Service
Energy, Resources & Utilities
Rebalance the energy equation
Highlights
- Utilities are moving beyond rule based automation to agentic AI that orchestrates workflows, synthesises knowledge, and delivers multimodal insights.
- Agentic AI empowers critical utility roles, from field operations and engineering analytics to customer experience, by augmenting human expertise with faster decisions, predictive intelligence, and context aware actions across the value chain.
- Scaling AI across utilities requires more than technology; it demands a clear enterprise strategy, strong governance, data readiness, AI ready operating models, and sustained upskilling to embed autonomy responsibly and at scale.
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Introduction
As utilities worldwide navigate the pressures of modernisation, sustainability, and customer expectations, there is an evolving shift from automation to autonomy. This pivot from traditional digital tools to autonomous, action-oriented intelligence enabled by agentic AI is rapidly reshaping utilities' operations. Let’s find out how agentic AI is reshaping grid operations, customer engagement, and enterprise decision-making through intelligent orchestration, multimodal insights, and enterprise-scale reliability.
Rise of agentic AI
Digital transformation is not new to utilities, as they have effectively streamlined operations, leveraging digital technologies. But the next step moves beyond automation to agents that reason, engage in multi-step planning, and execute tasks across IT and OT environments. Three pillars define the new intelligence paradigm:
- Agentic orchestration: Moving utilities from AI-assisted to AI-orchestrated via AI agents that independently manage load balancing, isolate faults, and enable self-healing grid operations. Utilities thereby progress from passive automation to autonomous execution.
- Multimodal integration: Synthesising structured and unstructured data such as documents, high-fidelity images, voice, video, and real time sensor streams—into cohesive insights. This enables immersive, context-aware interactions, say, visual validations for field repairs.
- Knowledge synthesise: Consolidating enterprise knowledge, historical patterns, and domain expertise to generate smarter decisions. LLMs form the reasoning core in multi-agent systems, where specialised agents collaborate to orchestrate complex workflows.
Together, these pillars create a dynamic AI system capable of driving operational excellence and business transformation.
Role empowerment
Agentic AI is more than a technology upgrade; it's a workforce multiplier. Across utilities, these agents are poised to transform how field teams, engineers, analysts, and customer-facing units work. They empower them with real-time insights and automated decision-making, enabling greater autonomy and faster outcomes while elevating the human work profile beyond mundane tasks.
This holistic approach ensures that AI is not just embedded but becomes the operating backbone of the utility.
Agentic AI is more than a technology upgrade; it's a workforce multiplier. Across utilities, these agents are poised to transform how field teams, engineers, analysts, and customer-facing units work. They empower them with real-time insights and automated decision-making, enabling greater autonomy and faster outcomes while elevating the human work profile beyond mundane tasks.
- Field operators benefit from proactive orchestration via execution-first agents using AR overlays and real time telemetry. These agents can autonomously pre-order parts and update digital twins, allowing humans to focus on complex physical repairs.
- Engineering and analysis teams gain access to predictive intelligence from agent-led feasibility studies and complex power-system simulations. As AI proposes optimised transmission routes derived from cost and environmental impact, utilities benefit from generative grid design.
- Customer experience improves fully autonomous resolution by agents without human intervention. About 80% of interactions can be autonomous with hyper personalised interaction, intent prediction, and consistent service quality.
By enhancing intelligence at every role level, utilities can drastically increase efficiency, responsiveness, and safety.
AI-native utility
Speed, complexity, and expectations are challenging today’s AI-native utilities. Workforce capabilities, oversight, and decision-making must adapt to this new reality.
- Governance model transition
Utilities must evolve from “human in the loop” oversight to “human on the loop,” where AI autonomously executes low risk tasks while humans manage high level supervision, ethical considerations, and exceptions. Decentralising AI expertise across every business unit is important for a federated governance model.
- Strategic pillars for 2026:
- Agent-ready data infrastructure: Using semantic mapping to reduce latent processing cycles by 20 times.
- Grid autonomy: Deploying "Edge" intelligence to manage volatile, bi-directional power flows.
- Perpetual upskilling: Set up AI academies to document internal expertise before retirements and train the management to supervise agent teams.
Examples of empowering roles in utility with action-oriented agents
- Field operations (Proactive orchestration):
- Workers transition from "seeking guidance" to being supported by action-oriented agents using AR overlays and real-time telemetry.
- Agents autonomously pre-order parts and update digital twins, allowing humans to focus on complex physical repairs.
- Engineering and analysis (Generative design):
- Agents conduct independent feasibility studies and run complex power system simulations.
- Enables "Generative Grid Design," where AI proposes optimised transmission routes based on cost and environmental impact.
- Customer experience (Autonomous resolution):
- Shift to fully autonomous resolution, where agents execute complete workflows (e.g., billing adjustments) without human intervention.
- Capable of handling 80% of interactions autonomously.
Scaling agentic AI
Scaling agentic AI is not just about technology or new capabilities. It is about leadership, operating models, and roles. Utilities must look to redesign work, align accountability, and evolve operating models to embed AI in everyday decision-making.
1. Technology considerations
- Data readiness: Ensure availability of trusted, high quality, and integrated data.
- Agentic orchestration: Define enterprise AI platform, agentic framework, architecture standards and guardrails.
2. People and organisational considerations
- Knowledge fabric: Have a knowledge management strategy in place to enable agents with curated knowledge.
- Business redesign: Rethink workflows and reskill workforce for human-AI collaboration.
- Operating model: Define AI, operating, and governance models.
Agentic AI represents a paradigm shift and demands a completely new approach. With the promise of operational resilience, unprecedented performance, and enhanced service experiences, utilities need to keep up with these developments. Utilities that take full advantage of agentic AI's force-multiplying capabilities are well-positioned to become future-ready.
The future: A unified experience for all
The integration of online and offline journeys through the Bridgital ecosystem represents the future of customer and citizen engagement. As more organisations from diverse sectors come together to create seamless, frictionless experiences, the opportunities for innovation and growth are endless. By embracing agentic AI, experience orchestration, and evolving discovery models, we move toward a future where interactions are not just transactions, but holistic engagements that enrich lives and communities.
In this new era, creating integrated, frictionless experiences isn't just a competitive advantage; it's a necessity. The Bridgital ecosystem is the foundation for staying relevant and thriving in a world where AI-driven interfaces, brand discoverability, and seamless journeys are no longer optional, but expected.