Tata Steel Has Deployed 300 AI Agents Across Its Business

Tata Steel and Google Cloud announced an expanded partnership on April 22, 2026, to deploy a unified agentic AI system across Tata Steel’s global value chain. Tata Steel says it has deployed more than 300 specialised AI agents in just nine months to improve efficiency and precision across global operations.

This is a strong India-relevant industrial AI story because Tata Steel is using AI inside manufacturing and business operations, not only in chat interfaces. The company is applying agents to maintenance prediction, shop-floor safety, HR helpdesk automation, invoice processing, GST classification, contract analysis, customer complaint handling, and decision support.

The rollout is built around two major internal platforms. The first is Zen AI, Tata Steel’s internal low-code platform. It allows employees who are not data scientists, including software developers and frontline managers, to build, test, and deploy specialised AI agents. Zen AI is built using Google Cloud’s Agent Development Kit and is integrated with BigQuery and Google Cloud Storage.

This matters because industrial companies usually have decades of operational data spread across systems, videos, documents, SOPs, spreadsheets, finance records, and plant-level tools. Zen AI gives Tata Steel a way to turn that data into working agents without making every team wait for a central data science team. It also means smaller teams can build agents for specific problems inside their own functions.

The second platform is Tata Steel Digital Assistant, or TDA. Tata Steel describes it as an internal portal that brings once-siloed information into a single decision-making interface. It can query global public data, internal enterprise systems such as operational APIs, SOPs, and financial records, and proprietary user data such as call recordings, spreadsheets, and PDFs.

The decision-support use case is especially important. Tata Steel says its agents can combine real-time global news and geopolitical sentiment with commodity price data to provide predictive market intelligence. For a steel company, this can support supply-chain planning, procurement decisions, market monitoring, and risk assessment.

The HR helpdesk example gives a clear operating result. Tata Steel says TDA resolves more than 70% of routine employee tickets autonomously. This reduces repetitive internal support work and gives employees faster answers for common HR queries.

The back-office use cases are also practical. Tata Steel is using business-process agents for intelligent invoice processing, GST creditable and non-creditable classification, and contract analysis. These are repetitive, document-heavy workflows where AI can extract information, classify cases, flag issues, and route work to the right team.

On the shop floor, Tata Steel is using AI for safety and operational performance. One specialised agent, Safety EyeQ, analyses live video feeds in high-risk zones to check adherence to Standard Operating Procedures. It can identify hazards such as large equipment moving near hot material or SOP deviations, then trigger real-time alerts for corrective action.

Maintenance prediction is another major industrial use case. Tata Steel’s Asset Sphere agents evaluate equipment health and provide proactive maintenance plans. The purpose is to prevent unplanned downtime, which is one of the most expensive problems in heavy manufacturing.

Customer service is also part of the deployment. Tata Steel says specialised agents automatically analyse complaint artifacts, detect complaint intent and defects from images, and route issues to resolver groups. The company says this has reduced average turnaround time by 50%.

The technical foundation is Google Cloud. Tata Steel says the deployment runs on scalable infrastructure built on Google Cloud Run, allowing the system to handle demand spikes and scale down when idle. The company also has access to more than 200 models on Google Cloud’s AI Agent Platform, allowing different tasks to use different models under lifecycle management and governance.

This is important for manufacturing because industrial AI cannot be treated like a loose chatbot. A plant-level AI system needs access controls, model governance, auditability, lifecycle management, data security, and clear responsibility. If an AI agent supports safety, maintenance, finance, or customer service, the company needs to know what data it used, what it recommended, who reviewed it, and what action followed.

Tata Steel’s scale makes the story more relevant. The Tata Steel group has annual crude steel capacity of 35 million tonnes, operations across five continents, consolidated turnover of around US$26 billion for the financial year ending March 31, 2025, and more than 76,000 employees. Deploying agents across an organisation of this size shows how industrial AI is moving into real operating environments.

For Indian businesses, the main lesson is that AI adoption does not need to start with a public-facing product. It can start with internal operations: employee support, compliance workflows, document review, maintenance alerts, customer complaint routing, market monitoring, and knowledge search. These are areas where companies can measure time saved, response-time reduction, issue resolution, downtime prevention, and process accuracy.

For manufacturing companies, Tata Steel’s deployment gives a practical pattern. Build a shared data foundation, create a governed low-code layer for teams, connect agents to real operational systems, start with measurable workflows, and keep humans in the loop where safety, finance, compliance, or customer impact is involved.

Tata Steel’s 300-agent rollout shows how AI agents are becoming part of industrial execution. The useful point is not the number alone. The useful point is where the agents are being used: equipment health, plant safety, HR tickets, invoices, GST classification, contract review, customer complaints, and internal decision-making.