Radio in the Age of AI

Notice

This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2026 | Volume : 04 | 01 | Page :
    By

    V. Basil Hans,

  • Rashmi Ammembala,

  1. Research Professor, Department of Commerce & Management and Humanities & Social Sciences, Srinivas University, Mangalore, Karnataka, India
  2. Assistant Professor, Coordinator, Department Community Radio, Manipal Institute of Communication MAHE, Manipal, Karnataka, India

Abstract

Artificial intelligence (AI) is changing the traditional world of radio broadcasting very quickly. It is changing the way material is made, curated, shared, and listened to. This article talks about how AI technologies like machine learning, natural language processing, and automated voice synthesis can be used in radio production and operations. It looks at how AI-powered solutions may make listening more personalised, give real-time audience statistics, automatically generate news, and make smart content recommendations, all of which improve audience engagement and operational efficiency. Broadcasters are using AI- driven automation to offer multilingual programming to a variety of audiences across digital platforms, save production costs, and increase scheduling accuracy. The study also looks at new problems that are coming up, such as worries about authenticity, the ethical use of synthetic voices, data privacy, and the possibility of replacing human talent in broadcasting jobs. The essay talks about how the job of radio professionals is changing in an AI-augmented world by looking at current trends and case examples. It stresses the necessity for hybrid abilities that combine creativity with technology knowledge. The paper contends that instead of supplanting traditional radio, artificial intelligence is reshaping its significance in the digital era allowing it to persist as a vibrant, accessible, and versatile medium within an ever more competitive media landscape.

Keywords: Artificial Intelligence, Radio Broadcasting, Personalisation, Automation, and Audience Analytics

How to cite this article:
V. Basil Hans, Rashmi Ammembala. Radio in the Age of AI. International Journal of Radio Frequency Innovations. 2026; 04(01):-.
How to cite this URL:
V. Basil Hans, Rashmi Ammembala. Radio in the Age of AI. International Journal of Radio Frequency Innovations. 2026; 04(01):-. Available from: https://journals.stmjournals.com/ijrfi/article=2026/view=244094


References

1. Soldati P, Ghadimi E, Demirel B, Wang Y, Gaigalas R, Sintorn M. Design principles for model generalization and scalable AI integration in radio access networks. IEEE Communications Magazine. 2024 Jul 15;63(1):84-91.

2. Tacchi J. The need for radio theory in the digital age. International Journal of Cultural Studies. 2000 Aug;3(2):289-98.

3. Kuyucu M. Artificial intelligence in media: Radio automation systems as the first artificial intelligence application in media in the terms of “threats” and “opportunities.”. Dijital dönüşüm ve süreçler & digital transformation and processes. 2020;2020:133-68.

4. Amato G, Behrmann M, Bimbot F, Caramiaux B, Falchi F, Garcia A, Geurts J, Gibert J, Gravier G, Holken H, Koenitz H. AI in the media and creative industries. arXiv preprint arXiv:1905.04175. 2019 May 10.

5. Gordon S, Mahari R, Mishra M, Epstein Z. Co-creation and ownership for AI radio. arXiv preprint arXiv:2206.00485. 2022 Jun 1.

6. Yu QY, Lin HC, Chen HH. Intelligent radio for next generation wireless communications: An overview. IEEE Wireless Communications. 2019 Aug 22;26(4):94-101.

7. Pham QV, Nguyen NT, Huynh-The T, Le LB, Lee K, Hwang WJ. Intelligent radio signal processing: A survey. IEEE Access. 2021 Jun 7;9:83818-50.

8. Langer S, Obermeier L, Ebert A, Friedrich M, Munisamy E, Linnhoff-Popien C. Content- based Recommendations for Radio Stations with Deep Learned Audio Fingerprints. arXiv preprint arXiv:2007.07486. 2020 Jul 15.

9. Goisauf M, Cano Abadía M. Ethics of AI in radiology: a review of ethical and societal implications. Frontiers in Big Data. 2022 Jul 14;5:850383.

10. Renzo MD, Debbah M, Phan-Huy DT, Zappone A, Alouini MS, Yuen C, Sciancalepore V, Alexandropoulos GC, Hoydis J, Gacanin H, Rosny JD. Smart radio environments empowered by reconfigurable AI meta-surfaces: An idea whose time has come. EURASIP Journal on Wireless Communications and Networking. 2019 Dec;2019(1):1-20.

11. MacFarland D. Future radio programming strategies: Cultivating listenership in the digital age. Routledge; 2013 Oct 18.

12. Vitullo A, Campbell HA, Mastrofini F, Di Pietro FP. From Radio to AI. Old and new trends in the Catholic Church’s approach to technological innovation. Church, Communication and Culture. 2025 Jan 2;10(1):45-59.

13. Lax S. The Prospects for Digital Radio Policy and technology for a new broadcasting system. Information Communication & Society. 2003 Sep 1;6(3):326-49.


Ahead of Print Subscription Review Article
Volume 04
01
Received 06/05/2026
Accepted 12/05/2026
Published 16/05/2026
Publication Time 10 Days


Login


My IP

PlumX Metrics