Our research into conversational AI performance shows clear gains in chatbot effectiveness across several industries and use cases.
Chatbot technology gets better every month. The AI behind the bots understands what users mean far more often than it did a few years ago, and it can reply without a human agent having to step in. We wanted to know by how much, in numbers that a business case can use. So we ran a benchmark and published the results as a whitepaper.
We scored conversational AI systems on accuracy, response time, user satisfaction and business impact. We used all four on purpose. A fast bot that gives wrong answers is no good to anyone, and neither is an accurate bot that people find tiring to talk to.
With modern AI models, average response accuracy improved by 34%. In enterprise deployments, user satisfaction scores went up by 28%. Resolution rates for customer service queries improved by 45% (more questions were settled in the chat itself), and costs were 60% lower than in traditional support channels.
Keep in mind that these are averages. One organization can end up well above or below them, depending on how good its content is, which languages it has to support and how deeply the bot is connected to its back office.
In the whitepaper we go through specific applications across healthcare, finance, retail, and technology sectors. Finance and healthcare stand out, because a good part of the project time there goes to personal data, audit logging and the moment a conversation is handed to a person, not to the language model.
Our expectation is that the pace will stay high and that we will see significant advances in the next 12 to 18 months. If you are starting a project now, take that into account and pick a setup in which you can swap the AI model later on.
Updated on 12 October 2026.
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