Gaurav Bhandari
Gaurav is the Content Lead at Kore.ai. He has over 10 years of experience creating content and designing marketing campaigns that drive organic growth for B2B companies. As both an engineer and a marketer, he excels at translating technical concepts into compelling marketing content. Outside of work, he enjoys long walks and playing badminton with his 10-year-old daughter.
Articles by this author
How to build voice AI agents in 2026: step-by-step guide
Building an enterprise voice AI agent requires moving beyond rigid IVR menus to real-time, two-way conversational systems. A successful build focuses on low-latency responses, live backend integration, and warm human handoffs. By defining clear use cases and utilizing modern multi-agent platforms, teams can design, test, and deploy production-ready voice agents without writing code, drastically lowering handle times while resolving complex customer requests.
9 real voice AI challenges every enterprise faces and how to fix
Voice AI fails when poor audio, high latency, or rigid scripting ruins the call experience. Overcoming these hurdles requires a parallelized streaming pipeline for sub-second responses and acoustic filters for compressed phone lines. Agents must also handle mid-call interruptions and integrate deeply with enterprise systems to resolve complex tasks. Building reliable voice agents depends on real-time execution, strict governance, and seamless human escalation rather than simple conversational scripts.
9 best AI agent builders in 2026 | Market guide
Building an AI agent prototype is easy, but taking it to production requires handling security, edge cases, and complex enterprise integrations. Choosing the right AI agent builder depends on your technical needs and existing ecosystem. Options range from pro-code developer frameworks like LangGraph to enterprise platforms like Kore.ai, Microsoft Copilot Studio, and Salesforce Agentforce. The best platforms combine natural language building, visual orchestration, and built-in governance to safely deploy reliable agents at scale.
8 challenges of implementing AI in customer service
AI in customer service often fails for operational reasons, not technical ones. Common problems include outdated knowledge bases, thin integration with legacy systems, AI bolted onto broken workflows, weak governance, data security gaps, customer distrust, unclear ROI as costs scale, and agent burnout from handling only the hardest cases. Fixing this means grounding AI in verified data, giving it real system access, enforcing clear rules, and keeping a visible path to a human.
7 real challenges enterprises face implementing AI in the workplace
Most enterprises roll out AI without fixing what sits underneath it. Seven real problems keep showing up: fragmented legacy data, pilots with no clear ROI, AI bolted onto old workflows instead of redesigned ones, weak governance, employee distrust, shadow AI use, and skill gaps. The fix starts with integration, not a company wide cleanup, tying AI to real P&L metrics, redesigning a few high friction workflows, and training employees for their specific job, not generic AI literacy.
EU AI Act: a GDPR moment for artificial intelligence
The EU AI Act is Europe's GDPR moment for AI, and it applies to any company whose systems affect people in the EU, regardless of where they are based. Enterprises need documented risk assessments, reasoning chain logs, tested human oversight, and clear classification of high risk systems. Fines reach up to 35 million euros or 7% of global turnover. Enforcement was pushed to December 2027, but procurement and dealmaking already expect governance now.