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AI-Readiness: Are You Building the Future or Falling Behind?
AI-Readiness: Are You Building the Future or Falling Behind?
Playlist
- Client Feedback & Quality Assurance in Web Design for Agencies
- Democratise Automation to Build Autonomy and Go-To-Market Faster
- Testing AWS applications locally and on CI with LocalStack
- Man Vs Machine: Finding (replicable) bugs post-release
- Shift-Left: Accelerating Quality Assurance in Agile Environments
- End-to-End Test Automation with Provar
- Fast-Tracking Project Delivery:Tips from a Recovering Perfectionist
- Crawl, Walk, Run...Fly - Take your build and test pipeline to the next level
- Revolutionizing Testing with Test Automation as a Service (TaaS)
- How Codemagic Mitigates Challenging Mobile App Testing Environments
- Fast and Furious: The Psychology of Web Performance
- Faster Feedback with Intelligent CD Pipelines
- Navigating the Future of Quality Engineering in 2024
- Using AI for Effective Test Generation
- Automated Test Execution Reporting
- How To Speed Up Our Work During Web Automation
- Building Products that Drive Better Results with Shortcut
- Shifting Accessibility Testing Left with LambdaTest and Evinced
- Testing Tomorrow: Unravelling the AI in QA Beyond Automation
- Flaky Tests from an Engineering Perspective
- Future Trends and Innovations in Gen AI for Quality Engineering
- Mastering User-Centric Mindset Unlocking Your Potential as a Tester
- From Brainwave to Inbox: Avo's Whimsical Adventure through AI-Powered Test Automation
- The Myth of βBest Practiceβ
- Impact and Potentials of GenAI to the IT Engineers
- Innovation Accelerated: The Intersection of AI and Quality Engineering
- Optimize Issue Tracking: Integrating SpiraTeam with LambdaTest
- The Power of Generative AI in Reducing Maintenance and Enhancing Speed
- Rethinking the Role of QA Profile
- Building Quality Software: AI-based testing approach with Jira and QMetry
- In-Depth with Playwright: A Modern Testing Framework
- Supercharge Your Data Quality Testing with AI/ML
- GenAI for Quality Transformation
- Creating Reliable and Scalable Test Automation Frameworks
- Upskilling Quality Engineers: A Success Story in SDET Transformation
- AI-Readiness: Are You Building the Future or Falling Behind
- Leading the Charge in Software Quality with Zero Bug Revolution
- Building a Test Automation Framework for TV Apps & Scaling at FX Digital
- Building High-Quality Teams: People, Process & Proof for QA Leadership
- Why Do We Have Bugs, and Why Do They Happen?
- GenAI in QA: Tiket's Approach to Evolving Quality Engineering
- Collaborative Remote Testing: How to Set Up & Run Effective Ensemble Sessions
- Simulating Real-World Scenarios: Balancing Precision and Practicality in Testing
- Building AI-Driven Test Automation Frameworks for QA Excellence : Challenges with Agile, DevOps, and AI-driven Testing
- Reinforcing Cybersecurity Beyond Functional Testing
- Building AI-Driven Test Automation Frameworks for QA Excellence
- Shift Happens: Driving Quality LeftβA Real-World Journey Across Five Teams
- Transitioning from Manual Testing to Test Automation with Cypress
- See Why Your Testing Framework Is Incorrect, Incomplete, or Inefficient β And Iβll Show You Why
- Quality First: Implementing Shift-Left Testing for Future-Ready Products
- Building Resilient Quality Engineering Teams: Exploring Emerging Trends and Best Practices
- Testing LLMs: Why It Matters, Common Data Challenges, and Proven Testing Strategies
- Observability in Software Test Modernization
- AARRR...Are you Test-Ready for AI? Discover If AI Can Transform QA Process
- Integrating Testing into Agile Workflows: Enabling Faster, Smarter Software Delivery
About The Video
Watch our latest episode of the LambdaTest Experience (XP) Series featuring πππ’π₯ππ¬π‘ πππ«ππ‘ππ«π’, VP - Head of Technology & Digital Natives, QualityKiosk. In this session, you will learn how AI-driven quality engineering is revolutionizing digital ecosystems by enhancing productivity, optimizing operational costs, and accelerating innovation. Discover how businesses are integrating AI to build intelligent, self-learning systems that adapt to rapidly changing demands.
Video Chapters
00:00 Welcome!
01:59 Guest Introduction
03:39 Understanding AIβs Role in Quality Engineering
05:59 Leveraging Data for Enhanced Testing
08:03 Adoption Challenges in Enterprise
12:35 Quality Engineering: Low-Hanging Fruits for AI
17:45 Redefining the Role of Quality Engineering
20:09 Upskilling for The Future of Quality Engineering
35:22 Conclusion
Key Topics Covered
AI's Impact on Quality Engineering:
AI-driven tools are transforming how quality engineering is conducted, helping businesses automate, enhance resilience, and boost efficiency in their software development processes.
AI is helping to identify issues earlier and improve user experience by leveraging real-time data.
Leveraging Data for Enhanced Testing:
AI facilitates better testing by using customer behavior data. This allows businesses to simulate real-world conditions and create more reliable test cases based on actual user journeys.
This data-driven approach reduces production issues and improves software quality.
Adoption Challenges in Enterprises:
While AI tools like OpenAIβs ChatGPT are gaining popularity, the broader adoption of AI in enterprise settings faces hurdles, primarily due to concerns around data security and integration into existing workflows.
Companies are still learning how to integrate AI to optimize operational costs and efficiencies, but adoption is accelerating.
Knowledge Graphs and AI in Testing:
The concept of a "Knowledge Graph" is introduced, where AI can bridge data silos between different teams (development, operations, support) to drive better insights and testing strategies.
This allows AI to make sense of large volumes of data across various stages of the product lifecycle.
Upskilling for the Future:
The video emphasizes the importance of upskilling for AI-readiness in quality engineering, as roles are evolving with the adoption of AI and other emerging technologies.
Observability and AI Ops:
AI is playing a crucial role in observability, helping organizations monitor, track, and derive insights from massive data sets to improve system reliability and performance.
Transitioning from simple observability to AI Ops (AI-driven operations) is the next step for many organizations in improving incident management and overall efficiency.
Related Blogs & Hubs
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LambdaTest is an AI-Native test orchestration and execution platform that allows you to perform both manual and automated testing across 3000+ environments, making it a top choice among other cloud testing platforms.
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