Adgully: The New Battle for Brand Visibility in AI Search

Adgully: The New Battle for Brand Visibility in AI Search

In a September 2026 interview with Adgully, Ambika Sharma explains why a top Google ranking does not carry into AI answers and how NeuroRank measures visibility as a rate across repeated fresh-token runs. She also sets out what brands need to stay discoverable in AI-first search over the next three to five years.

AI answers name two or three brands

Ambika Sharma starts with the format. Google returned ten links and the reader picked one. ChatGPT, Gemini, Claude, and Perplexity hand back a finished answer with two or three brands already chosen, and the reader acts on that shortlist. A model writing about a category repeats the brands its sources describe most consistently. She expects the SEO industry to dispute her view that backlink counts barely move that answer.

NeuroRank measures three things that decide the citation: whether the brand appears across repeated fresh-token runs, how it is represented, and which sources the answer was built from. Ambika  Sharma also states the limit. The weighting inside a model is not public and shifts with each release, so it cannot be measured.

Visibility is reported as a rate for each model

One screenshot proves little. The four models disagree with each other, and one model can change its answer within the hour. NeuroRank runs a fixed set of a category’s real buyer questions through fresh-token runs over weeks and reports a Brand Inclusion Score model by model, month by month.

The gap between models is the finding. A brand can lead in Perplexity and be missing in Gemini on the same question, and a blended number hides it. Each captured answer is classified through ORHL: Omitted, Replaced, Hallucinated, and Zero Leads. Visibility then reads as a diagnosis a brand can govern, model by model.

Ambika Sharma adds a caution. No language model is deterministic, so any promise of a guaranteed AI answer goes beyond what the technology can deliver.

Third-party sources carry most of the weight

The homepage counts for little in her account. She cites McKinsey’s October 2025 report on how small a share of a model’s reading comes from a brand’s own website. The rest is trade press, reviews, comparison pages, analyst notes, and reference sources such as Wikipedia and the databases behind Google’s entity panels. Models reward consistency across those sources, and a brand described three different ways gets averaged into something no customer would recognize.

She points to the May 2026 ruling by the Regional Court of Munich, which ordered Google to stop its AI Overviews calling two German publishers scam operations, a claim that appeared in none of the cited sources. Shaping those sources is the work NeuroRank calls RAG Conditioning.

Competitive gaps come back mapped

An AI answer has no position one to read. NeuroRank collects signals on a brand and its competitors at scale and runs them through content and EEAT analysis, the technical layer, and the signal strength a model weighs. The gaps come back mapped: where a rival is described more consistently, where its sources carry more weight, and where the brand is absent.

A lead mapped today is defended every month. The same sources stay open to rivals, and the models keep changing.

Three requirements for the next five years

Ambika Sharma separates brands that stay visible from brands that fade on three points:

• An owner on the leadership team who answers for what the four models say about the brand, on monthly numbers.

• Content that serves a reader and a model equally, answering real buyer questions with one consistent identity and genuine EEAT.

• Continuous analysis at a scale no human team can hold.

Her goal for brands is to become template-setters, the brands whose framing becomes a category’s default account.

Ambika Sharma summed up NeuroRank’s measurement approach this way.