Generative AI: A Business-Transforming Technology
Keywords:
Generative AI, Artificial Intelligence, Business-Transforming TechnologyAbstract
Generative AI (GAI) is a type of artificial intelligence that uses algorithms to learn from datasets and generate new outputs that are similar to, yet distinct from, the original data. These outputs can include text, question–answer responses, images, audio, video, design works, computer programming code, and synthetic data. Generative AI has become a topic of significant interest in the business sector, as it serves as a powerful tool for creators and designers across various industries. Its application in the workplace can enhance productivity and efficiency by saving time and improving workflow. Examples of its use in business include marketing, finance, education, manufacturing, services, artistic and entertainment creation, and content ideation.
This article focused on explaining the meaning, capabilities, and roles of Generative AI in the context of supporting operations within the business sector, enabling the business community to better understand and prepare for the changes currently taking place. Factors influencing the future of Generative AI in the business world include the adoption of key technologies, their impact on business operations, and the assessment of challenges and risks—ultimately guiding businesses toward effective adaptation in the future.
References
กนก บุนนาค.(2567).ปัญญาประดิษฐ์เชิงสร้างสรรค์กับการประยุกต์ใช้ทางการทหาร. วารสารวิชาการเทคโนโลยีป้องกันประเทศ, 13(6), 28-41.
Generative AI คืออะไร มีกี่ประเภท โดดเด่นแค่ไหน ใช้ทำอะไรได้บ้าง. (2566). Nerd Optimize. https://nerdoptimize.com/generative-ai/
Generative AI สำหรับชิ้นงานสร้างสรรค์. (2566). Adobe. https://www.adobe.com/th_th/products/firefly/discover/what-is-ai-art.html
ชินกฤต อุดมลาภไพศาลและกฤษณะ เชื้อชัยนาท.(2566). Generative AI ในงานสื่อสารมวลชน. วารสารเซนต์จอห์น., 26(39),179-194.
ณปภัช อโรชาสิริ.(2567).คุณค่า AI ปัญญาประดิษฐ์สมองกลอัจฉริยะ สู่การสร้างสรรค์ผลงานศิลปะแบบไร้ขอบเขต. วารสารศิลปกรรมศาสตร์วิชาการวิจัยและงานสร้างสรรค์, 12(2),65-79.
ธนาคารแห่งประเทศไทย (2566) . Generative AI : ขุมพลังขององค์กร?. https://shorturl.asia/OMKV4
นฤดล จันทรเพ็ชร์ และ อุบลวรรณ ส่งเสริม.(2568). ปัญญาปรุะดิิษฐ์ (AI) : การประยุกต์ใช้ทางการศึึกษา. วารสารครุุสภาวิทยาจารย์, 6(1), 1–13.
วิจัยกรุงศรี.(2566). Generative AI เทคโนโลยีพลิกโฉมโลก. https://www.krungsri.com/th/research/research-intelligence/generative-ai-2023
Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (FAccT '21). Association for Computing
Machinery, 610–623. https://doi.org/10.1145/3442188.3445922
Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P.,
Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R.,
Ramesh, A., Ziegler, D. M., Wu, J., Winter, C., … Amodei, D. (2020). Language models are few-shot learners. https://arxiv.org/abs/2005.14165
Buolamwini, J., & Gebru, T. (2018). Gender Shades: Intersectional Phenotypic Demographics for Benchmarking Facial Analysis Algorithms. Proceedings of the 1st Conference on Fairness, Accountability, and Transparency (FAT).
Carlini, N., Ippolito, D., Tramer, F. (2021). Extracting Training Data from Large Language Models. Proceedings of the 30th USENIX Security Symposium.
Davenport, T. H., & Ronanki, R. (2018). Artificial Intelligence for the Real World. Harvard Business Review, 96(1), 108-116.
Ekman, H., & Strand, E. (2024). AI: A helping hand for digital marketing agencies?. [Bachelor’s thesis]. Malmö University.
Greshake, K., Abdelnabi, S., Moura, L. (2023). Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection. arXiv preprint arXiv:2302.04940.
Khalil, M., & Ebner, M. (2023). The Potential of ChatGPT in Education: A State-of-the-Art Review. Education and Information Technologies.
Krenn, M., Häse, F., & Aspuru-Guzik, A. (2023). Synthetic data generation using generative models in industrial applications. Nature Machine Intelligence, 5(1), 12-24.
Ngotngamwong, R. (2020). Artificial Intelligence and Its Impacts on Employability. Human Behavior, Development & Society, 21(2), 51-56.
OpenAI. (2023). ChatGPT and its implications for education and the workforce.
Schuh, G., Gartzen, T., Kampker, A., & Potente, T. (2022). Industry 4.0 in Production – Applications of AI and Automation for Efficiency. Procedia CIRP, 57, 8-13.
U.S. Copyright Office. (2023). Copyright Registration Guidance: Works Containing AI-Generated Material.
