Essential industries like manufacturing, distribution, and building supplies have long relied on technology to navigate supply chain disruptions, rising costs, and labour shortages.
In recent years, cloud computing and Internet of Things (IoT) have become vital tools for streamlining operations and building resilience in the face of supply chain uncertainty.
Now, with the development and advancement of the possibilities of AI, we’re seeing an even bigger impact of technology on supply chains. Traditional AI has proven its value by automating processes, analysing data and providing useful insights — helping businesses streamline operations and reduce inefficiencies.
And with the rise of agentic AI, businesses can go a step further — and move beyond reactive crisis management towards AI driven predictive intelligence. The focus is no longer just on adapting to supply chain issues when they arise, but on the ability to anticipate and prevent disruptions before they occur.
The rise of predictive intelligence
Predictive intelligence relies on advanced AI capabilities, such as machine learning (ML) and agentic AI, to analyse vast amounts of data in real time. These technologies go beyond traditional AI systems by enabling more adaptive and autonomous decision making.
One of the most significant advantages of predictive intelligence is its ability to identify potential disruptions before they happen. By looking at historical data, market trends and external factors, AI can pick up on risks ahead of time like supplier delays, inventory shortages or demand fluctuations. This allows businesses to take preemptive action — whether that means changing production schedules, finding new suppliers, or reallocating their resources to prevent bottlenecks.
The role of agentic AI in supply chain optimisation
Unlike traditional AI, agentic AI can act autonomously within defined parameters, executing complex tasks without requiring constant human input.
One example of this in action is the automation of supplier communications and decision making, where AI can handle tasks such as sending requests for quotes (RFQs), parsing responses to compare pricing and delivery times, and helping to select the best option — all without manual intervention. This not only streamlines procurement but also minimises delays – optimising the supply chain.
Agentic AI is particularly effective in specialised domains like manufacturing and logistics, where efficiency and precision are key. By operating within defined boundaries — guided by ethical standards, policy constraints, and human oversight, it means that businesses can optimise operations without sacrificing control.
A practical approach to AI adoption
For businesses considering agentic AI, taking a strategic and measured approach is essential. This starts with looking at existing data structures to make sure they align with the capabilities of an AI system. Then, working with trusted digital transformation experts, such as enterprise resource planning (ERP) specialists, can help streamline AI powered integrations while dealing with concerns around cybersecurity and adaptability.
Working with such suppliers means software solutions deliver context based intelligence through an ecosystem of industry focused platforms, cloud technologies, and people centric AI capabilities – cognitive ERP. Gone are the days of transactional ERP and ahead lies a world of automated and increasingly cognitive business solutions to build a truly connected, efficient and resilient enterprise.
Research shows that nearly two thirds (63%) of high growth companies have embedded AI into their ERP and supply chain management systems, while 58% of organisations have already integrated generative AI into their digital supply chain operations. Don’t get left behind.
The future of AI in supply chains
As businesses continue to embrace AI, the focus will shift from reactive resilience to predictive intelligence — not just withstanding disruptions, but anticipating and preventing them.
Using agentic AI and predictive analytics, companies can transform their supply chains into agile, proactive ecosystems that drive long term growth and innovation.
The path toward predictive intelligence requires collaboration, investment, and a commitment to continuous improvement. But for those willing to take the leap, the rewards are game changing — a supply chain that is not just resilient, but intelligent, cognitive, agile, and future proof.
- AI in Supply Chain
