Learn about the latest conversational ai technology advances and automation tools for building chatbots and virtual agents
It's no secret, the past 12 months have caused a shakeup in the conversational AI space. Customer service and support channels across every sector have gone almost completely digital during the pandemic, with many businesses having kept and further expanded the infrastructure that was developed as a response to the global crisis.
A 2024 report by McKinsey & Company identified artificial intelligence (especially generative AI) amongst the key technologies more and more companies are taking benefits from. Executives across multiple industries identified AI as helping to position their companies better than previous years. Conversational AI was no doubt a driving factor in this. Chatbots and virtual agents present a scalable solution for reducing the customer service traffic spikes businesses are facing.
Like other technologies that have become indispensable, conversational AI underwent a period of significant growth in 2024. The basic, rule-based chatbot technology that underpins many solutions is no longer adequate. To meet consumer expectations, this technology is now required to provide customer experiences that rival in-person interactions.
This need, from both consumers and businesses, to do more with conversational AI has driven vendors to innovate. We are already beginning to see significant leaps forward in the technology in order to satisfy demand.
Here are seven key technological trends that will drive the conversational AI market forward in 2025 and beyond:
1) Generative AI integration:
With generative AI, virtual agents will craft dynamic, context-aware responses, making interactions feel more human-like and personalized. This allows businesses to create unique, tailored experiences for each user without relying on predefined answers.
As AI learns from interactions, it will continuously improve, ensuring richer, more conversational exchanges.
2) Self-learning AI:
Future AI systems will be able to autonomously learn and update themselves by analyzing vast amounts of real-time customer data. This will reduce the need for constant manual updates and adjustments by AI trainers.
A self-learning AI can refine its responses, expanding its capabilities and relevance in various customer interactions over time.
3) Advanced NLU (Natural Language Understanding):
AI will significantly enhance its ability to understand nuanced human language, managing thousands of customer intents with higher accuracy. This evolution in NLU means fewer misunderstandings in conversations and more accurate handling of complex requests.
By bridging language and meaning, virtual agents will deliver a more natural flow in conversations.
4) API-driven transactions
Conversational AI will move beyond answering FAQs to handling intricate, API-powered transactions, such as order processing and account management. Integration with multiple systems will make these virtual agents capable of executing complex actions directly within chat interfaces.
This enables users to complete tasks without ever leaving the conversation window, improving both efficiency and customer experience.
5) Scalability:
Future AI systems will be designed to handle sudden surges in customer interactions, such as during product launches or crises. They will be able to scale seamlessly without compromising on response time or accuracy. This flexibility ensures businesses can offer consistent support without needing to rapidly expand human support teams.
6) Voice and multimodal interfaces:
AI will incorporate both voice recognition and visual interfaces, allowing users to interact through multiple channels simultaneously. Voice AI will improve, enabling more natural conversations, while multimodal integration will combine text, voice, and visual cues to create richer, more immersive experiences. Legacy phone support systems are also due to be disrupted by a new technology called conversational IVR. Conversational IVR uses advanced NLU and integrations to give users the flexibility to speak naturally to an automated phone system.
This advancement will enhance accessibility and cater to diverse user preferences.
7) Cross-platform agents:
AI virtual agents will bridge multiple platforms, such as websites, mobile apps, and messaging services, providing a unified customer experience. Regardless of the channel a customer chooses, the virtual agent will maintain the context of the conversation, allowing seamless continuity.
This integration will ensure that businesses can maintain consistent service quality across all communication channels.
Businesses will be able to break down departmental and organizational silos, streamlining the customer experience. This implementation of conversational AI has already seen success in Finland, where the government is using a virtual agent network to help foreign entrepreneurs establish new companies in the country.