Juhi Tiwari
Juhi is a Research Lead at Kore.ai, driving market intelligence, competitive insights, and analyst-facing strategy that strengthens the company’s industry positioning. She also works as a content strategist, creating deep technical content that translates complex AI concepts into clear, compelling narratives. Her work supports Kore.ai’s vision, credibility, and thought leadership across the AI landscape.
Articles by this author
What to ask before you choose a voice AI platform: 10 questions for CX leaders
Choosing a voice AI platform for enterprise customer service? This guide gives CX leaders 10 questions to ask vendors, covering end-to-end voice turn control, latency at peak load, natural interruptions, accuracy on real phone lines, multilingual and multi-intent handling, PII and payment protection, failover, warm human handoffs, resolution versus containment, and predictable cost per resolved call. Test every claim against your own calls, systems, compliance rules, and outcomes before committing.
AI tokens explained: Why your AI bill keeps growing and how to fix it
AI token costs are climbing even as per-token prices fall, because agentic workflows consume 5–30x more tokens than chatbots. Most waste comes from architecture, not models: over-retrieval, context inflation, recursive agents and verbose output. Fixes include model routing, precise RAG, caching and output limits. Success is measured by cost per successful outcome and Token Efficiency Ratio, which tie spend to value.
Top Use Cases for Voice AI Agents in Retail
Voice AI agents are transforming retail customer service by moving beyond traditional IVRs and chatbots to understand requests, access data, and complete transactions. This article covers retail voice AI use cases including order tracking, returns, product discovery, inventory lookup, delivery scheduling, and multilingual support. A Kore.ai deployment handled approximately 520,000 monthly voice calls, achieving 75% voice containment and a 9% live-agent transfer rate.
How to evaluate contact center AI beyond the demo
Evaluating contact center AI requires more than a successful product demo. This article explains how enterprises can assess AI agents across real customer journeys, complex intents, knowledge grounding, voice performance, integrations, governance, observability, resilience, and ROI. It recommends testing edge cases and measuring production outcomes such as resolution, cost, reliability, and customer experience to determine whether contact center AI is truly production-ready.