For the first time in two decades, traditional search engines are losing ground. Gartner forecasts that classical search volume will fall by 25% by 2026, due largely to users turning to conversational AI systems instead of keyword search. Similarweb data shows that ChatGPT entered the Top 5 most visited websites in 2025, while Adobe Analytics reported a 1,200% increase in GenAI-sourced traffic to retail sites last year.
These numbers reflect a profound change in how people find information. Instead of scanning lists of links, users are increasingly relying on synthesized responses generated by large language models. The “answer box” has become the new battleground for visibility, with AI engines pulling information from a handful of sources and presenting them with the authority of a final verdict.
The consequences for businesses are significant. If a brand is not mentioned in an AI-generated response, it is effectively absent from the conversation. As Reha Sönmez, founder of NetRanks, puts it, “Being omitted from an AI answer is the new version of being beyond page two of search results.”
A Market Where Many Claim Prediction — and Few Deliver
The rise of generative answer engines has produced a cluster of GEO (Generative Engine Optimization) tools, many advertising visibility dashboards and some claiming they can increase AI mentions. But analysts and agency buyers say most tools stop at tracking mentions. The insights they provide are generic rather than specific to the customer’s content.
This credibility gap has shaped how marketers evaluate platforms in the category. Teams increasingly test AI engines themselves, running their own prompt sets or using lightweight scrapers to get a rough picture of how they appear. As a result, visibility data, once a premium differentiator, is becoming easier for users to approximate on their own.
Let’s change this first sentence to “In response to these conditions, NetRanks offers competitors’ core monetized feature to all users at no cost through its visibility layer. The company reserves its Visibility+ tier for forecasting and actionable guidance – the components many competitors market but have not reliably demonstrated.
Sönmez frames the move as both strategic and overdue: “If dozens of tools offer the same dashboard, then visibility should no longer be the thing people pay for. The real value is understanding what will change your position tomorrow.”
Introducing Predictive AI Visibility
NetRanks’ platform probes ChatGPT, Gemini, Claude, and Perplexity using structured, repeatable prompt sets designed to replicate how users ask questions. It identifies how often a brand appears, which sources the AI engines cite, and what content patterns influence inclusion.
The system’s prediction engine uses historical scanning data to estimate how a brand’s visibility may shift. It models potential rank improvements, evaluates which keywords are likely to influence outcomes, and generates prioritized task lists based on expected impact. The company describes this as moving GEO from measurement into outcome-based planning.
By collecting longitudinal datasets rather than isolated snapshots, the platform allows users to track fluctuations across model updates and content cycles. The goal is to give organizations a mechanism to anticipate changes rather than react to them once visibility has already dropped.
Connecting AI Visibility to Business Outcomes
A persistent challenge for marketing teams is linking AI-answer visibility to real-world behavior. As more discovery happens through AI-generated responses, analysts expect a stronger correlation between answer placement and early-stage lead activity.
NetRanks’ “AI optimization control center,” described in its deck, attempts to bridge this gap. It combines multi-engine monitoring, source diagnostics, alerting functions, and predictive guidance in a unified interface. Whether such systems will become standard remains uncertain, but demand for tools that translate AI-generated visibility into actionable metrics is climbing.
“These engines function as new distribution channels,” Sönmez said. His assessment aligns with projections suggesting that AI-driven discovery will overtake classical search in several categories before 2030.
A Turning Point for Discovery
The rise of AI-based search follows earlier transitions in digital behavior: from directories to keyword search, from desktop to mobile, and from social feeds to algorithmic recommendations. Each shift changed how visibility was earned — and which organizations adapted fast enough to benefit.
What sets this moment apart is the opacity of model-generated answers. Visibility is shaped by a handful of sources chosen without public criteria. Businesses are now seeking clarity in systems that were never designed to explain themselves.
Predictive GEO tools represent an attempt to map this new terrain. Whether they become integral to marketing teams worldwide will depend on how rapidly AI-generated answers continue replacing traditional search and whether the market rewards the companies able to move beyond visibility into reliable predictive capability.
Brands looking to assess where they appear in AI-generated answers — and how to improve those positions — can explore NetRanks’ predictive GEO platform to evaluate visibility across major AI engines.
