The Effect of AI-Generated Advertising on Consumer Purchase Decisions
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Abstract
About This Research Topic
Advertising has always required someone to write the words, shoot the images, and cut the footage together, a process that traditionally took weeks of coordinated creative work. Generative AI has started to strip much of that time out. Language models can draft ad copy in seconds, image and video generation tools can produce polished creative from a short text prompt, and AI-powered ad platforms can assemble and personalize thousands of ad variants automatically, at a scale no human creative team could realistically match.
This shift raises a question that brands are still working out in practice: does AI-generated advertising actually persuade consumers to buy, and does it do so as effectively as advertising produced the traditional way? This article draws on a study that disaggregated AI-generated advertising into three components, ad copy, visual and creative content, and personalization, and tested how each affects consumer purchase decisions, along with the extent to which perceived authenticity shapes how AI-generated content is received. The sections below set out the study's background, problem, objectives, and scope, along with definitions of its key terms, to give a grounded picture of what generative AI can and cannot be expected to deliver in advertising.
Main Abstract
This study examined the effect of artificial intelligence (AI)-generated advertising on consumer purchase decisions, at a time when brands increasingly use generative AI tools to produce advertising copy, visual and video creative, and personalized ad content at a speed and scale unattainable through traditional, fully human-led ad production. The study was guided by four objectives: to determine the effect of AI-generated ad copy on consumer purchase decisions; to examine the influence of AI-generated visual and creative content on consumer purchase decisions; to assess the effect of AI-generated ad personalization on consumer purchase decisions; and to evaluate the moderating role of perceived authenticity on the relationship between AI-generated advertising and consumer purchase decisions.
A survey research design was adopted, and a structured questionnaire was administered to 405 social media and digital platform users who had encountered AI-generated advertising, using a multi-stage sampling technique; 385 responses were retrieved and found usable, a response rate of 95.1%. Data were analysed using descriptive statistics (frequencies, percentages, means, standard deviation) and inferential statistics (Pearson correlation, hierarchical multiple regression, and chi-square tests) with SPSS version 26.
Findings revealed that AI-generated ad copy (β = 0.25, p < 0.05), AI-generated visual and creative content (β = 0.29, p < 0.05), and AI-generated ad personalization (β = 0.28, p < 0.05) each had a positive and statistically significant effect on consumer purchase decisions, jointly accounting for approximately 57.1% of the variance in purchase decisions (Adjusted R² = 0.571, F = 169.4, p < 0.05). Perceived authenticity significantly moderated the relationship (ΔR² = 0.037, p < 0.05), strengthening the positive effect of AI-generated advertising on purchase decisions among consumers who perceived the content as authentic and credible, and weakening it among those who perceived it as artificial, manipulative, or untrustworthy.
The study concluded that AI-generated advertising is a significant, multidimensional driver of consumer purchase decisions, but that its persuasive effectiveness is substantially conditioned by perceived authenticity, such that technically impressive AI-generated content that fails to feel genuine or credible risks underperforming relative to its creative and personalization potential. Recommendations included investing in AI-generated visual and creative content given its identified strength as a driver of purchase decisions, pairing AI-generated advertising with authenticity-reinforcing cues such as transparent disclosure and human oversight, avoiding AI-generated content that trends toward the uncanny or overtly synthetic, and continuously testing AI-generated ad variants against consumer authenticity perception rather than production efficiency alone.
Keywords: AI-generated advertising, generative AI, ad copy, ad creative, ad personalization, perceived authenticity, consumer purchase decision
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Background to the Study
Advertising has historically been a labour-intensive creative process: copywriters draft persuasive text, designers and photographers produce visual assets, and production teams assemble finished video or multimedia campaigns, all coordinated across weeks or months. Generative artificial intelligence has begun to compress this dramatically. Large language models now draft advertising copy in seconds; image and video generation tools produce polished visual and multimedia creative from a short text prompt; and AI-powered ad platforms assemble and personalize thousands of ad variants automatically, tailoring creative elements to individual audience segments at a scale no human creative team could match.
This capability, broadly termed AI-generated advertising, spans several interconnected dimensions. AI-generated ad copy uses natural language generation to produce headlines, product descriptions, and persuasive body text, often refined through iterative testing of multiple AI-drafted variants. AI-generated visual and creative content uses image and video generation models to produce advertising imagery, illustrations, or short-form video, in some cases without any traditional photography or filming. AI-generated ad personalization combines these generative capabilities with audience data to automatically tailor ad copy, visuals, or offers to specific segments or individual users, updating creative content dynamically based on performance data.
Adoption has accelerated across both large multinational brands and small businesses, driven by dramatically reduced production cost and time, combined with the ability to test and iterate creative at a scale previously reserved for the largest advertising budgets. Platforms such as Meta's Advantage+ and Google's Performance Max now incorporate generative AI directly into ad creation workflows, echoing broader trends explored in our review of AI's role in marketing decision-making, while a growing ecosystem of standalone generative AI tools now lets even small businesses produce advertising creative that would previously have required a professional agency.
Yet the growing prevalence of AI-generated advertising raises an unsettled question in consumer behaviour research: does content generated by AI persuade consumers as effectively as, more effectively than, or less effectively than, traditionally produced advertising, and under what conditions? A central concern running through early commentary and emerging research is perceived authenticity, the extent to which consumers perceive AI-generated advertising as genuine and credible rather than artificial, generic, or manipulative. Content that is technically well produced but perceived as inauthentic risks triggering scepticism rather than persuasion, a concern that regulators have begun to formalise: the U.S. Federal Trade Commission's updated Endorsement Guides now require that AI-generated or AI-modified advertising content, including synthetic endorsements, be truthfully and clearly disclosed to consumers.
It is against this background that this study investigates the effect of AI-generated advertising, disaggregated into AI-generated ad copy, AI-generated visual and creative content, and AI-generated ad personalization, on consumer purchase decisions, while also examining the extent to which perceived authenticity moderates this relationship, a question closely related to the growing evidence base on how algorithmic decision-making shapes consumer purchase behaviour more broadly.
Statement of the Problem
Despite the rapid and growing adoption of AI-generated advertising by brands seeking to reduce production cost and accelerate creative output, it remains unclear whether AI-generated advertising is as persuasive, in terms of actually influencing consumer purchase decisions, as advertising produced through traditional, fully human-led creative processes. Some brands report strong performance from AI-generated ad campaigns, while others have faced public criticism and consumer backlash over AI-generated advertising perceived as low-quality, generic, or unsettling, particularly where AI-generated imagery or video strays into uncanny or synthetic-feeling territory.
A related problem is that much of the existing commentary on AI-generated advertising treats it as a single, undifferentiated phenomenon, without adequately distinguishing between its constituent forms, AI-generated copy, AI-generated visual and video creative, and AI-generated personalization, despite these representing meaningfully different creative outputs with potentially different persuasive strengths and different susceptibility to consumer scepticism. Limited empirical marketing research disaggregates these dimensions to determine their relative contribution to consumer purchase decisions.
Furthermore, while perceived authenticity is frequently invoked in industry commentary as a critical success factor for AI-generated advertising, comparatively few studies formally test it as a statistical moderator of the relationship between AI-generated advertising and purchase decisions using rigorous quantitative techniques, and fewer still do so within emerging market consumer contexts, where exposure to and awareness of generative AI tools is still evolving. This creates practical uncertainty for brands and advertising agencies: without clear evidence on which AI-generated advertising dimensions most strongly drive purchase decisions, and how perceived authenticity conditions this effect, businesses risk over-investing in AI-generated content that fails to persuade, or under-investing in a genuinely effective and cost-efficient creative capability out of unfounded caution. This study addresses these gaps by empirically examining the effect of AI-generated advertising, disaggregated into its copy, visual/creative, and personalization dimensions, on consumer purchase decisions, and by assessing the moderating influence of perceived authenticity on this relationship.
Aim and Objectives of the Study
The aim of this study is to examine the effect of AI-generated advertising on consumer purchase decisions. Specifically, the study sought to:
1. Determine the effect of AI-generated ad copy on consumer purchase decisions.
2. Examine the influence of AI-generated visual and creative content on consumer purchase decisions.
3. Assess the effect of AI-generated ad personalization on consumer purchase decisions.
4. Evaluate the moderating role of perceived authenticity on the relationship between AI-generated advertising and consumer purchase decisions.
Research Questions
In line with the objectives above, the study sought to answer the following questions:
1. What is the effect of AI-generated ad copy on consumer purchase decisions?
2. What is the influence of AI-generated visual and creative content on consumer purchase decisions?
3. What is the effect of AI-generated ad personalization on consumer purchase decisions?
4. To what extent does perceived authenticity moderate the relationship between AI-generated advertising and consumer purchase decisions?
Significance of the Study
This study holds significance for a range of stakeholders across the advertising and marketing ecosystem. For brand managers and advertising practitioners, it offers empirical guidance on which dimensions of AI-generated advertising, copy, visual/creative, or personalization, contribute most meaningfully to consumer purchase decisions, supporting more informed investment of creative and technical resources. For advertising agencies and creative teams navigating generative AI adoption, it underscores the importance of managing perceived authenticity alongside production efficiency, reframing authenticity-conscious AI adoption as a performance lever rather than merely a reputational risk to be managed. For technology vendors developing generative AI advertising tools, it offers evidence on which content dimensions and design choices are most closely associated with positive consumer response. For the wider academic community, it extends the Elaboration Likelihood Model, Source Credibility Theory, and the Persuasion Knowledge Model into a disaggregated, empirically tested model of AI-generated advertising. Students and researchers working on related topics can access comparable methodology and structure through ScholarNestHub's research coaching service, which supports original work on AI adoption, advertising, and consumer behaviour.
Scope of the Study
This study focuses on examining the effect of AI-generated advertising on consumer purchase decisions among social media and digital platform users who have knowingly or unknowingly encountered AI-generated advertising content, whether AI-drafted copy, AI-generated imagery or video, or AI-personalized ad variants. It is delimited to consumers within the study area who report regular use of social media or digital advertising-supported platforms, and covers the three dimensions of AI-generated advertising (copy, visual/creative content, and personalization), their combined and individual effects on consumer purchase decisions, and the moderating role of perceived authenticity, with data collected within a defined period of the academic session.
Operational Definition of Terms
AI-Generated Advertising: Advertising content, including copy, visual, video, or personalized creative, produced wholly or substantially through generative artificial intelligence tools rather than fully human-led creative processes.
AI-Generated Ad Copy: Advertising headlines, product descriptions, or persuasive text produced using AI natural language generation tools.
AI-Generated Visual and Creative Content: Advertising imagery, illustrations, or video content produced using AI image or video generation tools.
AI-Generated Ad Personalization: The use of AI to automatically tailor advertising copy, visuals, or offers to specific audience segments or individual consumers.
Perceived Authenticity: The extent to which a consumer perceives advertising content as genuine, credible, and sincere, as opposed to artificial, generic, or manipulative, a concern reflected in FTC guidance on the disclosure of AI-generated advertising content.
Consumer Purchase Decision: The cognitive and behavioural process through which a consumer evaluates advertising-driven information and arrives at a decision to purchase, or not purchase, a product or service.
Conclusion
AI-generated advertising, across copy, visual and creative content, and personalization, is a genuine and statistically significant driver of consumer purchase decisions, and the study's findings suggest visual and creative content carries the strongest individual pull. But the size of that pull depends on something advertisers cannot fully automate: whether the content feels authentic. Technically polished AI-generated creative that reads as artificial or manipulative risks undercutting its own persuasive potential, which is why pairing generative AI adoption with transparent disclosure and human oversight matters as much as the technology itself. Readers working on related final-year research can browse more sample research projects on AI adoption, digital marketing, and consumer behaviour for further methodological reference.
Frequently Asked Questions
1. What is AI-generated advertising?
AI-generated advertising is advertising content, including copy, images, video, or personalized creative, produced wholly or substantially by generative AI tools rather than through a fully human-led creative process.
2. Does AI-generated advertising actually influence purchase decisions?
Yes. The study found that AI-generated ad copy, visual and creative content, and personalization each had a positive and statistically significant effect on consumer purchase decisions, together explaining a substantial share of the variance in those decisions.
3. Which type of AI-generated content is most persuasive?
AI-generated visual and creative content showed the strongest individual effect on consumer purchase decisions in this study, slightly ahead of ad personalization and ad copy.
4. What is perceived authenticity in advertising?
Perceived authenticity is the extent to which a consumer experiences advertising content as genuine, credible, and sincere rather than artificial, generic, or manipulative.
5. Why does perceived authenticity matter for AI-generated ads?
Because it moderates how persuasive the content is: AI-generated advertising perceived as authentic produces a stronger positive effect on purchase decisions, while content perceived as artificial or manipulative weakens that effect.
6. Do brands need to disclose when advertising is AI-generated?
Increasingly, yes. Regulatory guidance such as the FTC's updated Endorsement Guides requires that AI-generated or AI-modified advertising content, including synthetic endorsements, be clearly and truthfully disclosed to consumers.
7. What theories explain how AI-generated advertising persuades consumers?
The study draws on the Elaboration Likelihood Model, Source Credibility Theory, and the Persuasion Knowledge Model to explain how consumers process and evaluate AI-generated persuasive content.
8. How was this study conducted?
A survey research design was used, with a structured questionnaire administered to 405 social media and digital platform users who had encountered AI-generated advertising; 385 usable responses were analysed in SPSS version 26 using correlation, hierarchical regression, and chi-square tests.
9. Should brands avoid AI-generated advertising to prevent consumer backlash?
No. The study recommends that brands invest in AI-generated content, particularly visual and creative content, while pairing it with authenticity-reinforcing practices such as transparent disclosure and human oversight rather than avoiding the technology altogether.
10. What should brands prioritise when testing AI-generated ad variants?
The study recommends continuously testing AI-generated ad variants against consumer authenticity perception, not just production efficiency, and avoiding creative that trends toward the uncanny or overtly synthetic in tone or imagery.
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