Remote Intelligence Solutions (RIS)

Office of the Chief AI Officer (CAIO) Charter

Operational Intelligence for Electric Utilities — Powered by Responsible AI

Foreword from the Chief AI Officer

At Remote Intelligence Solutions, we believe that artificial intelligence must be applied, transparent, and measurable to make a real difference. Our mission is to help electric utilities transition from data collection to data intelligence — unlocking the full value of their inspection imagery, LiDAR, and field records.

As Chief AI Officer, my focus is on turning this belief into practice. The Applied AI layer we are building, beginning with Asset Recognition, establishes a foundation for automation, predictive capability, and informed decision-making across the utility sector. We are committed to ethical AI that enhances human expertise, strengthens operational safety, and accelerates modernization through insight that utilities can trust.

— Michael Kay, Chief AI Officer, Remote Intelligence Solutions

1. Purpose

The Office of the Chief AI Officer (CAIO) leads Remote Intelligence Solutions’ commitment to advancing applied artificial intelligence within the electric utility sector. Its purpose is to ensure that automation, prediction, and intelligent analysis are seamlessly integrated into the RIS platform and the everyday workflows of utility operators — transforming raw data into operational foresight.

The CAIO Office stewards RIS’s Applied AI layer, which powers the company’s flagship product, UTELinspect, and other utility-focused solutions. Through this framework, RIS reduces human overhead, enhances reliability, and delivers actionable intelligence to help utilities modernize asset inspection, maintenance, and risk management.

2. Mission

To make applied AI a practical, trusted, and measurable force in the modernization of electric utility operations — improving safety, efficiency, and decision-making while maintaining the highest standards of transparency and ethical responsibility.

3. Scope and Boundaries

The CAIO Office’s authority spans the design, implementation, and governance of AI-driven functions across RIS products, including:

  • Machine learning model development, validation, and deployment
  • Data lifecycle management, from collection to anonymization and re-use
  • AI ethics, compliance, and risk management aligned with ISO/IEC 42001
  • Cross-functional alignment of AI initiatives with product, data, and client operations

The CAIO Office does not oversee IT infrastructure, cybersecurity operations, or business intelligence unrelated to applied AI functions.

4. Strategic Objectives

  1. Operationalize Applied AI – Embed intelligent automation and predictive capabilities into RIS’s software solutions.
  2. Build AI Readiness Across Clients – Equip utilities to benefit from structured, labeled, and analyzable operational data.
  3. Advance Continuous Learning – Leverage supervised and unsupervised learning to enhance model accuracy across field environments.
  4. Ensure AI Governance and Ethics – Promote transparency, accountability, and fairness in every AI-driven decision.
  5. Accelerate Utility Transformation – Support utilities in transitioning from manual inspection methods to predictive, data-centric maintenance.

5. Functional Pillars of the Applied AI Layer

5.1 Asset Recognition

Computer-vision models automatically identify and classify poles, transformers, insulators, and other field assets within images, LiDAR, and video. This foundational capability enables structured data capture at scale, creating the visual and categorical context required for all downstream AI functions.

Within UTELinspect, it forms the basis for automated inventory validation, asset indexing, and AI-ready labeling of field imagery.

5.2 Condition Recognition

Building on accurate asset detection, this pillar uses image and sensor data to identify asset states, degradation, and anomalies. It delivers real-time diagnostics, fault detection, and alert generation, ensuring that utilities can act before issues escalate into outages or safety risks.

5.3 Continuous Supervised Learning

With consistent asset and condition data available, RIS models learn continuously from labeled datasets generated through everyday inspection workflows. This process strengthens prediction accuracy and allows learnings from one client or region to improve results across the network — creating collective intelligence among participating utilities.

5.4 AI-Enhanced Temporal Analysis

Once asset and condition histories are established, AI analyzes changes over time to reveal degradation trends, seasonal patterns, and behavioral shifts. Utilities can forecast maintenance, validate contractor performance, and quantify intervention impact through objective temporal analytics.

5.5 Unsupervised Insight Discovery

At the most advanced stage, AI autonomously uncovers hidden correlations and emerging phenomena without labeled inputs. This enables proactive identification of new risk factors, systemic inefficiencies, or unexpected asset behaviors — insights that traditional rule-based systems overlook.

6. AI Ethics and Data Responsibility

RIS adheres to principles consistent with ISO/IEC 42001, ensuring responsible development and deployment of AI systems. The CAIO Office is accountable for:

  • Transparency: Every AI decision must be explainable and traceable.
  • Fairness: Models are tested for bias across datasets and use cases.
  • Data Stewardship: All training data are anonymized, governed, and used within the bounds of contractual and ethical compliance.
  • Accountability: Clear ownership is defined for each AI-driven process, from model creation to deployment.
  • Security: Sensitive utility data are protected through multi-layered access controls and compliance with data protection regulations (e.g., GDPR).

7. Innovation Pipeline

  1. Identification – New AI use cases are proposed by internal teams or client partners.
  2. Validation – Concepts are tested through controlled pilots using representative datasets.
  3. Integration – Successful models are deployed to production within the UTELinspect ecosystem.
  4. Feedback Loop – Results from field performance feed back into the continuous learning framework, ensuring ongoing improvement.

This process ensures that innovation is measurable, iterative, and aligned with client priorities.

8. Governance and Collaboration

The CAIO reports directly to the Chief Executive Officer and works in coordination with the CTO and COO to align AI strategy with RIS’s business objectives.

Key governance components include:

  • AI Council: Cross-functional team that reviews ethical compliance, technical readiness, and risk management.
  • Research Partnerships: Engagement with leading research institutions such as MIT CSAIL Alliances for validation and applied AI research.
  • Client Collaboration: Work directly with electric utilities to tailor models to their asset profiles, environments, and risk priorities.

9. Performance Indicators

  • Reduction in manual inspection and QA effort
  • Improvement in model accuracy, precision, and recall
  • Reduction in false positives and missed detections
  • Number of AI-enabled features successfully deployed in UTELinspect
  • Verified client outcomes (e.g., reduced re-inspections, faster decision-making)

10. Expected Outcomes

  • Utilities gain faster and more reliable insights from inspection data.
  • Maintenance and investment decisions become data-driven and evidence-based.
  • Operational safety and compliance are strengthened through consistent AI validation.
  • RIS maintains its position as a trusted, research-engaged innovator in applied AI for electric utilities.