Video interview: How multimodal AI transforms business

Artificial intelligence is reshaping how businesses communicate across various channels including voice calls, messaging, video conferencing, and real-time transcriptions. In a recent video interview with UC Today's Susie Harrison, Telnyx CEO David Casem discusses how multimodal AI moves beyond traditional chatbots, becoming capable of understanding and responding seamlessly across multiple channels. Learn how multimodal AI is set to redefine business interactions, key industry applications, and steps your business should take now.

A new era of multimodal AI

David Casem explains that AI-driven communication has evolved far beyond basic IVRs. Thanks to rapidly falling costs, expanding context windows, structured decoding in large language models, and richer “memory” of past interactions, he predicts truly superhuman AI agents within a year—capable of understanding and responding across voice, text, video, and vision in real-time.

Solving real-world challenges across industries

Casem highlights how multimodal AI is poised to transform every sector reliant on phone calls or messaging:

• Travel & logistics: AI assistants can dynamically suggest personalized itineraries, check availability, and rebook activities all in real time.
• Healthcare: From scheduling appointments and managing prescription refills to verifying insurance and remote patient monitoring, AI handles routine tasks, freeing practitioners to focus on patient care.
• Customer support: Advanced speech recognition combined with deep integrations enables AI assistants to resolve tickets effectively and smoothly transition conversations to human agents when needed.

The Telnyx edge: low latency and integration

Real-time voice AI hinges on sub-200 ms latency—something public internet routes can’t guarantee. Casem emphasizes the importance of closely integrating telco and AI infrastructure to minimize delays, support HD voice, and ensure natural interactions. Telnyx does this with a CPaaS-native orchestration layer. Its turnkey APIs, SDKs, and "AI memory" features enable developers to deploy comprehensive voice and messaging agents without assembling multiple disparate tools.

Overcoming current limitations

While AI technology is rapidly advancing, Casem points out current limitations and common issues, such as AI's occasional inability to fully resolve problems due to fragmented systems or inadequate context. That often leads to clunky interactions. To mitigate these challenges, companies should start small, select specific use cases, and iterate rapidly.

Looking forward, Casem predicts that within the next two to three years, AI agents will pass Turing-test thresholds, making seamless, machine-powered conversations widespread. Organizations need to adapt quickly or risk falling behind in an increasingly AI-driven communication landscape.