AI Advice Or Subtle Persuasion: Could ChatGPT Influence What You Buy?

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Last Updated:February 04, 2026, 20:07 IST

Following Perplexity’s ad experiments, Microsoft Copilot, Google AI Mode, and Amazon Rufus incorporated ads and come 2026, OpenAI too announced testing ads in ChatGPT

For everyday users, the emergence of AI-driven conversational marketing signals both opportunity and caution. (Image: AI)

Riya Menon, a 29-year-old marketing executive from Kochi, opened ChatGPT one evening to plan a budget-friendly getaway. She asked for recommendations on destinations that fit her limited leave, preferred weather, and travel budget. Within seconds, the AI produced a carefully curated list of options — hill stations in the Western Ghats, coastal retreats along Kerala, and even hidden gems in Rajasthan — complete with estimated costs, travel tips, and seasonal considerations. At first, it felt unremarkable, the kind of helpful digital advice one might expect from a search engine or a travel forum.

But would those recommendations be more persuasive if Riya could see the property or what kind of clothes she should buy, from which brand store? OpenAI’s ChatGPT Search (late 2024) and ChatGPT Atlas browser (October 2025) marked the start of capturing behavioural data to fuel advertising, a turnabout from CEO Sam Altman’s earlier caution about ads in AI. Users have noted subtle paid placements, raising scepticism.

Ajay Verma, digital marketing expert, told News18, “AI-led conversational marketing differs from traditional targeted advertising by shifting from message delivery to real-time dialogue. Instead of pushing predefined creatives to segmented audiences, AI understands intent in the moment and responds contextually, helping consumers evaluate options rather than interrupting them."

Following Perplexity’s ad experiments in 2024, Microsoft Copilot, Google AI Mode, and Amazon Rufus incorporated ads. On 16 January 2026, OpenAI announced it would begin testing ads in ChatGPT’s free version. Ad revenue powers much of the internet already, shaping what users see and click. The pressing question now is whether AI, designed to feel neutral and helpful, can remain impartial once monetisation and sponsored recommendations are embedded into the answers we trust.

Will Your AI Chats Become Commercial?

The appeal of AI-driven chat platforms lies in their ability to deliver answers that feel personal, timely, and contextually intelligent. Unlike traditional search engines, which display lists of links and occasional ads, AI can craft natural-language responses, weaving guidance, recommendations, and instructions into a seamless dialogue. This very capability is what makes conversational AI an attractive avenue for marketers.

Verma who is the Managing Partner at 0101.Today explains how this form of marketing differs from conventional approaches, ““AI-led conversational marketing makes influence feel assistive, not intrusive. Its truly data led intelligent 121 marketing at play. The emergence of Agentic AI will further propagate this, today there are few brands, specially in BFSI space who are the prime movers. Currently seen as experimentative rather than conclusive."

The result is a subtle, yet powerful form of persuasion that can guide decisions without appearing overtly promotional. It transforms the user experience, but also raises the stakes in terms of transparency and trust.

Advice or Promotion: Who Decides?

One of the challenges conversational AI faces is how users perceive its guidance. Are these neutral suggestions, or are they influenced by commercial incentives? The distinction is delicate but crucial, users trust AI when it feels like a co-pilot rather than a salesperson. Clear disclosures about sponsored content and transparency in recommendations are likely to be decisive factors in preserving that trust.

Sheshgiri Kamath, Co-founder at Kapture CX, emphasises the importance of safeguards within AI systems to prevent unintended commercial bias, “Most safeguards today come from how AI systems are built and managed. Good AI systems keep decision-making separate from commercial interests. If a response is influenced by ads or partnerships, that should be clearly stated. Teams also use tools like audit logs and prompt tracking to check why an AI gave a certain answer. Still, safeguards only work if goals are set correctly."

Kamath adds, “Bias usually doesn’t come from bad intent—it comes from what the system is trying to optimize. That’s why oversight matters. AI decisions shouldn’t be a mystery; they should be reviewable. This is why many enterprise teams are choosing in-house, industry-focused models instead of general, ad-driven ones. At Kapture, our models are trained on domain-specific and synthetic data, without outside commercial influence. The goal isn’t idealism—it’s consistency. When AI is part of daily customer operations, even small bias can create real problems."

Verma addresses our concern of whether consumers perceive AI suggestions as advice or promotion, and how does that affect trust by explaining, “Consumers increasingly perceive AI suggestions as advice when the interaction is transparent, relevant, and problem-solving. Trust is built when AI explains why a recommendation is made, aligns with user intent, and avoids over-selling. The biggest reason for failure currently is our eagerness to sell quickly. These are newer tools and tech not magic wands, where the results will be there immediately. When responses feel biased or opaque, they are quickly classified as promotion and lose credibility. Some cosmetic brands have adopted this and are seeing early results. Our advice is to deploy this from an experimentative budget rather than performance, so the outlook is different and the medium does not suffer an early death."

What Users Can Expect From AI-Driven Personalisation?

Transparency and user agency, even if partial, are the cornerstones of trustworthy AI. Consumers may not need full insight into algorithms, but they do need control over how their preferences shape outcomes. Kamath highlights what users can realistically expect in terms of control over AI-driven personalisation, “In the near future, consumers should expect clear choices, not total control. Personalization works much like a navigation app. You don’t control every step, but you choose what matters—faster routes, fewer tolls, or fewer turns. In the same way, users should be able to decide what data is used, what it’s used for, and how long it’s kept. In simple terms, this means- Clear permission for different types of data, Easy explanations for recommendations, Options to reset or limit personalization. Users don’t need to see how the model works internally. What they need is control over the results. The goal isn’t for AI to know everything about you—it’s for AI to know just enough, with your permission."

Riya wasn’t alone in noticing the subtle shift in AI guidance. After receiving her travel recommendations, she paused at a few highlighted resorts. “At first, I thought it was helpful prioritisation," she says, “but then I realised these places were consistently emphasised over equally good options I’d read about elsewhere. It made me second-guess whether I was being steered toward something paid." Riya’s sense of agency — the freedom to explore options based on her preferences alone — was suddenly tinged with doubt.

Similarly, Arjun Sharma, a 35-year-old financial analyst in Mumbai, recounts a recent experience seeking investment advice. He asked an AI platform for low-risk mutual funds suited to his portfolio. “The AI gave a thorough list," he recalls, “but then certain funds came with more detailed breakdowns and glowing descriptions, while others barely got a mention. It felt subtly persuasive, even if I couldn’t see a clear reason why." Arjun admits that if he had known that commercial interests influenced these recommendations, his trust in the platform would have dropped sharply.

“When AI suggestions feel like advice, users engage willingly. But once they sense a nudge toward paid providers or sponsored options, trust diminishes quickly," explains Ajay Verma, Managing Partner at 0101.Today.

What This Means for Everyday Users?

For everyday users, the emergence of AI-driven conversational marketing signals both opportunity and caution. On the one hand, intelligent, context-aware guidance can save time and simplify decisions — whether planning a trip, managing finances, or seeking health information. On the other, the invisible hand of monetisation may subtly redirect choices, challenging the assumption of neutrality.

Experts suggest that transparency, clear labelling, and user agency are key safeguards. Kamath emphasises, “Personalization works much like a navigation app. You don’t control every step, but you choose what matters faster routes, fewer tolls, or fewer turns. In the same way, users should be able to decide what data is used, what it’s used for, and how long it’s kept."

The convergence of personal data with advertising objectives also introduces nuanced privacy risks. The implication is clear, even well-intentioned AI systems must operate with restraint, balancing personalisation with ethical limits. Kamath notes, “The biggest risk isn’t data leaks—it’s making too many assumptions. When AI combines personal behavior with advertising goals, it can guess sensitive things users never shared, like stress, health worries, or financial pressure. While this isn’t prohibited under current data regulations, it can still feel uncomfortable. The real problem is the gap between what users say and what systems assume. That’s where trust breaks down. To fix this, we need to limit not just data collection, but what AI is allowed to infer and act on. In AI, privacy isn’t only about protecting data—it’s about restraint. The systems people trust most will be the ones that know when not to use what they know."

By understanding the AI’s role and recognising where commercial influence may exist users can make more informed decisions without losing trust in the technology entirely. It also highlights the importance of gradual experimentation by brands and platforms, avoiding over-reliance on immediate monetisation that risks undermining the medium.