Over the past two years, we have witnessed unprecedented acceleration: from research to recommendations, from customer-facing interactions to internal processes, generative AI has evolved from a “promising new technology” to a behavioral infrastructure. It is the new first point of contact between people and information.
Yet, as models become more sophisticated, a frequently overlooked truth emerges: it’s not enough to have a model that works. You need a model that works in the real world. With real users. With unpredictable questions. With unwritten expectations.
And most importantly: based on how it represents your brand.
The limitations of AI trained (and tested) in the lab
Anyone who builds a generative model knows this well: weeks of fine-tuning, prompt engineering, internal validations, quality controls. Everything seems to work. The answers are consistent. Tests pass. Demos run smoothly.
Then the AI is put in front of a real person.
And people do what they always do: introduce noise, skip steps, ask ambiguous questions, mix languages, bring their own experiences. This is why, in complex digital projects, it’s essential to integrate methodologies like crowdtesting, which overcome the limitations of internal tests and observe how real users react to your systems — a concept we explore in “4 Reasons to Integrate Crowd Testing in Development”.
In an ideal scenario, a model can correctly respond to 100 planned prompts. But in the real world, those same models may struggle to interpret a single question from a user who doesn’t follow a script. This gap between the test environment and real-world usage underlies many failures.
Why AI readiness today is also brand readiness
Until recently, a brand’s reputation depended on what appeared in search engines, on social platforms, and in online reviews. Today, an increasing share of information comes from generative systems:
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chatbots integrated into digital services;
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voice assistants;
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advanced conversational tools;
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generative search engines.
When someone asks, “What is the best solution for…?”, they are not just looking for information: they are delegating part of their decision-making process to a system that interprets and synthesizes brands. In this context, the presence of your brand in AI-generated responses — and the quality of how it is mentioned — directly affects visibility, perception, and even conversion.
This phenomenon is closely related to the broader idea of user research and interaction contextualization, which we explore in “UX Research: How to Obtain Quality Results with Crowdtesting”, where we explain how understanding cognitive biases and real user experiences can turn a digital product from functional to truly relevant.
In other words, today we increasingly talk about AI Visibility: a new form of SEO where you don’t just optimize web pages, but your brand’s presence in AI-generated responses. It’s an emerging field but one that is becoming crucial for digital visibility.
The impact on KPIs: how to measure a truly ready AI
AI Readiness is not only about the quality of responses. It’s about a model’s ability to generate measurable value.
Here are some emerging KPIs that can make a real difference:
Brand mention accuracy
Does the model mention your brand correctly and consistently, in line with the desired tone and positioning?
Competitive visibility
In AI-generated responses, does your brand appear, or is it overshadowed by competitors?
Trust & credibility score
Do the responses build trust or create confusion? Do users perceive reliability?
AI search presence
Does your brand appear in generic sector-related queries, or is it bypassed by the AI?
Task success rate in real-world scenarios
When users assign a task to the AI (e.g., selecting a product or solving a problem), does the model guide them to a solution?
These KPIs are no longer just for technical teams; they are relevant for marketing, product management, digital strategy, and SEO — disciplines increasingly intertwined with AI-driven user experiences.
The critical variable: real users, not ideal scenarios
Automated tests and internal validations identify technical errors.
Tests with real people reveal tangible and unexpected consequences.
Crowdtesting allows your AI model to be exposed to a community of real users who:
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don’t know the development context;
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don’t know “how it should work”;
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communicate in natural language;
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bring diverse experiences;
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generate non-standard use cases.
This type of variability cannot be simulated with automated scripts. In this context, human testing becomes indispensable, just as it is crucial for validating complex conversational tools like chatbots, as explained in “Testing Chatbots Pre-Release: Methods and Tools.”
These observations capture contextual feedback, cultural biases, frustrations, interpretation errors, and workflow issues — insights that no automated test can provide.
AI Readiness is no longer just a technical step. It’s a strategy.
An AI model doesn’t just need to “work”: it needs to perform well in the real world, with real users and real scenarios.
AI Readiness is a process that involves multiple business disciplines:
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product management;
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marketing and communications;
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SEO and visibility strategy;
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UX and human-centered design;
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data science and analytics.
It’s a process that connects technical performance to brand visibility, experience quality, generative SEO, digital reputation, and perceived reliability, transforming AI from a simple tool into a strategic business pillar.
Do you want an AI that really works in the real world?
Then it needs to be put in the right hands: those of the users who will actually interact with it.
With UNGUESS, you can test your AI model with real people, in authentic scenarios, gaining actionable insights and measurable KPIs to improve not only technical performance but also brand visibility and user experience quality.
Discover our AI & Digital Experience Testing services (custom crowdtesting for your project).