The Next Marketing Challenge Is Being Chosen by AI: Ambika Sharma

MUMBAI: If brands once fought for clicks, they are now fighting for citations. That shift captures one of the most significant changes underway in marketing. As consumers increasingly turn to AI assistants for answers, recommendations and purchasing guidance, the traditional rules of digital discovery are being rewritten. Instead of presenting a page of search results, AI platforms such as ChatGPT, Gemini, Claude and Perplexity are increasingly acting as decision-making intermediaries, narrowing choices and influencing consideration long before a consumer reaches a brand’s website.

For marketers, this raises a fundamental question, if AI is telling a brand’s story, how accurate, visible and competitive is that story? From omission and misinformation to competitor displacement, a new set of risks is emerging that many organisations are only beginning to understand. At the same time, concepts such as AI Visibility Intelligence and Large Language Model Optimisation (LLMO) are gaining prominence as brands seek to ensure they remain discoverable and relevant in an AI-first ecosystem. More on this as we have Ambika Sharma, Product Architect, Neuro Rank and Chief Strategist, Pulp Strategy, in conversation with Indian Television Dot Com. READ ON……..

On how AI-powered search is reshaping brand discovery beyond Google. 

Google gave the consumer a list. AI gives the consumer an answer. That is the whole shift in one sentence. On Google, the consumer saw ten links and chose. The brand could compete for the click on every one of them. In an AI answer, the consumer asks a question and gets a short recommendation. Two or three names. No page two. The brands in that answer get considered. The rest do not.

The numbers show how fast this moved. About 60 percent of searches now end without a click. Around 80 percent of consumers rely on AI summaries for at least 40 percent of their searches.

The deeper change is control. Traditional search rewarded the brand that controlled its own pages. AI search rewards the brand the model trusts enough to recommend, and the model builds that trust from sources the brand often does not own. Discovery is no longer something a brand stages on its own channel. It happens inside the machine first.

On why brand representation across AI platforms matters to marketing leaders. 

Because it is happening whether they watch it or not, and most have never looked. Every CMO knows their Google rank. Almost none can tell you what ChatGPT says about their brand when a buyer asks which product to choose. No tool in the standard marketing stack shows it. The brand is being described to buyers every day, and the team has no visibility into what is being said.

The stakes are high because of who these buyers are. AI-referred traffic converts at 14.2 percent against 2.8 percent for Google organic. Five times more valuable per session. These are buyers who arrive informed and close to a decision. If the AI omits the brand or gets it wrong, the brand loses its highest-intent prospects and never sees the leak.

This is why we built NeuroRank. The first time a CMO runs a diagnostic and sees what the four engines say about their brand, the reaction is always the same. They have never looked. What they find is rarely what they expected.

On the key risks of AI-driven recommendations for brands.

Three risks, and they map to three distinct failure modes we see in almost every brand audit. Omission. The brand does not appear in the answer to a buying question in its own category. Not ranked low. Absent. The buyer gets a shortlist of competitors and the brand is not on it.

Misinformation. The model states something false or outdated as current fact. Old pricing. A discontinued product. A claim figure from three years ago. In a regulated category, this can constitute misrepresentation, even though the brand never made the claim. The model said it. The trust damage lands on the brand.

Competitor displacement. The model hands the brand’s strength to a competitor, or recommends a competitor in the exact answer where the brand should appear. This is the most direct money risk. G2 research found that 69 percent of B2B buyers chose a different vendor than they originally planned based on AI guidance, and 33 percent bought from a vendor they had never heard of. Those deals moved, and the brand that lost them rarely knows why.

What makes all three dangerous is that they are invisible. No bounce. No lost-lead flag. No missed click. The decision happened on a surface the brand was not watching.

On AI Visibility Intelligence and its role in brand governance.  

AI Visibility Intelligence is the discipline of knowing, measuring, and governing how AI engines perceive, describe, and recommend a brand. It is to the AI answer what analytics was to the website.

Here is why it becomes governance, not marketing. When AI mediates discovery across every function of a company, what the machine says stops being a marketing concern alone. Investors ask AI about the company’s financials. Customers ask about service. Candidates ask about culture. Regulators ask about compliance. The CMO does not own most of those domains, and right now nobody is accountable for the answer the machine gives.

That is a governance gap. A board is responsible for what the company says about itself in its filings and its advertising. It is now answerable for what the AI says about the company to the market, because that answer is shaping decisions at scale.

NeuroRank was built as the instrument for this. It does not stop at telling a brand it is invisible. It deconstructs how the four engines see the brand, diagnoses what is broken, prescribes the fix, conditions the sources the models read, and tracks the result month over month. That closed loop turns a blind spot into a governed discipline with assigned ownership and documented controls.

On how LLMO differs from SEO and how brands can improve AI visibility.  

SEO optimizes one surface the brand controls, the website, for one engine, Google, measured in clicks. LLMO conditions a network of sources the brand does not own, across four engines that disagree with each other, measured in citations. The disciplines share maybe a third of their muscle. After that they diverge completely.

The critical difference is the source layer. Google sent the searcher to the brand’s page. The AI engine builds its answer from third-party reviews, named industry mentions, structured data, and expert commentary, far more than from the brand’s own site. Optimizing the website harder does not move the AI answer, because the AI was not reading the website to begin with.

Practical steps brands should take now.

Audit what the engines actually say, from clean, un-personalized environments, not from a team member’s logged-in account, which gives a flattering and false picture.

Identify the failure mode. Omitted, misdescribed, or displaced by a competitor. Each one has a different fix.

Condition the sources the models read. Owned, earned, and third-party. Get the brand’s facts accurate and consistent across every surface the model consults.

Measure against buying-intent prompts, not vanity mentions. Being cited when someone asks a general question produces awareness. Being cited when someone asks which vendor to shortlist produces a lead.

Govern it continuously. The models retrain, and last quarter’s cited position is not guaranteed this quarter.

On preparing brands for an AI-first discovery and recommendation landscape.

Stop treating AI visibility as a future problem. The behavior is already here. 51 percent of B2B software buyers now start their research in an AI chatbot more often than Google. AI-driven traffic to retail sites grew 693 percent year over year in the last holiday season. The buyer has moved.

Three things to do now.

First, look. Most marketers have never seen what the AI says about their brand. The highest-value action available today is to audit it, because the gap between what the team assumes and what the buyer is shown is almost always a surprise.

Second, treat this as a discipline, not a campaign. The brands that win govern their AI presence month over month, the way they once managed SEO and social, not run one audit and hope. Models change. Competitors condition their own presence. The position has to be held, not won once.

Third, move early. AI engines learn from what they already say. The brand cited today is cited more tomorrow. The brand absent today is harder to surface next quarter. The window to establish a cited position in a category is open now and will not stay open as competitors move in.

The brands that prepare for an AI-first ecosystem are not the ones with the biggest budgets. They are the ones who looked first, governed continuously, and earned the model’s trust before their category got crowded.

When AI tells your customer your story, is it telling the truth? Every marketer should be able to answer that. Today, almost none can.