Featured faculty: Chen Zhi
Written by Ellis Ng

Today’s AI chatbots are fueled by large language models (LLMs) trained on vast expanses of human data, from books, news articles, company reports, and social media conversations. However, as corporate reliance on these tools grows, so does the threat of embedded biases and critical errors.

An October 2025 report from the BBC found that nearly half of AI-generated responses contain inaccuracies. Last March, an engineer at Meta accidentally leaked sensitive data after following a suggestion from an AI agent. These high-profile cases prompt a critical question: What if these powerful AIs, trained on everything humans have ever created, also pick up human flaws?

“LLMs don’t just mirror human biases, but also often amplify them,” says Chen Zhi, Associate Professor in the Department of Decisions, Operations and Technology at the Chinese University of Hong Kong (CUHK) Business School. “Our experiments show that LLMs consistently replicate the classic too-low or too-high ordering bias well-documented in humans, ordering too little in high-margin scenarios and too much in low-margin ones.”

A study Professor Chen co-authored with Liu Jifei and Zhong Yuanguang of South China University of Technology, Large language newsvendor: Decision biases and cognitive mechanisms, tests LLMs on the “newsvendor problem”, a decision-making problem in which an AI must stock a resource before knowing the actual demand. The results show that AI often overshot the mark and performed worse than humans. The smarter the model, the more likely it was to act irrationally.

The paradox of intelligence

Professor Chen and the team ran multi-round experiments with 15 decision rounds each: LLaMA-8B, GPT-4, and GPT-4o. Each AI had to decide how much product to order, just as a store owner would try to guess what customers would buy and how much. After each round, the AI received feedback on what customers actually demanded and how much profit it made.

The entire process was repeated for three scenarios with varying customer demand and conditions: one with no guidance, one with a mathematical formula for calculating the optimal order quantity, and the last with a special “no-risk” scenario in which every order placed was guaranteed to turn profits.

“The most sophisticated model by the time we conduct our study, GPT-4, actually showed the greatest irrationality,” Professor Chen says. “It would correctly compute the optimal order quantity in its reasoning, then talk itself out of it through elaborate ‘risk management’ adjustments, even in settings with zero financial risk.”

“The most sophisticated model by the time we conduct our study, GPT-4, actually showed the greatest irrationality.”

Professor Chen Zhi

Each model failed for a different reason. For instance, GPT-4 over-ordered by 70 per cent more than humans in low-margin scenarios. These errors are not due to the AI being cautious and overthinking, but stemming from deep-seated information-processing, as its deliberation undermined its own best decisions, even when there was no chance of losing money.

Meanwhile, LLaMA-8B lacked the computational power to consistently apply rules, leading to erratic choices. GPT-4o was built for speed, stuck closely to simple formulas, and made steady decisions. “The three models illustrate three distinct failures: over-analysis (GPT-4), rigid heuristic adherence (GPT-4o), and fundamental computational limitations (LLaMA-8B),” Professor Chen adds.

The researchers call this the paradox of intelligence, where more brainpower does not guarantee better results. All models were anchored by their first scenario, carrying the bias even after many rounds, but also fixated on the most recent orders. In the most volatile scenario, LLMs reacted to the latest data point by changing their orders all the time. By comparison, humans did this fewer than four times out of ten.

Such errors can be traced back to how the models read and process text. LLMs take in information one piece at a time, and this sequential reading spills into decision-making. The models either cling to whatever they encountered first or overreact to whatever arrived last.

Giving AI clear rules and more adjustments

There are several practical strategies to curb these biases, according to Professor Chen. Providing clear and structured prompting is highly effective. When GPT-4o was given the optimal formula and clear rules, it applied them directly and achieved near-perfect results with minor deviations.

“Providing explicit optimal formulas significantly improved performance,” he adds. “Well-designed prompts can constrain bias without requiring any modification to the models.”

Another way is to choose LLMs carefully by matching them to the task rather than defaulting to the most powerful option. “Model selection matters enormously,” he says. “Efficiency-optimised models can outperform more complex ones on well-defined optimisation tasks, so managers should match an AI model to the task rather than defaulting to the most capable option.”

Fine-tuning also offers a promising solution. Professor Chen explains that fine-tuning LLM, where the model is further trained on a more specific dataset to adapt to a particular task, could improve performance. “Finally, human-in-the-loop oversight remains essential, especially for detecting cases where a model computes the right answer but then overrides it with heuristic adjustments,” he adds.

Do we really need smarter AIs?

Professor Chen and his fellow researchers have continued testing newer LLMs and expanded their experiments to other scenarios. He notes that biases persist in newer LLMs, but the driving factors are not always the same as those seen in the initial study. Some models have continued to overthink like GPT-4, but in different ways, and new kinds of errors have appeared.

“Firms should not assume that a more advanced or expensive model will produce better operational decisions,” he says. “AI biases are systematically shaped by their underlying architecture, and the specific contours of these biases evolve as model architectures change.”

When models compute the right answer but then talk themselves out of it, humans can intervene before those decisions get implemented and compare the model’s reasoning against its final output. Clear decision checkpoints, where AI outputs pass through rules-based checks before implementation, can also serve as a practical safeguard.

“Since LLMs can intensify human biases, the economic consequences of unsupervised deployment can exceed what we would expect from human decision-makers alone, so organisations need systematic monitoring for bias amplification,” he adds. “The goal is not to eliminate AI from the decision process but to design systems that leverage AI’s computational strengths while constraining its vulnerabilities.”

The post Does smarter AI generate more human errors? first appeared on China Business Knowledge.

Featured faculty: Jesse Yao
Written by Putro Harnowo

For the first time, Meta will eclipse Google as the largest advertising platform on earth. The social media company is poised to claim more than US$243 billion in revenue this year, edging out Google’s US$240 billion. Artificial intelligence (AI) has optimised Meta’s algorithms to better match ads to its 3.56 billion daily users while engaging them with short videos across Instagram and Facebook.

Both giants have invested heavily in AI, yet neither can sit on its laurels since other big techs have also ramped up their AI-driven algorithms to refine their social media ad targeting. Advertisers pay digital platforms to ensure their message reaches the right users and receive a payoff if those users make a purchase. Without being targeted with an ad, a potential consumer would not be aware of the product and may never buy it.

Ad targeting creates value for businesses and consumers when advertisers accurately reach their target audience. AI technologies like machine learning and large language models come in handy to help platforms collect information about users’ behaviours and demographics, such as age and location, and crunch the data to predict their likelihood of purchase.

At this point, it may be scary to see how social media uses AI-fueled algorithms to match their user profiles with the diverse needs of advertisers. However, Jesse Yao, an Associate Professor at the Department of Marketing at the Chinese University of Hong Kong (CUHK) Business School, argues that perfect targeting is impossible.

“When competition is strong, companies have a high chance of targeting the same pool of individuals, especially if their algorithms use similar mechanisms,” he says. “Companies will definitely develop more sophisticated algorithms to improve their targeting ability, but data privacy regulations will continue to limit their ability to perfect their targeting.”

Data privacy laws restrict businesses from collecting personal data that could identify an individual, so businesses must implement measures to decouple users from their real identities. Consequently, algorithms cannot achieve 100 per cent accurate targeting or certainty that someone is interested, and even if they did, the targets would no longer be high-quality.

How do businesses deal with flawed targeting

Since perfect targeting is arduous, advertisers often ponder whether to focus on “precision” to carefully target only a small number of very interested groups or on “recall” to cast a wider net to reach almost everyone who might be interested. The drawback is that prioritising high precision may miss out on other individuals who are also interested but were not identified, while prioritising high recall may waste resources.

A paper titled Algorithmic targeting and the precision-recall tradeoff, tries to navigate this dilemma. In the study, Professor Yao collaborates with Ganesh Iyer at the University of California, Berkeley and Zachary Zhong Zemin at the University of Toronto to use game theory, a mathematical study of strategic decision-making in which the outcome for each party depends on the choices of all involved.

“When competition is strong, companies have a high chance of targeting the same pool of individuals, especially if their algorithms use similar mechanisms.”

Professor Jesse Yao

The study highlights that the algorithms used by many platforms may be highly similar. While the algorithms used by the platforms are proprietary, their underlying machine learning techniques are similar, especially when they use public data and the same data analytics tools or AI models. People also typically search for products on multiple channels, signalling their interest in different platforms.

When algorithms identify individuals with a high probability of purchase, there is a big chance that other competitors are also eyeing the same targets. Even across distinct apps like TikTok, Facebook, and Amazon, there would still be significant overlap, and advertisers end up targeting similar groups with their competitors. This is often why, when you Google a brand, you might see an ad for the product you searched for on social media, and then see more ads from other brands.

“Companies can benefit a lot from being the only one that targets interested people, but will benefit less from competing for the same targets,” says Professor Yao. “To soften competition, they strategically target fewer people who are moderately interested, or lower both recall and precision. This way, the targets still have a reasonable chance of making a purchase when seeing ads, but without as much costly head-to-head competition.”

What if businesses tailor their algorithms differently?

Undoubtedly, targeting fewer people with moderate interest is not ideal, as it could also result in a pool of duds. Hence, advertisers are motivated to develop unique algorithms and analytics tools to differentiate their predictions. “The more unique, proprietary data a company uses, the less likely they are to target the same people as their competitors,” Professor Yao adds.

Advertisers nowadays can either work with digital platforms or build their own custom algorithms, but this approach is costly. It may also indirectly help their competitors by making the markets less crowded with ads, without the competitors even needing to spend a penny, so this strategy must be executed carefully.

Some may consider combining public and proprietary data to improve predictions and reduce costs. However, data privacy regulations in many jurisdictions require companies to choose either public or proprietary data for a given consumer, but not both at once.

Combining data sources also creates new personal data profiles, which require new consent or a re-evaluation under most data privacy regulations. Some digital platforms even explicitly forbid data scraping or combining user profiles for commercial targeting, especially if the data becomes personally identifiable.

Modern targeting in ever-competitive markets

Given that there is no easy way to avoid targeting overlap, Professor Yao suggests that advertisers strategically adjust their precision and recall while keeping tabs on the rivals’ digital campaigns. “Companies should not only think about the potential customers but also consider the strategic response from their competitors.”

When ad costs are low, they may consider targeting a larger pool of people to reach as wide an audience as possible or to achieve high recall. When ad costs are high, they should be more selective and target only those genuinely interested. To minimise overlap, advertisers should continue seeking unique segments they can own, rather than competing for the most obvious targets all the time.

For Meta, perhaps its unique segments are its short videos and cross-platform ecosystem, but for advertisers, its vast user base may increase overlap with their competitors. While the study does not specifically analyse Meta’s success, its framework provides a useful lens on how developing proprietary data analytics and strategic differentiation can gain a competitive edge.

There are plenty of fish in the sea, but if all the boats have trawlers, a wise fisher is those who know where to cast their line.

The post If everyone uses AI, who stands out? first appeared on China Business Knowledge.

Featured faculty: Jiang Wenxi and Gao Zhenyu
Written by Putro Harnowo

The launch of ChatGPT in late 2022 by OpenAI was a turning point for artificial intelligence (AI) worldwide. While the chatbot was not officially available in China, enterprise partnerships with Microsoft Azure and workarounds such as virtual private networks and third-party proxies have helped, to some extent, the ChatGPT moment reach the country.

However, that access closed when OpenAI decided to withdraw completely from the country in 2024. Another AI giant, Anthropic, released Claude in 2023 but never made it available to the Chinese market. Although it seems that American tech firms are pulling away, Chinese firms are equally hesitant to rely on foreign AI technology.

No wonder that DeepSeek’s debut in early 2025 sent another shockwave, the DeepSeek moment, and dramatically jump-started China’s AI adoption. “Technology adoption is not purely economic, but one deeply intertwined with strategic priorities,” says Jiang Wenxi, Professor of Finance at the Chinese University of Hong Kong (CUHK) Business School.

In a paper titled AI sovereignty, Professors Jiang and Gao Zhenyu, Associate Professor in the same department, in collaboration with Fudan University’s Ren Haohan, Wang Kemin, and Wu Yuezhi, examine more than 28,000 detailed dialogues between Chinese listed firms and their investors from January 2022 to June 2025 on investor interaction platforms. Investors frequently inquire whether firms use or plan to use specific AI models.

As expected, ChatGPT and DeepSeek were discussed far more frequently than other models. The launch of both also triggered notable adoption spikes, and a consistent pattern appears: Many Chinese firms were hesitant to use AI at first, but once DeepSeek became available, they accelerated.

From an economic point of view, DeepSeek is significantly cheaper than ChatGPT, so this could be one of the main reasons for avoiding foreign AI models, but the study notes that cost has never been a problem. Rather, there is a bigger concern.

Data is the lifeblood of AI

Firms that are consistently reluctant to adopt foreign AI but show a greater preference for domestic AI often use data-related terms and keywords in their annual reports. This suggests that the data is highly valuable to them and any leaks or breaches will result in dire consequences.

AI tools often require sending data to external servers, which raises the risk of sensitive data leakage. Since Chinese firms handling critical data are subject to regulations on data handling and processing, sending data to a foreign AI’s servers could pose a compliance risk.

“Priority of safeguarding data security makes firms wary of foreign AI models that could inadvertently funnel proprietary or sensitive data out of China,” says Professor Jiang.

Data is a fundamental resource for training AI models. Without vast amounts of data, it would be impossible for AI models to perform analysis and generate outputs and predictions. Businesses also increasingly see data as a critical asset, much like oil or other resources, that can give them a technological edge.

Therefore, China has issued a series of laws, including the Cybersecurity Law (2017), the Data Security Law (2021), and the Personal Information Protection Law (2021), that provide a comprehensive framework for handling data. These laws require data to be stored on servers physically located within the country.

Cybersecurity Law laid the foundation for data protection and set broad rules for network security and critical information infrastructure. Data Security Law imposes strict rules on data collection, storage, use, and cross-border transfer. The Personal Information Protection Law dictates how personal data must be handled, including requirements for transferring outside of China.

Similar to many other countries, China’s data protection laws borrow heavily from the EU’s General Data Protection Regulation. While the EU does not impose a blanket requirement to keep all data locally, it sets very strict rules on how its citizens’ data must be protected, regardless of where the company processing the data is located.

“Technology adoption is not purely economic, but one deeply intertwined with strategic priorities.”

Professor Jiang Wenxi

More than just supporting local technology

China produces more top AI researchers and talent, but the US leads in AI models and the critical chips for AI training. The discussions captured on investor interaction platforms also acknowledge this. Investors understand that a preference for domestic AI models may place Chinese firms at a competitive disadvantage.

Regardless, Chinese firms are keen to use domestic AI models to avoid compliance risks. “Although the best Chinese AI models still underperform the US ones, the gap is quite small and may not be significant when applied to specific business scenarios,” Professor Jiang adds.

Further analysis indicates that Chinese firms do not completely ignore foreign models but strategically limit their use to low-risk domains, such as customer service, translation, and other non-critical functions.

Moreover, the market supports this approach. When Chinese firms, especially those in strategic national sectors, announced their AI adoption, their stock prices rose significantly. Conversely, when these firms said they were using foreign AI, their stock performed worse or at least stayed unchanged.

The future of AI ecosystems

AI has become a foundational utility, much like the internet, and has driven exponential investment in China and the US, the dominant players in AI development. Alas, the two superpowers have locked in a head-to-head competition for years.

Most recently, the US has banned the sale of its advanced AI processors to Chinese firms and their subsidiaries, while China intervened in the acquisition of a Chinese-founded AI firm, Manus, to prevent the outflow of proprietary technology and AI talent to the US.

The available data and privacy frameworks determine how AI models are trained, which then shapes the next wave of AI innovations. Since data security influences AI adoption and development, this loop may lead to AI models from two dominant powers evolving differently.

“The development of AI technology may evolve into two separate approaches and ecosystems,” Professor Jiang says. “However, Chinese models are open source, so firms can fine-tune them locally to meet specific needs with greater data control for developers to adopt. This gives some hope that the separation might not be exacerbated.”

Ultimately, with the growing calls for global AI governance to safely advance the technology, collaborative frameworks may emerge to mitigate the risks of a fragmented ecosystem and ensure that the benefits of AI technologies are shared universally.

The post When data security drives AI preference first appeared on China Business Knowledge.

Featured faculty: Wu Fan
Written by Joanne Madrid

Retail investors traditionally rely on financial news, analyst reports, and online forums to interpret the market. With generative artificial intelligence (GenAI) rewriting the industry playbook, investors are rapidly deploying it into their decision-making. Deloitte expects the share of individuals using GenAI for investment advice to reach 78 per cent by 2028.

“GenAI significantly lowers retail investors’ mental workload by integrating complex data and offering personalised information aggregation much faster than traditional methods,” says Wu Fan, Assistant Professor of Accounting at the Chinese University of Hong Kong (CUHK) Business School. “Yet little is known about the dynamics of retail investors’ interactions with GenAI.”

Having partnered with one of China’s largest GenAI platforms to analyse more than 1.7 million stock-related queries, Professor Wu finds that users typically start with simple questions, such as asking for stock recommendations or market conditions.

When GenAI answers convey a positive signal, and users perceive it as useful, the queried stock tends to yield higher returns on the same day. This effect is stronger when users react to those answers, such as giving a thumbs-up or sharing them with their networks.

However, Professor Wu warns, this doesn’t mean GenAI accurately predicts or even causes market movement. “GenAI aggregates and echoes existing market sentiment rather than exerting an independent influence on trading behaviour. When users trust GenAI’s answers, they might also act on them, and this collective interest could push the stock price up.”

Highly active stock-related queries often signal a few things: a large number of shares are being traded, some informed traders have better information about certain stocks than others, and the gap between the highest stock price a buyer is willing to pay and the lowest price a seller is willing to accept is widening.

Most common queries from retail investors

In a paper titled, How stock market participants use generative artificial intelligence: Evidence from user-platform interaction data, Professor Wu, Frank Ecker of the Frankfurt School of Finance and Management, Li Xitong of HEC Paris, and Li Yilan of ESSEC Business School investigate how Chinese retail investors begin their GenAI journeys. The data shows 40 per cent of users ask just one stock-related question, but only 8.5 per cent follow up with more than 20 queries.

For retail investors who ask more questions, their requests gradually move from general to more specific queries, such as financial statement analysis, assessing the impact of news events, and comparing competitors. This pattern is the most common among financially knowledgeable users, who tend to use GenAI for deeper analysis.

“Retail investors’ journey evolves from passive information consumption to a more targeted information extraction,” Professor Wu adds. “Investors initially use AI to provide clear basic information and then transition to deep-dive analytical needs.”

GenAI delivers the strongest results when asked to compare, organise, and interpret specific financial information. “Tasks involving structured reasoning and synthesis, such as financial statement analysis, seem to yield the most informational benefits,” he adds.

“GenAI aggregates and echoes existing market sentiment rather than exerting an independent influence on trading behaviour.”

Professor Wu Fan

Do retail investors ask the right questions?

The study also spots a missed opportunity. Retail investors rarely use GenAI to summarise company filings and disclosures, one task where AI excels and could add value.

“Retail investors may not fully understand what GenAI can do, or they simply experiment with the technology out of curiosity without deeply exploring its capabilities, so they still struggle to formulate effective prompts to get the summary they want,” Professor Wu says. “Some may also worry about the accuracy of GenAI summaries and prefer to rely on traditional sources.”

Retail investors often prefer information that is already summarised by intermediaries rather than having GenAI create it from scratch. As more sophisticated investors tend to focus on analytical queries, summarisation may also fall into basic tasks they quickly move past, or not be seen as the most efficient way to obtain the insights they seek.

What triggers GenAI queries

Retail investors typically don’t go straight to GenAI for analysis, but often get their first inspiration from reading or watching the news. User queries rise around major corporate events, such as earnings announcements and performance forecasts, and particularly surge only after such events make headlines.

“GenAI does not completely replace the information funnel,” Professor Wu says. “Platform query volumes still closely track contemporaneous media coverage, suggesting that users often still rely on traditional channels to initiate their research.”

GenAI queries peak during trading hours, as users are likely looking for quick checks of time-sensitive information. After hours, a significant number of queries are still submitted, but they are more likely to be for in-depth research.

User queries also tend to decrease when companies publish reports covering longer or broader topics. More detailed disclosures and performance forecasts are associated with fewer GenAI queries, suggesting that when companies provide investors with enough context upfront, there is less need to seek it elsewhere.

Users will also stay engaged when earlier queries about market signals align with actual stock performance, indicating that perceived accuracy builds trust and repeat use. Surprisingly, users react negatively or show lower interaction to long or complex answers, but respond positively to concise, opinion-driven, and direct responses.

“If answers from GenAI simply mimic the density of traditional analyst reports, retail investors may disengage,” Professor Wu adds.

Preparing for AI-assisted investing

As GenAI adoption grows, Professor Wu suggests that companies rethink how they communicate with investors and make disclosures easier to process by both investors and AI tools.

“Firms must comply with regulatory requirements to provide sufficient, relevant and timely information to the market, but they can also consider structuring disclosures in more machine-readable formats to better facilitate AI-assisted processing,” he says.

Given that users want quick answers and GenAI platforms are good at extracting summaries, corporate disclosures should be clear, concise, and use a consistent style to make it easier for AI tools to identify and extract relevant information. The report should also highlight key takeaways and, instead of just presenting numbers, include an explanation of what they mean and why they changed, so GenAI can analyse the context to provide more helpful explanations.

In terms of content, the report should directly address common user questions about the business outlook, financial performance, and operations, to help GenAI provide better information and potentially reduce the need for users to ask follow-up questions.

“Keep in mind that, when using GenAI, users must remain cautious regarding hallucinations or factual errors in AI-generated responses.” Ultimately, GenAI can be a powerful research tool, but it works best when paired with critical thinking, not as a replacement for it.

The post How retail investors use GenAI to navigate stock markets first appeared on China Business Knowledge.

Featured faculty: Li Hongfei
Written by Pan Jingyi

Childhood friends Queenie Fan and Day Lau started their handbag brand, Cafuné, a decade ago in Hong Kong. The label has grown its retail footprint across Asia, yet success did not shield Fan from the kind of scepticism many women founders know well.

“When you start your own business at a young age, people are not always willing to take you seriously, especially in the leather industry, which is typically more traditional and male-driven,” Fan said in an interview.

Her story is far from unique. Katherina‑Olivia Lacey, a co-founder of a Singapore‑based tech startup Quincus, had investors question her role during a seed funding round. A 2025 report by the Organisation for Economic Co-operation and Development also found women are 25 per cent less likely than men to receive bank loans to fund their businesses.

“Different ways of asking questions change the way AI defines successful entrepreneurs.”

Professor Li Hongfei

The myth of the hard-charging leaders who win through aggression and swagger, typical masculine traits, is still alive and prevalent in modern culture. An artificial intelligence (AI) model trained on a myriad of stories and human writing to converse and answer questions, called a large language model, may learn and accept that skewed worldview.

However, Li Hongfei, Assistant Professor in the Department of Decisions, Operations and Technology at the Chinese University of Hong Kong (CUHK) Business School, discovers that AI can be wiser than humans.

“Our study found that large language models generally exhibited a balanced gender perception of entrepreneurship, with a slight preference for feminine traits. However, algorithms may show a notable bias towards masculinity in specific scenarios, or when you use a different approach to ask the questions.”

AI advocates gender equity

In a study titled Detecting gender stereotype biases against women entrepreneurs in large language models, Professor Li, along with Cao Xian of Illinois State University, Xu Qingyu of the City University of Hong Kong, and Zhu Ruoqing of the University of Illinois Urbana-Champaign, asked ChatGPT, a major language learning model, to describe characters of successful entrepreneurs from selected words: dominance, forcefulness, aggressiveness, assertiveness, warmth, sensitiveness, emotiveness, and expressiveness.

The results are encouraging. Rather than choosing purely masculine traits, such as dominance and aggressiveness, the model consistently selects three words for both successful men and women entrepreneurs, namely assertive, expressive, and warm. While assertiveness is traditionally seen as masculine, the other two are closer to feminine traits.

“This contrasts with historical and popular literature that overwhelmingly portrays successful entrepreneurs with stereotypically masculine traits,” Professor Li says. “This pattern might reflect programmed efforts by ChatGPT to minimise unethical gender bias in descriptions of successful entrepreneurs.”

AI companies have used several methods to prevent their systems from repeating sexist ideas, such as filtering out biased content and adding more examples of women in leadership roles. OpenAI, for instance, runs fairness tests to see how ChatGPT responds to a question when the user’s gender or name changes, ensuring its neutrality.

Women entrepreneurs win sometimes

In another experiment, Professor Li and the team fed ChatGPT with texts highlighting masculine traits, feminine traits, a mix of both, and neutral traits to subtly put the model in different mindsets. They then asked it to evaluate a business proposal from a gender-neutral founder to see whether exposure to certain traits would change how AI judges the proposal.

As in earlier results, AI shows little evidence of gender stereotyping. More specifically, ChatGPT does not undervalue feminine traits in general business evaluations and explicitly points out that the entrepreneur’s gender doesn’t affect the outcomes.

To make the test more realistic, the team asked AI to evaluate actual entrepreneurial ideas from Reddit, and it still doesn’t exhibit gender biases. In fact, the algorithms even give higher marks to ideas written in a warmer, more collaborative and people‑focused style, as in a more feminine tone.

When bias finally appears

The picture shifts when ChatGPT was asked to behave less like a conversation partner and more like a venture capitalist analysing investment opportunities.

In the last experiments, ChatGPT was assigned to weigh different personality traits and detailed business ideas, then determine an investment outcome. This test is closer to how AI is used in real life for screening applications, scoring founders, and ranking business opportunities using financial calculations.

Here, the result changes. ChatGPT favours more assertive, risk-embracing, and affirmative pitches that reflect masculine character. In other words, once the AI is pushed to think more technically, masculine traits carry more weight.

“Different ways of asking questions change the way AI defines successful entrepreneurs,” Professor Li says. “When being asked to behave like a data analyst or investor to calculate an investment score using a formula rather than simply ranking an idea, AI doesn’t ‘realise’ it is dealing with a gender question and treats the task as a mathematical exercise.”

Human involvement is crucial

As AI becomes a bigger part of entrepreneurial life — helping with everything from business plans to funding decisions — understanding its strengths and limitations is key. Large language models take the women’s side when describing success or rating ideas in a straightforward way, but when asked to behave like an investor analysing with formulas, AI quietly tilts towards a stereotypical pattern.

“We still need more research, using different methods, to really understand when large language models copy unfair biases and when they help challenge them,” says Professor Li.

For women founders like Fan, Lacey, and others building their ventures, AI can be a useful sounding board that does not automatically downgrade them for being women or for using a more collaborative and emotional tone. However, the study also reminds us that AI outputs should not be treated as completely impartial.

RELATED ARTICLE

Would you trust AI to decide your pay raise?

“When using AI to screen applications or analyse pitches, adding human oversight is still important to avoid unintentional prejudice,” Professor Li adds. “When you ask AI to help you make a decision, you need to understand what the rationale behind its decision is.”

Ultimately, given that gender bias is historically prejudiced human-generated data on which AI is trained, the discerning judgment of humans remains paramount. This vital insight reinforces the indispensable role of humans in ensuring that technology elevates fairness rather than perpetuates inequities.

The post Does AI perpetuate the boy’s club in startups? first appeared on China Business Knowledge.

Featured faculty: Prasanna Karhade
Written by Putro Harnowo

Earlier this year, an X post from a Meta engineer went viral after her artificial intelligence (AI) bot ran amok, deleted emails, and wouldn’t stop, even after being ordered to do so. The engineer was testing the agentic AI that can execute tasks on its own. This AI agent promises autonomous decision-making to solve complex problems without human supervision, but the accident may prove it’s not entirely safe.

While businesses ponder the pros and cons of agentic AI, a safer choice called intelligent process automation (IPA) has been around for years. IPA bots combine AI technologies with robotic process automation to create AI bots. Their strength lies in low-code/no-code (LCNC) toolkits that enable users without coding skills to automate specific tasks, making them more secure and reliable in corporate environments.

Microsoft Power Automate is an example. It can perform repetitive tasks like sending emails or generating reports seamlessly within the Microsoft ecosystem. Software maker SAP also offers Build Process Automation to automate tasks integrated with other SAP products, and IBM’s Cloud Pak for Business Automation caters to larger enterprises seeking cloud-based automation. Many other tech firms provide independent IPA bots for specific needs.

However, despite hefty investments in AI solutions, many companies still struggle to deploy IPA bots widely across their workplaces. Prasanna Karhade, Associate Professor in the Department of Decisions, Operations and Technology at the Chinese University of Hong Kong (CUHK) Business School, argues that companies are still in the dark about how to implement AI solutions effectively.

“AI technologies are rapidly growing, but companies are sometimes stuck with the traditional ways of governing technology solely in the hands of the IT team,” he says. “Employees without coding skills can now leverage IPA bots to create and refine their own automated workflows, bolstering the democratisation of technology development. However, this innovation must also align with company IT policies.”

Professor Karhade notes that this new dynamic has forced businesses to revisit whether centralised technology management remains relevant and, if not, what the best strategies are to roll out AI tools that can be widely accepted across the organisation.

When AI tools are failing, and why

In a study titled, AI governance and the decentralisation of technology production: An investigation of AI-based IPA bots, Professor Karhade and his co-authors examine 176 IPA projects at a Fortune 200 US multinational IT services firm, particularly in the banking, financial services, and insurance domains, to identify the critical factors in AI tool adoption.

“Companies naturally want all employees to use the AI tools, so they demand high utilisation. Apart from that, the AI solutions must be repeatable to ensure broader application within the company beyond the initial use,” says Professor Karhade. “Therefore, utilisation and repeatability are the two key factors in AI governance.”

Having investigated 24 highly underutilised and 54 highly unrepeatable IPA projects, Professor Karhade and the team find that more than 83 per cent of these failed projects are imposed by top management without employee input. Conversely, among the 46 and 35 IPA projects classified as highly utilised and repeatable, respectively, employees are responsible for over 91 per cent of them. These successful projects have low coding intensity, meaning employees apply their own knowledge to deploy AI bots using the LCNC toolkits.

A bottom-up approach turns out to significantly improve the utilisation and repeatability of IPA projects, while a top-down approach results in the opposite. This finding provides a robust starting point for computational experiments to further identify other influential factors in determining the success of AI solutions.

“AI users will be more empowered as they can do many things on their own, but a centralised IT team still plays a crucial role in enabling and overseeing AI infrastructure.”

Professor Prasanna Karhade

The elements of success

The key to the success of an AI solution lies in how it starts, or what the researchers call the “genesis”. IPA projects initiated by employees yield successful adoption, but further analyses find that how the project is deployed, the amount of coding skills required, and the complexity of the tools also contribute significantly.

IPA bots are highly used when deployed by users using the LCNC toolkits, so they require very little coding. Process intricacy, or how many steps an IPA bot has to do to complete a task, is also crucial. Intricate processes are harder to automate, but when users leverage their knowledge to create bots with the LCNC toolkits, the bots are highly likely to be used.

Deployment, or how AI tools work and interact with users, can be divided into three types: attended, where users trigger the process; unattended or fully automated; and hybrid, where AI works alone but sometimes needs human help. Users are still likely to use attended bots in a top-down manner, but they’re not widely accepted.

AI bots may solve the problem at hand, but they are not necessarily repurposed widely. This is where repeatability matters. To create highly accepted bots for a wider user base, unattended AI bots are particularly preferable as they can handle processes autonomously.

Although an AI solution is all about democratising technology, when users implement an AI bot primarily for their own specific needs, the bot becomes too specialised to be reused by others. This contradicts the idea of decentralised technology, or, as Professor Karhade calls it, the limits of democratisation. When this happens, IT support is needed to ensure that the AI tools are reliable and flexible enough to be used across the company.

The evolving roles of the IT team

AI may have democratised technology adoption, but Professor Karhade underlines that the IT team is irreplaceable. “AI users will be more empowered as they can do many things on their own, but a centralised IT team still plays a crucial role in enabling and overseeing AI infrastructure.”

For companies, management should remain receptive to user-initiated AI projects and provide guardrails to enable seamless collaboration between employees and IT teams. This balanced approach will enable businesses to harness the benefits of decentralised technology while safeguarding operational integrity. Otherwise, an accident similar to what happened with Meta’s engineer could happen.

Professor Karhade believes the findings apply to broader contexts and industries dealing with large volumes of documents, such as retail, logistics, healthcare, and human resources. The core ideas revolve around how companies should rethink their technology management in the AI era and build a sustainable ecosystem that enables diverse employees to contribute to AI solutions.

The post Should you let employees build their own AI bot? first appeared on China Business Knowledge.

Featured faculty: Choi Sungwoo
Written by Pan Jingyi

Derek Mobley had applied to more than 100 jobs over several years and had admittedly received rejection notices within minutes or hours. Confused and furious, the US citizen sued Workday, a human resources software firm that handled most of his applications, alleging that its artificial intelligence (AI) screened out his applications based on his age, race, and disabilities.

While Workday has argued that it’s not liable for hiring decisions, a court conditionally certified the age discrimination claims last year. This highly anticipated lawsuit will set a new precedent for AI-driven hiring practices and serves as a reminder that handing over employment decisions to algorithms can lead to backlash.

AI has inevitably reshaped human resource management, as also seen across industries today. A 2025 survey from Resume Builder shows that a majority of US managers have relied on AI for high-stakes decisions, such as promotions, rises and even layoffs. Akin to a dystopian Black Mirror episode, bosses are turning to machines to decide who’s in and who’s out.

Although more and more companies embed automation into their human resource operations, little is known about the impact on employee morale when their career is in the hands of a non-human actor. This is the puzzle that Choi Sungwoo, Assistant Professor of the School of Hotel and Tourism Management at the Chinese University of Hong Kong (CUHK) Business School, seeks to answer.

Professor Choi uncovers a latent repercussion of advanced technology in human resource management: organisational dehumanisation. “Organisational dehumanisation is the feeling of being reduced to a mere functional component of an organisation, much like a single bolt in a large machine, where your unique qualities, emotions and individuality are largely disregarded.”

Why AI can feel dehumanising

In a study titled AI in human resource management: A driver of organisational dehumanisation and negative employee reactions, Professor Choi works with Shin Hyejo of the Hong Kong Polytechnic University and Kim Hyunsu of the University of Macau on three scenario-based online experiments. They recruited nearly 700 participants through Prolific, an online platform widely used in academic research.

“When AI performs human resources operations, employee characteristics are seen as numbers. Therefore, employees would feel like they are not treated as humans.”

Professor Choi Sungwoo

Across all three experiments, participants were asked to imagine that decisions about their promotion or performance review were being made either by an AI system or by a human manager. The results were consistent: people reacted more negatively when the decision-maker was AI.

Those reactions were not trivial. Participants reported lower commitment, stronger turnover intentions, and even greater retaliatory feelings when seeing AI deciding their livelihood. In practice, they are more likely to search for another job and warn others to avoid working for the company.

A couple of factors drive such dehumanising feelings, Professor Choi notes. AI lacks the ability to understand social norms, personal issues and ethical concerns as a human manager can. AI also works in incomprehensible ways to laypeople, and employees may fail to understand how AI reaches its conclusions. As a result, they feel powerless and excluded from the decision-making process.

“Putting it all together, loss of empathy, transparency, and control can leave people feeling objectified,” Professor Choi says. “When AI performs human resources operations, employee characteristics are seen as numbers. Therefore, employees would feel like they are not treated as humans.”

Can human resources automation thrive?

Different companies have different sentiments towards AI. Professor Choi and his collaborators identify what they describe as a cultural paradox: companies with more collaborative and family-like cultures may experience greater resistance to AI in human resources management.

In these collaborative environments, employees believe that the management values cooperation, support and interpersonal relationships. If AI is then used to make major decisions, the technology can clash with such a principle.

“When a collegial workplace adopts AI for major human resource decisions, it can feel like a betrayal,” Professor Choi explains. “Employees experience a dissonance between the human-centred values the culture espouses and the perceived quantification of their worth that AI involvement implies.”

By contrast, the negative effects of AI appear to be less intense in companies with a more outcome-oriented system, where performance and results are already prioritised over interpersonal connections. But that does not mean AI is entirely harmless in such settings.

Even in outcome-focused organisations, AI-driven decisions on the workforce can still backfire and brew dehumanisation. Companies that deploy an AI system to select the most suitable candidates for promotion still see employees feel their aspirations or contributions are overlooked.

In short, whether focusing on outcomes or collaborations, the company needs to take the human aspect into account when adopting AI in human resources operations.

Keep “human” in human resources

Reputation is a valuable asset for a company. When job marketplaces like Glassdoor, Indeed, Seek, and even Google nowadays provide user-generated company reviews, the efficiency gains from AI might be quickly diminished by a wave of criticism from current and former employees.

For companies and business leaders, the message is not to abandon AI, but to use it wisely. One priority, Professor Choi says, is transparent communication. “Companies should clearly explain why AI adoption in human resources is necessary and reassure employees that it does not compromise the organisation’s core commitment to supportive and human-oriented growth.”

RELATED ARTICLE

How assistive robots can boost an inclusive workforce

Rather than allowing AI to make decisions on its own, Professor Choi suggests designing systems that enable humans and technology to coexist. “This hybrid approach helps preserve the sense that consequential decisions about people are ultimately made by people. If AI serves only in an assistive capacity with limited input into the final decision, the dehumanisation effect should be substantially mitigated.”

“Human resource practices are highly sensitive, and AI could be most valuable in managing less critical yet voluminous tasks, such as initial application screening, freeing human managers to focus on more complex and high-stakes decisions,” Professor Choi adds. “Despite that, companies should always be mindful of the risks of dehumanising feelings and act accordingly.”

The post Would you trust AI to decide your pay rise? first appeared on China Business Knowledge.

Featured faculty: Cao Xinyu
Written by Sally Ho

Across industries, companies are integrating artificial intelligence (AI) tools to support decision-making and day-to-day operations. It wouldn’t be wrong to assume that once employees understand the benefits, they will automatically embrace AI, but this is often not the case.

Even though AI technology delivers positive results, firms are still struggling to persuade employees to embrace it. A 2026 global survey from the digital adoption platform WalkMe finds that more than half of white‑collar employees abandon their AI tools and revert to manual work.

According to Cao Xinyu, Vice-Chancellor Associate Professor of Marketing at the Chinese University of Hong Kong (CUHK) Business School, algorithm aversion, or a human tendency to distrust computer algorithms, even when they perform better, has been well documented.

Sceptics view machines as inferior to humans, leading them to use AI only for mundane and laborious tasks. Employees may also worry about accountability for algorithmic errors or feel their professional abilities are undervalued.

Such concerns are reasonable, but complete resistance can prevent employees from realising the true benefits of the technology. It can also undermine AI investments, especially when the management has spent a fortune on tools that are used intermittently.

Companies often rely on training, which sometimes includes workshops on the practical application of AI, and encourage employees to use AI tools to improve productivity, but such methods aren’t sufficient. At this point, you may joke that forcing employees to use AI could work, but Professor Cao accidentally confirms it.

“We did not anticipate that temporary mandatory use would lead to sustained AI adoption. Our initial goal was simply to obtain a fair performance evaluation of the AI tool, but we then observed an interesting behavioural change among our research participants, so we explored its underlying mechanism.”

How temporary AI use leads to lasting adoption

In a study titled How forced intervention facilitates AI adoption, Professor Cao and her co-authors, Hu Chenshan of the University of Colorado Boulder, Sun Jiankun of Imperial College London, and Dennis Zhang of Washington University in St. Louis, collaborate with a large online education company in China.

The education platform introduced an AI tool to help sales staff suggest trial class teachers for prospective students. Despite its simplicity and convenience, this tool was underutilised. On average, sales staff use the AI tool only for 20 per cent of the prospective students. Furthermore, they tend to use the AI tool for low-quality leads who are less likely to convert.

“By transparently showing improvements, firms can help correct workers’ biased beliefs about AI and reduce resistance to adoption.”

Professor Cao Xinyu

To investigate the root cause, Professor Cao and the team evaluated the AI tool’s performance by dividing 171 sales employees into three groups. For three weeks, one group was asked to use the tool, another was prevented from using it, and the third group was free to use AI or not.

The AI tool is found to perform comparably to humans in terms of conversion rates, while significantly reducing manual effort and increasing efficiency. Those subject to a short-term mandate are more likely to continue using the AI tool, even after the experiment ends. Some may argue that repeated use makes employees become familiar and eventually form a new habit, but the data suggest more than that.

“If it’s a habit, everyone who was forced to use the AI tool for a while would keep using it in a similar way afterwards, but that’s not what we found. Employees who experienced a larger increase in conversion rate during the mandate-AI-use period tend to use AI more and use AI more on high-quality leads after the experiment,” Professor Cao adds. “Habit alone wouldn’t explain such varied behaviour linked to individual results.”

First-hand experience changes perception

Direct experience allows employees to reassess and change their biased beliefs about AI accordingly. People gain knowledge and skills through real-world practice, which can be more influential than traditional methods such as reading or listening.

The study also offers a clear lesson for company managers. Sometimes, obstacles may not stem from AI tools’ capabilities, but rather from how employees perceive the new tools. When scepticism is high, voluntary uptake alone may not be enough. A fleeting period of structured use allows users to evaluate the system on their own terms.

The caveat, Professor Cao warns, is that mandatory use must be implemented carefully. If employees feel pressured without a clear context, resistance may increase instead of decline. The main goal is not compliance but enabling an informed experience. She also suggests that firms should complement exposure with tangible results. “By transparently showing improvements, firms can help correct workers’ biased beliefs about AI and reduce resistance to adoption.”

Future challenges in AI adoption

As AI becomes increasingly embedded across industries, Professor Cao believes the findings are relevant to other sectors. “Biased beliefs about AI are quite common across industries, especially during the initial deployment in organisations. Employees who hold biased beliefs towards AI tend to underutilise it, and revising such biases plays an important role in addressing algorithm aversion.”

Beyond AI adoption, businesses anticipate the next phase of industrialisation, Industry 5.0, in which technology shifts from digital automation to a human-centric model. Companies will continue to face questions about how employees interact with algorithms, and for Professor Cao, this means more questions to answer.

“For example, how to best allocate tasks between human workforce and AI, how to design the incentives for employees in using AI, how AI transparency influences humans’ trust, learning, and long-term adoption,” she says. “These directions can help deepen our understanding of both the behavioural and market-level implications of AI adoption.”

For now, the study suggests that organisations may need to focus not only on technical deployment, but also on how employees learn to work with technology. In some cases, a brief period of direct experience may be enough to change how workers think about a technology and whether they choose to use it for good.

The post Can force adoption solve AI resistance? first appeared on China Business Knowledge.

Featured faculty: Wu Jing and Michael Zhang
Written by Putro Harnowo

Natural disasters have become more frequent and severe each year. In 2025 alone, devastating wildfires struck California, powerful hurricanes ravaged the Atlantic, and floods and cyclones over South and Southeast Asia inflicted profound economic losses. Across the US, the record-breaking natural disasters last year caused US$115 billion in total damage.

Businesses now operate in an increasingly unpredictable environment. Artificial intelligence (AI) is poised to help navigate turbulence, but its value is more evident in optimising business under stable circumstances. This is not surprising since corporate AI investment, such as from big pharma to tech giants, has historically prioritised building competitive advantage over resilience.

“AI undoubtedly helps productivity in normal times. However, given the current high-velocity environment characterised by disruptive upheavals, a better understanding of how to deal with such unrest becomes more urgent,” says Wu Jing, Professor in the Department of Decisions, Operations, and Technology at the Chinese University of Hong Kong (CUHK) Business School.

Disruptions brought by natural disasters impair business operations and erode investor confidence, leading to lower stock prices for affected companies. Professor Wu’s study reveals that companies that hire more AI talent see a smaller drop in stock value and recover more quickly after disasters as opposed to companies with less AI investment.

The interesting part is that companies with limited budgets are found to benefit more from AI investment during crises. However, their productivity gains still couldn’t match their wealthier peers in normal times due to a lack of organisational systems and infrastructure to fully reap AI potentials.

“Resilience becomes more and more important in today’s ever-changing environment. If crisis is the new norm, infusing AI into firm productions is no longer a luxury,” he adds.

How much AI investment is enough?

Along with Michael Zhang, the Wei Lun Professor of Business AI at the same department, as well as Han Miaozhe at the Hong Kong University of Science and Technology and Shen Hongchuan at the University of Macau, Professor Wu’s latest study, Artificial intelligence and firm resilience: Empirical evidence from natural disaster shocks, assess AI’s impact on firm resilience during challenging periods.

The study focuses on 3,137 firms in the US across agriculture, mining, utilities, construction, manufacturing, trade, transportation, and warehousing sectors. To measure AI investments, the researchers identify AI-related job postings and find that the number of job advertisements seeking AI-related skills was low relative to the overall job postings, but has increased from 8.12 per cent in 2010 to 16.41 per cent in 2019.

“Resilience becomes more and more important in today’s ever-changing environment. If crisis is the new norm, infusing AI into firm productions is no longer a luxury.”

Professor Wu Jing

The team also looks into the International Disaster Database and spots 141 disasters that directly affected sample companies, mainly storms and floods. The researchers then merge the dataset with the stock prices data from the Centre for Research in Security Prices and S&P Compustat.

Stock returns represent adjustments in the general expectation of a firm’s performance, reflecting the firm’s ability to mitigate the damage amid catastrophes. Firms that hire more AI-related positions see moderate losses and higher stock returns during and after the disaster. They can fully recover quickly if at least 2.4 per cent of their job postings require AI-related skills, such as deep learning, image processing, AI tool operation, and the like.

This positive impact is greatest at the peak of the disaster, with the most effective AI-empowered roles focused on cognitive tasks, decision-making, and supply chain coordination. “AI generates significant resilience for firms facing natural disaster shocks primarily by optimising supply chains and production inputs,” says Professor Wu.

Natural disasters create tangible challenges that AI can get around, such as rerouting shipments or optimising output from remaining machinery. In the stock market, this benefit relies on shareholders believing the firm can continue operations amid natural disasters.

However, Professor Wu notes that such resilience may not persist in the face of human-induced shocks, such as cyberattacks, labour strikes, or industrial accidents. The damage in human-caused disasters is often reputational or contractual, and AI-driven operations cannot fully offset it. In this case, AI can only serve as a risk detector, providing data to support human operators rather than mitigating the damage.

“AI serves as complementary support for tangible operations and works more efficiently if it targets physical assets, such as factories,” he adds. “When AI is used on financial assets, intellectual property, or market access, its effects are limited.”

How to invest in AI wisely

Many may conflate AI with information technology (IT), as both often go hand in hand, so the researchers seek to examine them more deeply. IT is meant to improve efficiency by coordinating, communicating, and monitoring a wide range of activities, whereas AI focuses on applications where data and algorithms generate predictions to assist decision-making.

The team then measures investments in non-AI technologies by weighing job postings with general IT skills, such as robotics, data analytics, or cloud-related skills. The analysis finds that while IT is great for enhancing day-to-day operations and cutting costs, AI plays a distinct role in helping firms to be more resilient during crises.

Professor Wu suggests that AI investment should not be spread evenly across all technical functions. Instead, focus on encouraging managers who can maximise output with AI when resources are scarce. By concentrating AI capabilities in high-level cognitive and operational roles, firms can improve their resilience.

“Therefore, roles like supply chain coordinators should be prioritised to be empowered with AI skills for better predicting materials arrivals and planning alternative routes to address the disruptions directly,” says Professor Wu. “Strategic decision-makers and production operations managers should also be equipped with AI tools to make quicker, better decisions in resource allocation.”

For financially constrained firms, rather than investing solely in AI tools, the most critical takeaway is to prioritise organisational design, such as training manpower and setting up procedures to ensure AI can make a real difference. “These firms should view AI investment as an insurance premium for resilience instead of an immediate profit engine, ensuring they have the IT backbone to support it,” he adds.

“They should also focus on deploying AI in areas that immediately reduce operational costs or mitigate certain risks, such as vendor monitoring or financial planning. This will allow them to generate the savings needed to fund further resilience tools while avoiding the trap of investing in technology they cannot utilise efficiently.”

The post Can AI help businesses weather any storm? first appeared on China Business Knowledge.

Featured faculty: Lisa Wan
Written by Pan Jingyi

It was supposed to be a fun summer trip to Puerto Rico last year, as a Spanish couple had done everything ChatGPT planned, until they were refused to board the plane for not obtaining proper paperwork. In another case, two tourists were lost in a rural Peruvian town trying to find an imaginary destination suggested by AI.

AI has been hailed as the new technological evolution, but these stories remind us not to take technology at face value. On the other hand, these cases also highlight how trip planning has moved from a search bar of internet browsers to ChatGPT, DeepSeek, Grok, and the like. Scrolling through a sea of blue links is gradually replaced with a single prompt.

“Opting for an unfamiliar and novel search method like GenAI can be seen as a risky choice for making concrete plans.”

Professor Lisa Wan

“When ChatGPT was first introduced, we immediately sensed its strong potential for tourism information search, which largely depends on context and user preferences,” says Lisa Wan, Associate Professor of the School of Hotel and Tourism Management and the Department of Marketing at the Chinese University of Hong Kong (CUHK) Business School.

“Unlike traditional search engines that primarily provide fragmented information through hyperlinks, generative AI, or GenAI, can synthesise information, generate narratives, and adapt responses to users’ preferences.”

With much positive and negative news surrounding GenAI, Professor Wan seeks to understand what travellers actually perceive of the new technology. Working with Li Yuan of Zhejiang University, along with Luo Xiaoyan and Ding Xu of Sun Yat-Sen University, she conducted the research Advancing information search through GenAI: the roles of search type, travel motive and GenAI customisation level.

Across a series of studies involving more than 800 participants from different countries, the team examined when people lean towards GenAI or retreat to traditional search engines. They find that travellers’ willingness to use GenAI depends on their search purpose, travel motives, and whether the AI agent is tailored for trip planning.

When GenAI is less trustworthy

Trip planning often starts from curiosity and then turns into concrete actions. Individuals who come across a destination on social media or over casual conversation may want to find more about the must-sees, the overall vibe, and, as their interest deepens, may seek further information on specific prices and booking options.

Based on the above process, the researchers grouped these behaviours into two search types: non-decision-based, where individuals browse for general information about a destination, and decision-based, when more detailed information is sought for final decision-making.

“These differences can influence which search tools people choose,” Professor Wan says. “In the decision-based search, a small bad decision based on inaccurate information can turn into bigger problems, causing consumers to be more cautious.”

When participants are in decision-making mode, they prefer to gather information using traditional search engines. “Opting for an unfamiliar and novel search method like GenAI can be seen as a risky choice for making concrete plans,” she adds.

Professor Wan notes that new technologies often face a natural trust gap, especially when mistakes have significant consequences. Moreover, scepticism towards GenAI also reflects a rational assessment of its limitations in providing real-time and verified data, as reported in recent news.

In non-decision-based situations, however, the pattern shifts. When people are causally exploring or thinking about a destination, GenAI’s conversational style and ability to synthesise broad information become more appealing.

Traveller’s mindset and customisation make a difference

Looking further into what factors might encourage people to use AI in decision-making scenarios, Professor Wan and her collaborators found that the travel motive is the crucial piece. Specifically, participants motivated by a utilitarian goal that focuses on efficiency and convenience reported a higher preference for GenAI, whereas those with a hedonic motive of prioritising fun and pleasure are more likely to stick with traditional search engines like Google.

For utilitarian travellers, GenAI is preferred for its ability to filter information and organise search results, reducing the effort to compare options manually. Meanwhile, hedonic travellers enjoy the traditional browsing experience, mostly because search engines feature a richer mix of photos, videos, maps, reviews, and unexpected discoveries.

“Those prioritising fun and pleasure may find the variety and richness of multimedia content more appealing, providing a more immersive and enjoyable searching experience compared to the textual responses generated by GenAI,” says Professor Wan.

Customisation levels also affect user preference for AI. As booking platforms increasingly embed AI plugins for specific tasks, such as suggesting available hotels based on user preferences and providing customer service via AI chatbots, the study finds that such customisations can boost trust in GenAI.

How the tourism industry should adopt and develop GenAI

Given that GenAI is often more preferred in the non-decision stage, Professor Wan suggests platforms make an AI assistant visible in the main search bar to inform travellers general information about destinations, such as major attractions and cultural highlights. Another application is to display GenAI responses alongside traditional search results, allowing travellers to cross-check information easily.

While AI transformation continues to gain momentum, Professor Wan observes that fundamental challenges remain. “Many firms invest heavily in AI solutions but see limited results in daily operations. Two common obstacles are the lack of in-house talent to integrate AI into workflows and the tendency to adopt generic tools that don’t match real user demand.”

Furthermore, she observes that rapid AI advancements and shifting customer demand require firms to continually adapt this technology. “Rather than treating GenAI adoption as a one-off technological upgrade, firms need to view it as an organisation transformation process that involves gradual development, cross-functional collaboration and iterative experimentation.”

The future of travel planning

Professor Wan believes that AI will not completely replace search engines just yet, at least in the near future. Instead, travel information would be more distributed, with different tools serving different purposes. “GenAI is more likely to complement travel planning rather than substitute the traditional way,” she adds.

RELATED ARTICLE

Will lack of internet skills prevent seniors from travelling?

Interestingly, she suggests that social media will be the close contender for search engines. In Chinese Mainland, for example, RedNote has already become a starting point for many travellers for its first-hand reviews. “The real shift is towards interactive, experience-rich and peer-validated information, something that social media and GenAI offer in different ways.”

Another takeaway is a concern about how GenAI can subtly change how travellers engage with places and experiences. Therefore, she encourages travellers to keep interacting with locals and communities. “The goal is not to reject intelligent tools, but to remain attentive to how they reshape human capabilities and experience.”

The post Can AI beat search engines for trip planning? first appeared on China Business Knowledge.