ManageEngine eyes new growth avenues via AI, ML solutions

CHENNAI: ManageEngine (ME), Zoho Corp’s 22-year-old enterprise IT management arm is building its AI and machine learning (ML) arsenal, hoping to onboard global firms on the emerging tech bandwagon.

At a time when process automation, service delivery and cybersecurity, among others, have undergone sea changes, due to the evolving nature of business, ME is targeting CIOs or chief information officers to offer product solutions that will enable the businesses to function efficiently. “Tech infrastructure management is an opportunity,” says Rajesh Ganesan, president, ManageEngine, who explains the rationale of creating ME, which was aimed at disrupting the market.

Pitching the suite of ME product solutions as an open, easy-to-use model marketing via digital marketing, he says the consumption of technology is dynamic. In a world where hackers thrive, it has become imperative for players like ME to outsmart them by enabling security functions on every device and every component through robust safeguard mechanisms.

Enterprises are now embracing AI and ML, providing a perfect foil for ME to offer its solutions, given that it has already been working in these spheres for some time. Ramprakash Ramamoorthy, who joined as the first intern for the AI experiment of Zoho in 2011, has seen the transition from rolling out vanilla products to AI-powered features that are now capable of detecting statistical anomalies, predictive analytics, forecasting and beefing up security for enterprises.

It has moved from predictive to prescriptive, he says as he notes that AI is able to recommend solutions now, bringing in confidence in the implementation phase.

The effort behind building and providing simple tools to serve the market in North America and small businesses has resulted in ME transitioning from making available a suite of products to becoming a platform itself, notes Ganesan, as he dwells on the AI roadmap that global clients demand. Unified end point management services has seen the maximum traction, he says, going on to add that many customers seek strategies based on conversational AI; to negotiate insurance deals, for instance.

“If not specific uses, they are asking about ME roadmap for AI,” he says, and adds the intent to draw one such for BFSI (banking, financial services and insurance) is a reason for enterprises to align with ME, as it is working to remain a step ahead in these emerging technologies. ME has been also going full steam on generative AI over the last two years. Blockchain has not seen much traction on AI-ML use since customers are not clear about their use cases, leading the intra team to pivot to cryptography wherein secure enclaves leading to adding layers of security remain the focus areas for Zoho.

AI, IoT, 5G and cryptography are specialised areas for ME, chips in Shailesh Davey, co-founder and VP, Engineering, Zoho Corp, as he elaborates on the three phases of AI development, ranging from classical ML to classical deep learning (less than 100 mn parameters) to generative AI (500 bn parameters) to LLM or large language models (500 bn parameters) besides small (less than 7 bn parameters) and medium (20-50 bn). These approaches have led to the emergence of contextual intelligence which does root cause analysis, says Ramamoorthy.