Infosys launches AI agent to streamline energy operations
Infosys has introduced an AI agent built to improve day-to-day operations in the energy sector. The system automates reporting, processes real-time data, and delivers predictive insights with early warnings. For operations teams, that means faster decisions, fewer avoidable errors, and immediate access to critical information.
What's inside the stack
The solution brings together Infosys Topaz, Infosys Cobalt cloud services, Microsoft Copilot Studio, Azure OpenAI in Foundry Models, and ChatGPT4o. It uses conversational AI so frontline teams can ask questions in plain language, get context-aware answers, and trigger actions without digging through dashboards.
What it does for operations teams
- Automates recurring reports and logs, reducing manual effort and cycle times.
- Surfaces predictive insights and early warnings from live operational data.
- Guides planning and scheduling to cut delays, rework, and safety incidents.
- Provides instant retrieval of SOPs, EHS guidance, and asset history during field work.
High-impact starting points
- Maintenance: work order triage, parts readiness checks, and suggested schedules based on condition data.
- Incident response: early alerts on anomaly patterns, with step-by-step playbooks.
- Shift handovers: concise summaries of alarms, asset status, and pending actions.
- Compliance: auto-generated evidence packs and variance explanations for audits.
Metrics that show it's working
- Mean time to detect (MTTD) and mean time to respond (MTTR).
- Unplanned downtime vs. planned maintenance ratio.
- Report preparation time and first-pass accuracy.
- Work order schedule adherence and rework rate.
- Near-miss frequency and audit findings closed on time.
Implementation checklist for operations leaders
- Data readiness: map critical sources (SCADA/OT, historian, CMMS, EAM, EHS, ERP) and latency needs.
- Access and security: define roles, approvals, and audit trails for every action the agent can trigger.
- Knowledge grounding: connect SOPs, site procedures, and manuals so answers reflect your standards.
- Human-in-the-loop: require approvals for high-impact changes and track overrides to improve prompts.
- Pilot scope: pick 1-2 sites, 3-4 clear use cases, and a cross-functional squad (operations, maintenance, safety, IT/OT).
- Change adoption: train crews on the chat interface, mobile access, and exception handling.
Why this matters
Energy operations run on fast decisions and clean data. An AI agent that can read live signals, flag what's about to break, and close the loop on reporting gives teams more room to focus on safety, quality, and throughput.
What Infosys says
"The energy sector faces ongoing challenges in managing complexities of vast volume of operational data while making real-time decisions that ensure safety, efficiency, and peak performance. Our AI Agent solution, powered by Microsoft's AI and Cloud capabilities, Infosys Topaz, and Infosys Cobalt, directly tackles these challenges by transforming raw data into actionable insights through intuitive conversational AI. We see this as a pivotal move toward an AI-first future, where intelligent automation and predictive analytics redefine productivity and operational excellence," said Ashiss Kumar Dash, EVP & Global Head - Services, Utilities, Resources, Energy, and Enterprise Sustainability, Infosys.
Learn more
Explore the underlying tech: Microsoft Copilot Studio and Azure OpenAI Service.
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