Utility operations specialist monitoring AI-enabled grid systems and real-time network data from a modern control room, supporting coordinated decision-making, operational visibility and resilient utility performance.
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AI Agents for Utility Operations

Objective driven AI that can reason, coordinate and prepare actions across utility systems expanding operational capacity while keeping critical decisions under human control.
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Automation handles the predictable. Utility operations rarely stay predictable
WHY THIS OPERATIONAL AUTOMATION CHALLENGE?
People are still the integration layer

Maintenance, incident management and engineering workflows often require employees to gather information, interpret it, move between systems and coordinate the next action manually. Traditional automation works well when workflows are predefined but becomes less effective when tasks change with context or depend on unstructured information.

Key benefits
Move beyond task automation to objective driven execution

AI Agents can take an objective, determine the required steps and coordinate authorized actions across systems reducing manual orchestration while preserving human oversight.

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Turn objectives into coordinated action

Agents can reason through the steps required instead of relying only on rigid, predefined workflows.

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Make systems work together, not around people

Reduce manual information transfer between applications, departments and operational teams.

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Prepare action before experts step in

Gather asset history, procedures and resource information so specialists can focus on validation and judgment.

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Scale expertise without scaling administration

Extend specialized operational knowledge across larger teams without increasing coordination workload at the same rate.

How NTT DATA helps

01
Design agents around real utility functions

Create specialized capabilities for maintenance, operations, engineering, assets, workforce and customer operations rather than generic AI assistance.

Utility operations specialist using AI-enabled grid intelligence to analyze network conditions, coordinate real utility functions and support faster, more informed operational decisions from a modern control environment.
02
Orchestrate knowledge and systems around an objective

Combine reasoning, enterprise knowledge, workflow orchestration and controlled system access to support multi step operational processes.

Utility field engineer using a digital tablet at an electrical substation to access geospatial network information and operational knowledge, connecting field context with utility systems to support faster, better-informed maintenance and operational decisions.
03
Move from information to prepared action

Enable agents to retrieve history, assess previous interventions, identify procedures, verify resources and prepare recommendations for human approval.

Utility operations specialist transforming AI-assisted grid intelligence into prepared operational action by reviewing asset data, network conditions and prioritized work information to support faster, more coordinated utility response.
04
Set autonomy according to operational risk

Apply permissions, APIs, workflow controls and approval gates so higher-risk actions remain subject to stronger safeguards and validation.

Utility field engineer reviewing AI-assisted operational guidance on a tablet inside a critical infrastructure facility, applying human oversight and risk-based controls to determine when automation can act and when expert authorization is required.
05
Keep control visible

We don’t treat autonomy as the goal. We design for traceable actions, approved data sources, cybersecurity controls and escalation to human experts.

Utility field engineers reviewing operational information together at an electrical substation, maintaining visible human oversight, shared accountability and expert validation as AI-assisted decisions move into real-world grid operations.
High-voltage transmission network at sunset reflecting resilient utility infrastructure and the operational impact of AI-assisted decision intelligence across critical grid environments.
Proven impact
Less coordination. Faster execution. More expert capacity where judgment matters.
Results that matter
Where automation ends, objective-driven execution begins

Indicative objectives depend on process complexity, system integration, governance and the degree of automation deployed.

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Reduce administrative and coordination workload

Reduce workload by 20–40% by automating information gathering, system coordination and routine process preparation across teams.

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Accelerate process preparation

Reduce preparation time by 20–40% by bringing together asset history, procedures, operational context and required resources automatically.

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Increase knowledge-intensive task productivity

Reduce preparation time by 20–40% by bringing together asset history, procedures, operational context and required resources automatically.

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Cut manual system interactions

Reduce manual system interactions by 30–50% by enabling authorized agents to retrieve and coordinate information across connected operational and enterprise platforms.

Move utility operations from manual coordination toward controlled, objective-driven execution

Put AI Agents to work across the processes that matter most

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