Agentic AI at Scale
Agentic AI is moving from pilot projects into core energy operations, increasing automation in safety and mission-critical systems. However, IDC projects that by 2030, fewer than half will have mature agent architecture and lifecycle management in place.
Gaia Gallotti, research director for IDC’s Energy Insights, says the biggest barrier to scaling agentic AI in core energy and utility operations is not the technology itself but the underlying data environment.
She explains that most still operate with fragmented, siloed data and lack the governance models needed to support autonomous systems in mission-critical workflows.
“Production forecasting and bid optimization are where we expect agentic AI to first support these organizations in a meaningful way,” she says. “We are very far away from agentic AI running core grids or making operational decisions.”
WATCH: Artificial intelligence continues to be a top trend in IT for energy, oil and gas companies.
Unified IT/OT Compute and Edge Modernization
Energy and utility operators are accelerating the convergence of IT and OT as they modernize for AI-driven, low-carbon operations. According to research firm Gartner, organizations are standardizing cloud-edge architectures that support real-time decision-making at the edge while using centralized cloud platforms for analytics and scale.
“Combining Internet of Things sensors with edge computing — which is only enabled with the cloud — offers much better real-time decision-making that supports asset life and health overall,” Gallotti says. “That helps companies improve operational efficiency and lower the cost to serve end customers.”
By 2027, according to Gartner, AI-powered asset intelligence will reduce outages by 40%, delivering measurable operational savings.
“You can protect assets before something really devastating happens, rather than dealing with much more extensive damage that costs significantly more to fix,” Gallotti says.
DIVE DEEPER: How to manage IT/OT convergence securely.
From Digital Twins to Simulation Twins
Energy and utility companies are evolving digital twins into more advanced simulation twins that go beyond monitoring assets to actively supporting planning, safety and operations.
These next-generation twins combine real-time operational data, AI models and scenario simulations to test what-if conditions and recommend next-best actions. Gartner describes this progression as a maturity path from static and real-time twins to simulation, immersive and adaptive intelligence.
Gallotti says the long-term value lies in using those simulations to guide operational choices, not just visualize risk.
“That’s how you start driving better operational efficiency and a lower cost,” she says.
