Nowadays, AI chatbots influence what customers buy, but researchers warn that the biggest risks of using AI in business aren’t bad solutions, but hidden incentives requiring full transparency in the future regulation, behind every AI-powered recommendation.
The role of AI in business analytics to accommodate consumer choices is changing online-built trust.
Ziff Davis, CEO of Vivek Shah, recently expressed to Vox Media’s Channels podcast his concern about the sneaky way chatbots affect our decisions.
“Where we get information matters. And so, if you start to look into citations in LLM chatbots, you’re going to see that sources have gone from journalism sources to marketing sources,” said Shah.
That reveals disadvantages of AI in business and how it can turn mere objective advice into sponsored persuasions. Chatbots like ChatGPT, Google Gemini, and Anthropic’s Claude now reduce data from sources that aren’t always revealed, so users aren’t aware of who might profit from their buying choices.
In Shah’s mind, humans are susceptible to depending on the authority of AI, even when the data informing those recommendations is unclear. It highlights just how essential data quality for AI has become, especially as generative systems start replacing traditional product reviews and research sites.
In a simple experiment pitting four chatbots against one another with the question, “Are Meta Ray-Ban Display glasses a good purchase?”, the results were all over the place.
Some are directed to vendor sites, others to publishers, proving algorithm outputs are not generated equally. Such disparity is part of what researchers call algorithm aversion where people lose faith in AI after experiencing its flaws.
Why Transparency Must be Built In
The issue is not bad advice in generative AI in business operation but how we typically don’t see that advice’ driver. As governments consider how to regulate machine-based recommendations, some experts look for AI to soon be treated like a financial advisor, being required to show its reasoning and reveal its data trail.
For companies learning how to use AI in business development, that change could redefine how AI tools are developed and deployed. Businesses would need to be able to show accountability for every suggestion their systems make, whether it’s product recommendation or internal decision making.
The need for explainability is connected to how to measure ROI of ethical AI implementation in enterprises, because trust can be a business metric itself. Explainable AI systems may strengthen brand loyalty, whereas unexplainable ones can lead to regulatory backlash and consumer distrust.
Despite that, the CEO remains optimistic about the future of AI in business process automation.
“I’m actually very bullish about AI in terms of what it can do in the context of our business, and we’re seeing some really smart implementations right now,” Shah admitted.
As generative systems spread to intelligent business analytics, AI business operations, and process automation, the need for transparency grows. Without accountability, even the sagest chatbots can deepen manipulation, exposing once again the persistent bias risks of AI in business.
AI’s growing presence in decision making isn’t just about convenience, but control. When a chatbot quietly favors one brand over another, that subtle influence shapes markets, trust, and even behavior. The dangers of AI in the workplace and consumer space aren’t only technical; they are ethical.
As organizations scale up AI technologies, they must balance automation and integrity. For example, understanding the advantages and disadvantages of AI in the workplace is a component of building systems that guide rather than mislead.
In project management, new methods like cognitive project management for AI aim to guide responsible adoption, putting understanding and fairness first. The actual danger is not that AI will give bad advice, but that its advice will subtly advance concealed interests.
To protect consumers and ensure safe practical application of generative AI for project managers, regulators and companies alike will need to ensure that every generated recommendation is transparent, traceable, and grounded in trustworthy data. Because as AI becomes a daily advisor, acknowledging and limiting the bias risks of using AI in business can quite likely be the most important safeguard.
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