ElevenLabs debuts Conversational AI 2 0 voice assistants that understand when to pause, speak, and take turns talking

Over the last decade, it has become hard to imagine retail without e-commerce, thanks to the endless digitalization of everyday activities and the unprecedented use of mobile devices. Further enhancing agent expressiveness, Conversational AI 2.0 allows multi-character mode, enabling a single agent to switch between different personas. This capability could be valuable in scenarios such as creative content development, training simulations, or customer engagement campaigns.

Enterprise-grade standards and pricing plans

By analyzing vast datasets, it can provide actionable insights that aid strategic planning. From being just a chatbot, conversational AI is heading toward the core of business strategy—reshaping how decisions are made, problems are solved and value is created. The company recently launched Agentforce in Slack, bringing task-specific digital teammates that can update CRM records, post in channels, and assist with employee onboarding. Early results show Salesforce’s sales team saving 66,000 hours annually through AI assistance with deal insights and executive briefings.

Ethical AI And Explainable Systems

Enterprises must invest in diverse, high-quality datasets and perform regular testing to ensure outputs are inclusive and accurate. I expect this domain expertise to turn conversational AI into a strategic asset—enhancing precision, reducing errors and saving time. Explainable systems can also ensure AI remains accountable, making it easier to detect errors, manage risks and build user confidence. According to a MarketsandMarkets report, the conversational AI market—valued at $13.2 billion in 2024—is expected to expand to $49.9 billion by 2030, growing at 24.9% CAGR. OpenTable handled 73% of restaurant web queries using Salesforce’s Agentforce AI in just three weeks, while payment processor Engine reduced average handle time by 15% and projects $2 million in annual cost savings. Perhaps more intriguingly, Slack will introduce contextual message explanations that activate when users hover over unfamiliar terms, acronyms, or project references.

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A conversational AI solution should be able to use the abundant history available from existing enterprise interactions, including chat and voice transcripts, transactions and other preexisting corpora of enterprise data to learn. What’s more, you need AI that can converse, suggest, recommend and engage based on these learnings. As chatbots failed to deliver on expectations, the enterprise market in particular has turned toward conversational AI platforms, especially in complex use cases such as banking, insurance and telecommunications. The hype that bots would become the next great thing can be attributed directly to app fatigue. Consumers currently spend most of their time using apps created by Apple, Google and Facebook.

From understanding user intent to generating coherent responses, conversational AI platforms help business create lifelike conversations that meet customer needs efficiently. Conversational AI platforms are software solutions that leverage the innovations of AI, deep learning, and NLP technologies to enable automated, human-like interactions between computers and users through natural language. The computer’s ability to understand human spoken or written language is known as natural language processing. NLP combines computational linguistics, machine learning, and deep learning models to process human language. This feature enables the conversational AI system to comprehend and interpret the nuances of human language, including context, intent, entities, and sentiment.

• Regulatory frameworks like GDPR, HIPAA and CCPA demand stringent data handling protocols.
• Future iterations of conversational AI will provide personalized assistants that both serve and predict users’ needs.
• ElevenLabs reinforces these compliance-focused features with enterprise-grade security and reliability.
• Companies are increasingly deciding that many of the AI capabilities they need are strategically important and should be developed in-house.
• And, it is seeing good demand, with one source projecting that the market will grow 20% year on year to $32 billion by 2030.

Certainly, but only if you plan on overcoming the challenges and limitations that prevent it from reaching its full potential. For those interested in learning more, ElevenLabs encourages developers and organizations to explore its documentation, visit the developer portal, or reach out to the sales team to see how Conversational AI 2.0 can enhance their customer experiences. In addition to these core features, ElevenLabs’ new platform supports multimodality, meaning agents can communicate via voice, text, or a combination of both. This flexibility reduces the engineering burden on developers, as agents only need to be defined once to operate across different communication channels. The feature caters to global enterprises seeking consistent service for diverse customer bases, removing language barriers and fostering more inclusive experiences. The true power of conversational AI lies not in its ability to mimic human speech but in how it reshapes decisions, builds trust and adapts to complexity.

Integration With Legacy Systems

Now, however, companies in various verticals are deploying conversational AI to solve more compelling business problems, and many prefer to control the tools and training themselves. Integration capability is an important feature of any modern-day digital solution, especially for conversational AI platforms. Seamless integration with third-party services like CRM systems, messaging platforms, payment gateways, or ticketing systems allows businesses to provide personalized experiences. The market has grown quickly, with hundreds of vendors developing a variety of tools, technologies and platforms for everything from first-generation chatbots all the way up to the most sophisticated conversational AI systems. Thousands of successful deployments over the past few years have shown that conversational AI can deliver 24/7 service, as well as a positive financial ROI. However, its applications have expanded far beyond chatbots and virtual assistants handling queries.

Here is a head-to-head comparison summary of the best conversational AI platforms. As adoption of conversational AI spreads and companies become more aware of its benefits and limitations, finding the right balance between AI and the humans will become more critical, Sutherland says. For example, a biotech firm that’s developing a conversational AI system to assist with the development of novel compounds will likely much more specific data than, say, a mattress store would need, Sutherland says. Assuming your firm’s data is as safe as possible, give your AI systems unhindered access to every database that they need to perform tasks successfully. No one wants to be on an airliner that is short of jet fuel, and companies can’t afford to be in the same disastrous situation with their AI systems. This capability ensures that the agent can recognize the language spoken by the user and respond accordingly within the same interaction.