IBM's Pushpinder Singh Talks Agentic AI in Supply Chains
Supply chains are evolvingāand AI is no longer just a supporting tool.
Enter agentic AI, a new generation of intelligent agents capable of making decisions, learning and adapting in real time.
As global supply chains face unprecedented complexity, businesses investing in AI are seeing greater revenue growth than their peers.
Pushpinder Singh, Global Supply Chain Transformation Leader at IBM Consulting, tells Supply Chain Digital how to unlock its full potential across modern operations.
How does agentic AI differ from traditional AI in supply chain operations?
I break down the AI journey into three phases: traditional AI, generative AI and agentic AI.
Traditional AI—this is the AI we’ve been using for years. It’s great at handling well-defined tasks: think of rules-based forecasting, inventory optimisation, etc.
Then came generative AI, which opened a whole new set of possibilities. Gen AI can generate content, understand language, summarise reports or suggest actions by using LLM's—but only when prompted. It boosts productivity, but it is still reactive to the user’s input.
Now, agentic AI—this is where it gets exciting. Agentic AI combines the power of foundation models with autonomous agents that can perceive, reason, act and learn. These agents don’t just respond—they pursue goals, monitor progress and adapt their actions based on real-time data. For example, agentic AI can autonomously rebalance inventory, identify risks, reach out to suppliers or even negotiate contracts.
While many leaders may think of AI agents and autonomous supply chains as something far off in the future, the reality is it’s already here. In fact, our research shows 61% of CEOs say they’re actively adopting AI agents and preparing to scale them across their operations.
What real-world benefits are organisations seeing from investing in agentic AI?
Early adopters of agentic AI are already exploring how AI agents can better enable their teams – piloting agents to automate many of the high-volume, decision-intensive tasks that typically consume a significant amount of time.
So far, this has led to benefits such as improved inventory turns, faster disruption response, enhanced service levels and reduced manual workload for planning teams.
As agentic AI continues to mature, these benefits will only increase, enabling autonomous decision-making across planning, sourcing, manufacturing and delivery. It will also unlock new levels of optimisation by using real-time data to track, analyse and adjust logistics operations as they occur, enabling faster responses to disruptions and improved efficiency. Eventually, it will allow supply chains to adapt on the fly and even begin to “self-heal,” reducing the need for manual intervention.
Companies investing in this technology now are laying the foundation for intelligent, adaptive and resilient supply chains, and many are already seeing the impact. Recent IBM research shows that organisations with higher AI investment in supply chain operations report 61% higher revenue growth than their peers.
How can agentic AI help supply chains become more agile and resilient?
Agility in supply chain operations hinges on real-time visibility, dynamic decision-making and orchestrated execution. AI agents enable this by sensing disruptions, simulating responses and autonomously executing decisions.
On the resilience front, agentic AI strengthens proactive risk monitoring, exception handling and multi-tier visibility. Unlike traditional AI, which can analyse data to predict and assess risk factors, agentic AI goes a step further by autonomously acting on that data in real-time—responding to issues as they arise with minimal human intervention.
For example, a pharmaceutical company can use agents to reroute APIs during geopolitical disruptions to preserve operational continuity.
With geopolitical risks and global trade tensions ranking as top challenges for supply chain leaders, these capabilities can mean the difference between playing catch up versus staying ahead.
What are the biggest misconceptions about agentic AI in business processes?
Business leaders are excited about the potential of agentic AI, but there is still the common misconception that AI can simply be bolted onto outdated processes without addressing the underlying challenges.
AI agents hold the potential to help move businesses beyond incremental productivity gains, but it’s not a quick fix. To get the meaningful, long-term results they want, organisations need to take a comprehensive approach to re-engineering entire business processes. This includes everything from ensuring employees understand how to work with AI agents responsibly and effectively, to re-evaluating how data is managed across the enterprise and establishing the right AI governance guardrails.
To see the real ROI, supply chain leaders will need to address these challenges head-on. By doing so, they’ll be able to leverage agentic AI in a way that recasts their operations as an engine for growth and differentiation.
What steps should leaders take to successfully implement agentic AI in supply chains?
Successfully implementing agentic AI is not just a technology project—it’s a strategic leadership initiative. It requires rethinking roles, workflows, governance and how humans and machines collaborate in real-time decision-making environments.
Many supply chain leaders are still navigating the complexities of AI adoption. The rise of agentic AI adds new dimensions—autonomy, adaptability and continuous learning—which can feel overwhelming. But with a structured approach, it becomes a powerful opportunity to reshape how supply chains operate.
One of the most important steps is defining how teams will work alongside AI agents. Leaders need to determine which tasks require human judgment and which can be managed autonomously. Just as critical is implementing human oversight frameworks to monitor agent performance, address edge cases and ensure trust and accountability.
Equally essential is aligning the AI strategy with core business objectives. Ask: what are our biggest growth or resilience challenges—and where could AI agents help us accelerate progress?
Leaders don’t need to start with everything. Focus on high-value, low-friction use cases such as demand signal reconciliation, inventory rebalancing, supplier risk monitoring, logistics exception handling and contract drafting agents. These early wins build credibility, unlock business value, and create the momentum needed for broader scale.
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