The AI inflection level: How one can flip provide chain volatility into foresight
We’re in an age where volatility is the new normal. Warfare, sanctions, and climate instability can fracture supply chains and upend business plans with no warning.
At the same time, businesses are rushing to adopt AI, which promises to enable faster decisions, better planning, and more resilience against global turmoil.
Regional Vice President EMEA at Kinaxis.
Many organizations have adopted early generative AI tools and systems that sit alongside existing processes rather than being fully integrated into decision-making.
While this has accelerated analysis, it often leaves systems disconnected from key data, constraints and an understanding of wider business impact, ultimately increasing risk, rather than reducing it.
Agentic AI represents the next wave of this evolution, as it can both analyze information and act on it. This significantly ups both the potential value and the stakes involved.
But when AI operates without full context or appropriate guardrails, the consequences can be immediate and costly – from sending stock to the wrong market and overproduction, to costly regulatory breaches.
We have reached an inflection point in AI adoption. Agentic AI will shape the future of supply chain decision-making, but success will depend less on adopting AI itself and more on how deeply and responsibly AI is integrated into core supply chain systems and workflows.
Trap or transformation
Business leaders face a defining choice as they deploy AI to foresee and manage disruption.
On one side is a trap: generative AI tools and copilots that are bolted on to existing processes. While these are easy to deploy and promise quick wins that are impressive in isolation, they sit outside the workflows where real decisions are made.
As a result, they operate on siloed data and produce recommendations that lack context, traceability and clear business accountability.
In complex supply chains, these gaps and miscalculations can quickly cascade across inventory, finance, logistics, and customer service, badly damaging trust and increasing risk rather than reducing it.
The alternative is a more disciplined approach to AI adoption, where intelligence is integrated directly into decision-making workflows. At its most advanced, this takes the form of agentic AI systems that have access to real-time data, constraints and financial context, and can coordinate responses across the enterprise.
When AI, particularly agentic systems, are fully embedded in this way, it enables organizations to not just react to disruption but to anticipate it, align trade-offs, and act with speed and confidence before issues escalate into crises.
Human oversight as a design principle
As organizations move toward more advanced, autonomous forms of AI, best practice depends on preserving clear human oversight and accountability.
Fears are understandably widespread that AI will replace humans, but agentic systems that are well-designed should work in partnership with humans, ensuring transparency and oversight.
Humans remain in control of the core decision processes. They set guardrails and goals for agents, approve high-impact decisions, and retain responsibility for outcomes. This governance is most effective when AI systems operate with real-time data and a single, trusted source of truth.
Within this framework, autonomous agents can focus on the daily operational tasks such as monitoring signals, coordinating across parts of the business, and rapidly creating response options that can be analyzed and audited.
This allows people to focus on the decisions where human insight into ethics, legality and context is essential.
Crucially, when agentic systems are embedded into decision-making workflows, oversight can be applied at the outset, meaning unsafe or non-compliant options are automatically blocked, instead of having to be screened out or undone.
With lawmakers in regions such as the EU placing increasing scrutiny on businesses to ensure AI processes are transparent, transparency into how decisions are reached is crucial.
A human–agent partnership, built on explainability and governance by design, offers organizations a way to scale decision-making while maintaining trust, resilience and compliance.
Agentic AI in action
When agentic AI is embedded properly into decision-making workflows, it can help organizations better manage disruption, providing coordinated data and response options in quickly changing high-risk situations.
Imagine a pharmaceutical supplier facing a sudden shortage of a key material because of a legislative change, just as a batch of critical medicines nears expiry.
Where AI tools operate in isolation, disconnected from core workflows, shared data and governance, teams are forced to react under pressure, often operating with multiple versions of the truth and often incomplete, outdated information. In such situations, delays and misjudgments can have real life-or-death consequences.
But with agentic systems fully embedded into decision-making, the response can look very different. Agents can both detect the supply chain problem and flag the inventory risk simultaneously, drawing on real-time supply, inventory, logistics, and finance data.
They generate coordinated response options for humans to then make the final decisions on, allowing leaders to assess trade-offs and approve the best course of action. Once decisions are made, agents can help execute changes across the business in parallel.
The result is faster, more reliable outcomes, where patients get their medication on time, in accordance with compliance rules, and every decision is made through auditable, transparent processes.
This is humans and AI agents working together to achieve speed, resilience and trust at scale.
Trust is central
Supply chains aren’t at risk because companies lack data, but because many lack the systems to make decisions that are reliable, transparent and properly coordinated at speed.
As global instability increases, it’s not about being the first to adopt AI, but adopting responsibly — embedding intelligence into core decision-making processes, subject to clear rules, and with full human oversight and accountability.
The companies that thrive in the new era of constant disruption will be those best placed not just to respond to disruption, but through embedded systems, to anticipate change with confidence and align decisions across the business without introducing new risk.
In this environment, trust is not a by-product of faster decisions. It is the foundation that makes faster, more autonomous decision-making possible at all.
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