Leading T&D utility in the US Accelerates GenAI Adoption with HCLTech

A leading US transmission and distribution utility advances GenAI strategy and accelerates minimum viable product (MVP) delivery

HCLTech delivers AI assessment, strategy, roadmap and agentic AI MVPs

5 min read

Overview

Automating regulatory monitoring and enhancing customer intelligence for our client. HCLTech enables AI-driven regulatory docket summarization and centralized customer sentiment analysis, addressing the challenges of manual, time-consuming document reviews and fragmented interaction insights. The solution helps process high volumes of regulatory data, extract actionable insights and provide real-time visibility into customer interactions, enabling faster compliance actions, improved decision-making and enhanced operational efficiency.

The Challenge

Manual monitoring and fragmented insights

Manual monitoring of regulatory dockets is time-consuming and may lead to missed opportunities or non-compliance; the Texas government publishes 50–60 regulatory dockets daily and each docket requires multiple logins, clicks, downloads and review of 2–200 page PDFs.
Customer service interactions across C360 Desktop Agent and monitored social media platforms required manual, fragmented analysis and lacked centralized insights, causing delays in issue triaging and missed opportunities for proactive service
Platform and operational challenges included:
- Siloed data, manual workflows and legacy infrastructure constraints
- Difficulty scaling due to limited infrastructure, manual deployments and lack of centralized orchestration
- Limited monitoring and observability capabilities
- Low governance maturity, with fragmented AI policies and predominantly manual oversight

The Objective

To align GenAI capabilities with our client’s mission and establish a scalable and compliant AI operating model

To enhance operational efficiency, innovation and customer experience.
Establish a unified AI platform architecture and DevOps/LLMOps delivery models.
Define build vs buy recommendations and operational support models.
Ensure robust security, privacy and end-to-end regulatory compliance.
Enhance customer experience through intelligent automation and personalization.
Create a scalable and resilient platform to support diverse use cases and pursue gradual, low-risk adoption of cloud technologies.
Manage Total Cost of Ownership (TCO) with predictable spending and clear ROI.
Close internal knowledge and skill gaps through training and new opportunities.

The Solution

AI-led transformation combining targeted use cases with enterprise GenAI capabilities

HCLTech addressed the client’s business and technology challenges through a combination of targeted AI use cases and a comprehensive enterprise GenAI strategy, architecture and roadmap, enabling scalable, efficient and data-driven operations. Delivered a dual-track approach combining enterprise GenAI transformation (strategy, architecture, governance) with rapid MVP execution to demonstrate business value.

Regulatory Docket AI Summarization and Decision Support
Implemented an Agentic AI solution to automate regulatory docket analysis and action identification, delivering decision-ready insights.
Customer Contact Summarization
Developed an AI-powered solution to analyze customer interactions and generate sentiment, incident and operational insights.
Integrated GenAI, Data and Infrastructure Strategy
Designed a scalable GenAI architecture integrating data, AI and infrastructure across on-premises and cloud environments.
Data Domain Gap Analysis and Recommended Approach
Assessed data domains, identified key gaps and defined a prioritized modernization roadmap.
AI Assessment, Strategy and Roadmap Development
Evaluated AI capabilities and developed an enterprise AI strategy and phased adoption roadmap.
Responsible AI, Governance and OCM Enablement
Established governance, compliance and change management frameworks for responsible AI adoption.
Data, Monitoring and Operational Enablement
Implemented data management, monitoring and operational frameworks to support reliable AI operations.