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.”

The post Can you truly trust AI recommendations? first appeared on China Business Knowledge.

Featured faculty: Philip Zhang Renyu and Jenny Jin
Written by Joanne Madrid

Large language models or LLMs, the AI systems behind chatbots like ChatGPT, are increasingly being used as stand-ins for humans in behavioural research. Dubbed synthetic surveys or silicon sampling, people can now ask AI to complete surveys at a fraction of the cost and time of human participants.

AI startups like Simile, Aaru, Artificial Societies, Uxia, and many others offer human behaviour simulations to predict market trends, consumer preferences, and even political views. This synthetic survey is a direct application of social simulation, in which LLMs act as humans in experimental settings.

Yet there is a catch. Critics say that social simulation may not be a reliable substitute for human respondents, as AI sometimes struggles to capture nuanced opinions and shows bias due to its vast training data. Although LLMs have successfully reproduced simple experiments, they often fail to behave like real humans in complex scenarios.

According to Philip Zhang Renyu, Professor in the Department of Decisions, Operations and Technology at the Chinese University of Hong Kong (CUHK) Business School, such limits may stem from a deeper structural challenge in how LLMs understand and reason about their tasks.

“LLMs in behavioural and social simulations can fail not only because of flawed reasoning, but also because of the limitations in how they interpret their tasks,” he says. “Even if they understand the tasks, their reasoning process may not align with human thinking.”

Professor Zhang’s latest study, Improving behavioural alignment in LLM social simulations via context formation and navigation, co-authored with Jenny Jin, Assistant Professor in the same department and their PhD student Kong Letian, proposes a two-stage method to improve LLMs’ ability to simulate human behaviour.

Beyond clever prompts

Most AI users engaged in prompt engineering have already recognised the importance of clear, specific instructions, but many popular prompts still fall short in social simulations. Therefore, the study aims to find whether a more comprehensive approach is more effective.

Specifically, Professor Zhang and the team turn to a classic Human Problem-Solving Theory developed by Herbert Simon and Allen Newell. Both celebrated scientists, with the help of programmer Cliff Shaw, developed Logic Theorist, the first computer programme designed to perform automated reasoning that marked the birth of AI.

Simon and Newell argue that humans solve problems by forming a mental picture of the task and selectively searching for solutions within it. Inspired by this theory, the team then develops a two-stage framework to better align LLMs with human behaviours in social simulations.

The first stage is context formation, or explicitly specifying experimental design so LLMs can understand the tasks and their background by defining their goals, what information matters, and how all the pieces fit together. The second is context navigation, or guiding the reasoning process so LLMs can further strategise, update their assessments, and weigh the best options.

For LLMs to behave like humans, Professor Zhang notes that the two-stage framework, context formation and context navigation, should be clearly defined so the AI understands the task and its role perfectly.

“Even really smart AI still needs a crystal-clear instruction to make good decisions. While AI sometimes can reason autonomously in simpler tasks, context navigation remains crucial in complex scenarios to choose the most relevant behavioural signals.”

Professor Philip Zhang Renyu

Comparison with popular prompts

Current popular prompting techniques can improve task performance but are not always suitable for social simulations. For instance, chain-of-thought prompting focuses on step-by-step instructions, yet if the AI misunderstands the rules and setup, it often yields inconsistent results. Few-shot prompting guides the AI model with examples, but it may not be reliable when the social experiment differs from the examples.

Tree-of-thought prompting asks the AI model to explore several paths to choose the best solution, but it fails to determine which one is most human-like. Meta-prompting can organise reasoning by designing a reusable prompt, but still requires humans to specify the setting and the standard for a valid result.

“Context formation provides a systematic way to explain the rules and setup of an experiment to the AI models, which helps it understand and adapt to specific scenarios rather than relying on a few examples or generic instructions,” Professor Zhang says.

“Context navigation directs the AI models selectively towards the crucial information and strategic considerations, rather than requiring an open search across reasoning paths. These stages help the AI figure out why it still mismatches human behaviour and see if it misunderstood the setting, if its reasoning was incorrect, or both.”

Putting the method to the test

To evaluate the framework, the team tested it across three existing social experiments with publicly available human data to directly compare the method against real human choices. The instructions in these experiments were then given step by step, as in chain-of-thought prompting, to the most capable LLMs at the time — GPT-4o, GPT-5, Claude 4 Sonnet Thinking, and DeepSeek-R1 — to see how far they replicated human behaviour.

The first human experiment found that the waiting time to get a product can signal its quality. When the same instructions in the experiment were fed to LLMs, all models boiled the problem down to a cost analysis and ignored social cues humans perceived.

Adding context formation that some queuing buyers already knew the product’s quality helped the LLMs understand the instruction, but still could not replicate human behaviour. Only after providing context navigation, that queue length can be a meaningful cue for quality, did the LLMs start to act like human shoppers.

The second experiment asked humans to choose whether to invest in a project without knowing its true quality. Some project owners offered investors a refund if they failed to reach full funding, which human investors viewed as a signal of a good project.

However, even after being provided with investment rules, all LLMs significantly funded fewer good projects than humans. Only when the LLMs received clear context navigation to interpret the refund scheme as a positive signal did they replicate human investors.

Not every situation demanded both steps. The third experiment simply found that human purchases decreased as the price increased. Oddly, upon receiving the instructions, LLMs bought more when the price rose, and as the cost soared further, they eventually reduced their purchases.

When LLMs were given a clear explanation of the experimental setup, they successfully replicated human behaviour. Here, providing context formation was sufficient. The contrast points to a useful rule of thumb: simple choices often need only context formation, while more complex scenarios involving several parties require a two-stage method.

As AI models improve, explicit guidance on how to reason may matter less for simpler tasks since the models can work it out for themselves, but this doesn’t mean that context navigation becomes less important. “Even really smart AI still needs a crystal-clear instruction to make good decisions,” Professor Zhang adds.

“While AI sometimes can reason autonomously in simpler tasks, context navigation remains crucial in complex scenarios to choose the most relevant behavioural signals.”

The post Can AI really mimic our decision-making? first appeared on China Business Knowledge.

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: 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: 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.

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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.

By Pan Jingyi, Principal Writer, China Business Knowledge @ CUHK

Following the launch of ChatGPT in late 2022, artificial intelligence (AI) has supercharged the possibility to revolutionise the modern industry, rushing Microsoft to invest US$10 billion in OpenAI. This step signalled a race among big tech to incorporate AI into their products. A few months later, Meta introduced its own AI called LLaMa, while Apple recently announced its “Apple Intelligence” system by integrating AI into its gadgets.

The US is not the only one that is trying to get its hands on AI. When OpenAI launched its video-generation model Sora in February this year, many were amazed by its ability to create videos just by typing a cue on a keyboard. Fast forward in June, China’s short-video platform Kuaishou unveiled a text-to-video model named Kling, making it a worthy opponent for Sora. The UK, France, Germany, Israel, India, Japan, and Singapore have also entered the ring in the AI competition.

“AI will not replace humans, but those who don’t make good use of AI [will be replaced].”

Professor Dominic Chan

According to a 2024 AI index report by Stanford University, generative AI funding surged to US$25.2 billion in 2023, nearly nine times higher than the previous year and 30 times the amount recorded in 2019, with generative AI made up more than a quarter of all AI-related private investment. The report also indicates that AI outperforms humans in certain benchmarks like image classification and English understanding, but it still falls short in complex cognitive tasks.

But not everyone is amused. A Global views on AI 2023 survey by market research firm Ipsos found widespread concern about the negative impacts of this advanced technology on employment. Approximately, 57 per cent of 14,782 working adults across 31 countries anticipated that AI would change the way they do their current jobs, while 36 per cent were worried about AI taking their jobs.

How is AI going to impact our society and transform industries? What can people do to get themselves ready for this new era? Dominic Chan, Associate Professor of Practice in Entrepreneurship of the Department of Decisions, Operations and Technology at the Chinese University of Hong Kong (CUHK) Business School, shared his thoughts on these questions in a masterclass for the school’s EMBA programme titled Embracing technological disruption: Thriving as leaders in the age of AI in April.

Assisted intelligence

In a nutshell, AI refers to the technology that enables computers and machines to emulate human intelligence and tackle problem-solving tasks. The words people hear a lot such as machine learning, deep learning, natural language processing and generative AI, are all interconnected fields within the broader domain of AI. These advanced technologies have been applied to various sectors and industries to streamline processes and improve efficiency. In the realm of business, AI encompasses a wide range of applications, including chatbot assistants, fraud detection, and task automation, among others.

“AI can help you to organise your information or help you automate the process, which makes a big difference in productivity and quality of the work,” Professor Chan says, adding that AI can amplify human abilities.

Professor Chan also highlights that people should view AI as “assisted intelligence”. This perspective underscores the notion that AI serves as a tool to assist people rather than replace them. He notes that AI lacks the ability to think independently and operates based on the training it receives from human input. “AI makes decisions based on mathematics not ethics,” he adds.

Will I lose my job to AI?

Along with the emergence of new technologies throughout history, the topic of job security has once again become a prominent concern, with people expressing apprehensions about being replaced by AI.

To illustrate which types of jobs are most threatened by AI and which types of jobs are likely to be safer, Professor Chan refers to a model proposed by a renowned businessman and computer scientist, Lee Kai-Fu, in his book titled AI superpowers: China, Silicon Valley, and the New World Order. According to this model, jobs that primarily require optimisation rather than compassion to be most likely to be replaced, such as truck drivers. Conversely, jobs involving compassion, creativity, or strategic thinking, such as social workers, are less likely to be replaced.

Furthermore, from the analysis of Age Earning Profile, Professor Chan observed very clear shifts in income trends across generations as people age, indicating that the income curve peaks at an earlier age with each subsequent generation. Separately, Professor Chan observed those born between 1986 and 1995 earning less than the previous generations. This phenomenon is partly attributed to the rapid development of technology. Knowledge and skills accumulation held significant value before the 1990s because computers, the internet and AI had not yet become widely available. Now, with the advancement of technology, human knowledge and skills depreciate rapidly.

“The way for most people to create value today is the ability to use technology,” says Professor Chan.

AI poses challenges not only for rank-and-file employees but also for the management. A 2023 report titled Leadership in the age of AI by IBM indicates that 82 per cent of more than 1,600 senior leaders surveyed have deployed or planned to implement generative AI in 2024. Alongside opportunities, the report highlights that business leaders across all sectors grapple with challenges related to skills, ethics, privacy and data security.

“In the past, managers primarily managed people, but today they have to manage the machines as well,” says Professor Chan. “Managers also need to oversee and facilitate the technological interactions between their own organisations and other firms.”

Make good use of technology

While AI has undoubtedly reduced tedious work and enhanced productivity, it is not without flaws. For instance, if you ask AI to generate a picture on the subject of a secluded temple on a mountain, it can present you with glorious images of temples and mountains. However, a human tasked with the same assignment may have different outcomes, which would feature symbols or metaphors that go beyond the literal interpretation of the request. Professor Chan notes that AI doesn’t think by itself but relies on what humans have already done.

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Whether their current job falls into a safe or dangerous zone, everyone needs to be well-prepared for the future coexisting with AI. Professor Chan suggests that individuals who possess complex problem-solving skills, critical thinking, creativity, communication, and compassion (or he calls them 5C abilities) will be better equipped to deal with the challenges ahead.

Finally, Professor Chan encourages people to embrace AI technology and enjoy the ride, as it will make individuals more productive. “AI will not replace humans, but those who don’t make good use of AI [will be replaced].”

The post Why you should and shouldn’t be afraid of AI first appeared on China Business Knowledge.