Himshikha ShuklaWith more than two decades of experience across PR, communications, brand strategy and entrepreneurship, Roshan Mohan, Co-Founder and CMO of FlowBlinq, has spent his career tracking how brands earn attention and stay relevant. Today, that focus has moved into a new territory: AI-led discovery and commerce. As conversational platforms such as ChatGPT, Gemini, Claude and Perplexity increasingly influence how consumers search, compare and choose, Mohan believes brands must learn to become not just visible to people, but understandable and recommendable to machines. In this conversation with Woman’s Era, he discusses the shift from search to AI, the changing customer funnel, and what businesses must do to remain competitive in an AI-first world.
- What key turning points took you from communications to co-founding an AI infrastructure company?
The common thread in my journey has been understanding where attention is moving next. I started my first event management venture at 19. Later, through Pepper Interactive Communications and PCG, I helped brands build visibility and credibility.
The real turning point came when consumers began moving from search and social platforms towards conversational AI. AI was interpreting information, comparing options and influencing consideration. FlowBlinq came from that realisation: helping brands remain visible in a world where machines increasingly mediate attention.
- Across events, communications, content and AI, what has remained constant in your entrepreneurial approach?
Curiosity has been the biggest constant. I have always been interested in change before it becomes obvious to everyone else.
The industries have changed, but my approach is similar: identify a shift, understand its commercial impact and build around the problem.
The other constant is execution. Businesses are built by solving real problems. That is why FlowBlinq was designed not just to identify AI visibility gaps, but to implement fixes and measure their impact.
- When did you realise AI could reshape how brands are discovered and chosen, not just marketed?
The shift became clear when people started using AI to make decisions, not just create content.
If someone asks an AI assistant, “What is the best product for my requirement?”, that is different from a Google search. Search gives consumers links to compare. Conversational AI can perform much of the research and shortlisting before a consumer reaches a brand’s website.
The intent has not changed, but the effort has. People increasingly describe their needs and allow AI to identify suitable options. The question for a brand becomes less “Where do I rank?” and more “Am I part of the answer?”
- What market gap led to FlowBlinq, and why were existing SEO and AI visibility tools not enough?
We saw two gaps. The first was visibility. Businesses had optimised for search engines for years, but AI systems need structured data, schema, entity signals, crawlability and trustworthy information. Our May 2026 study of more than 500 websites across 71 parameters found roughly two in three were effectively invisible to AI systems.
The second gap was execution. Many tools diagnose problems but leave companies with a report to implement. We wanted FlowBlinq to implement fixes as deployable code and monitor visibility across ChatGPT, Gemini, Claude and Perplexity.
Commerce is the next step, with AI agents needing live catalogues, pricing, inventory and transactions.
- How do FlowBlinq’s AI visibility and AI commerce capabilities work together for brands?
We see it as a progression from being understood, to being recommended, to being transactable.
First, we assess how a brand appears across major AI platforms, benchmark competitors and identify gaps in structured data, product information, crawlability and credibility. Then we improve its presence in relevant AI conversations and monitor as models evolve.
Through our AI Commerce Protocol, live catalogues, real-time pricing and inventory can be exposed to AI agents through one integration. AI can then check availability, compare pricing and facilitate a transaction. Visibility gets you into the conversation; commerce turns it into action.
- How is conversational AI changing the way consumers discover and evaluate brands?
The biggest change is that discovery is being compressed. Traditionally, a consumer might search on Google, open several websites, read reviews, visit marketplaces and compare specifications. Conversational AI can synthesise much of that into one interaction.
A customer can explain their needs, budget and priorities, and AI can do much of the comparison.
Brands are therefore no longer competing only for clicks. They are competing for inclusion in the AI’s response. AI systems can consider company information alongside credible third-party sources, editorial coverage and reviews. The brand AI can understand and trust most confidently may have the advantage.
- How does AI-led shortlisting change the traditional customer funnel?
I do not think we are simply redesigning the funnel; we are changing where it begins.
Earlier, awareness was followed by search, website visits, comparisons and reviews. Increasingly, several of those stages can happen within one AI conversation before the user reaches a brand’s website.
AI may now perform the filtering and shortlisting. That makes the funnel less visible. A business may never know it was considered and rejected because its information was incomplete or a competitor appeared more credible.
Marketers need to think not only about traffic and conversion, but whether their brand enters the AI-generated consideration set.
- What will make an AI agent choose one brand over another?
An AI agent needs confidence. It must understand the product, compare it accurately and verify the information through trustworthy sources.
That makes complete, consistent product information critical. Specifications, identifiers, schema, pricing and availability need to be machine-readable. If information is incomplete, the AI may have greater confidence in a competitor.
Credible editorial coverage, genuine reviews and authoritative third-party references become important trust signals.
As agentic commerce develops, live inventory, current pricing and system access for AI agents will matter too. Being a good brand is not enough; the evidence has to be accessible and verifiable.
- What makes a brand visible, credible and recommendable to AI platforms?
There is no single optimisation. AI visibility comes from several signals working together.
AI systems need clear product descriptions, schema markup, entity signals, product codes, specifications and accessible pages. Our research found that 62 percent of Indian brand websites had product descriptions insufficiently detailed for AI systems, while more than half lacked product codes. We also found that 91 percent did not clearly explain product catalogues in a way AI could understand, and nearly half were unintentionally restricting ChatGPT’s web crawler.
Credibility matters equally. AI systems look to independent publications, reviews and trusted sources. A brand becomes recommendable when AI can understand it, compare it accurately and find enough evidence to mention it confidently.
- Which traditional digital strategies will remain relevant, and what new AI capabilities will brands need?
Search, social, performance marketing, content and PR will continue to matter, though their roles will change.
SEO supports structure and discoverability. Social shapes perception. Performance marketing captures demand. Credible PR may become even more valuable because AI systems look beyond a brand’s own claims when assessing trust.
Brands now need an additional capability: Generative Engine Optimisation, or GEO. They need to know how often they appear in AI responses, which questions they appear for, which competitors are recommended instead and why.
Marketers will increasingly track AI citations, share of voice, citation accuracy and AI-generated referrals alongside conventional metrics.
- How will AI agents change the conventional e-commerce journey?
The e-commerce journey will likely become shorter and more conversational.
Today, we discover a product, visit a website or marketplace, filter options, compare specifications, check prices and availability, and then purchase. An AI agent can compress many of those steps into one conversation.
You may describe what you want, and the agent could identify products, compare them, check stock, pricing and facilitate the transaction.
I would still distinguish between “AI helping me decide” and “AI deciding for me”. With expensive or safety-critical purchases, consumers are likely to retain final approval. But research, comparison and logistics can increasingly be delegated.
For brands, e-commerce infrastructure must work for both humans and machines interacting directly with commerce systems.
- How different is building a brand for AI systems versus a human audience?
The fundamentals are closer than they appear. Both humans and AI systems need clarity, consistency and trust. The difference lies in how those qualities are communicated.
Humans respond to storytelling, emotion, creativity and context. AI systems need facts to be explicit, structured, accessible and verifiable. A beautiful campaign cannot compensate for product information a model cannot interpret.
I would not frame this as building brands for algorithms instead of people. Brands still have to mean something to humans. What is changing is that a machine increasingly sits between the brand and consumer.
The best brands will create meaning for people while giving AI enough structured evidence to understand and recommend them.
- What should CEOs and CMOs do today to stay discoverable and competitive in an AI-first future?
First, understand your position. Ask the questions your customers are likely to ask ChatGPT, Gemini, Claude or Perplexity and see whether your brand appears, how it is described and which competitors appear instead.
Then fix the fundamentals. Make company and product information complete, structured and machine-readable. Check schema, entity signals, technical documentation and crawler accessibility. Do not leave important information buried inside PDFs or image catalogues.
Strengthen independent credibility through trusted editorial coverage, genuine reviews and consistent third-party information. Start measuring AI share of voice and citation accuracy alongside traditional SEO metrics.
Finally, prepare the commerce layer. If AI agents are going to check inventory, pricing and transact for customers, your systems need to expose that information securely and in real time. AI readiness is becoming part of core brand and commercial infrastructure.