Why enterprise AI needs governance before autonomy

After two years of rapid experimentation with generative AI, organisations are increasingly looking beyond proofs of concept towards production deployments. Yet while the technology itself continues to evolve rapidly, many enterprises are finding that the real challenge lies elsewhere: integrating AI into business operations without compromising governance, security, compliance and operational control.

As organisations begin exploring agentic AI, where AI systems can reason, coordinate and execute business tasks with minimal human intervention, the focus is shifting from building individual AI agents to creating enterprise environments where those agents can operate safely at scale.

In this exclusive interaction with Express Computer, Kaushal Kurapati, Group Vice President, Applications Development, Oracle, discusses why governance is becoming the defining factor in enterprise AI adoption, why many AI initiatives struggle to move beyond pilots, and how enterprises should rethink the way they build AI-powered business applications.

Enterprise AI requires more than better models

According to Kurapati, the conversation around enterprise AI has moved beyond model capabilities.

While large language models have dramatically lowered the barriers to building intelligent applications, organisations are now discovering that deploying AI inside business-critical environments requires far greater attention to operational controls than to model performance alone.

“The next generation of enterprise applications is not simply about adding AI to existing software. It is about creating systems where specialised AI agents can monitor, coordinate and execute business processes while operating within enterprise controls.”

Rather than relying on isolated AI assistants or copilots, Kurapati believes enterprises will increasingly adopt applications built around teams of specialised AI agents working together towards specific business outcomes. These systems need to remain tightly connected to enterprise data, workflows and governance frameworks instead of operating independently.

Why enterprises struggle to move beyond pilots

Despite growing investment in generative AI, many organisations continue to face difficulties when moving AI initiatives into production.

Kurapati attributes much of this challenge to the way early AI projects were developed.

Many proofs of concept focus on demonstrating AI capabilities but pay relatively little attention to enterprise requirements such as identity management, approvals, audit trails, security policies and regulatory compliance. These issues only emerge later, often delaying or preventing deployment altogether.

He argues that organisations should treat governance as part of the application architecture from the outset rather than attempting to retrofit it after development is complete.

This philosophy underpins Oracle’s recently announced AI-first Builder Experience for Oracle AI Agent Studio, which brings no-code, low-code and pro-code development into a single framework designed to create agentic applications while inheriting governance, security and auditability from Oracle Fusion Applications.

Development is becoming accessible to both business and IT teams

Kurapati believes enterprise AI development is also becoming more inclusive.

Business users are increasingly able to create applications using natural language interfaces, while professional developers continue to use familiar environments such as Visual Studio Code alongside AI-assisted development tools.

Rather than replacing developers, he sees AI significantly reducing the effort required to assemble enterprise applications. “We don’t want building applications to become the bottleneck. Whether someone is a business user or a developer, the objective is to accelerate development while ensuring governance, testing, auditability and security remain built into the application.”

The goal, he says, is not merely to generate AI applications faster but to ensure they are production-ready from the moment they are deployed.

Governance is becoming enterprise AI’s competitive advantage

For Kurapati, governance extends well beyond data security.

As AI systems become increasingly autonomous, enterprises must establish mechanisms to ensure AI decisions remain predictable, transparent and accountable.

That includes maintaining detailed audit trails, validating model behaviour, testing applications before deployment and incorporating structured human oversight for high-impact business decisions.

One area Oracle is focusing on is reducing non-deterministic AI behaviour by converting business policies into executable code, allowing AI systems to execute enterprise rules consistently rather than interpreting policy documents differently with each interaction.

Equally important, Kurapati argues, is preserving human accountability.

For critical financial, HR or operational decisions, AI should prepare recommendations while designated business leaders retain authority to approve or reject actions. “The agent performs the reasoning and prepares the recommendation, but organisations still need human oversight where accountability matters. AI reduces the effort required to reach decisions without removing enterprise responsibility,” he says. 

Trust will determine enterprise AI adoption

Kurapati believes organisations will ultimately adopt AI only when they trust its operational behaviour.

That trust depends not only on model performance but also on an organisation’s ability to understand how decisions are made, replay execution paths, investigate failures and demonstrate regulatory compliance.

As enterprises increasingly deploy multiple AI agents across finance, HR, supply chain and customer operations, these operational capabilities are likely to become as important as AI itself.

“Confidence comes from knowing applications have been tested, governed and built with the safeguards enterprises expect. Once organisations have that confidence, the barriers to production adoption become significantly lower,” he points out. 

India is well positioned for enterprise AI development

Kurapati also expects India to play an increasingly important role in the enterprise AI ecosystem.

He points to the country’s large developer base, expanding global capability centres and growing partner ecosystem as important contributors to building and deploying enterprise AI applications.

With enterprise development becoming increasingly AI-assisted, he believes organisations will be able to create sophisticated business applications far more quickly while maintaining enterprise governance requirements.

As enterprise AI matures, Kurapati argues that competitive advantage will depend less on access to increasingly commoditised AI models and more on an organisation’s ability to combine governance, enterprise workflows, developer productivity and human oversight into a cohesive operating framework. For enterprises moving beyond experimentation, that operational foundation may ultimately determine whether AI delivers sustainable business value at scale.