AI-powered assistants are rapidly emerging as the operational backbone of modern supply chains, according to new research from the IBM Institute for Business Value (IBV), conducted in partnership with Oracle and Accelalpha. The study shows that organizations investing more deeply in AI-enabled supply chain operations are already seeing material gains in responsiveness, predictability, and execution speed.
Based on a survey of more than 300 global chief supply chain officers (CSCOs) and chief operating officers (COOs), the study finds that AI assistants are the practical entry point for transforming supply chain decision-making. These assistants aggregate data, surface insights, and accelerate communication across planning, sourcing, manufacturing, and logistics workflows.
From Disconnected Data to Real-Time Insight
Supply chain leaders continue to face volatility driven by geopolitical risk, trade tensions, and persistent disruption. In this environment, the ability to synthesize large volumes of operational and external data has become a critical differentiator. The report finds that AI-powered assistants significantly improve access to insights that were historically locked in disconnected systems.
Of the CSCOs respondents, 70% say generative AI has improved responsiveness and communication with customers, while 55% report that AI reliably validates and aggregates information for employees. Among organizations with higher AI investment, that figure rises to 69%, showing the link between AI maturity and operational clarity.
Surveyed executives also identify operational performance and predictability as the top areas benefiting from generative AI today. By embedding assistants directly into supply chain workflows, organizations are shortening the time between insight and action without removing humans from decision loops.
Driving Measurable Business Value
The study also links AI adoption directly to financial outcomes. Organizations with a larger AI investment in supply chain operations report 61% greater revenue growth compared to that of their peers. This performance premium reflects improvements in efficiency, service levels, and the ability to anticipate and respond to disruptions.
AI assistants are also expanding beyond analytics. Use cases highlighted in the research include trade compliance automation, logistics optimization, and improved coordination across global operations. These assistants help employees focus on exception handling and strategic decisions rather than manual data reconciliation.
Importantly, the study shows AI adoption following a capability continuum. Most organizations begin with process automation and machine learning, then progress to generative AI assistants embedded into workflows. This stage lays the foundation for more advanced autonomous capabilities without requiring a wholesale redesign of operations.
Preparing for the Next Phase
While AI assistants can deliver immediate value, the study makes clear these tools are not the end state. Instead, they act as a bridge between traditional automation and the agentic AI operating models now emerging. By improving data quality, visibility, and trust in AI outputs, assistants can help organizations prepare for more autonomous supply chain execution.
What This Means for ERP Insiders
AI assistants are becoming everyday tools for supply chain teams. IBM’s research shows that AI assistants embedded in supply chain systems are moving beyond pilots and proofs of concept into daily use. These assistants are increasingly providing teams access to operational data, helping to understand what is happening across their supply chain network, and coordinating responses when conditions change.
Value realization depends on workflow integration. Organizations seeing the strongest results are not treating AI as a separate analytics layer. Instead, they are embedding AI assistants directly into ERP and supply chain workflows so that insights lead quickly to decisions and actions, rather than sitting in dashboards waiting to be interpreted.
AI assistants lay the groundwork for autonomy. By consistently aggregating data, validating information, and earning user trust, AI assistants prepare organizations for the next phase of supply chain automation. This foundation makes it easier to introduce systems that can adjust plans and execute responses on their own, with people overseeing outcomes rather than managing every step.
