Impact of AI on native advertising revenue growth

The application of synthetic intelligence (AI) in the native advertising system has brought changes that are revolutionary in themselves in terms of content production, user targeting, an increase in bidding, and in estimating revenue. In this review, the technological premises and business considerations of AI-enabled native advertising have been discussed with regard to its role in revenue growth, engagement uplift, and efficiency in operations. This paper examines the mechanisms of machine learning, reinforcement, natural language processing, and real-time recommendation systems of AI, and their contributions towards improving ad similarity, retention, as well as return-on-ad-spend. The review also issues platform-based empirical standards and sketches out the challenges of infrastructural and ethical scaling of AI-driven systems. It ends up by outlining major research avenues that need to be subjected to future studies in this rapidly changing field.

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Publication Details

Journal
World Journal of Advanced Engineering Technology and Sciences
Published
2025-07-24
DOI
https://doi.org/10.30574/wjaets.2025.16.1.1229
Primary Topic
Impact of AI and Big Data on Business and Society
Type
article
Field-Weighted Citation Impact
0.00
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article

Impact of AI on native advertising revenue growth

Divij Pasrija
World Journal of Advanced Engineering Technology and Sciences
Impact of AI and Big Data on Business and Society
article

Impact of AI on native advertising revenue growth

Divij Pasrija
article en

Abstract

The application of synthetic intelligence (AI) in the native advertising system has brought changes that are revolutionary in themselves in terms of content production, user targeting, an increase in bidding, and in estimating revenue. In this review, the technological premises and business considerations of AI-enabled native advertising have been discussed with regard to its role in revenue growth, engagement uplift, and efficiency in operations. This paper examines the mechanisms of machine learning, reinforcement, natural language processing, and real-time recommendation systems of AI, and their contributions towards improving ad similarity, retention, as well as return-on-ad-spend. The review also issues platform-based empirical standards and sketches out the challenges of infrastructural and ethical scaling of AI-driven systems. It ends up by outlining major research avenues that need to be subjected to future studies in this rapidly changing field.

World Journal of Advanced Engineering Technology and SciencesVol. 16(1)
Openalex Percentile: Top 79%
Impact of AI and Big Data on Business and Society
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