Utilities customer experience
Storm-day performance is the KPI.
Electric, water, and gas utilities must serve customers on the worst day of the year, not the average. We design AI programs that hold up through outages, meet NERC CIP boundaries, and answer PUC reliability metrics.
Illustrative target ranges used for planning and baselining. Actual results depend on current-state maturity, channel mix, data quality, and regulatory constraints.
What problems are utilities organizations facing?
The operational and customer-service problems utilities leaders raise most — and the regulatory context that shapes every decision.
Storm-event surge
Call volume can spike 50x during major outages — legacy IVRs and staffed queues collapse.
Outage status accuracy
Customers demand real-time restoration ETAs; wrong answers erode trust and drive PUC complaints.
Critical-infrastructure boundaries
AI touching OT systems, SCADA data, or grid telemetry falls under NERC CIP scope.
Life-safety escalations
Gas leaks and downed lines must reach dispatch in seconds, not minutes.
Regulated billing complexity
Rate cases, disconnect protections, and assistance programs require nuanced handling.
Aging workforce
Institutional knowledge is retiring — agent-assist and knowledge automation are workforce continuity, not just efficiency.
Regulatory, security, privacy, governance, accessibility, and operational requirements that shape architecture, data handling, and vendor eligibility in utilities.
Where can AI create measurable value in utilities?
KPI-first, vendor-neutral, and built to survive audit — not just the pilot.
Storm-day KPI baseline
Peak concurrent calls, ETA accuracy, life-safety time-to-dispatch — measured before tooling.
OMS-integrated virtual agent
Real-time outage status, restoration ETA, and callback registration with fallback to human dispatch.
CIP-aligned architecture
Clear separation between customer-facing AI and OT/grid systems, with documented boundaries.
Elastic surge capacity
Voice + digital channels that scale to storm-day load without pre-provisioning peak headcount.
What does a responsible AI transformation roadmap look like?
A sequence that establishes outcomes, readiness, and governance before technology selection — then stays engaged through adoption and measurement.
- 01
Phase 1 — Discovery & readiness
Baseline the operational and customer-service metrics leadership already reports on, then assess data, process, security, and organizational readiness against sector requirements.
- 02
Phase 2 — Prioritization & governance design
Rank use cases by measurable value, feasibility, and risk. Define the governance model, human-review points, data handling rules, and audit evidence before any platform is selected.
- 03
Phase 3 — Vendor-neutral selection & implementation
Evaluate suppliers against documented requirements, then oversee integration, testing, and change management with your teams and existing systems of record.
- 04
Phase 4 — Governance, measurement & optimization
Operate with monitoring, controls, and audit evidence in place; measure against the original baseline and tune workflows, models, and adoption over time.
Investor-owned electric utility — 2.1M meters
An IOU faced a PUC investigation after a major storm overwhelmed its contact center with 4-hour wait times and inconsistent restoration ETAs. We designed an OMS-integrated voice AI, stress-tested for 50x surge, and hardened the escalation path for downed-line reports.
“The PUC hearing became a non-event. More importantly, our line workers stopped getting yelled at because customers finally had accurate ETAs.”
— Director Customer Operations, IOU Electric Utility
Composite example based on typical engagements. Details anonymized.
Ready to design for utilities KPIs?
A 45-minute session with a senior advisor. No sales pitch — just your metrics, your constraints, and where AI actually fits.