Enterprises are Now Realising That AI-Native Shift Doesn't Happen in Isolation

The enterprise software ecosystem is facing a structural overhaul. For years, corporate adoption of artificial intelligence involved merely patching large language models or predictive algorithms onto existing software to handle isolated tasks. However, turning “AI-native” requires a deeper architectural shift.

The transition from traditional software-as-a-service (SaaS) to AI-native architecture involves a complete reimagining of the corporate tech stack. While traditional SaaS sells tools, and AI-enabled SaaS adds intelligent features to those tools, an AI-native setup completely flips the operational hierarchy.

"The AI is native in two aspects," says Krupesh Bhat, Founder of enterprise technology platform Melento (formerly SignDesk). 

"One is making the product as AI-native, which means the entire outcome is owned by me. Instead of selling a sales CRM, I will promise that I'll procure X number of leads, screen them, clean up the data, enrich the data, and then set up voice-based agents to speak to customers. If required, I'll bring humans in the loop," he tells AIM.

In highly specialised sectors like banking, financial services, and insurance (BFSI), where Melento says it processes document and onboarding automation for nearly 80% of India's private banks, this architecture redefines entire lifecycles, such as loan underwriting and execution.

"The loan underwriting will also be agentic... all the way it reaches the loan management system," Bhat explains, emphasising that the AI-native approach must be machine-first.

Collaborative vs Autonomous Intelligence

Despite the technological capability to run systems fully autonomously, enterprise leaders emphasise that the immediate future of corporate AI relies heavily on "collaborative intelligence." While some SaaS providers aggressively push for total automation within tight timelines—often leading to system instability—a grounded approach balances automation with human oversight. 

"We're not really actively pushing that narrative, the autonomous part, because the market is not ready to go fully autonomous. There's also some sensitivity around it, and people also don't feel safe using a completely autonomous system for various reasons, including regulatory frameworks, observability, and explainability,” explains Bhat, adding that accountability remains central to this transition.

This sentiment is echoed globally. Sapna Chadha, VP for Southeast Asia and South Asia Frontier at Google, speaking at the Fortune Brainstorm AI Singapore conference, said, "You wouldn't want to have a system that can do this fully without a human in the loop." Chadha argued that agentic platforms must request user approval at key decision points, ensuring transparency and accountability as these systems become more powerful.

In an AI-native setup, human capital is strategically deployed where it matters most: negotiation, complex decision-making, and exception handling.

The Regulatory Blueprint

Building an AI-native enterprise is not a one-size-fits-all endeavour. Global applications often operate on highly standardised, rigid rules. However, deploying enterprise AI in heavily regulated markets like India requires deep local compliance integration, particularly under the Digital Personal Data Protection (DPDP) Act, alongside guidelines from regulators such as the RBI and SEBI.

Previously, data collected by one business unit within a large enterprise frequently leaked to adjacent sectors, resulting in rampant data privacy violations. Modern agentic architecture must build privacy directly into its core orchestration network.

"Today, the DPDP Act is primarily helping us in building that data ownership," Bhat notes. "Data privacy is built in as part of the network. Even access control at the agent level is right—if you give access to this agent, it should not be like a black-box blanket approval. The agents will also have different roles to play."

Furthermore, enterprise workflows must adapt to distinct regional behaviours. While Western markets strictly adhere to digital rules, regional markets frequently demand operational flexibility.

"In the US and other companies, the rule is a rule. If an override is generated, it's a true exception," says Bhat. "In India, sometimes even though this is a process, there's always an option to override. So, how do you handle those exceptions?"

The primary challenge is balancing standardisation with local nuance. 

Kit Cox, CTO and Founder of Enate, a process orchestration software provider, argues that enterprise architects must learn to deliver consistency whilst accommodating geographic distinctions—much like how a global brand adapts its offering per market without sacrificing its core identity. "It is the consistency that is delivered. Agentic AI differs from previous waves of enterprise technology in that its pace and ease of use are disruptive and, in the worst cases, a risk to the organisation, making thoughtful architectural governance more critical than ever,” he says.

The Next Frontier: Voice and Orchestration

As enterprises move away from isolated AI tasks, the demand is shifting towards advanced voice-based processes and low-code orchestration systems that allow businesses to deploy custom agents natively.

Unlike the rigid, rule-based chatbots of the past, generative AI voice agents are becoming critical tools for customer interaction, account servicing, and overcoming vernacular language barriers between regional offices and corporate headquarters.

"The technology has gotten so advanced that you feel that you're interacting with a real agent because of all the vernacular support, the natural language, and more than that, the ability for AI to give accurate answers and be empathetic," says Bhat. "Unlike before, these were all rule-based voice agents that could only ask 10 questions. Now, actually, it can answer like a human being."

Ultimately, the goal for the next generation of enterprise architecture is to bypass the multi-step process of stringing together seven or eight legacy systems. By utilising unified workflow and agent orchestration platforms, enterprises can achieve single-step execution. This allows organisation leaders to scale rapidly and focus strictly on core operations whilst the underlying AI-native architecture securely owns the process end-to-end.