AI adoption is rapidly transforming the enterprise landscape, with organizations moving from experimentation to large-scale implementation. In this exclusive interaction, Rajeev Ranjan, Editor, Digital Terminal, speaks with Rishi Mehtani, VP and Head – Technology and Multicloud, Oracle India, about Oracle’s AI strategy, multicloud innovations, data management, security, and how enterprises can unlock measurable business outcomes through responsible AI adoption.
Rajeev: AI adoption has accelerated significantly across Indian enterprises. How has this impacted Oracle India’s business growth and customer demand for AI and multicloud solutions?
Rishi Mehtani: India’s IT leadership has always been built on strong foundations of talent, scale, entrepreneurship, and digital infrastructure. Today, enterprises are moving beyond cost-efficient delivery models towards innovation through cloud, AI, cybersecurity, and digital platforms. This shift is driving AI adoption from experimentation to enterprise-scale deployments.
For Oracle, this has created strong momentum across cloud infrastructure, databases, and applications. With more than 33 years of presence in India, Oracle has been solving complex business challenges across industries. Today, we are helping enterprises use AI to improve productivity, automate workflows, enhance customer service, and unlock greater value from their data.
We are seeing strong AI adoption across sectors such as BFSI, telecom, manufacturing, and healthcare, where organizations are focusing on areas like fraud detection, network optimization, supply chain planning, intelligent automation, and better utilization of data.
Rajeev: Many enterprises are experimenting with AI but struggle to scale it beyond pilot projects. What are the key challenges and your advice for CIOs and CTOs?
Rishi Mehtani: The biggest shift organizations need to make is treating AI pilots as business transformation initiatives rather than technology experiments. Every AI project should start with a clear business problem, defined outcomes, and ownership for scaling the initiative.
One of the biggest challenges enterprises face is fragmented data, along with governance and integration issues. To successfully scale AI, organizations need a trusted data foundation, strong security frameworks, responsible AI practices, and an architecture that brings AI closer to the data instead of continuously moving sensitive information.
AI delivers real value when it is embedded into applications, workflows, and decision-making processes. Enterprises need collaboration between business, technology, and data teams to move from isolated experiments to enterprise-wide adoption.
Rajeev: With enterprises adopting multicloud environments, how can they avoid data silos and ensure AI has access to trusted information?
Rishi Mehtani: Multicloud strategies should be designed around a unified data approach rather than treating each cloud as a separate environment. Enterprises need consistent governance, security, metadata management, and access policies across platforms.
The objective should not be moving all data into one place but allowing data to remain where it is most suitable while ensuring it remains accessible and usable. Reducing unnecessary data movement helps organizations avoid duplication, latency, higher costs, and governance challenges.
Strong data integration, APIs, event-driven architectures, and security frameworks are essential for building a trusted multicloud environment where AI applications can securely access real-time information.
Rajeev: How important is a unified data strategy for enabling AI to deliver measurable business outcomes?
Rishi Mehtani: A unified data strategy is critical because AI can only deliver enterprise-level value when it has secure and consistent access to trusted data. The challenge today is not just storing data but making it discoverable, governed, and usable.
A significant amount of enterprise data exists in unstructured formats such as documents, emails, images, and videos. Bringing this data together with structured enterprise data enables AI to support more meaningful business decisions.
With solutions like Oracle Database 26ai, enterprises can combine structured, unstructured, and vector data with strong governance and security. This helps organizations move beyond isolated AI use cases and build capabilities that improve productivity, customer experience, risk management, and compliance.
Rajeev: As enterprises balance AI innovation with security, compliance, and data sovereignty, what best practices should they follow?
Rishi Mehtani: Security and governance must be built into the AI lifecycle from the beginning and cannot be treated as an additional layer. Enterprises should establish clear AI governance frameworks with defined responsibilities across business, technology, legal, and risk teams.
Organizations must focus on trusted data, strong access controls, encryption, monitoring, and compliance requirements while designing AI systems. A risk-based approach is also important because different AI applications require different levels of governance.
Human oversight remains essential, especially for high-impact decisions. Responsible AI should not be viewed as a limitation but as a foundation that enables enterprises to scale AI with confidence.
Rajeev: How should CIOs and CTOs approach compliance requirements such as the DPDP Act while adopting AI?
Rishi Mehtani: Compliance requirements need to be taken seriously because they are designed to protect businesses and customers. Ignoring compliance can create significant financial and reputational risks.
Organizations should consider regulatory requirements from the early stages of AI planning and ensure that governance, security, and privacy controls are integrated into their technology decisions. The government and regulatory bodies are working towards strengthening India’s digital ecosystem, and enterprises should view compliance as an enabler for secure and sustainable innovation.
Rajeev: What differentiates Oracle’s approach to AI adoption with its integrated portfolio of AI infrastructure, databases, and multicloud capabilities?
Rishi Mehtani: Oracle’s approach is differentiated because we bring together infrastructure, data, applications, and multicloud capabilities on a single integrated platform. Oracle Cloud Infrastructure provides the high-performance computing, networking, and GPU capabilities required for AI workloads. At the data layer, Oracle Autonomous AI Database and AI Database 26ai help enterprises bring AI directly to their trusted business data instead of moving sensitive information across different platforms.
Our multicloud capabilities allow customers to use Oracle database services across OCI as well as other cloud environments, helping them address requirements around data residency, latency, security, and compliance.
Oracle believes AI should not be treated as a standalone service. It needs to be embedded across infrastructure, databases, and applications to help enterprises achieve scalable and measurable business outcomes.
Rajeev: What is your message for CIOs, CTOs, and Oracle partners supporting enterprise AI adoption?
Rishi Mehtani: Partners are a very important part of Oracle’s ecosystem because they understand customer requirements and help organizations apply technology to solve real business challenges. For CIOs and CTOs, the focus should remain on governance, security, responsible AI adoption, and choosing the right business use cases. Enterprises should leverage their trusted data and bring AI closer to where the data resides. As organizations move from AI experimentation to enterprise-scale adoption, these principles will play a critical role in building successful AI strategies.
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