Indiantelevision: The Next Marketing Challenge Is Being Chosen by AI

In a June 2026 interview with Indiantelevision.com, Ambika Sharma describes the move from search results to AI answers as a change in control. She sets out three risks for brands, defines AI Visibility Intelligence as a governance discipline, explains how LLMO differs from SEO, and lists the steps brands can take now.

AI hands the buyer an answer

Ambika Sharma opens with the contrast. Google gave the consumer ten links and a choice. An AI answer gives a short recommendation with two or three names and no second page, and the brands in it are the ones considered. On Google, owning your pages was enough to compete. In an AI answer, the deciding factor is whether the model trusts the brand enough to recommend it, and that trust comes largely from pages the brand does not control.

She supports the argument with third-party research on zero-click search, the conversion of AI-referred visitors, and B2B buying behavior. The figures are in the original article.

Three failures stay hidden from the brand

Ambika Sharma maps three risks to the failures NeuroRank finds in brand audits.

• Omission: the brand is absent from the answer to a buying question in its own category.

• Misinformation: the model states something false or outdated as current fact, such as old pricing or a discontinued product. In a regulated category this can amount to misrepresentation the brand never made.

• Competitor displacement: the model gives the brand’s strength to a competitor, or recommends the competitor where the brand belongs.

Each one happens without a bounce, a lost-lead flag, or a missed click. The decision is made on a surface the brand is not watching.

AI Visibility Intelligence is a governance discipline

Sharma defines AI Visibility Intelligence as knowing, measuring, and governing how AI models perceive, describe, and recommend a brand. She compares its role to the one web analytics played for websites. Investors, customers, candidates, and regulators all ask AI about a company, and the CMO owns only some of those subjects. A board answers for what a company says in its filings and advertising, and she argues it now answers for what AI tells the market.

NeuroRank is built as the instrument for that loop. It deconstructs how ChatGPT, Gemini, Claude, and Perplexity see a brand, diagnoses what is broken, prescribes the fix, conditions the sources the models read, and tracks the result month over month.

LLMO works on sources the brand does not own

SEO optimizes one surface the brand controls, its website, for one search engine, measured in clicks. LLMO works across many sources outside the brand’s control and four models that often disagree, and its unit of success is the citation. Ambika Sharma puts the overlap between the two disciplines at about a third.

Her five steps for brands:

• Audit what the models say from clean, un-personalized sessions. A logged-in team account gives a flattering picture.

• Identify the failure: omitted, misdescribed, or displaced. Each has a different fix.

• Condition owned, earned, and third-party sources so the brand’s facts are accurate and consistent everywhere a model looks.

• Measure against buying-intent prompts. A citation on a shortlist question produces a lead.

• Govern continuously, because the models retrain.

Look first, then govern

Her advice for an AI-first market is to audit what AI says now, run the work as a monthly discipline, and move early, since a brand cited today tends to be cited again. In her reading, the brands that win are the ones that looked first and earned a model’s trust before their category got crowded. Budget comes second.

Her opening answer set the frame for the whole interview.