, WMS, TMS, and planning platforms have become standard configurations, and APIs and AI are connecting them into a smarter whole. However, the road to the "autonomous supply chain" remains bumpy. Early adopters may pull ahead of observers within the next 12 to 24 months, while data governance, organizational change, and trust-building will determine the ultimate depth of this transformation.