As enterprises accelerate their AI adoption journeys, customer experience is emerging as one of the most critical frontiers for innovation. The shift from rule-based automation to Agentic AI is redefining how support systems operate—moving beyond scripted interactions to autonomous, outcome-driven resolution. In a conversation with Vikas Garg, Co-founder and CPO at Kapture CX, we explore how Agentic AI is enabling enterprises to transform customer support into a proactive, intelligent, and fully orchestrated function.
Some edited excerpts:
How does Agentic AI change the way customer support systems operate compared to conventional AI chatbots or automation tools?
Agentic AI represents a shift from rule-based automation to autonomous problem resolution. Traditional chatbots typically operate within predefined scripts or decision trees; they can answer FAQs or guide users through simple workflows, but they struggle when a situation falls outside those parameters.
Agentic AI, however, is designed to understand intent, reason through complex workflows, and take action across systems. Instead of merely responding to queries, it can retrieve contextual data, execute tasks such as initiating refunds, updating orders, or creating support tickets and close the loop autonomously.
What role does real-time data and analytics play in enabling proactive customer experience management on the platform?
Real-time data is the backbone of proactive customer experience management. Traditionally, support systems have been reactive; they respond only when customers raise an issue.
With real-time analytics, platforms can detect patterns and signals before they escalate into problems. For example, if shipping delays are detected across a specific region, the system can proactively notify customers, update delivery timelines, or offer compensation options.
How is Kapture integrating emerging AI technologies such as LLMs, private deployments, or BYO-LLM strategies into its platform?
The AI ecosystem is evolving rapidly, and enterprises increasingly want flexibility and control over how they deploy AI. At Kapture, we are integrating large language models (LLMs) in a way that balances performance, security, and customization.
Our platform supports hybrid AI architectures, including private LLM deployments for organizations with strict data governance requirements. We are also enabling BYO-LLM strategies, allowing enterprises to plug in their preferred models whether open-source or proprietary while still leveraging Kapture’s orchestration layer for workflows, integrations, and automation.
This approach ensures that businesses can retain control over their data and AI strategy while benefiting from advanced conversational intelligence and automation capabilities.
What are the biggest technological challenges companies face when deploying AI across customer support channels like voice, chat, and email?
One of the biggest challenges is maintaining consistency across channels. Customers today interact through multiple touchpoints such as voice, chat, email, social media and each of these channels generates different types of data.
Ensuring that AI systems can understand context across these channels and maintain conversation continuity is a complex engineering challenge.
Another major hurdle is data integration. AI systems are only as effective as the data they can access. Many organizations still operate with fragmented systems like CRMs, ticketing platforms, and logistics databases making it difficult for AI to provide accurate and actionable responses.
Finally, governance and security remain critical concerns. Enterprises must ensure that AI systems comply with data privacy regulations and maintain strict access controls while still delivering real-time intelligence.
What trends are you currently observing in how enterprises are adopting AI for customer experience and service operations?
We’re seeing three major trends in enterprise AI adoption.
First, companies are moving from chatbots to AI agents that can complete tasks autonomously rather than simply answering questions.
Second, there is a growing emphasis on AI orchestration across the entire customer journey, not just within support. AI is now being used for sales assistance, onboarding, retention, and upselling.
Third, enterprises are prioritizing domain-specific AI. Instead of generic AI tools, companies are adopting platforms trained on industry-specific workflows such as travel, logistics, retail, or fintech to deliver more accurate and contextual responses.
How do you see the role of human agents evolving as AI becomes more capable in handling customer interactions?
AI will not replace human agents; it will elevate their role. As AI takes over repetitive queries like order tracking, password resets, or policy information, human agents will increasingly focus on complex problem-solving, relationship management, and high-empathy interactions.
AI will also act as a real-time co-pilot for agents, providing contextual information, suggested responses, and workflow automation during conversations. This reduces cognitive load and allows agents to deliver faster and more personalized support.
Ultimately, the goal is to create a human + AI collaboration model, where technology handles scale and efficiency while humans provide judgment, empathy, and strategic engagement.
Looking ahead, what technological shifts will define the next generation of customer experience platforms?
The next generation of CX platforms will move toward autonomous experience management, where AI systems not only respond to queries but also predict issues, take action across systems, and resolve problems proactively. This shift will be driven by Agentic AI platforms that replace fragmented automation tools, unified customer intelligence layers that bring together data from voice, chat, and behavioral signals, and AI-native CX platforms built with orchestration, predictive analytics, and autonomous workflows at their core enabling businesses to deliver more proactive, personalized customer experiences at scale.