Distributed manufacturing networks, geopolitical volatility and shifting consumer demand are forcing companies to rethink how they plan, act and adapt. For Kinaxis, the answer to this complexity combines two distinct forms of AI with a modern, unified data foundation.

Andrew Bell, Chief Product Officer at Kinaxis, says that AI is a spectrum of capabilities, each with a distinct role.“ When we talk about AI, we should not think of it as just one thing,” he explains.

Two types of AI, one unified purpose

Kinaxis distinguishes between predictive and prescriptive AI, encompassing machine learning, mathematical optimisation and deep modelling, and agentic AI, which is changing how decisions are made. Predictive AI processes huge volumes of internal and external data to generate the most optimal supply chain plan.

Agentic AI drives three distinct outcomes:

• Productivity through autonomous decision-making

• Democratisation of access for nontechnical business leaders

• Composability of end-to-end workflows that were previously created by human effort

Together, these capabilities allow supply chain teams to move from generating insights to taking real-time action.

The data problem for AI

Before AI can deliver on these promises, it needs a strong data foundation.

Andrew explains:“ Planners spend the vast majority of their time pulling data together, not making decisions.”

When that data is contradictory, latent or incomplete, the resulting decisions are flawed regardless of the intelligence applied to them. Physical supply chains are also becoming more complex.

Nearshoring, offshoring and hybrid transportation strategies mean more data points, more variables and a greater need for real-time visibility.

The Kinaxis data fabric, augmented by Databricks, supports the Maestro platform, unifying internal enterprise data with outside-in signals covering demand forecasting, geopolitical risk, transport availability and material supply.