AI Readiness Checklist for Energy & Utilities

AMI/smart meter data is collected at granular intervals and stored for demand analytics.

SCADA and grid sensor data streams into a time-series database for real-time monitoring.

All grid assets have digital records with specifications, maintenance history, and condition data.

Hyper-local weather data feeds into generation forecasting and demand prediction models.

Critical infrastructure data is managed in compliance with NERC CIP cybersecurity standards.

Solar, wind, and other renewable generation data is captured for intermittency modeling.

Historical outage, fault, and event data is structured for predictive reliability models.

Granular emissions data per generation source and grid segment supports carbon analytics.

Cloud infrastructure meeting energy & utilities security and compliance requirements is operational.

A secure API gateway manages access to AI services with authentication and rate limiting.

Infrastructure for deploying and serving ML models is available with appropriate latency.

Continuous integration and deployment pipelines support AI model versioning and rollback.

Sufficient compute resources (GPU/TPU) are available for model training and inference.

A centralized model registry tracks model versions, metadata, and deployment history.

Model performance monitoring tracks accuracy, drift, latency, and resource usage.

Data encryption in transit and at rest meets NERC CIP (Critical Infrastructure Protection) requirements.

Domain experts who understand AI capabilities are identified to champion adoption and validate outputs.

Data scientists with energy & utilities domain expertise are on staff or available.

Staff across the organization receive training on AI tools, limitations, and responsible use.

AI projects include domain experts, data scientists, engineers, and compliance.

MLOps engineers manage model deployment, monitoring, and lifecycle in production.

Product managers translate business needs into AI requirements and manage AI roadmaps.

Teams can evaluate and manage AI vendors for energy & utilities applications.

The organization invests in AI education through conferences, certifications, and knowledge sharing.

A formal AI policy defines acceptable use, risk levels, and approval workflows aligned with NERC CIP (Critical Infrastructure Protection).

An ethics framework addresses fairness, transparency, and accountability for energy & utilities AI.

AI models undergo structured validation testing before deployment to production.

Regular bias audits evaluate AI models across relevant demographic and operational dimensions.

An incident response plan addresses AI model failures, biased outputs, and data issues.

Structured change management guides organizational transitions from AI implementation.

Vendor AI solutions undergo proper evaluation for quality, compliance, and data handling.

Post-deployment monitoring tracks model performance, drift, and business impact continuously.

High-impact AI use cases for energy & utilities are documented with expected outcomes.

An ROI model quantifies AI impact including System Average Interruption Duration Index (SAIDI) and System Average Interruption Frequency Index (SAIFI).

C-suite executives actively champion AI initiatives with dedicated budget and resources.

A 2-3 year AI roadmap aligns with the organization's energy & utilities strategic plan.

AI maturity is benchmarked against energy & utilities industry peers and leaders.

AI initiatives directly map to improving key stakeholder metrics and satisfaction.

Organization embraces AI experimentation with acceptance of measured failures.

Strategic partnerships with AI vendors, cloud providers, and domain experts are established.