Conversational AI may seem in full bloom today, but analysts say it’s just the beginning.
A new study by Juniper Research projects that global revenue from conversational AI services will grow from $14.6 billion in 2025 to over $23 billion by 2027.
Most growth will come from implementation of agentic AI — an advanced AI model that can operate independently, learn from interactions, and automate customer service tasks like answering inquiries and scheduling appointments.
The research team urges conversational AI vendors to integrate agentic AI into their offerings. However, there are few factors that developers should pay attention to as the sector continuously evolves. Thus, early-stage AI agent implementation requires careful monitoring to prevent errors or misleading responses, ensuring business confidence in AI-driven automation. Therefore, AI vendors must balance automation with human supervision while integrating AI into enterprise systems.
Once the market confidence and stable operation frameworks are established, companies that integrate agentic AI into their systems can tap into a rapidly expanding market. Agentic AI helps businesses reduce reliance on human agents, streamlining customer service across messaging platforms. It has an enormous potential for financial services, experiencing the most substantial growth in venture capital activity. Despite the infancy of the technology, startups working on autonomous agents and digital co-workers attracted record funding in 2024, surpassing even highly widespread generative AI applications in customer support operations.
Of course, the mass implementation of AI agents into enterprise management requires solving some sensitive data privacy issues. The study envisions more widespread development of AI-powered enterprise solutions that streamline customer interactions across messaging platforms in the next three years. However, for agentic AI to handle these interactions throughout the entire customer journey, it must be integrated with business support systems that store and manage customer data.
At present, storing and sharing customer data with AI systems raises concerns about breaches, unauthorized access, and compliance with regulations like GDPR or CCPA. Besides, AI’s decision-making capabilities must align with industry regulations as well as the company’s vision to avoid negative outcomes such as misinformation, customer dissatisfaction, or legal liabilities.
Some tech companies are already working on solutions to these core issues. For example, Secret Network and Eliza Labs have joined their efforts to enable AI agents to deliver business results autonomously and securely within encrypted environments. By integrating their respective innovative solutions, the partners hope to enable AI agents to handle sensitive data during transactions and strategic operations across blockchain ecosystems without compromising it.
