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Ajay Verma, digital marketing expert, told News18, \u201cAI-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.\u201d<\/p>\n
Following Perplexity\u2019s 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\u2019s 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.<\/p>\n
Will Your AI Chats Become Commercial?<\/strong><\/p>\nThe 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.<\/p>\n
Verma who is the Managing Partner at 0101.Today explains how this form of marketing differs from conventional approaches, \u201c\u201cAI-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.\u201d<\/p>\n
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.<\/p>\n
Advice or Promotion: Who Decides?<\/strong><\/p>\nOne 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.<\/p>\n
Sheshgiri Kamath, Co-founder at Kapture CX, emphasises the importance of safeguards within AI systems to prevent unintended commercial bias, \u201cMost 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.\u201d<\/p>\n
Kamath adds, \u201cBias usually doesn\u2019t come from bad intent\u2014it comes from what the system is trying to optimize. That\u2019s why oversight matters. AI decisions shouldn\u2019t 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\u2019t idealism\u2014it\u2019s consistency. When AI is part of daily customer operations, even small bias can create real problems.\u201d<\/p>\n
Verma addresses our concern of whether consumers perceive AI suggestions as advice or promotion, and how does that affect trust by explaining, \u201cConsumers 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.\u201d<\/p>\n
What Users Can Expect From AI-Driven Personalisation?<\/strong><\/p>\nTransparency 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, \u201cIn the near future, consumers should expect clear choices, not total control. Personalization works much like a navigation app. You don\u2019t control every step, but you choose what matters\u2014faster routes, fewer tolls, or fewer turns. In the same way, users should be able to decide what data is used, what it\u2019s used for, and how long it\u2019s 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\u2019t need to see how the model works internally. What they need is control over the results. The goal isn\u2019t for AI to know everything about you\u2014it\u2019s for AI to know just enough, with your permission.\u201d<\/p>\n
Riya wasn\u2019t alone in noticing the subtle shift in AI guidance. After receiving her travel recommendations, she paused at a few highlighted resorts. \u201cAt first, I thought it was helpful prioritisation,\u201d she says, \u201cbut then I realised these places were consistently emphasised over equally good options I\u2019d read about elsewhere. It made me second-guess whether I was being steered toward something paid.\u201d Riya\u2019s sense of agency \u2014 the freedom to explore options based on her preferences alone \u2014 was suddenly tinged with doubt.<\/p>\n
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. \u201cThe AI gave a thorough list,\u201d he recalls, \u201cbut 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\u2019t see a clear reason why.\u201d Arjun admits that if he had known that commercial interests influenced these recommendations, his trust in the platform would have dropped sharply.<\/p>\n
\u201cWhen AI suggestions feel like advice, users engage willingly. But once they sense a nudge toward paid providers or sponsored options, trust diminishes quickly,\u201d explains Ajay Verma, Managing Partner at 0101.Today.<\/p>\n
What This Means for Everyday Users?<\/strong><\/p>\nFor 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 \u2014 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.<\/p>\n
Experts suggest that transparency, clear labelling, and user agency are key safeguards. Kamath emphasises, \u201cPersonalization works much like a navigation app. You don\u2019t 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\u2019s used for, and how long it\u2019s kept.\u201d<\/p>\n
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, \u201cThe biggest risk isn\u2019t data leaks\u2014it\u2019s 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\u2019t 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\u2019s 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\u2019t only about protecting data\u2014it\u2019s about restraint. The systems people trust most will be the ones that know when not to use what they know.\u201d<\/p>\n
By understanding the AI\u2019s 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.<\/p>\n<\/div>\n
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