The Enterprise Testing Challenge
Is your testing organization keeping pace with the speed of business change?
Enterprise IT teams are under pressure: application landscapes are expanding across GenAI and Agentic applications, ERP, cloud, and custom platforms, releases are accelerating, and quality expectations keep rising. Traditional testing — siloed, manual, and hard to scale — can't keep pace, driving up cost, risk, and delayed releases.
Birlasoft's NextGen AI-native Testing CoE combines AI, automation, governance, and domain expertise into one outcome-driven Quality Engineering model, helping enterprises test faster, reduce cost, expand coverage, improve application stability, and release with confidence.
The NextGen AI-TCoE turns testing from a cost center into an autonomous, outcome-driven quality engine for enterprise applications.
Trust First: Proven Enterprise Quality Transformation
Enterprise buyers need more than a testing partner. They need confidence that quality can scale with transformation.
~45%
Reduction in cost of quality
~50%
Faster Time to Market
1%
Defect Leakage
~100%
Test Coverage
~40%
Effort reduction in test case design
Quality engineering outcomes that move the business forward
Core Capability Areas
Robust QA Strategy
Robust QA Strategy
Centralized QA Center of Excellence, test standardization, roadmap definition, and KPI-driven governance. Dedicated focus on AI for QE and QE for AI respectively.
Scalable Operating Model
Scalable Operating Model
Dynamic demand management, targeted upskilling, domain-aligned leadership, and flexible capacity planning.
AI Workforce Enablement
AI Workforce Enablement
Knowledge management, structured transitions, security integration, and AI-enabled workforce transformation.
Business Alignment
Business Alignment
Strategic QA alignment tied to faster delivery, productivity gains, and outcome-based engagement models.
Innovation & AI Integration
Innovation & AI Integration
AI-based impact analysis, automated test generation, self-healing automation, AI-powered test data management, defect prediction, and predictive reporting.
From Testing Bottleneck to Business Enabler
Testing Cannot Keep Pace with Release Velocity
Challenge
Frequent upgrades, patches, transformation programs, and continuous delivery cycles place increasing pressure on testing organizations. Manual testing and legacy QA approaches lengthen release cycles and delay business outcomes. Need for fresh perspective to certify non-deterministic Agentic applications.
Our Approach
Birlasoft introduces AI-assisted test design, automated scripting, intelligent execution, impact analysis, reusable automation, and flexible testing capacity through a centralized AI-native Testing CoE.
Value Delivered
- Faster testing cycles
- Accelerated release readiness
- Increased automation coverage
- Up to 2X faster turnaround
- Approximately 50% faster time-to-market
Quality Ownership is Fragmented
Challenge
Testing is often distributed across business units, regions, applications, vendors, and delivery teams. Different standards, metrics, tools, and operating practices create inconsistent quality outcomes and limited executive visibility.
Our Approach
Birlasoft establishes centralized quality ownership with standardized testing processes, KPI-driven governance, domain-aligned leadership, quality certification frameworks, and outcome-based reporting.
Value Delivered
- Single ownership of enterprise quality
- Consistent testing standards
- Increased automation coverage
- Greater stakeholder transparency
- Stronger governance and accountability
- Better visibility into quality, coverage, and release readiness.
Automation is expensive to build and difficult to maintain
Challenge
Traditional automation frameworks require specialized skills, significant scripting effort, and continuous maintenance. Application changes frequently break scripts and increase automation debt.
Our Approach
Birlasoft Lynx brings GenAI-based test generation, automatic script creation, low-code/no-code automation, reusable components, and AI/ML-powered self-healing into the automation lifecycle.
Value Delivered
- Up to 50% faster test design
- Up to 50% lower automation maintenance effort
- Reduced dependence on specialized automation skills
- Increased automation scalability
- Greater reuse across programs and applications
Quality Risks Become Visible Too Late
Challenge
Traditional testing frequently identifies defects after significant development and testing effort has already occurred. Fragmented reporting makes it difficult for leaders to understand release risk early enough to act.
Our Approach
AI-powered impact analysis, intelligent test prioritization, defect prediction, real-time analytics, automated reporting, and predictive quality insights move risk identification earlier in the lifecycle.
Value Delivered
- Reduced defect leakage
- Better application stability
- Improved release confidence
- Faster risk-based decision-making
- 1% critical defect leakage demonstrated in large-scale transformation.
From Fragmented QA to an AI-native Quality Engineering Organization
01
Assess and Align
Understand the current application landscape, testing maturity, automation coverage, quality risks, operating model, demand patterns, and business priorities..
Outcome: A quality engineering roadmap aligned to enterprise transformation objectives
02
Standardize & Govern
Establish centralized quality ownership, common processes, enterprise testing standards, governance structures, KPIs, and quality certification mechanisms.
Outcome: Consistent quality practices and greater executive transparency.
03
Automate and AI-Enable
Introduce intelligent automation across requirement analysis, test design, scripting, data preparation, execution, maintenance, and reporting.
Outcome: Higher productivity, increased coverage, and reduced manual effort.
04
Predict & Optimize
Use AI-powered impact analysis, intelligent orchestration, defect prediction, analytics, and risk-based testing to continuously optimize execution.
Outcome: Earlier risk detection, improved release confidence, and greater application stability.
05
Continuously Transform
Operate efficiently. Generate savings. Reinvest those savings into innovation. Continuously modernize quality engineering.
Outcome: A sustainable model where operational efficiencies help fund future innovation.
This model runs on the Innovation Flywheel — Run, Optimize, Elevate, Transform — a self-funding cycle for continuous value generation, where operational savings are reinvested into continuous innovation and delivers compounding business value.
AI Across the Testing Lifecycle
Requirement Analysis
AI Use Cases: Impact analysis and traceability
Potential Impact: Up to 40% effort reduction
Potential Impact: Up to 40% effort reduction
Test Design
AI Use Cases: AI-generated test cases
Potential Impact: Up to 50% faster test design
Potential Impact: Up to 50% faster test design
Test Data Management
AI Use Cases: Automated data generation and compliance
Potential Impact: Up to 40% lower preparation effort
Potential Impact: Up to 40% lower preparation effort
Test Execution
AI Use Cases: Intelligent execution and orchestration
Potential Impact: Up to 40% lower execution effort
Potential Impact: Up to 40% lower execution effort
Automation Maintenance
AI Use Cases: Self-healing automation
Potential Impact: Up to 50% lower maintenance effort
Potential Impact: Up to 50% lower maintenance effort
Defect Prediction
AI Use Cases: Historical analysis and proactive risk identification
Potential Impact: Up to 30% lower defect leakage
Potential Impact: Up to 30% lower defect leakage
Reporting
AI Use Cases: AI dashboards and automated reporting
Potential Impact: Up to 50% lower reporting effort
Potential Impact: Up to 50% lower reporting effort
Birlasoft Lynx – Agentic AI for the Next Generation of Enterprise Testing
Birlasoft Lynx is an Agentic AI-powered test automation platform designed to make quality engineering increasingly autonomous, intelligent, predictive, and scalable
Key Differentiators:
- AI-First by DesignAI embedded at every stage, not bolted on
- Test OptimizationTracks dependencies and ripple effects across requirements, tests, and scripts
- Plug-and-PlayWorks seamlessly with existing enterprise tools (Jira, Azure DevOps, Xray, TestRail)
- Low-Code / No-CodeReduces dependency on specialized automation skills
- Man-Minimum, AI-MaximumSignificantly reduces manual intervention
Turn Quality Engineering Into Measurable Enterprise Value
A NextGen AI-native Testing CoE can help organizations move beyond activity-based QA metrics toward business outcomes.
Faster
- Reduced test-cycle times
- Accelerated releases
- Faster business transformation
- Up to 2X faster turnaround
Better Quality
- Greater enterprise test coverage
- More consistent quality certification
- Reduced production defects
- 1% critical defect leakage
More Efficient
- Automation-driven productivity
- Reduced testing effort
- Improved resource flexibility
- 40–50% reduction in testing spend
- Climate-Related Financial Disclosures
More Resilient
- Improved application stability
- Fewer production incidents
- Predictive quality management
- Stronger business continuity
Better Governed
- Single ownership of quality
- Standardized enterprise KPIs
- Outcome-based governance
- Increased executive visibility
Birlasoft Advantage
Enterprise quality transformation requires more than automation technology
Independent Testing CoE
- Scalable & Flexible Operating Model
- AI optimized Test Process
- Outcome-Driven Approach with Transparent Reporting
Proven Experience
- 20+ large-scale Testing CoE implementations
- Experience across SAP, Oracle, D365, JD Edwards, Salesforce, Guidewire, and enterprise transformation programs
Accelerators
- GenAI based Test Automation to reap the benefits of automation
- Agentic AI based Testing Assistants
- JumpStart Kit (Test Templates, TCOE/TAAS Playbook, Reports, Dashboards etc.)
Partnerships
Birlasoft's Testing CoE model is built on deep platform partnerships and delivery experience across leading enterprise ecosystems — including SAP, Oracle, JD Edwards, Salesforce, Microsoft Dynamics 365, and Guidewire — combined with independent, outcome-driven Testing-as-a-Service delivery recognized across large-scale enterprise transformation programs.
Lynx is designed to work with established engineering and test-management ecosystems, including Tosca, Opkey, Zephyr, Jira, Azure DevOps, Xray, and TestRail.
FAQs
Most testing setups operate as decentralized, project-based functions with no single owner of quality. Birlasoft's model establishes one centralized, governed Testing CoE with standardized processes, shared accountability, and AI embedded across the lifecycle — not just automation layered on top of existing gaps.
Traditional automation still requires heavy scripting and constant maintenance when applications change. Lynx uses GenAI and self-healing automation to generate, adapt, and maintain tests automatically, cutting maintenance effort by up to 50% and reducing dependency on specialized coding skills.
AI can be applied across requirement analysis, test-case generation, script creation, test-data preparation, execution orchestration, automation maintenance, defect prediction, and reporting. The objective is not AI for its own sake. It is measurable improvement in productivity, quality, coverage, speed, and risk visibility.
Engagements typically follow a phased model — assessment and strategy, centralization, AI-led automation rollout, and continuous optimization. Clients have seen up to 2X faster turnaround and measurable cost reduction within the first phases of transformation, with full-scale value realized over 12–18 months.
Yes. Birlasoft's NextGen AI-native Testing CoE , powered by Birlasoft LynX, supports both Run and Transform initiatives. Lynx is designed to be plug-and-play, integrating with leading enterprise tools like Jira, Azure DevOps, Xray, and TestRail, and supports testing across SAP, Oracle, D365, Guidewire, and custom applications without replacing existing toolchain. This allows organizations to maintain application stability while simultaneously supporting upgrades, modernization, cloud programs, ERP transformation, and continuous business change.
The model is built on KPI-driven governance with a single point of accountability for quality outcomes. Standardized reporting, predictive analytics, and outcome-based engagement ensure consistent compliance and executive visibility across every application and business unit.
The operating model incorporates dynamic demand management, capacity planning, flexible resourcing, targeted upskilling, and domain-aligned leadership. This allows testing capacity and specialist skills to adjust as application portfolios and transformation programs evolve.
Yes. Self-healing automation can detect application changes and adapt test automation, accordingly, reducing the manual effort required to maintain scripts. Birlasoft's AI use-case model identifies potential maintenance-effort reductions of approximately 50%.
Birlasoft operates on an outcome-based engagement model, aligning commercial terms to measurable business results — such as reduced cost of quality, faster time-to-market, and lower defect leakage — rather than simply billing for tools or headcount.
The starting point is an assessment of your testing landscape, maturity, automation coverage, application portfolio, governance model, demand patterns, quality risks, and transformation priorities. From there, Birlasoft can define an AI-enabled quality engineering roadmap aligned to the outcomes that matter most to your organization.
Ready to Transform Testing Into a Strategic Advantage?
Make quality engineering move at the speed of your enterprise
Build an AI-native QA engine that releases faster, scales intelligently, reduces quality costs, and strengthens application resilience — powered by Birlasoft LynX.
Tell us where quality engineering is slowing your transformation. We’ll help you identify opportunities across operating model, automation, AI adoption, governance, and testing productivity.
Talk to a Birlasoft Quality Engineering Expert →