As artificial intelligence becomes deeply embedded in modern business infrastructure, it is no longer just the domain of tech teams—it’s now a boardroom priority. From compliance to customer experience, the impact of GenAI and Agentic AI is reshaping how enterprises operate, especially in highly regulated sectors like finance, healthcare, and cybersecurity. But with innovation comes complexity: How do you deploy AI at scale while staying explainable, compliant, and globally adaptable?

In a candid conversation with Kush Kaushik, Chief Operating Officer at Scrut Automation Inc., ObserveNow explores how the company is navigating the evolving landscape of AI adoption. From infrastructure bottlenecks and integration challenges to cloud sovereignty and CRM transformation in India, Kush shares valuable insights on balancing speed with responsibility. “We don’t see explainability as a trade-off—it’s part of what makes an AI system truly effective and trustworthy in the real world,” he notes.

What are the challenges of implementation of AI and its derivatives like Gen AI, and Agentic AI in your line of business? Are there any particular infrastructural challenges that you face?

Answer:

Implementing AI, especially Generative AI and Agent-based AI, definitely comes with its own challenges. One of the biggest hurdles in our line of work is ensuring the technology fits into our existing systems and processes without causing too much disruption.

Infrastructure Readiness: AI systems, especially Gen AI models, require a lot of computing power and storage. Our current infrastructure wasn’t originally built to handle such heavy workloads, so we’ve had to upgrade servers, improve cloud support, and ensure network speeds can keep up.

AI performance hinges on data quality; much business data is inconsistent or unstructured, requiring extensive cleaning. Integrating AI with existing tools is challenging. User trust and adoption, data privacy, security, and the cost of experimentation are also significant hurdles. Compliance with acts like NYC 144 and the EU AI Act adds further complexity.

What are the key strategic considerations and challenges in managing data sovereignty, data residency, and cross-border data flows in your multi-cloud environment, especially with evolving global regulations?

Answer:

As we move more of our operations into the cloud—often across multiple providers—managing where our data lives and how it’s handled becomes increasingly complex. With global regulations constantly evolving, we face several strategic challenges around data sovereignty, data residency, and cross-border data flows that we can’t afford to overlook.

Data residency and cross-border data flows are key concerns. Some countries mandate data storage within their borders (data residency/localization) for specific data types, necessitating careful cloud provider selection. Global operations require data transfer, but differing international rules demand caution to avoid penalties or business disruption. Strategically, collaboration with legal, compliance, and IT teams is vital to choose localized cloud options, understand jurisdictional data governance, map data flows, and build adaptable architecture for emerging regulations. It’s not just a technical or legal issue—it’s about protecting our customers’ trust, avoiding legal risk, and ensuring we can scale globally without facing regulatory roadblocks.

Give a broader perspective of use cases of advanced AI in CRM platforms in India. Is India ready for the sea change? Can you identify a few gaps?

Answer:

As artificial intelligence continues to evolve from a technological frontier into an everyday business enabler, one domain that’s experiencing a transformative shift is Customer Relationship Management (CRM). While globally, enterprises are integrating advanced AI, including generative AI and agentic AI, to elevate customer interactions, the Indian CRM landscape is now on the cusp of its own intelligent evolution. 

Gen AI helps CRM systems analyze diverse Indian customer interactions to create customized messages across WhatsApp, email, and SMS. Sales teams in fintech and real estate utilize AI for lead conversion, with AI assistants offering real-time suggestions and insights. Support functions deploy AI chatbots that understand natural language, including regional Indian languages. Generative AI aids agents in drafting responses, summarizing tickets, and suggesting solutions. Enriched CRM data allows AI models to score leads based on conversion likelihood.

Voice and Multilingual Interfaces: Voice-based CRM is gaining traction, particularly in Tier 2 and Tier 3 cities. AI can now transcribe, translate, and interpret voice inputs, enabling field agents to operate in their preferred language and mode, thereby improving adoption and reducing training needs.

Is India Truly Ready for This Transformation?

India has several factors working in its favor: A mobile-first digital economy, where cloud-based CRMs are accessible even to small teams, a tech-literate young workforce, eager to adopt tools that increase productivity and a robust digital infrastructure, including Aadhaar, UPI, and other public tech platforms that make integration easier. However, readiness isn’t uniform. While digitally mature enterprises in metro cities are already investing in AI-infused CRM, many small and mid-sized businesses are just beginning the journey.

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India is not just ready—but ripe for AI-powered CRM adoption. However, to unlock its full potential, the ecosystem needs more than just technology. It requires: Strategic education around AI’s practical use, Stronger data foundations, Thoughtful design for language and regional needs and Policy and infrastructure support for smaller players.

As AI capabilities continue to mature, CRM platforms will become not just systems of record—but systems of intelligence. For Indian businesses, the opportunity lies in moving early, investing smartly, and ensuring that the human side of customer relationships remains central—even as machines get smarter.