Sinkron : Jurnal dan Penelitian Teknik Informatika
Vol. 7 No. 2 (2023): Research Article, Volume 7 Issue 2 April, 2023

TOGAF Framework For an AI-enabled Software House

Crosley, Nathaniel (Unknown)
Indrajit, Richardus Eko (Unknown)
Dazki, Erick (Unknown)



Article Info

Publish Date
02 May 2023

Abstract

The integration of artificial intelligence (AI) in software development has revolutionized the industry, leading to faster and more accurate results. However, the implementation of AI requires a robust framework to ensure effective planning, design, implementation, and maintenance of AI-enabled software systems. The Open Group Architecture Framework (TOGAF) provides such a framework, enabling organizations to develop a structured and integrated approach to AI-enabled software development. In this journal, we present a case study of how a software house utilized the TOGAF framework to integrate AI in their software development processes. We discuss the challenges faced by the organization in the integration process and how the TOGAF framework provided a structured approach to overcome these challenges. We also highlight the benefits that the organization realized through the implementation of AI-enabled software systems. The case study presented in this journal demonstrates the applicability of the TOGAF framework in AI-enabled software development, and its potential to enhance the capabilities and competitiveness of software houses. The TOGAF framework provides a structured approach to the integration of AI in software development, ensuring that organizations can effectively leverage the benefits of AI while minimizing the associated risks and challenges.

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Journal Info

Abbrev

sinkron

Publisher

Subject

Computer Science & IT

Description

Scope of SinkrOns Scientific Discussion 1. Machine Learning 2. Cryptography 3. Steganography 4. Digital Image Processing 5. Networking 6. Security 7. Algorithm and Programming 8. Computer Vision 9. Troubleshooting 10. Internet and E-Commerce 11. Artificial Intelligence 12. Data Mining 13. Artificial ...