ManageEngine, Zoho's enterprise IT management arm, focuses on expanding the workforce, opening new data centers, investing in AI research and strengthening the city's overall ground presence. The company also believes there is growing interest from AI agents companies. As India is ready to become the second largest market by next year, Rajesh Ganesan, president of ManageNentine, talks to Sudhir Chowdhary about expansion plans and how Genai is beginning to influence IT management. excerpt:
How do you plan to accelerate the growth of the Indian market?
India's strategy is rooted in long-term commitment. That means making strategic investments in a local talent, customer support and partner ecosystem. We continuously bring our products local to expand our ground presence across the city and meet the requirements specific to operations, compliance and industry needs.
From a product perspective, it is to provide enterprise-grade IT management solutions that can be accessed by organizations of all sizes. This includes simplifying onboarding, providing flexible deployment models (cloud, on-premises, hybrid), and integration of AI-driven capabilities to solve real-world operational challenges, such as IT service management, endpoint security, or observability.
Going forward, India will not only see it as a market but also as a strategic innovation hub. The R&D team here plays a crucial role in shaping the roadmap for global products, especially in areas such as Agent AI and Privacy-First Analytics. As AI adoption grows, we are also working on basic models that are particularly suitable for Indian companies. This is a model that understands regional context, language, and business logic.
How do businesses integrate AI into business operations?
We have observed that today's enterprise IT leaders are approaching AI with a more balanced outlook –
They are not just excited about AI, they are paying a little more attention. Their focus is not on adopting all new tools, but on finding solutions that truly drive business outcomes. Not in a hurry – and of course. Whether in India, the US or elsewhere, clients are trying to solve real-world problems and are responsible for being a technology provider that uses AI meaningfully to address those needs.
Today, AI is heading for exciting innovation in areas such as real-time analytics, personalized customer experiences, and advanced anomaly detection. However, to adopt AI at scale, companies must overcome data quality issues, workforce skills gaps, and organizational silos. Strategic approaches that emphasize reliable data governance, training initiatives and transparent AI lifecycles can alleviate these challenges.
There is also a growing interest in AI agents, or “intelligent and autonomous” workflows. Our deep experience in building business applications puts us in a strong position to develop our own agent capabilities. This is an AI agent that functions autonomously throughout the system.
Most importantly, AI conversations are evolving. It's not just about what technology can do anymore, but how it works, responsibly, ethical and governance in place.
But why do companies struggle to expand their AI projects beyond their initial pilots?
One of the most common challenges we see is that companies treat AI projects as isolated experiments rather than integrated extensions of core operations. Pilots often succeed in a controlled setting, but fail to scale because they are not rooted in the daily business context or tied together to clarify operational goals.
We recommend three important practices to meaningfully scale your AI: First, it is based on AI initiatives in actual business needs. Start with well-defined issues and look for ways that AI can improve results and efficiency. When AI is tied to measurable impacts, it becomes easier for stakeholders to buy into.
Second, build existing systems and data. Many companies are already matured their IT infrastructure. The most successful AI implementations we've seen are those that layer intelligence into these systems rather than replacing them.
Third, AI is not a one-off development. Clear policies regarding data quality, security, explanability and monitoring are required. In particular, having proper checks and balance is essential in IT management where AI-driven actions can impact critical infrastructure.
How do you see AI agents and human workers working together in an enterprise setting?
We can see collaboration with humans being evolved into a symbiotic model. This takes over the everyday, repetitive tasks that AI agents can automate, allowing employees to focus on the strategic, creative and interpersonal aspects of their role. This is not about exchanges. That's about enhancement. The goal is not to empower people, but to empower people.
