AI reputation is becoming a live business risk for brands as customers increasingly use large language models (LLMs) to compare products, shortlist vendors, and form opinions before visiting a company website.
Sprinklr’s recent launch of LLM Insights puts that risk into clearer focus. The solution is designed to help brands understand how they appear in AI-generated answers across tools such as ChatGPT, Gemini, and Perplexity.
The launch follows a shift explored in CX Today’s recent interview with Sean Jackson, Lead AI Architect at Sprinklr. Jackson argued that customer discovery is moving upstream into AI interfaces, where brands may be recommended, misrepresented, or omitted before their own channels enter the journey. Jackson framed the problem as an invisible loss of influence:
“Traffic drops, conversations soften, and nobody can point to why, because the damage happened upstream, inside the model, before the customer ever got near your site.”
For CX and marketing leaders, the risk is a combination of lower web traffic and the possibility that pricing, product fit, customer experience, or competitive positioning gets summarized inaccurately at the point where a buyer is narrowing the field.
AI Reputation Starts With Missing or Weak Information
Jackson identified three recurring causes when a brand receives an incomplete, inaccurate, or unfavorable AI-generated answer.
The first is a content gap. If a brand has not published a clear answer to the detailed question a customer is asking, the LLM will draw from whatever related information it can find.
Jackson pointed to the difference between a broad keyword such as “best CRM software” and a detailed customer prompt with constraints, integrations, use cases, and evaluation criteria. He described that gap as the difference between a simple keyword and the full question a customer is actually asking.
Source quality creates a second problem. A brand may have accurate information, but that information may sit in a format or location that does little to shape the AI answer.
Jackson warned that useful content can lose influence if it is not discoverable or properly represented:
“A great answer buried in a PDF nobody can crawl isn’t doing you any good. Meanwhile an unanswered question rotting on a forum somewhere is quietly shaping the narrative, just not the way you’d want.”
The third issue is organizational ownership. Marketing may own the website, Product may own technical knowledge, Care may own the help center and Social teams may manage community conversations.
When no team owns the question “how does AI describe us,” misalignment can go unnoticed until a prospect or executive encounters an answer that does not match the brand’s intended positioning.
Third-Party Narratives Can Shape Buyer Perception
Sprinklr said early LLM Insights deployments found cases where AI-generated answers surfaced competitors more prominently or described brands through inaccurate pricing and product narratives.
Jackson said those issues often came from influential third-party domains, including review aggregators, comparison blogs, analyst reports, and forums. The problem was not necessarily hostile content. In many cases, outdated or incomplete material carried more weight because clearer information was unavailable or harder to access.
In those deployments, brands queried LLMs with prompts closer to the language customers actually use. Examples included questions such as “what’s the best platform for enterprise social management” or “which CX tool has the strongest analytics.”
According to Jackson, the results exposed a practical gap between how brands positioned themselves and how AI tools represented them at the moment of comparison.
