Engineering Quality with Intelligence
An AI-Powered Software Quality Engineering Transformation Through a Center of Excellence

The client

Our client is a leading global energy services organization headquartered in the U.S. The client operates in over 80 countries creating innovative technologies, products, and services that help its customers to maximize their value throughout the life cycle of an asset and advance into a sustainable energy future.

Market Trends Driving Software Quality Engineering Transformation

Software quality engineering (QE) transformation in the energy industry is fueled by the rapid pace of digital innovation, agile adoption, and enterprise modernization. Organizations are under increasing pressure to deliver high-quality software faster, more efficiently, and with greater resilience. In this evolving environment, traditional QE approaches, often siloed, reactive, and manual, are proving inadequate. Many energy service organizations are seeking AI-powered solutions to modernize their QE capabilities.

Challenges

The client faced growing pressure to improve software delivery speed, reduce operational costs, and enhance system reliability. However, its existing quality engineering processes were fragmented and reactive. To overcome these challenges, the client began exploring AI-powered quality engineering solutions. After careful consideration, the client initiated a strategic software quality engineering transformation to elevate its QE capabilities across a complex application landscape—including finance, HR, supply chain, BI, and enterprise platforms. This transformation was driven by the need to align with the demands of rapid technological advancement and agile delivery, ensuring robust, scalable, and efficient software quality across the enterprise.

Some of the key challenges primarily faced by the client’s QA leadership

The lack of a centralized repository for reusable quality artifacts led to duplicated efforts and inconsistent practices across teams.

The absence of real-time dashboards and key performance indicators (KPIs) within Azure DevOps (ADO) limited visibility into software health. This hindered leadership’s ability to monitor quality and make informed decisions.

Automation efforts were ad hoc and lacked prioritization, governance, and a structured framework for identifying suitable candidates, resulting in fragmented execution and low ROI.

Quality checks were often performed late in the software development lifecycle (SDLC), leading to delayed defect detection, increased rework, and longer time-to-market.

The client’s quality engineering landscape was fragmented, with siloed team practices, inconsistent deployment of skilled resources, and a lack of standardized tools. This led to inefficiencies, inconsistent testing standards, increased complexity, and delivery risks.

LTIMindtree’s Solution: Transforming Quality Engineering Through a Center of Excellence

To address these challenges, the client partnered with LTIMindtree to establish a Testing Center of Excellence (TCoE) and transform its QE function. The solution was built on five strategic pillars: governance, process optimization, agile alignment, tooling, and workforce development.

Governance and Strategy

A KPI-driven dashboard was implemented to provide end-to-end visibility into testing effectiveness. Governance processes were standardized to ensure seamless coordination across teams, with continuous tracking of value delivered to stakeholders.

Process Optimization

Test management processes were standardized using defined templates and feedback loops. Estimation techniques were introduced, and quality gates were strengthened to ensure delivery excellence.

Agile Alignment

Testing practices were aligned with the organization’s agile methodologies. To embed quality early, a shift-left approach was applied across all sprints and project phases. A flexible operating model was adopted to cater to varying delivery needs.

Workforce Development

A centralized pool of skilled testers was created, supported by a core-flex resource model. LTIMindtree built a multifaceted workforce of SDETs (Software Development Engineer in Test) with domain expertise and promoted cross-skilling and upskilling to future-proof resources.

Tool and Framework Standardization

Based on project demands, best-fit tools were recommended. Automation frameworks were optimized, and tools like Jira were standardized for managing testing activities. AI and GenAI capabilities were leveraged to enhance automation resilience and reduce maintenance overhead.

AI-Powered Test Automation

Using UFTOne, LTIMindtree introduced intelligent automation features, such as:

Self-healing scripts to adapt to UI changes
Natural language test authoring for simplified script creation
AI-based step recording to convert user interactions into executable steps
Synthetic test data generation using AI models to simulate edge cases and reduce dependency on production data

Strategic QA Enablement

A managed service CoE was established to support key business domains. The CoE team provided governance for functional testing and UAT, including test planning, defect management, metrics collection, and release support.

Client Collaboration

LTIMindtree collaborated closely with internal QA teams and application SMEs to align QA resources with evolving project needs. Stakeholder engagement was actively fostered through webinars, boot camps, and centralized dashboards, driving adoption and continuous improvement across the quality ecosystem.

Automation Integration

Automation of SAP, Salesforce, and legacy applications was achieved using Microfocus UFT and Azure DevOps. Over 4000 test cases were developed and executed, forming a robust regression suite for daily ART and BVT runs.

Benefits: AI-Powered Quality Engineering in Action

Maintained >99% UAT defect prevention, ensuring stable releases and reducing production issues.
Identified and resolved 5000+ quality observations , improving delivery standards and minimizing downstream defects.
Restored access for 4300+ SAP users , preventing disruption to month-end close activities. Updates were shared with IT leadership every 4 hours, ensuring transparency and responsiveness.
Transitioned knowledge tracking to Azure DevOps , streamlining onboarding and ramp-up. Migrated 110+ testing artifacts to a centralized ServiceNow knowledge base , improving accessibility and traceability.
Enhanced Power BI dashboards for QA, boosting productivity and closing gaps. Provided strategic oversight for test approaches across 35+ enterprise applications .
Configured the Tosca environment and automated 350+ pilot test scripts . Enabled unattended test execution via ADO pipelines. Integrated Tosca and UFT scripts into a unified pipeline for end-to-end testing.
Achieved 100% execution of BVT & ART, ensuring early defect detection and robust application stability.

Building a Future-Ready Quality Engineering Ecosystem

LTIMindtree’s strategic partnership with the client has redefined software quality engineering through a comprehensive, AI-powered quality engineering and governance-led transformation. By establishing a Testing Center of Excellence, standardizing processes, and embedding quality early in the SDLC, the client now benefits from improved delivery efficiency, reduced costs, and enhanced software reliability.

The solution not only addressed immediate operational challenges but also laid the foundation for continuous improvement and innovation. With intelligent automation, centralized governance, and agile alignment, the client is well-positioned to meet future demands and maintain its leadership in the energy sector.

Testimonial

– Sunie Paul, Associate Vice President, Quality Engineering.

“At LTIMindtree’s EGUL (Energy and Utilities Business Unit), delivering measurable business value is at the core of every engagement. In our client partnership, we leveraged an AI-powered automation service model, anchored by a robust Testing Center of Excellence (COE). We integrated it into the client’s quality engineering ecosystem to achieve over 99% UAT defect prevention, restore SAP access for thousands, and accelerate delivery speed and reliability. These results reflect our commitment to sustainable, scalable impact. I’m proud of how our teams consistently drive operational excellence, fulfill client commitments, and set new benchmarks for innovation and quality. We don’t just imagine what’s possible: we deliver it every day.”

Ready to elevate your software quality engineering capabilities?
Partner with LTIMindtree to build a future-ready quality engineering ecosystem that boosts efficiency, reduces costs, and ensures delivery excellence.

Write to us at eugene.comms@ltimindtree.com to learn more.

Engineering Quality with Intelligence

The client

Market Trends Driving Software Quality Engineering Transformation

Challenges