ManageEngine, the enterprise IT management arm of Zoho, is expanding its operations—focusing on growing their workforce, opening new data centres, investing in AI research and enhancing their on-ground presence across cities. The company is also seeing increasing interest from enterprises for AI agents. With India poised to become its second-largest market by next year, Rajesh Ganesan, CEO of ManageEngine, speaks to Sudhir Chowdhary on its expansion plans and how GenAI is beginning to impact IT management. Excerpts:
How do you plan to accelerate growth in the Indian market?
Our strategy for India is rooted in a long-term commitment. That means making strategic investments in local talent, customer support, and partner ecosystems. We’ve expanded our on-ground presence across cities and are continuously localising our offerings to meet requirements specific to operational, compliance, and industry needs.
From a product standpoint, our focus is to deliver enterprise-grade IT management solutions that are accessible to organisations of all sizes. This includes simplifying onboarding, offering flexible deployment models (cloud, on-premises, and hybrid), and integrating AI-powered capabilities that solve real operational challenges – whether in IT service management, endpoint security, or observability.
Looking ahead, we see India not just as a market but as a strategic innovation hub. Our R&D teams here play a pivotal role in shaping our global product roadmap, particularly in areas like agentic AI and privacy-first analytics. As AI adoption grows, we’re also working on foundational models specifically suited for Indian enterprises – models that understand regional context, languages, and business logic.
How are enterprises integrating AI into their business operations?
We’ve observed that enterprise IT leaders today are approaching AI with a more balanced outlook –
they hold a bit of caution rather than just excitement for AI. Their focus is not on adopting every new tool but finding solutions that genuinely drive business outcomes. There’s no rush – and rightly so. Whether in India, the United States, or other regions, our clients are looking to solve real-world problems, and the onus is on us as technology providers to use AI meaningfully to address those needs.
Today, AI is moving towards stimulating innovation in areas like real-time analytics, personalised customer experiences, and advanced anomaly detection. However, to adopt AI at scale, businesses must surmount data quality issues, workforce skill gaps, and organisational silos. A strategic approach – emphasising reliable data governance, training initiatives, and a transparent AI life cycle – can mitigate these challenges.
We are also seeing increasing interest in AI agents, i.e., ‘intelligent and autonomous’ workflow. Our deep experience in building business applications puts us in a strong position to develop our own agentic capabilities – AI agents that can work autonomously across systems.
Most importantly, the AI conversation is evolving. It’s no longer just about what the technology can do but also how it performs – responsibly, ethically, and with governance in place.
But why do firms struggle to scale AI projects beyond initial pilots?
One of the most common challenges we see is that enterprises treat AI projects as isolated experiments rather than integrated extensions of their core operations. Pilots often succeed in controlled settings but fail to scale because they’re not rooted in the day-to-day business context or tied to clear operational goals.
To scale AI meaningfully, I would recommend three key practices. First, ground AI initiatives in real business needs. Start with well-defined problems and look for ways AI can improve outcomes or efficiency. When AI is tied to measurable impact, stakeholder buy-in becomes easier.
Second, build on existing systems and data. Many enterprises already have mature IT infrastructures. The most successful AI implementations we’ve seen are those that layer intelligence onto these systems rather than replacing them.
Third, AI isn’t a one-time deployment. You need clear policies around data quality, security, explainability, and monitoring. Especially in IT management, where AI-driven actions could impact critical infrastructure, having the right checks and balances is essential.
How do you see AI agents and human workers collaborating in enterprise settings?
We see human-AI collaboration evolving into a symbiotic model – one where AI agents take over routine, repetitive tasks that can be automated, allowing employees to focus on the strategic, creative, and interpersonal aspects of their roles. This isn’t about replacement; it’s about augmentation. The goal is to empower people, not displace them.