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Revolutionizing BA-QA Team Dynamics: AI-Driven Collaboration Platforms for Accelerated Software Quality in the US Market

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  • Mohammed Majid Bakhsh
  • Md Shaikat Alam Joy
  • Gazi Touhidul Alam

Abstract

In today’s fast-paced software development environment, the collaboration between Business Analysts (BAs) and Quality Assurance (QA) teams is essential for delivering high-quality products efficiently. However, traditional methods often lead to inefficiencies due to silos and misalignment between these teams. This article explores how Artificial Intelligence (AI)-driven collaboration platforms are transforming BA-QA dynamics, offering a more integrated, data-driven approach to software development. By leveraging AI technologies such as predictive analytics, automated test case generation, and real-time collaboration tools, businesses can enhance decision-making, improve communication, and optimize testing strategies. This paper discusses the key benefits of AI in accelerating software quality, highlights real-world case studies of AI applications, and examines the future potential of AI in revolutionizing BA-QA collaboration, particularly in the US market. It also addresses the emerging trends and challenges that come with adopting AI, emphasizing the importance of continuous learning, training, and integration of AI tools with other technologies like IoT and blockchain. As AI continues to evolve, its role in streamlining BA-QA collaboration will become increasingly critical, offering organizations a competitive edge in delivering high-quality software at an accelerated pace.

Suggested Citation

  • Mohammed Majid Bakhsh & Md Shaikat Alam Joy & Gazi Touhidul Alam, 2024. "Revolutionizing BA-QA Team Dynamics: AI-Driven Collaboration Platforms for Accelerated Software Quality in the US Market," Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023, Open Knowledge, vol. 7(01), pages 63-76.
    • Handle: RePEc:das:njaigs:v:7:y:2024:i:01:p:63-76:id:296
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    References listed on IDEAS

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    1. S A Mohaiminul Islam & MD Shadikul Bari & Ankur Sarkar, 2024. "Transforming Software Testing in the US: Generative AI Models for Realistic User Simulation," Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023, Open Knowledge, vol. 6(1), pages 635-659.
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    Cited by:

    1. Sandeep Pochu & Sai Rama Krishna Nersu & Srikanth Reddy Kathram, 2024. "Zero Trust Principles in Cloud Security: A DevOps Perspective," Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023, Open Knowledge, vol. 6(1), pages 660-671.
    2. Sandeep Pochu & Sai Rama Krishna Nersu & Srikanth Reddy Kathram, 2024. "Enhancing Cloud Security with Automated Service Mesh Implementations in DevOps Pipelines," Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023, Open Knowledge, vol. 7(01), pages 90-103.

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