By Enterprise IT teams are flooded with AI promises. The practical problem is not whether AI can be applied to IT operations, it is knowing which AI investments will reduce real workload versus which will become shelfware after the first demo cycle. Without a repeatable prioritization framework, product teams ship features that look good in keynotes but never move adoption metrics in production.
In this interview on TFiR, Rakesh Jayaprakash, Director of Product Management at ManageEngine, walks through how ManageEngine decides which AI features make it into products spanning IT service management, observability, and endpoint security, and how the team measures whether those features are actually being used after launch.
Guest: Rakesh Jayaprakash, Director of Product Management at ManageEngine
Show: The Agentic Enterprise
LinkedIn: Rakesh Jayaprakash
Watch the discussion on YouTube
Here is what every IT product manager and platform engineer needs to know.
Technical Deep Dive
Q: How should IT product teams decide which AI features to build versus deprioritize?
Rakesh Jayaprakash, Director of Product Management at ManageEngine, says the primary filter is identifying where customers spend the most manual time. Across ManageEngine’s portfolio covering ITSM, observability, and endpoint security, the team maps each domain to its highest-volume manual tasks and targets AI investment there first. The goal is not to add AI for visibility but to eliminate the manual work before it becomes a support burden.
“What we look at is what are the things that are consuming a lot of time for our customers. The idea is where is the customer spending a lot of manual time, which can be automated.”
Rakesh Jayaprakash, Director of Product Management, ManageEngine
Q: How can AI reduce IT ticket volume before incidents are even reported?
Jayaprakash describes two specific examples from ManageEngine’s portfolio. On the ITSM side, the target is resolving end-user issues before a ticket is raised at all. On the endpoint security side, AI agents embedded in endpoint software monitor workstation state proactively, so IT admins are alerted to problems like application crashes before the user ever submits a report. Both approaches aim to remove the ticket as the trigger for action entirely.
“We are trying to do things which avoid the issue getting reported in the first place. That by itself is a huge advantage for customers in terms of cutting down the time it takes to sit and reply and respond to tickets.”
Rakesh Jayaprakash, Director of Product Management, ManageEngine
Q: How do you measure whether an AI feature is actually being used after it ships?
Jayaprakash points to telemetry as the primary mechanism. Because the majority of ManageEngine applications run on the cloud, the team has direct visibility into usage statistics for each feature across their own data centers. For on-premise deployments, customers can opt in to share anonymized usage data, giving the product team a comparable signal. This data layer is what separates features with real adoption from those that only perform well in demos.
“We can see the usage statistics for the various applications running in our own data centers. This gives us a sense of how extensively our customers are actually using these features.”
Rakesh Jayaprakash, Director of Product Management, ManageEngine
Q: What is the risk of shipping an AI assistant too early, before the technology is mature enough to drive adoption?
Jayaprakash uses Ask Zia as a direct case study. ManageEngine released Ask Zia as a conversational IT assistant eight years ago, built on NLP before large language models existed. The feature was functional but adoption was low. The lesson he draws is that feature availability alone does not create use: the technology maturity, the interface, and the moment of market readiness all have to align for an AI feature to move from available to actually used.
“We released it at least eight years ago, before the whole LLM phenomenon emerged. It was designed for people to ask questions and get immediate answers. But in reality, not many people were actually using it.”
Rakesh Jayaprakash, Director of Product Management, ManageEngine
Q: How do smaller regional events improve AI product feedback loops compared to annual user conferences?
Jayaprakash notes that a single annual user conference is not sufficient to maintain a continuous feedback loop, particularly across a global customer base. ManageEngine supplements it with smaller half-day and full-day seminars in cities across regions, including deliberate geographic coverage across the east coast, west coast, and central areas of the US. These smaller formats allow for more direct and candid customer input, which feeds back into product decisions alongside the telemetry data.
“It’s a combination of telemetry data from our cloud usage and insights from our interactions with customers, not just at conferences but also during individual customer visits and smaller seminars.”
Rakesh Jayaprakash, Director of Product Management, ManageEngine
Resources & Documentation
- ManageEngine, IT management software portfolio covering ITSM, endpoint security, and observability
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Swapnil Bhartiya: Every vendor has AI on their roadmap. How does your team decide whether it’s based on customer feedback or your own roadmap, that which AI feature should get into managed engine product versus the ones that should just stay on the bench on the white bull for now. How do you decide that?
Rakesh Jayaprakash: Okay, so one of the biggest measuring sticks for us to decide where to put AI is first and foremost, the reason why people want to even invest in AI is to help them cut down on the time it takes for them to do a lot of the manual stuff. So what we look at is what are the things that are consuming a lot of time for our customers? And you know, managing has a wide portfolio of products from IT Service management, observability or endpoint security and so on. So if you look at each of these domains, the idea or our thought process is where is the customer spending a lot of manual time, which can be automated. So if you look at the IT service management side, nobody wants to deal with tickets. So how can you even try to resolve the end user’s issue even before the ticket gets raised? And when you look at endpoint security before a customer tells you that there is something wrong with their particular workstation, let’s say an application crashed. If we put AI in the endpoint agents and if they can kind of run and understand what’s happening on that particular endpoint, proactively, IT admins will know that something is wrong. And so, yeah, so we are trying to do things which avoid issue getting reported in the first place. So that by itself is a huge, huge advantage for customers in terms of cutting down the time it takes for them to sit and reply and respond to tickets.
Swapnil Bhartiya: These user constants, you know, as you said, in the us, you do globally, but once a year in the us, that cannot be enough, you know, to get that. So what kind of feedback loop you have, how do you actually, what you explain, how do you actually measure, you know, whether an AI feature is actually delivering the value that you talk about versus it looks great in the demo, it looks great in the keynote slide, but when it goes to the customer, nobody actually even used that feature.
Rakesh Jayaprakash: I mean, that’s a very true statement and something that I personally relate to a lot because the conversational assistant that we’ve been talking about as part of this conference, which is Ask Zia, has been before LLMs, we used nlp. It was working on NLP technology. We released it, at least I would say, eight years ago, before the whole LLM thing landed, which was aimed at people going in, asking questions, getting answers immediately. But in reality, not a lot of people were even using it. So what we do is of course anonymously custom. I mean, because majority of our applications are on the cloud, we can see the usage stats for the various applications that we are running in our own data centers. So that gives a sense of, you know, how much of these features are actually being used by our customers. And two, even in the on premise world, again, customers can choose to share anonymized data in terms of that usage. That is a big, you know, data point or data set that can give us immediate feedback in terms of whether a particular feature is being used or not. And of course, in addition to the larger user conference format, we also do a lot of smaller seminars, which is probably for half a day or one day across the globe where it’s in like smaller cities. We ensure that we kind of COVID for example, if you’re talking about us, we ensure that there’s one in the east coast, west coast, and of course everything in the middle. So yeah, it’s a combination of telemetry data that’s coming in from our cloud usage as well as our interactions with our customers, not just at the conference, but also in the individual customer visits or the smaller format seminars.
