Nornickel Achieves +3% Productivity and $100M Profit: How AI Agents Manage Production

By 2026, companies are transitioning from experimental projects to scaling AI solutions, achieving measurable economic impact. Nornickel, one of the world's largest metal producers, is already using AI agents to manage production processes. Algorithms predict equipment status 15 minutes in advance and maintain optimal load at 85% (compared to 60–70% manually). This has resulted in a +3% increase in productivity and approximately $30 million from just one algorithm, with the total AI effect for the company reaching $100 million annually.

Today's businesses face staff shortages and rising salaries amid slowing revenue growth. Technology investments are becoming more expensive, forcing companies to balance costs and efficiency. Digitalization is no longer optional: pressure on businesses is only intensifying. Meanwhile, routine tasks that consume budgets can be automated, and productivity can be increased, relieving staff burden and opening up new growth opportunities.

Business Pain: Staff Shortages and Accelerating Pace

Modern business operates under dual pressure. On one hand, personnel costs are rising: salaries are increasing, but revenue growth isn't keeping pace. On the other hand, hiring is becoming more challenging. The supply and demand in the labor market are increasingly mismatched: most vacancies are for blue-collar jobs, while job seekers prefer office positions and flexible work arrangements. Demographic factors only exacerbate the situation: in the coming years, the market will face a reduction in the number of young specialists.

In such conditions, increasing efficiency by expanding staff is no longer feasible. The only sustainable way to compensate for staff shortages and rising costs is to boost labor productivity, including through digitalization and the implementation of artificial intelligence.

From Manual Control to AI Agents

Before the implementation of AI agents, production process management at Nornickel, like in many other industrial giants, relied on the experience and intuition of operators. A human could maintain equipment utilization at 60–70%, but this was the limit. Predicting failures, optimizing flows, and making real-time decisions while managing numerous parameters simultaneously was extremely difficult. A tool was needed that not only collected data but actively participated in management, predicting and correcting processes.

The company turned to AI agents, which can process vast amounts of data, identify hidden patterns, and make decisions beyond human capabilities due to cognitive limitations and speed.

How the AI Agent for Production Was Designed

AI agents were designed as operator assistants capable of real-time operation. The main task was to predict equipment status 15 minutes in advance. To do this, the agent continuously analyzes data from sensors, equipment operating history, external factors, and technological parameters. Based on this analysis, it provides the operator with recommendations to maintain optimal load and prevent failures.

Key functional blocks of the agent:

• Data collection and processing module. Automatically aggregates information from various systems: SCADA, MES, ERP, as well as external sources.
• Predictive module. Uses machine learning algorithms to predict the probability of failures, deviations, and optimal operating modes.
• Decision-making module. Based on predictions, it generates recommendations for the operator to adjust process parameters.
• Operator interface. Visualizes the current state, forecasts, and recommendations in a user-friendly format.

The goal was not to replace humans but to empower them with superpowers, enabling them to make more informed and timely decisions.

Implementation and Scaling

Implementation began with pilot projects in selected production areas where the effect could be quickly assessed and the methodology refined. Gradually, as efficiency was confirmed and algorithms were fine-tuned, best practices were scaled to other facilities. An important aspect was personnel training: operators learned to trust the AI agent's recommendations and use it as a tool, not a threat. This phased approach allowed for minimizing risks and ensuring a smooth integration of new technologies into established production processes.

Results of AI Implementation at Nornickel

AI effectively helps the operator make real-time decisions. By predicting equipment status 15 minutes in advance, the company significantly increased efficiency. These results demonstrate how the transition from experiments to systemic implementation of AI agents transforms entire industries, generating tens of millions of dollars in profit.

How to Implement This in Your Company

If your company has processes where decisions are made based on large volumes of data, and speed and accuracy are critical, an AI agent can deliver measurable results. Here's how to start:

• Identify bottlenecks. Where do employees spend a lot of time on routine analysis, or where are errors frequent due to human factors? These are ideal candidates for AI agents.
• Focus on data. Ensure you have access to high-quality data that can be used to train the AI. Without it, any model will be ineffective.
• Start small. Launch pilot projects in small but representative areas. This will allow you to quickly test hypotheses, refine processes, and get initial results without major risks.
• Engage the team. Explain to employees how AI agents will help them in their work, rather than replace them. Provide training on how to use the new tools.

If this case sounds like what's happening in your company, our manager can help: he'll analyze your business and niche for free and point out where an AI agent would bring a real result in your case. Message the manager