While many pursue human-indistinguishable AI, ElevenLabs' CEO argues this misses the point for use cases like customer support. Users prioritize fast, accurate resolutions over a perfectly "human" interaction, making the uncanny valley a secondary concern to core functionality.
A one-size-fits-all AI voice fails. For a Japanese healthcare client, ElevenLabs' agent used quick, short responses for younger callers but a calmer, slower style for older callers. This personalization of delivery, not just content, based on demographic context was critical for success.
Don't worry if customers know they're talking to an AI. As long as the agent is helpful, provides value, and creates a smooth experience, people don't mind. In many cases, a responsive, value-adding AI is preferable to a slow or mediocre human interaction. The focus should be on quality of service, not on hiding the AI.
Instead of fully automating conversations and risking sounding robotic, use AI to provide real-time suggestions and prompts to a human sales rep. This scales expertise and consistency without sacrificing the human touch needed to close deals.
Deciding whether to disclose AI use in customer interactions should be guided by context and user expectations. For simple, transactional queries, users prioritize speed and accuracy over human contact. However, in emotionally complex situations, failing to provide an expected human connection can damage the relationship.
Superhuman designs its AI to avoid "agent laziness," where the AI asks the user for clarification on simple tasks (e.g., "Which time slot do you prefer?"). A truly helpful agent should operate like a human executive assistant, making reasonable decisions autonomously to save the user time.
Don't fear deploying a specialized, multi-agent customer experience. Even if a customer interacts with several different AI agents, it's superior to being bounced between human agents who lose context. Each AI agent can retain the full conversation history, providing a more coherent and efficient experience.
The most significant near-term impact of voice AI will be in call centers. Rather than simply replacing agents, the technology will first elevate their effectiveness and productivity. Concurrently, voice bots will handle initial queries, solving the common pain point of long wait times and improving overall customer experience.
A tangible way to implement a "more human" AI strategy is to use automation to free up employee time from repetitive tasks. This saved time should then be deliberately reallocated to high-value, human-centric activities, such as providing personalized customer consultations, that technology cannot replicate.
A common objection to voice AI is its robotic nature. However, current tools can clone voices, replicate human intonation, cadence, and even use slang. The speaker claims that 97% of people outside the AI industry cannot tell the difference, making it a viable front-line tool for customer interaction.
Instead of forcing AI to be as deterministic as traditional code, we should embrace its "squishy" nature. Humans have deep-seated biological and social models for dealing with unpredictable, human-like agents, making these systems more intuitive to interact with than rigid software.