Over the next three years, conversational AI will generate about $57 billion in revenue, originating from enterprise spend on these platforms as companies begin to understand the benefits of enriching their services with agentic AI, a new study has found.
Specifically, global revenue from conversational AI services will grow from $14.6 billion in 2025 to more than $23 billion by 2027, according to the recent study published by Juniper Research, an analyst house specializing in telecommunications markets, on February 18.
In 2023, the conversational AI market was valued at $10.57 billion, with chatbots accounting for its largest share, according to the data published in July 2024 by The Brainy Insights, which at the time also predicted that the market would reach $97.64 billion by 2033.
Per Juniper’s experts, agentic AI, which is a subset of AI empowering solutions to act independently to reach a specific preset objective, while also learning from previous interactions, is attractive to enterprises for a number of demonstrated use cases.
These refer to task automation, for instance, service inquiries and appointment scheduling through various conversational channels, which would result in less reliance on human agent intervention.
Commenting on the study’s findings, its author Molly Gatford explained:
“Conversational AI vendors must carefully moderate the outputs of agentic AI models during early-stage implementations. (…) Issues around liability arising from hallucinations or erroneous communications must be avoided before enterprises’ trust in agentic AI can be established. This will best position conversational AI vendors to capitalize on this substantial revenue growth over the next three years.”
Hence, the report has urged conversational AI vendors to integrate agentic AI into their communications technology stack. This would allow them to create enterprise solutions that automate customer interactions across messaging channels. That said, this integration would require an interplay with business support systems holding customer data.
Agentic AI vs. generative AI
As a reminder, agentic AI differs from generative AI in that it takes autonomous capabilities to a whole new level through a digital ecosystem of large language models (LLMs), machine learning (ML), and natural language processing (NLP) to carry out tasks independently, on behalf of the user or another system, without the explicit need for human prompts or oversight.
On the other hand, while offering similar creative abilities, generative AI, such as ChatGPT, can create original content, such as text, images, video, audio, or software code, in response to a user’s prompt or request. To accomplish this, it relies on deep learning models – algorithms simulating the learning and decision-making processes of the human brain – and other technologies, including robotic process automation (RPA).
In other words, agentic AI focuses on decision-making, problem-solving, autonomy, interactivity, and planning, whereas generative AI’s primary focus is on creating the actual new content, data analysis, adaptability, and personalization.
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