Global conflicts, shifting tariffs, trade disruptions and regulatory uncertainty are forcing companies worldwide to rethink how they manage supply chains.

For many, the challenge is not only moving goods from one place to another but understanding, in real time, how a disruption in one part of the world could ripple through factories, suppliers, inventory, transportation costs and customer demand.

Canada-based Kinaxis is trying to help companies find the right answers faster. Its flagship platform, Maestro, uses predictive AI, advanced scenario modeling and agentic AI to help businesses forecast demand, test possible scenarios and adjust plans when conditions change.

“What customers need is to be very adaptable and very agile. And that’s the way Maestro plays a very critical role,” says Razat Gaurav, Kinaxis’ CEO. With the ongoing Strait of Hormuz conflict, for instance, customers have increased scenario modeling by more than 120% from pre-conflict levels, he notes.

The tool is used by global companies, from automakers and technology giants to energy and shipping leaders, to manage complex supply chains, respond to shortages and model the potential impact of tariffs and other disruptions. For Kinaxis, there’s a broader global shift: supply chains that once ran on stable assumptions now need systems that can absorb constant change. 

Maestro is essentially an orchestration platform for end-to-end supply chain management. It brings together data from across a company, including sales orders, product information, production capacity and inventory, and combines it with external signals such as weather, market shifts and breaking news.

Rather than managing demand planning, inventory, logistics and production in separate systems, companies can evaluate these interconnected functions in one tool, powered by predictive AI and AI agents, helping planners understand how changes in one area affect the rest of the business and respond more quickly to disruptions.

“Part of the innovation we’ve brought…” says Gaurav, “is to really look at the entire supply chain network in one single model.”

The tool, which runs on Azure, Microsoft’s cloud platform, is built to tackle the complexity of supply chains by combining large-scale data processing, advanced modeling and multiple layers of AI, says Andrew Bell, Chief Product Officer at Kinaxis.

Founded in Ottawa in 1984 by software engineers, Kinaxis has grown from a Canadian startup into a global supply chain software company serving hundreds of customers worldwide, including Fortune 500 companies. The company first introduced RapidResponse to improve manufacturing and supply chain planning across operations. In 2024, it launched Maestro, a cloud-native, AI-driven evolution of its platform.

The timing has been significant. The COVID-19 pandemic exposed structural weaknesses in global supply chains. Companies have since redesigned networks to increase flexibility, diversify sourcing and, in some cases, move production closer to end markets. More recently, geopolitical instability and tariff uncertainty have intensified the need for scenario planning. 

Microsoft Azure is a critical part of Maestro’s global cloud infrastructure. Kinaxis uses Azure Kubernetes Service (AKS) and Azure Databricks to run the platform, manage large volumes of supply chain data and support real-time operational views and forecasting workloads. Other Microsoft technologies, including Azure OpenAI, Azure Cosmos DB, Azure AI Content Safety and Foundry IQ, support capabilities such as natural language interactions, structured data storage, governance and search.

Kinaxis says it chose Microsoft technology because it is trusted, secure and able to support the needs of large enterprises. Azure gives Maestro the cloud computing power to operate at scale, while Azure OpenAI helps Kinaxis bring advanced AI into customer systems in a more controlled way.  

A hybrid decision-making force

Bell describes Maestro as a layered system. The foundation is a data fabric that brings together internal and external data to create a near real-time view of the supply chain.

Above that, an intelligence layer applies predictive AI, optimization models and machine learning to help anticipate demand shifts, evaluate constraints and recommend actions.

The final layer is the user interface, where planners and analysts can use dashboards, reports and generative AI agents as their assistants to ask questions, explore scenarios and automate parts of the planning process.

Kinaxis is working toward developing integrations that would allow Maestro agents to work with Microsoft Copilot, the AI assistant for work.

The platform can draw on different AI models depending on the task, from forecasting demand to evaluating supply constraints or generating recommendations. “It’s really the full spectrum of different AI technologies applied throughout the platform and the products,” says Bell.

Kinaxis has also adopted an agentic software development lifecycle using GitHub and GitHub Copilot, Microsoft’s AI-powered coding assistant. AI agents now help manage work across the development process, generating tasks and submitting them as pull requests for human review. This allows engineers to spend less time on routine coding work and more time on architecture, judgment and validation.

One of Maestro’s key capabilities is demand forecasting, explains Chantal Bisson-Krol, Kinaxis’ Senior Vice President for AI Innovation. The platform helps companies anticipate changes in customer demand and adjust plans accordingly.

Kinaxis has enhanced that capability with AI and machine learning, and it uses Azure OpenAI to support recent innovations, including Enterprise Demand Forecasting, a module designed to help organizations refine their forecasts in complex, changing environments.

“The models used for one customer… have nothing to do with the models of another customer,” Bisson-Krol notes.

Demand forecasting can involve thousands of customer-specific models that are retrained regularly as data and operating conditions change. Each implementation is configured to reflect a company’s operations and Maestro supports customer data separation through separate customer environments.

Kinaxis emphasizes responsible AI as part of the platform’s design. AI agents operate within established permissions and safeguards, security controls, identity management, testing and evaluations help support responsible use.

Another emerging capability is agentic AI, which is beginning to change how planners interact with Maestro.

Kinaxis says customers can configure specialized agents to handle parts of a workflow, such as detecting issues, generating response scenarios and coordinating follow-up actions. Multiple focused agents can work together under an orchestrator agent, operating within defined instructions and guardrails to call platform tools, evaluate options and propose recommendations. The agents act as virtual users inside the system, says Bisson-Krol, but human planners remain responsible for final decisions.

A “detector agent,” for instance, focuses only on identifying events and triggering the right tools, she says. A “resolver agent” handles exceptions, evaluates options and generates recommendations.

“You stitch together your detector agent and then your resolver agent, and you (the planner) orchestrate them in the way that you want them to operate,” Bisson-Krol explains.

In the end, Bell points out, Maestro is not just solving singular problems “but we’re actually solving end-to-end workflows that go horizontal across an organization using agentic AI.” According to Kinaxis, the agents, introduced earlier this year, are already being used by a small group of customers.

For the executive, the larger point is that companies are not simply trying to move faster. They are trying to make higher-quality decisions in environments where the number of variables keeps growing. 

“It’s not just about accelerating decisions and automating decisions. It’s about making better decisions by considering more elements,” Bell says.

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All photos by Kinaxis.

Juan Montes writes about how AI and digital innovation are reshaping industries and decision‑making across Latin America and Canada. His reporting spans stories from multinational companies deploying AI agents for executives to public‑school teachers adopting technology in classrooms. Born in Madrid, he worked as a journalist in Spain and Guatemala and was a foreign correspondent for the Wall Street Journal in Mexico, Central America and the Caribbean. You can contact him on LinkedIn. 

Gustavo Lo Valvo is an editorial designer specializing in new storytelling formats. Previously, he served as design director at the Argentine newspaper Clarín, where he led visual architecture and innovation in journalistic storytelling across both print and digital platforms. You can contact him on LinkedIn.