By Enterprise IT teams face a common trap when adopting AI: the assumption that existing management architecture must be rebuilt before AI can deliver value. That assumption stalls projects, consumes budget, and puts proven infrastructure at risk. The real bottleneck is not architecture; it is whether the underlying data foundation is structured well enough for AI to consume.
In this interview on TFiR, Romanus Prabhu Raymond, Director of Technology at ManageEngine, explains how AI is being integrated across ManageEngine’s suite as an acceleration layer on top of existing product architecture, not as a replacement for it.
Guest: Romanus Prabhu Raymond, Director of Technology at ManageEngine
Show: The Agentic Enterprise
Here is what every IT administrator and platform engineer needs to know.
Technical Deep Dive
Q: Does adopting AI in enterprise IT management require rearchitecting existing products?
Romanus Prabhu Raymond, Director of Technology at ManageEngine, is direct on this point: rearchitecting for AI is neither practical nor necessary. ManageEngine’s existing customers are actively consuming the product suite right now, and rebuilding the underlying architecture would disrupt that continuity. Instead, AI is introduced as a separate layer that sits on top of the proven product foundation, accelerating and enhancing what already works rather than replacing it.
“AI is being added as a layer to make sure that your existing product architecture is effectively utilized.”
Romanus Prabhu Raymond, Director of Technology, ManageEngine
Q: How does ManageEngine apply AI contextually across different IT management domains?
Raymond explains that AI application is scoped to the specific context of each domain. For endpoint management, AI is leveraged in ways that serve IT administrators managing endpoints. For identity management, AI is applied to the identity-specific environment. The approach is deliberate: AI is not a single generic layer applied uniformly, but a contextually targeted capability tuned to the operational reality of each product area within the suite.
“AI has to be leveraged in such a way that how AI can be used in that context, by IT administrators who are managing the endpoints, for those who are managing identity.”
Romanus Prabhu Raymond, Director of Technology, ManageEngine
Q: What is the role of structured data and unified platform visibility in enabling AI for IT operations?
Raymond identifies the underlying data architecture as the true prerequisite for effective AI outcomes. The foundation involves how data is organized, how it is discovered and ingested, and how a unified platform surfaces it for visualization. Without that structured foundation, AI cannot correlate signals across endpoints, identity, and applications effectively. The unified platform layer is what makes AI-driven remediation and action possible across managed assets.
“Unless you have that foundation, that essential basics are not properly done, you will not be able to have an effective outcome with the AI.”
Romanus Prabhu Raymond, Director of Technology, ManageEngine
Q: How does AI replace manual cross-console correlation in IT management?
Raymond describes a common pain point: IT administrators needing to open multiple consoles, pull data from each, and manually correlate it to answer a question such as who accessed which application from which endpoint using which identity. AI with access to structured data from across the management suite can answer that question directly, giving administrators unified visibility without the manual aggregation step. The outcome is faster, more accurate correlation at operational scale.
“Instead of going into different consoles and getting the data and then correlating it, you are talking to AI which actually has the access to the structured data from different tools and then gives you better visibility.”
Romanus Prabhu Raymond, Director of Technology, ManageEngine
Resources & Documentation
- ManageEngine, unified IT management suite covering endpoints, identity, and applications with AI-layer capabilities
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👇 Click to Read Full Raw Transcript
Swapnil Bhartiya: From a technology standpoint, what has changed in how ManageEngine architects its product? So AI runs at scale across the whole suite.
Romanus Prabhu Raymond: All right, that’s a great question because so basically what I hear is, are you rerating the product for the AI era? No, that cannot be done. Right. So we have existing product, existing customers who are consuming it right now. This is kind of, we have philosophy as our CEO was talking about, AI is a technology which has to be consumed. So contextually, if I am looking at it from the endpoints, then AI has to be leveraged in such a way that how AI can be used in that context for IT administrators who are managing the endpoints, for those who are managing identity, how can AI be leveraged? So it’s more focused on that particular environment. So the underlying architecture of how the data is organized, how do you discover the data and add it, and how do you have unified platform for that to be visualized? And from there how do you do remediations or actions on the endpoints or any other assets which you manage remains the same. Now AI layer is actually accelerating or making it more efficient in terms of correlation, in terms of say, for example, you want to understand who has done what in terms of identity, in terms of which endpoint they used, in terms of which application is being accessed or things like that. Instead of going into different consoles and getting the data and then correlating it, you are talking to AI which actually has the access to the structured data from different tools and then gives you better visibility. So I think AI is being added as a layer to make sure that your existing product architecture is effectively utilized. Unless you have that foundation that essential basics are not properly done, you will not be able to have an effective outcome with the AI. So it’s not much changes, it’s being consumed as a separate layer.
