How Eversource Energy Can Transform Utility Operations and Clean Energy Transition with Agentic AI

Agentic AI for utilities is quickly moving from an emerging concept to a practical way to improve reliability, speed up interconnection, and reduce the operational drag created by manual, cross-system work. For a utility like Eversource, the opportunity is especially clear: electrification is driving load growth, distributed energy resources are scaling fast, storms are intensifying, and experienced workforce capacity is tight.

Against that backdrop, the winning strategy isn’t deploying another chatbot. It’s deploying agentic AI for utilities to take ownership of repeatable workflows across systems like OMS, DMS/ADMS, GIS, WFM, and CRM while keeping humans firmly in control of critical decisions. When agents can plan work, take approved actions, and verify outcomes, teams spend less time chasing information and more time improving grid performance.

What “Agentic AI” Means for Utilities (In Plain English)

Definition: Agentic AI vs. Chatbots vs. Traditional Automation

Agentic AI for utilities refers to AI systems that can plan multi-step tasks, take actions in connected tools, and verify results with guardrails and approvals. Instead of only answering questions, an agent can execute workflows like opening a ticket, routing a work order, drafting regulator-ready narratives, or coordinating handoffs across departments.

Here’s the simplest way to distinguish the three:

• Chatbots answer questions and summarize information.

• Traditional automation follows fixed rules in predefined paths.

• Agentic AI plans + acts + verifies, adapting to context while respecting constraints and approval gates.

In utility environments, “actions” can be practical and auditable, such as:

• Creating an outage incident record from incoming signals

• Assigning a job to a crew based on skills and location

• Triggering a load forecast refresh for a planning team

• Drafting a compliance packet that pulls evidence from approved systems

Why Now: The Utility Pressure Cooker

Utilities are being squeezed from multiple directions at once.

• First, load is changing fast. EV adoption, building electrification, and new commercial demand create planning uncertainty and operational strain, especially at the distribution edge.

• Second, DER growth is accelerating. Solar, storage, and managed charging introduce variability and interconnection complexity that many processes were never designed to handle at scale.

• Third, climate-driven storms are testing restoration playbooks more often. Extreme weather doesn’t just increase outages; it increases the complexity and pace of decision-making.

• Finally, there’s a workforce reality: retirements and institutional knowledge loss make it harder to execute consistently, especially during peak events. Agentic AI for utilities is emerging as a way to capture “how work gets done” and make it repeatable, measurable, and safer.

Eversource’s Highest-Impact Opportunities for Agentic AI (Quick Map)

A Simple “Value x Feasibility” Framework

A practical way to prioritize agentic AI for utilities is to score each workflow on two axes.

Value levers:

• Reliability improvements and resilience

• Cost-to-serve reduction through utility operations automation

• Safety and procedural consistency

• Speed of execution (cycle time, handoffs, restoration)

• Customer experience in high-stakes moments

Feasibility factors:

• Data readiness (signal quality, access, ownership)

• Process standardization across territories

• Integration complexity (OMS/DMS/WFM/GIS/CRM)

• Regulatory and safety risk

• Change management effort and labor alignment

In practice, the best early wins are high-volume, evidence-heavy workflows where humans currently spend time collecting, reconciling, and re-entering information.

The 7 Use Cases This Article Will Dive Into

Below are top agentic AI for utilities use cases that are especially relevant for Eversource:

1. Outage management and storm restoration (OMS + field ops)

2. Predictive maintenance and asset health

3. Field work management, safety, and knowledge capture

4. DER and EV interconnection workflow automation and grid planning support

5. Customer operations: billing, contact center, proactive communications

6. Regulatory reporting, compliance, and audit readiness

7. Engineering knowledge and documentation workflows

Each can be designed with human-in-the-loop controls so agents accelerate work without taking unsafe or unauthorized actions.

Use Case #1 — Outage Management and Storm Restoration (OMS + Field Ops)

Pain Points Agentic AI Can Reduce

Outage response is a classic example of “high-stakes, multi-system coordination.” During storm events, utilities face:

• Signal overload from AMI last gasp, SCADA alarms, OMS incidents, and customer calls

• Fragmented context spread across OMS, DMS/ADMS, GIS, and crew systems

• Manual triage and re-triage as conditions change

• Restoration estimates that require constant updates and consistent narratives

• Customer communication bottlenecks and inbound call surges

Even when each team is strong, the system-level coordination can break down under stress.

What an “Outage Response Agent” Could Do

An outage response agent built as agentic AI for utilities can help operators and storm teams close the loop from detection to restoration documentation.

Typical workflow capabilities include:

• Ingesting signals from AMI, weather feeds, SCADA, OMS, and customer reports

• Grouping incidents that likely share a common upstream cause

• Recommending probable fault location using GIS topology and recent events

• Suggesting switching and isolation options as an advisory, not an autonomous action

• Creating or updating OMS tickets and routing work orders to WFM

• Drafting customer updates and ETR narratives with clear confidence flags

• Producing post-event summaries for internal review and regulator-facing documentation

The big shift is that the agent doesn’t just surface information. It coordinates the workflow and verifies steps were completed, escalating when it detects conflicts or missing data.

KPIs to Track

To evaluate agentic AI for utilities in outage operations, track metrics that connect to reliability and execution speed:

• Time to detect and confirm an outage cause candidate

• Time to assign a crew and create a complete work package

• Time to restore (overall and by outage class)

• Reduction in manual ticket reclassification

• Reduced average handle time in outage-related customer contacts

• Improvements in SAIDI/SAIFI where applicable and appropriately measured

Implementation Notes (Practical)

Outage is a workflow where guardrails matter.

Integrations commonly required:

• OMS

• ADMS/DMS

• WFM

• GIS

• AMI event streams

• CRM or customer messaging tooling

Operational guardrails that keep this safe:

• Role-based approvals for switching or protection-related actions

• A strict “advisory only” mode for any operational switching recommendations unless a human explicitly approves

• Full audit logs: what data was used, what was recommended, what was approved, what was executed

• Post-event replay capability for improvement and regulator confidence

Use Case #2 — Predictive Maintenance and Asset Health (Substations, Feeders, Transformers)

Where Agentic AI Goes Beyond Predictive Models

Many utilities already run predictive maintenance models. The gap is what happens next.

Agentic AI for utilities goes beyond “risk scoring” by owning the workflow around maintenance decisions:

• Compiling the evidence behind a risk score (events, inspection notes, loading, environmental factors)

• Proposing maintenance actions aligned to standards and operating procedures

• Checking constraints like crew availability, access requirements, and parts

• Scheduling work and generating job packets

• Verifying completion and updating the asset record so the system learns over time

This is the difference between analytics and operational closure.

Data Inputs to Prioritize

Asset-health agents improve quickly when fed consistent, high-signal inputs such as:

• SCADA and condition monitoring

• Infrared inspection results and imagery summaries

• Preventive maintenance records and corrective work history

• AMI voltage events and service quality issues

• Customer trouble calls and repeat outage clusters

• Weather exposure and vegetation risk overlays

Even partial coverage can produce value if the agent is explicit about confidence and gaps.

Outcomes and KPIs

For predictive maintenance for utilities, the most defensible measures include:

• Reduction in unplanned outages tied to asset failure

• Reduced truck rolls through better prioritization

• Improved maintenance backlog burn-down rate

• Better capex/opex allocation by focusing field time on the highest-risk segments

• Shorter time from detection of a risk signal to scheduled corrective action

Use Case #3 — Field Work Management, Safety, and Knowledge Capture

“Crew Copilot” for Work Packages

Field teams often lose time assembling job context from multiple repositories. A crew copilot, designed as agentic AI for utilities, can automatically assemble a complete, consistent job packet:

• Permits, prints, and one-lines

• Switching orders and relevant approvals

• Prior work history on the same asset or circuit

• Known hazards, access constraints, and environmental notes

• Lessons learned from similar jobs and near-miss summaries

This is utility operations automation in its most practical form: fewer callbacks, fewer missing documents, fewer avoidable delays.

Real-Time Assistance in the Field

Once crews are on site, an agent can support execution without trying to replace trained judgment:

• Voice-first Q&A on procedures using only approved documentation sources

• Quick retrieval of equipment specs, standards, and step sequences

• Parts availability checks and substitution guidance based on inventory policies

• Optional photo-based damage classification if policies and safety rules allow it

In storm conditions, the ability to retrieve the right procedure fast can matter as much as any prediction model.

Safety and Labor Considerations

Safety is where agentic AI for utilities must be most disciplined.

Core principles:

• Advisory vs. directive must be clear in every interaction

• Procedures should be grounded in approved documents, not free-form generation

• Union and workforce engagement should happen early, with the message that agents reduce administrative load, not skilled roles

• Offline-ready workflows should be considered for storm restoration scenarios where connectivity is limited

Use Case #4 — DER / Solar / Storage / EV Interconnection at Scale

The Interconnection Bottleneck

Interconnection is one of the most visible friction points in the clean energy transition utilities are leading. The bottlenecks are familiar:

• Incomplete applications and document back-and-forth

• Manual engineering reviews and inconsistent triage

• Queue management challenges and limited transparency

• Coordination across engineering, protection, metering, and customer/installer communications

When interconnection cycle time grows, everyone feels it: customers, installers, regulators, and internal teams.

What an “Interconnection Agent” Could Do

An interconnection agent built with agentic AI for utilities can reduce rework and cycle time by owning the workflow end-to-end.

Common capabilities:

• Triage applications for completeness and flag missing requirements immediately

• Classify the request type and route to the correct review path

• Run pre-screen rules and initiate hosting capacity checks where applicable

• Draft clear communications to installers with specific next steps

• Coordinate internal approvals and capture decisions in a consistent format

• Track SLA timers, queue position logic, and escalation rules

A practical 8-step interconnection workflow an agent can support:

1. Ingest application and attachments

2. Validate completeness and identify missing items

3. Classify program type and review track

4. Run pre-screen rules and hosting capacity checks

5. Route to engineering/protection/metering review queues

6. Draft results and required customer actions

7. Track customer responses and schedule inspections/tests

8. Confirm PTO requirements are met and generate final documentation draft for approval

Grid Planning and DER Forecasting Agents

Interconnection doesn’t exist in isolation. Agentic AI for utilities can also support planners by:

• Continuously updating load forecasts with EV adoption and electrification assumptions

• Monitoring DER pipeline trends and scenario shifts

• Proposing targeted upgrade options instead of blanket reinforcement strategies

• Producing consistent planning narratives that tie model outputs to decisions

Metrics That Matter

To evaluate DER orchestration and interconnection workflow automation improvements, focus on:

• Cycle time to PTO

• Queue backlog size and aging

• Rework rate due to incomplete submissions

• Engineering touch time per application

• Installer/customer satisfaction indicators and complaint volume

Use Case #5 — Customer Operations: Billing, Contact Center, and Proactive Communications

Where Utilities Lose Trust (and Time)

Customer operations are often where operational complexity becomes public. High-bill complaints, billing corrections, payment arrangements, and outage surges can overwhelm teams.

In those moments, speed matters. Consistency matters. And mistakes can become expensive, both financially and reputationally.

Agentic AI Workflows (Not Just Chat)

Customer service automation utilities can benefit most when the agent resolves cases, not just answers questions.

A case resolution agent can:

• Gather facts from CRM, billing, AMI summaries, and outage records

• Identify likely issue categories and required resolution paths

• Draft a proposed resolution for review (bill correction, explanation, escalation)

• Generate a customer-ready response with clear reasoning

• Log outcomes, update case tags, and trigger follow-up tasks

A proactive messaging agent can:

• Segment customers by outage status, medical need flags, or priority tiers (where policy allows)

• Generate consistent updates based on OMS status and restoration narratives

• Reduce inbound contact volume by preempting confusion and misinformation

Governance and Compliance

Customer workflows require strict protections:

• No unverified statements: responses must be grounded in account and system-of-record data

• PII controls including redaction and access policies

• Clear retention and audit trails for customer communications

• Guardrails that prevent the agent from taking credit-related actions without approval

Use Case #6 — Regulatory Reporting, Compliance, and Audit Readiness

Why This Is a Prime “Agent” Use Case

Compliance work is repetitive, evidence-heavy, and high stakes. Reporting often requires pulling information from multiple systems, reconciling differences, and producing consistent narratives under time pressure.

Agentic AI for utilities fits well here because the goal is not creativity. It’s completeness, traceability, and speed.

What a “Compliance Agent” Can Do

A compliance agent can:

• Pull data from approved sources and reconcile discrepancies

• Build evidence packages tied to specific controls and obligations

• Draft narratives that match required filing formats and terminology

• Maintain a control library and highlight missing evidence early

• Trigger alerts when metrics drift or when required artifacts are not updated

A practical 7-step compliance workflow an agent can support:

1. Identify upcoming reporting obligations and deadlines

2. Pull required datasets from systems of record

3. Reconcile totals and flag mismatches

4. Compile required evidence and supporting documentation

5. Draft the narrative sections and change summaries

6. Route for internal review and approval

7. Produce an audit-ready package with immutable logs

Guardrails

To make regulatory compliance automation credible:

• Human approval should be mandatory before any filing submission

• Immutable logs should capture data sources, transformations, and edits

• Explainability should be available for key metrics and variances

• Separation of duties should be enforced through role-based access

Architecture: How Eversource Could Deploy Agentic AI Safely

Reference Architecture (High-Level)

A safe approach to agentic AI for utilities typically includes four layers:

Data layer:

• Warehouse or lakehouse for analytics-ready data

• Streaming inputs for events like AMI last gasp or SCADA alarms

• A semantic layer that standardizes definitions (assets, circuits, incidents, customers)

Tool layer:

• Secure connectors to OMS, DMS/ADMS, WFM, GIS, CRM, document repositories

• Read-first integration patterns before controlled write actions

Agent layer:

• Orchestration that manages multi-step workflows

• Bounded memory and strict context rules

• Evaluation checks that detect uncertainty, missing data, or policy violations

Observability layer:

• Logs and traces per workflow run

• Cost controls and throttling during storm surges

• Drift monitoring and continuous performance evaluation

Human-in-the-Loop by Design

Agentic AI for utilities should be designed with explicit approval gates, especially for:

• Switching actions or protection-related recommendations

• Customer credits, billing adjustments, or account changes

• Regulatory narratives and any external filings

Other practical controls:

• Confidence thresholds with automatic escalation paths

• Safe fallbacks that revert to “draft mode” when uncertainty is high

• Clear ownership: every automated action should map to an accountable role

Cybersecurity, Privacy, and Model Risk

Utilities can’t afford an AI layer that weakens security posture. Cybersecurity for AI in utilities should include:

• Zero trust principles and least-privilege access

• Secrets management for system credentials

• Strong controls against prompt injection and data exfiltration attempts

• Vendor risk management, incident response alignment, and testing

Just as importantly, the system should enforce “only use approved sources” for operational decisions and customer-facing statements.

Implementation Roadmap (90 Days → 12 Months)

Phase 1 (0–90 Days): Pilot with Measurable ROI

The fastest path to real value is one workflow with strong metrics and clear guardrails. For agentic AI for utilities, two strong candidates are:

• Outage triage and restoration communications support

• Interconnection triage and installer communications

Steps that keep a pilot grounded:

1. Define KPIs and establish baselines (cycle time, touch time, rework rate)

2. Map the workflow and decision rights before building

3. Start with read-only access; add tightly controlled write actions later

4. Define approval gates and audit logging from day one

5. Run parallel operations initially so teams can compare outputs safely

Phase 2 (3–6 Months): Expand and Integrate

Once the pilot is stable:

• Add additional systems (GIS, WFM, CRM) to reduce handoffs

• Expand into adjacent steps of the workflow so the agent can verify completion

• Implement an evaluation harness for accuracy, safety, latency, and cost

• Introduce role-specific interfaces for operators, engineers, and compliance staff

Phase 3 (6–12 Months): Scale Across Regions and Programs

Scaling agentic AI for utilities is as much operational as technical:

• Standardize playbooks across service territories

• Create an internal “agent ops” team to manage workflows, monitoring, and improvements

• Establish a governance board that includes IT, OT, security, operations, and regulatory teams

• Build continuous feedback loops so the system gets better without introducing risk

What Competitors Often Miss

Agents Need Process Redesign, Not Just Tech

A common mistake is trying to layer agents on top of messy, inconsistent processes.

The most successful deployments:

• Map the workflow first

• Identify what can be automated safely vs. what must remain advisory

• Clarify decision rights so approvals are unambiguous

• Standardize definitions and handoffs across teams

Agentic AI for utilities performs best when the “happy path” is clear and exceptions are handled intentionally.

Measuring Real Impact

It’s tempting to measure adoption by message volume or time spent chatting. Utilities should measure impact where it matters:

• Time-to-restore during outages

• Interconnection cycle time and backlog reduction

• Error rates in reporting and evidence collection

• Reduced rework and reduced manual reconciliation effort

Change Management

Field teams, control room operators, and compliance leaders will adopt what they trust.

Trust is built by:

• Transparent guardrails

• Consistent performance under pressure

• Fast feedback loops and visible improvements

• Clear positioning that agents remove administrative burden, not accountability

Conclusion: A Practical Path for Eversource’s Clean Transition

Agentic AI for utilities is most valuable when it closes the loop on real workflows: plan, act, and verify across the systems utilities already rely on. For Eversource, the most realistic near-term wins are the ones that combine high volume, clear process steps, and measurable outcomes.

The best starting points are:

• Outage triage and customer communications that reduce restoration friction

• Interconnection triage and queue transparency that accelerates clean energy

• Compliance evidence gathering that improves audit readiness and reduces manual burden

If the goal is to move from promising pilots to durable operational change, the next step is straightforward: run a 90-day pilot focused on one KPI-heavy workflow, with approvals and audit logging designed in from the beginning.

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