10-Point AI Readiness Checklist

Are your critical operational data sources digitized and accessible?

Consider:

• SCADA systems and real-time grid monitoring data
• Smart meter data and consumption patterns
• Asset maintenance records and equipment telemetry
• Weather and renewable generation forecasting data
• Network topology and capacity information

Your Rating: ☐ Strong ✓ | ☐ Partial ~ | ☐ Gap ✗

Why it matters: AI-powered grid optimization, demand forecasting, and predictive maintenance require high-quality real-time data. Legacy systems with siloed data severely limit AI capabilities.

Quick win: Audit data quality from one substation or region to identify gaps in sensor coverage, data completeness, and integration readiness.

Do you have governance frameworks for AI in critical infrastructure?

Consider:

• Ofgem compliance and reporting requirements
• UK AI Act obligations for high-risk AI systems
• GDPR for customer and employee data
• Grid Code compliance and safety requirements
• Security of Network Infrastructure regulations

Your Rating: ☐ Strong ✓ | ☐ Partial ~ | ☐ Gap ✗

Why it matters: Energy is critical national infrastructure. Using AI without proper regulatory consideration creates compliance risks, safety hazards, and potential grid instability.

Quick win: Map regulatory requirements to your planned AI use cases and flag high-risk scenarios for legal review before pilot deployment.

Do you have the technical foundation to deploy AI at scale?

Consider:

• Cloud infrastructure with appropriate security for energy data
• Edge computing capabilities for real-time decision-making
• API integration with legacy SCADA and OT systems
• Network connectivity across distributed assets
• Data lake or warehouse for historical analytics

Your Rating: ☐ Strong ✓ | ☐ Partial ~ | ☐ Gap ✗

Why it matters: Energy AI often requires real-time processing at the edge (substations, wind farms) combined with cloud-based analytics. Poor integration means limited AI deployment scope.

Quick win: Assess API availability and data accessibility from your top 3 critical systems (SCADA, DMS, billing).

Does your team understand AI capabilities, limitations, and risks in energy contexts?

Consider:

• Executive understanding of AI opportunities in energy operations
• Control room operators’ ability to interpret AI recommendations
• Engineering team capability to validate AI predictions
• IT/OT convergence skills and cybersecurity awareness
• Training programs for safe human-in-the-loop AI operation

Your Rating: ☐ Strong ✓ | ☐ Partial ~ | ☐ Gap ✗

Why it matters: Grid operators must understand when to trust AI recommendations and when to intervene. Blind reliance on AI in critical infrastructure creates safety and reliability risks.

Quick win: Run a workshop for control room staff on AI-assisted grid management—using real scenarios to build understanding and confidence.

Have you identified which AI use cases deliver the highest value for your operations?

Consider:

• Current operational pain points (unplanned outages, demand spikes, renewable intermittency)
• Strategic objectives (Net Zero, grid modernization, cost reduction)
• Regulatory drivers (Ofgem incentives, emissions reporting)
• Quick wins vs. transformational long-term projects
• Safety-critical vs. efficiency-focused applications

Your Rating: ☐ Strong ✓ | ☐ Partial ~ | ☐ Gap ✗

Why it matters: Not all AI use cases are equal in energy. Predictive maintenance on critical transformers has different risk/value profiles than demand forecasting. Start with high-value, lower-risk applications.

Quick win: List your top 3 operational challenges and research which AI solutions specifically address them in energy contexts.

Can you assess energy AI vendors objectively without lock-in?

Consider:

• Understanding of energy AI vendor landscape (specialist vs. general)
• Criteria for evaluating vendor claims and case studies (especially in energy)
• Integration requirements with legacy SCADA/DMS systems
• Data ownership, security, and exit clauses
• Track record in critical infrastructure deployments

Your Rating: ☐ Strong ✓ | ☐ Partial ~ | ☐ Gap ✗

Why it matters: Many “AI for energy” vendors overpromise on grid optimization or predictive maintenance. Energy-specific expertise and proven critical infrastructure deployments are essential.

Quick win: Create a vendor evaluation scorecard that includes energy-specific requirements (SCADA integration, real-time performance, grid code compliance).

Have you established processes to ensure AI doesn’t compromise grid safety or reliability?

Consider:

• Testing and validation in non-critical environments first
• Human-in-the-loop override mechanisms for critical decisions
• Fallback procedures if AI systems fail during peak demand
• Cybersecurity assessment for AI systems with grid access
• Impact analysis for AI-driven load balancing or switching decisions

Your Rating: ☐ Strong ✓ | ☐ Partial ~ | ☐ Gap ✗

Why it matters: AI failures in energy can cause blackouts, safety incidents, or grid instability. Testing and failsafes are non-negotiable in critical infrastructure.

Quick win: Define acceptance criteria for AI accuracy in grid operations (e.g., 99.5% uptime, <0.1% false positive rate for fault detection).

Do you have a plan to drive adoption of AI tools across operations teams?

Consider:

• Executive sponsorship for AI initiatives in operations
• Communication strategy for control room staff and field engineers
• Pilot project approach starting with non-critical assets
• Feedback loops to improve AI systems based on operator input
• Incentives aligned with AI-assisted decision-making

Your Rating: ☐ Strong ✓ | ☐ Partial ~ | ☐ Gap ✗

Why it matters: Control room operators and field engineers have deep domain expertise. AI adoption requires demonstrating value and building trust—not mandating blind compliance.

Quick win: Identify an operational champion (control room manager, senior engineer) who will advocate for AI and run your first pilot.

Have you allocated realistic budget for AI adoption in energy operations?

Consider:

• Initial capital investment (software, edge hardware, integration)
• Ongoing operational costs (subscriptions, cloud compute, support)
• Internal resource time (project management, training, validation)
• External expertise (consultants, integration partners, energy AI specialists)
• Grid modernization costs (sensors, IoT, connectivity upgrades)

Your Rating: ☐ Strong ✓ | ☐ Partial ~ | ☐ Gap ✗

Why it matters: Energy AI often requires infrastructure upgrades (sensors, edge compute, connectivity) beyond software licensing. Underfunding leads to failed pilots.

Quick win: Model a 3-year total cost of ownership for one priority use case (e.g., predictive maintenance on HV transformers), including hidden costs.

Do you have KPIs defined to measure AI impact on energy operations?

Consider:

• Baseline metrics before AI implementation (outage frequency, demand forecast accuracy, maintenance costs)
• Clear success criteria tied to operational outcomes
• Real-time monitoring dashboards for AI system performance
• Attribution methodology (AI impact vs. other factors)
• ROI calculation including reliability improvements and regulatory compliance

Your Rating: ☐ Strong ✓ | ☐ Partial ~ | ☐ Gap ✗

Why it matters: In energy, AI success isn’t just cost savings—it’s reliability, safety, and compliance. Clear KPIs ensure AI delivers measurable operational improvements.

Quick win: Define 3 KPIs for your first AI pilot that directly tie to operational outcomes (e.g., 20% reduction in unplanned transformer outages).