Can you truly trust AI recommendations?

The digital world is awash with AI-spun content as companies exploit generative engines for visibility, but this game of influence could backfire

Featured faculty: Tony Ke and Liao Chenxi
Written by Putro Harnowo

Generative AI, or GenAI, has rapidly changed how consumers find products. AI chatbots like ChatGPT, DeepSeek, and Claude AI have become the go-to tools for daily inquiries, much like Google in the old days, while traditional search engines like Google and Bing are stepping up by displaying AI-generated answers at the top of search results.

No surprise that American software company Adobe reported traffic from AI sources to retail sites surged by almost seven times in 2025 compared to the previous year. This shift has propelled a new digital strategy called generative engine optimisation (GEO), which aims to ensure the product shows up when users ask an AI a specific question.

Since GenAI is trained on massive amounts of data, companies can increase their visibility by creating synthetic content, or favourable product mentions across articles, blogs, social media, online forums, and more. The goal is to let AI models learn that their products are desirable, thereby increasing the likelihood of being recommended.

“A particularly powerful GEO strategy is deliberately crafting synthetic content so that GenAI learns and reproduces desired associations between consumer questions and product offerings,” says Liao Chenxi, Associate Professor of Marketing at the Chinese University of Hong Kong (CUHK) Business School.

For instance, a brand covertly creates blogs or articles on the top 10 products in the market but disproportionately highlights its own products, or posts a question on forums like Quora or Reddit and then uses another account to give a detailed answer recommending its own products.

Not to be confused with fake reviews, synthetic content is strategically created to influence GEO by feeding information that can be easily parsed by AI engines without explicitly targeting consumers. When content becomes misleading to consumers or includes fabricated claims, such as fake reviews, it can be considered deceitful.

In fact, the abundance of fake reviews has driven regulators in the US and the UK to ban them altogether. While it is not meant to mislead consumers, at its core, synthetic content aims to influence GenAI, which some consumers may consider as manipulation.

In her new study titled Synthetic corpus and consideration manipulation in generative engine optimisation, Professor Liao finds that, despite helping increase visibility, firms caught red-handed deploying synthetic content are perceived by consumers as lower quality. However, banning it entirely may backfire, as synthetic content can help consumers discover good products.

“AI engines work like a ‘black box’, but some consumers have an expectation that content manipulation exists on the internet, so they do not always blindly follow what GenAI recommends.”

Professor Liao Chenxi

Mapping player interactions with GenAI

In collaboration with Tony Ke, Professor of the Department of Marketing, and Xu Xiaoyan of Southwestern University of Finance and Economics, Professor Liao uses game theory to examine how firms strategically deploy synthetic content to influence consumers and GenAI, and how consumers respond to the firms’ actions.

The game-theoretic model in the study involves a firm selling a product of either high or low quality, GenAI, and consumers. Quality products naturally attract plenty of positive reviews, so a high-quality firm can rely on genuine, organic content. A low-quality firm has little organic content, so it must create synthetic content to make its product appear more attractive.

GenAI treats all content, whether organic or synthetic, as input data for its recommendation system. If the content provides sufficient evidence of a useful product, GenAI will recommend it. Consumers choose the product based on perceived quality, but are unaware of its true quality.

Before deciding, consumers go through a three-stage purchase funnel. The first is the consideration stage, in which consumers discover products. In traditional digital marketing, search engines and ads serve as beacons to discover the products, but consumers nowadays turn to GenAI.

Next, in the evaluation stage, consumers typically check other sources to confirm product quality. Consumers who consider GenAI recommendations then conduct independent evaluations. While some consumers fully rely on AI, the model focuses on how AI influences the options consumers consider. The same content that drew the AI’s attention serves as evidence for consumers to decide.

However, consumers are not entirely fooled. Some still cross-check with third-party sources and verify product quality through their own inspection. If they find out the content recommended by GenAI is synthetic, they will perceive the featured product negatively, regardless of its actual quality. GenAI would not be blamed, as consumers use it primarily to find products.

“After initial discovery with the help of AI, consumers have many independent channels to evaluate the product,” says Professor Liao. “AI engines work like a ‘black box’, but some consumers have an expectation that content manipulation exists on the internet, so they do not always blindly follow what GenAI recommends.”

In the final stage, consumers have formed expectations about the product’s quality. When the perceived quality matches personal preferences, consumers are more likely to buy it.

To manipulate or not to manipulate?

Synthetic content may successfully trick AI models, but it is not always effective on consumers. If consumers are good at spotting it, high-quality firms can keep their reputations by avoiding it, but risk being thwarted by synthetic content from low-quality rivals. High-quality firms can still leverage synthetic content, but the benefits depend on other market players.

If low-quality firms promote extremely poor products, allowing synthetic content is detrimental as it can harm consumers. For high-quality firms, the reputational damage from being caught using synthetic content becomes so severe that they risk being mistaken for having awful products, too.

However, when low-quality rivals promote moderate-quality products, synthetic content from high- and low-quality firms broadens market demand and helps all products be recommended by GenAI without risking severe reputational damage if consumers find out. High-quality products that lack organic buzz can benefit from a synthetic nudge to be endorsed by GenAI.

Ultimately, given that being spotted using synthetic content may lower consumer trust, Professor Liao emphasises that the strategy should be applied wisely. For firms with genuinely high-quality products, another way to improve GEO is to optimise organic content by encouraging satisfied customers to leave reviews and fostering genuine discussions on social media and online forums.

“When real consumers spontaneously praise a product online, it naturally provides quality data that AI engines incorporate in their algorithms, increasing the recommendation probability,” she adds. “Companies absolutely need to pay attention to GEO to stay visible, but it doesn’t mean that it can substitute traditional organic signals. In essence, GEO is a marketing tool for visibility. To build real trust, firms must make sure their product quality matches their claims.”