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
How to make enterprise AI agents deliver real customer experience
Enterprise AI agents must do more than answer questions. They need to resolve customer needs and deliver measurable customer experience (CX) outcomes. This article explores AI agent design, outcome-based testing, omnichannel customer service, intent mapping, conversation management, and AI agent ROI. It explains why enterprises should measure objective completion and business outcomes alongside accuracy. Kore.ai helps connect agent design, integrations, testing, and measurement to customer outcomes.
The AI governance gap: Why enterprise AI needs more than just guardrails
The AI governance gap occurs when enterprise AI systems have guardrails but lack the controls needed to govern agent behavior in production. This article explains why AI governance, policy enforcement, auditability, and real-time controls must be built into the architecture rather than added later. It distinguishes reactive guardrails from proactive governance, covering decision-level audit trails, per-agent policies, bypass-resistant controls, streaming enforcement, and compliance reporting. The article also presents a four-level AI governance maturity model and explains how Kore.ai addresses enterprise governance requirements for production AI.
Why a great demo is not enough to choose an enterprise AI platform
Evaluating agentic AI platforms requires looking beyond an impressive vendor demo. This article explains how enterprises should assess AI agent governance, observability, security, compliance, continuous evaluation, scalability, integrations, and lifecycle management before moving from pilot to production. It highlights why controlled demos cannot reveal how agents perform with real enterprise data, workflows, edge cases, and regulatory requirements. The article provides seven questions buyers should ask vendors and explains what production-ready enterprise AI platforms should demonstrate.
Why AI agents fail in production, and what it actually takes to fix it
Enterprise AI often fails in production not because of model capability, but because the surrounding operational infrastructure is not rigorous enough. This article explains how incomplete integrations, unrealistic testing, requirements drift, long-horizon tasks, and ineffective AI review processes create production risk. It outlines five principles for reliable AI agents: persistent specifications, real-world testing, independent evaluation, preventive rules, and separating AI capability from reliability. Kore.ai emphasizes AI governance, runtime guardrails, observability, auditability, and continuous learning as architectural foundations for trustworthy enterprise AI at scale.
Best agent management platforms for enterprise-scale operations
AI agent management platforms provide a centralized control plane for governing, monitoring, evaluating, and managing enterprise AI agents across frameworks, clouds, and vendors. This article compares leading platforms and explains core capabilities including AI agent observability, security, policy enforcement, pre-production evaluation, agent lifecycle management, A2A and MCP support, cost attribution, and auditability. It highlights Kore.ai’s cross-framework Agent Management Platform, designed to provide unified governance, evaluation, execution tracing, continuous policy enforcement, and operational visibility across heterogeneous agent estates.
The voice AI surge: From adoption to outcomes
Enterprise Voice AI is moving from call routing to agentic voice, where AI agents can understand requests, reason over enterprise knowledge, execute transactions, verify outcomes, and resolve customer issues end-to-end. This article examines the rapid growth of voice AI adoption, the technology and governance shifts enabling production deployment, and measurable ROI across FCR, AHT, CSAT, and agent productivity. It also highlights Kore.ai’s approach to enterprise voice AI across banking, healthcare, retail, and telecommunications.