We had the opportunity to interview Anupam Anand, a seasoned expert in quality engineering with over 20 years of experience. As AI continues to revolutionize software testing, Anupam shared his insights on how intelligent automation, predictive analytics, and AI-driven frameworks are transforming the industry. From enhancing testing practices in financial services to leveraging AI for quality assurance in Testing Centers of Excellence, he provided valuable perspectives on the evolving role of AI in enterprise applications. Here’s what he had to say.
- With over 20 years in quality engineering, how have you seen AI transform software testing?
AI has revolutionized software testing by introducing self-learning test automation, predictive analytics, and intelligent defect detection. In the early years, testing relied heavily on manual efforts and script-based automation, which were time-consuming and prone to human errors. AI-powered solutions now autonomously generate test cases, self-heal automation scripts, and optimize test execution, significantly reducing test cycle time. Additionally, AI-driven anomaly detection and risk-based testing help in proactive defect identification, making testing more efficient and robust.
- In what way is AI reshaping testing practices in financial services and consulting?
AI is reshaping financial services and consulting by ensuring faster, more accurate, and risk-focused testing. In this domain, even minor defects can lead to security breaches, financial losses, or compliance violations. AI-driven testing enhances fraud detection, regulatory compliance validation, and automated reconciliation of financial transactions. For instance, AI models analyze large-scale transactional data to detect anomalies, ensuring that financial applications remain robust. AI-powered chatbots also help in automating customer service testing, making BFSI applications more resilient and customer-centric.
- In your Testing Center of Excellence, how do you use AI for enhancing quality assurance?
In our Testing Center of Excellence (TCoE), AI is leveraged to streamline and enhance QA through intelligent test case generation, autonomous test execution, and defect prediction. We use AI-powered RPA bots for repetitive validation tasks, reducing human intervention and boosting accuracy. AI-based test data generation and augmentation help create realistic datasets, ensuring comprehensive test coverage. Additionally, we use predictive analytics to prioritize test scenarios based on historical defect patterns, ensuring maximum risk mitigation with minimal testing effort.
- Can you please share an example where AI helped make testing faster or more efficient?
One notable example is when we automated regression testing for a large-scale banking platform using AI-driven test automation. Earlier, regression cycles took weeks to execute manually. By implementing an AI-based test suite, we reduced execution time by 70%, allowing faster releases without compromising quality. AI-driven self-healing test scripts eliminated maintenance overhead, while predictive defect analytics helped preemptively fix issues, making the testing process faster and more efficient.
- In what ways AI-based tools enhance test coverage, speed, and overall efficiency?
- Test Coverage: AI-driven exploratory testing helps uncover edge cases and hidden defects that traditional testing might miss.
- Speed: AI automates test case generation and prioritization, reducing execution time from days to hours.
- Efficiency: Self-healing automation frameworks minimize maintenance efforts, while AI-powered parallel execution optimizes test runs across environments. AI’s ability to analyze logs and error patterns further refines test effectiveness, making it an indispensable tool for modern software development.
AI-based tools bring intelligence, adaptability, and automation to testing. They enhance:
- What are the most significant challenges of AI-based test automation, and how can industries overcome them?
- Data Dependency: AI models require vast amounts of high-quality training data. Solution: Use synthetic data generation techniques.
- Model Bias and False Positives: AI may misinterpret test failures or defects. Solution: Continuously refine AI models using diverse datasets.
- Integration Complexity: Legacy systems may not be compatible with AI-based testing. Solution: Implement API-based test automation for seamless integration.
- Cost and Skill Gap: AI implementation requires specialized expertise and can be costly. Solution: Upskill teams in AI-based testing frameworks and adopt AI-driven low-code test automation tools.
While AI-based test automation offers transformative benefits, it comes with challenges such as:
- How do you maintain seamless collaboration among testing, development, and product teams while using AI?
- Implementing AI-powered TestOps: AI provides real-time test analytics dashboards for developers, testers, and product owners.
- Using Intelligent CI/CD Pipelines: AI-driven test automation is embedded within DevOps pipelines, ensuring continuous feedback loops.
- Automated Defect Assignment: AI classifies and assigns defects to the right team members, reducing resolution time.
- AI-assisted Test Documentation: Automated test reporting ensures that insights are shared across all stakeholders, keeping the process transparent and aligned.
Collaboration is key to AI-driven testing success. We ensure seamless interaction between teams by:
- What advice would you give to people who aspire to build a career in AI-based testing and automation?
- Mastering AI & ML Fundamentals: Understand AI concepts, NLP, and machine learning models.
- Learning AI-Driven Test Automation Tools: Gain expertise in Selenium with AI, Test.ai, Applitools, and AI-based RPA tools.
- Understanding AI in DevOps: Learn how AI integrates into CI/CD pipelines for automated testing.
- Developing Analytical Thinking: AI-based testing is data-driven, so being able to analyze test results and defect patterns is crucial.
- Staying Updated with Industry Trends: AI in testing is evolving rapidly. Engage in webinars, AI forums, and practical projects to stay ahead.
To build a successful career in AI-based testing and automation, consider:
- What are other AI project is being taken care by you.
At Apptad, we are currently working on multiple AI initiatives, including Agentic AI-driven automation frameworks and collaborations with PSUs to drive IndiaAI initiatives. One of our key projects focuses on deepfake detection technology, addressing the growing threat of manipulated digital content. Deepfake technology poses a significant risk to digital content integrity, enabling malicious actors to spread false information, manipulate public perception, and create security vulnerabilities. Current detection methods often lack real-time verification and broad integration across digital platforms, necessitating a more robust and scalable solution. Our AI-driven approach aims to enhance real-time deepfake detection, ensuring digital authenticity and security.
- How do you see Agentic AI evolving in enterprise applications, and what role does it play in decision-making?
Agentic AI is revolutionizing enterprise applications by enabling autonomous decision-making through context-aware, adaptive agents. These agents dynamically learn from real-time data, execute multi-step reasoning, and refine responses based on changing conditions. For example, in financial risk management, Agentic AI can proactively analyze fraud patterns, trigger preventive actions, and optimize credit risk assessments without human intervention. The challenge lies in balancing autonomy with governance—implementing explainable AI models that align with regulatory frameworks while ensuring transparency in AI-driven decisions.
- What are the major challenges in deploying AI for large-scale data-driven systems, and how can enterprises mitigate them?
The biggest challenges include data scalability, model interpretability, and bias mitigation. As data volumes grow, traditional AI pipelines struggle with performance bottlenecks. To overcome this, enterprises must adopt federated learning, edge AI, and optimized model compression techniques to ensure real-time decision-making without compromising performance. Additionally, interpretability is critical—leveraging SHAP and LIME for explainability ensures trust in AI decisions. Bias mitigation requires continuous monitoring through adversarial testing and ethical AI governance frameworks.
- Generative AI is transforming industries—how do you see it being leveraged for digital security and misinformation detection?
Generative AI has a dual role—while it powers content creation, it also poses security threats like deepfakes. A robust AI-driven misinformation detection system requires multi-modal learning models that analyze text, voice, and visual elements to differentiate between authentic and synthetic content. For instance, in our work with PSUs under the IndiaAI initiative, we are developing a real-time deepfake detection engine using diffusion models and adversarial networks. This enhances digital security by enabling proactive detection of AI-generated media before it spreads misinformation.
- How should enterprises structure their AI innovation strategy to drive maximum business impact?
- AI-First Architecture – Transitioning from AI-enabled to AI-first, embedding LLMs and reinforcement learning into core business functions.
- Responsible AI Frameworks – Implementing bias detection, AI fairness metrics, and regulatory compliance mechanisms.
- Agile AI Governance – Creating an AI Center of Excellence (CoE) that continuously evaluates emerging technologies like agentic AI, synthetic data, and knowledge graphs to drive business transformation.
A successful AI innovation strategy must be aligned with business outcomes, scalable, and ethical. Enterprises need to adopt a three-pronged approach:
