Indian Enterprises Can’t Rely on Traditional Service Models Anymore: Exclusive Insights from Aditya Garg

Indian enterprises are operating at massive digital scale, especially across banking, retail, ecommerce, telecom, and public services. As interaction volumes grow, organisations are realising that traditional service models cannot deliver the speed, personalisation, and consistency customers now expect. In India, AI is no longer viewed as optional - it has become an operational imperative for managing experiences at scale while still preserving empathy and personalisation.  

What is accelerating this shift is the growing maturity of AI and the move from basic automation toward more autonomous, outcome-driven orchestration. We have moved beyond traditional conversational AI and large language models (LLMs) toward Large Action Models (LAMs), which can understand intent, reason across workflows and execute actions end-to-end.  

Successfully adopting and scaling autonomous CX calls for deliberate shifts across technology, workforce and governance - and all three need to move together. On the technology side, the first shift is moving away from fragmented, legacy architectures toward a unified experience orchestration platform that connects people, data, systems and intelligence in one place because autonomous AI cannot drive outcomes when it is operating in silos. The second — and more transformative — shift is moving from large language models (LLMs) to Large Action Models (LAMs). While LLMs are designed to understand language and generate responses, LAMs add the ability to reason, take action and complete tasks across enterprise systems within defined guardrails. This moves AI beyond simply answering questions toward autonomously resolving customer needs end-to-end.  

On the workforce side, the shift is centred on human and AI collaboration. As agentic AI takes ownership of routine, high-volume interactions, employees can evolve into experience designers, AI supervisors and complex problem-solvers who focus on what humans do best: empathy, creativity and judgment. Enterprises that invest in helping employees evolve alongside AI often see benefits that extend beyond efficiency gains, including stronger employee engagement, better customer outcomes and more adaptive organisations overall. Because ultimately, we see customer experience improves most sustainably when both human and AI capabilities are orchestrated together.  

The shift toward autonomous CX also brings a growing need for governance frameworks that can enable AI to operate responsibly at scale.  As AI becomes more autonomous, organisations need robust guardrails, explainability and auditability built directly into the architecture. Responsible autonomy requires transparency, compliance and accountability from the outset, especially in highly regulated and trust-sensitive markets like India.

Genesys helps organisations modernise customer experience through AI-powered experience orchestration delivered on a cloud-native platform. Rather than relying on disconnected point solutions, Genesys Cloud orchestrates customer journeys by connecting people, systems, data and AI across the enterprise within a unified experience orchestration platform that can continuously adapt as needs evolve. e.  Here, AI is embedded across every layer and the platform natively integrates agentic, generative, conversational and predictive AI capabilities to automate tasks, augment employees, personalise and optimise interactions continuously. 

In India specifically, Genesys has continued investing in local innovation, including becoming the first global CX organisation to establish an in-country cloud deployment, and launching the Genesys Executive Briefing Centre in India to advance AI-Powered experience orchestration e innovation. 

A point to note is that enterprises in India are at different stages of AI maturity. To help organisations navigate this evolution, Genesys has developed a maturity model for experience orchestration that maps progress across six stages — from basic reactive engagement to fully autonomous, intelligent orchestration. These include: 

• Level 0: Zero Orchestration 

• Level 1: Menu-based navigation 

• Level 2: Pre-defined Dialogue Automation 

• Level 3: System-generated conversations 

• Level 4: Agentic Experience Orchestration 

• Level 5: Universal Agentic Orchestration 

This framework enables organisations to adopt AI progressively based on their operational readiness, while continuously innovating without disrupting existing business operations. Because the platform is AI-native and cloud-based, businesses can rapidly adapt to changing customer expectations, integrate new AI capabilities and continuously optimise experiences using real-time analytics, orchestration and automation. Ultimately, it allows organisations to move from reactive customer service models toward proactive, intelligent and outcome-driven engagement at scale. 

As enterprises accelerate their AI adoption journeys, the focus is no longer just on deploying automation tools, it is about delivering efficient, intelligent and trusted customer experiences at scale. However, moving toward AI-led CX comes with a unique set of operational, technological and governance challenges that organisations must navigate carefully.  

One of the biggest challenges is balancing automation with trust. Enterprises want AI to improve efficiency and reduce friction, but they also need experiences to remain accurate, compliant, transparent, and empathetic. Many organisations are also dealing with fragmented systems and inconsistent customer journeys that make AI adoption harder to scale responsibly. Genesys addresses this by embedding governance, guardrails, and orchestration directly into the platform helping businesses deploy AI that is secure by design and capable of delivering reliable, explainable outcomes in real time. 

Genesys’ agentic virtual agent combines large action models (LAMs) with built-in orchestration and governance, allowing enterprises to scale autonomous AI without sacrificing control, transparency or trust.  

Unlike traditional LLM-based virtual agents that are primarily optimised for conversation, LAM-powered agents are designed to reason, make decisions, and take multi-step action across workflows within enterprise-defined guardrails. By combining orchestration with large action model innovation, enterprises can automate increasingly complex customer interactions while maintaining oversight, governance, and compliance. This allows organisations to move toward autonomous CX models without sacrificing control, transparency, or customer trust. 

Organisations can define permissions, policies, escalation paths and behavioral boundaries helping to ensure AI actions remain explainable, auditable and compliant. 

The integration of Scaled Cognition’s deterministic APT-1 Large Action Model further strengthens reliability by reducing hallucinations and enabling more predictable execution across workflows. This helps enterprises confidently move from AI that simply converses to AI that can safely complete customer outcomes end-to-end.