Episode 01: Conversational AI in 2026: From hype to enterprise reality

Voice AI: What everyone gets wrong, and how to get it right

In this episode, Samantha Rosendorff (VP of Global Pre-Sales, boost.ai [http://boost.ai]) joins Kane Simms (CEO, VUX World) to dismantle the most common misconceptions about Voice AI. They provide a blueprint for how to design, deploy, and scale Voice AI that actually works, moving beyond the hype to deliver enterprise-grade efficiency. Key Takeaways: * The Voice Value Gap: Why most organizations miss the mark with voice automation and the specific ways it impacts your bottom-line efficiency. * Dismantling Misconceptions: Debunking the biggest myths about Voice AI implementation that lead to costly deployment failures. * Designing for Dialect & Intent: How to build voice experiences that handle the complexity of natural speech without forcing customers back to human agents. * Scaling the Right Way: The technical and strategic requirements for moving from a voice pilot to a secure, global-scale deployment.

What "good" looks like for AI Agents in customer service

Accuracy is only the beginning. To build truly effective AI Agents, organizations must establish a clear standard for what quality looks like in practice and how to maintain it as they scale. In this episode, boost.ai's Simeon Kristofferson (Customer Education Manager) and Shiv Chibber (Customer Engagement Manager) sit down to provide a practical mental model for assessing AI agent behavior. They move beyond basic metrics to help you identify issues early and prioritize the improvements that result in reliable, high-performing automation. Key Takeaways: * Defining Quality: Why AI agent quality is notoriously difficult to pin down and what it actually means in a real-world service environment. * Practical Assessment: A grounded approach to evaluating your agent’s performance to ensure it meets operational standards. * Issue Prioritization: How to recognize performance gaps early and decide which optimizations will have the greatest impact on your results. * Building for Reliability: Whether you are tuning an existing agent or launching your first, learn how to strengthen your evaluation process and move forward with confidence.

Designing conversational AI that sounds like your brand

In this episode, Shiv Chibber (Customer Engagement Manager, boost.ai) sits down with Kane Simms (Founder & CEO, VUX World) to discuss the strategic importance of Designing Conversational AI That Sounds Like Your Brand. They move past surface-level "personality" to explore how technical guardrails and consistent identity drive customer trust and ROI. Key Takeaways: * The Identity Crisis: Why generic LLM outputs dilute brand equity and how to implement architectural guardrails to prevent "personality drift." * Trust as a Metric: The direct correlation between brand-consistent CX and self-service adoption. If your bot doesn't sound like you, your customers won't trust it with their data. * Operationalizing Voice: Moving from static scripts to dynamic, brand-aligned generation. How to maintain a consistent "voice" across millions of unique, unscripted interactions. * The Governance Shift: Why brand voice is now a risk mitigation tool, ensuring that AI agents reflect corporate values while operating within highly regulated environments.

Episode 01: Conversational AI in 2026: From hype to enterprise reality

The era of AI experimentation is over. To capture the projected $80B in labor savings in 2026, enterprises must pivot from “proof of concept” to responsible, large-scale deployment. In this inaugural episode, Shiv Chibber sits down with boost.ai [http://boost.ai]’s Clay Cardozo (VP of Product Development) and Nick Mitchell (CRO) to discuss the market realities of scaling AI. Key Takeaways: -The Scaling Gap: Why most AI pilots fail to reach production. -Market Realities: Moving past the hype to address security and governance. -ROI Framework: How to measure the tangible impact of AI Agents on the bottom line. -Strategic Deployment: The shift from “Can it work?” to “Can it scale responsibly?”