Why IT Consolidation Must Come Before AI | Rajesh Ganesan, ManageEngine | TFiR

Swapnil Bhartiya: If you look at the modern enterprise IT stack, IT keep getting bigger and more complex. You’re looking at security, endpoints, identity services, and you can also throw Observatory in there and they all live in separate tools. Now AI is also landing on the same framework, same infrastructure. On top of that, it’s adding new workloads, new risks and of course new expectations from the IT teams. So the question is simple. Will AI tie all of this together or will IT become another layer to manage? This week in Orlando, Florida, ManageEngine is getting its customers to understand whatever customers needs are. And also they are putting AI in front and center of this decision. And today we have with us Rajesh Ganeshan, CEO of Manage Engine. Rajesh, first of all, it’s great to have you on the show.

Rajesh Ganesan: Swapnil, thank you, thank you for being here.

Swapnil Bhartiya: It’s my pleasure. I mean if you look at Zoho or if you look at strategic at manageengine, first of all, you are competing with some of the big players out there. And the most important thing is that meeting customers where they are in their own journey, solving their problem. Manage Intern itself is not only expanding across the markets, but also when it comes to the whole product line. You are here this conference talking to customers. Can you talk about what are customers asking for most that is driving this momentum?

Rajesh Ganesan: Actually you said it’s a very simple question. The answer is not and what are customers asking? So obviously top of the mind comes AI, which is what I used to think too. So 2026 and 2025, but not just customers. In the U.S. i get the opportunity to travel across the world. I ask this question to our customers, the CAO’s particularly, what are the top three things in your mind? AI is obviously one. But before all that, their day to day problems, their immediate objectives are top of the mind. So the first thing they say is tool consolidation. Platform consolidation is number one. We’re already drowning in how many tools that we have. For each problem there is a different tool. How can consolidation help? Number two is cyber security, rightfully so. And third comes AI. They understand the promise, the potential, but they are still not waiting to take the plunge immediately unless they fix the first two. So this is the recurring theme that is something I’m also hearing today at this event at our user conference, our biggest so far in the us. How can you help us with fixing today? First, at the same time, this is the roadmap I have that includes AI. Tell me, how are you going to solve this? How would you map what you are going to build with what I need is what we repeatedly hear.

Swapnil Bhartiya: Excellent.

Rajesh Ganesan: Thank you.

Swapnil Bhartiya: And that also kind of perfect segue because of course convent you folks make some announcement also. So how many of those announcements align with those pain points that you just mentioned?

Rajesh Ganesan: I would say all of them. See, consolidation is not an afterthought for us. So one of the founding beliefs, founding values for us was we always wanted to be this end to end player. It’s not like focus on only one area. You always had this holistic view about solving problems. We started small, attacked the problem in steps, we introduced multiple tools. But in the last 10 years our mission has been singular. So how do we bring all these products together in a seamless, flexible way? And that continues and I spoke about it in the keynote today, how we are going to bring all of our products behave consistently. It’s not easy, but this is what we need to attend. I spoke about cybersecurity a lot and that is an area we are giving a lot of importance. We are investing heavily. So you see a product like endpoint detection response that came in earlier this year. ManageEngine started as IT Operations Management, IT Service Management focused company. That focus still says but a lot of emphasis has come on cyber security also. So today we are a service management, operations management and a cyber security company. So this path was shown to us by our customers. Like you cannot look at security in isolation. That is a lesson that we took. There are pure play security vendors, but there is a value to doing security fundamentally part of your other core domains. That is the approach that we have taken.

Swapnil Bhartiya: Although security has now changed. It used to be an afterthought. It used to be someone else’s problem. Now security. You have to start thinking of security from the day one when you’re writing the piece of code because the way we are deploying, it’s too late by that it’s like thinking about putting brakes or airbags after we have sold the car. It doesn’t make that model doesn’t work. Now when it comes to security or consolidation, that is more or less like evolution of the product journey. AI is something that has been just thrown at us all. So how are you or Manage Engine thinking about either embedding AI into your products or like bolting on or building it as a standalone capability.

Rajesh Ganesan: How do you that approach fundamentally is something we don’t want to take. Throw a into something or bolt a into something. Then we are basically sort of how to say, like you said, security cannot be an afterthought. Similarly, AI cannot just be bolt in into the things. So this approach is something we are not going to go after. We want to start with understanding what our customers really want. You have a as a challenge, you have security as a challenge. But they are all like just challenges. But what does the business really want? What is their outcome? What is the process that they have? What is the kind of model they want to go with? So what is their economics to doing all this? We start from there. So like how I said, we evolved into doing cyber security the right way, which is not as an afterthought. We are applying the same principle to doing AI also. In that sense we are more like a non FOMO company. I learned this word just now. We are not in any race, we are not in any AI race. We are just going to use AI as a very powerful tool to solve problems of modern day enterprises. So that is the approach that we take to that count. When you just throw in AI, it may help you with certain benefits, but it is also going to bring you additional challenges, additional headaches, things like sovereignty, things like data protection, things like token economics. How does a company handle all that when you just throw AI in? So we want to consider all these aspects. We build our own language models. We have not praised our AI yet, so we are thinking to never praise it because it just additional feature inside your product which helps you with better productivity. Why would you look at AI separately, make it charge separately, all of that. So we take that approach. We want to bring that approach to how we do AI the way it benefits our customers. So that is the path we are taking.

Swapnil Bhartiya: Now when it comes to AI. Of course frontier models, they started the whole. Initially it was OpenAI, then entropy became the Gemini. Now a lot of models are there and then open weight models. I personally in my own usage feel that some open weight models better. GLM is actually better than Sonnet in most cases. But when it comes to these general purpose models, if you look at enterprises, they of course learn logs, tickets, they have their own telemetry observability. If you look at it, those models don’t even have the data set on that. So what is time to decide when purpose built models you folks have built a lot of internally are better than plugging into a general purpose LLM? Also there is a big debate about bigger is better, which is not the case actually.

Rajesh Ganesan: Exactly. This calls for conviction. We have our own conviction, but we cannot force it down the throat of all our customers. So this conviction has to be built by each company. One frontier model can promise that they can do everything for you. If I can take Anthropic, for example, the kind of language Anthropic speaks is this is the only technology we’ll ever need. You don’t need anything else at all. We don’t want to take that stance yet. But from their vantage point, what they say is probably right for them. It appeals to certain companies, good for them if it works. But this model is not affordable, not sustainable, not pragmatic for all businesses in this world. The world is about coexistence. You need multiple models, you need multiple belief systems, multiple convictions. That is where we come in. We operate with our own set of belief. We exist to serve businesses, serve customers that have a different approach, different philosophy to how an anthropic would think or how customers of Anthropic would think. They need the alternative. That is also one of the reasons we got founded and still operate out of India. We wanted to show the world you could build big tech. You could build global tech from rural India, could be done from rural Africa, could be done from remote Latin America. What stops? Because these are geographies, these are regions that need technology the same way as any developed world would. We focus there. So there we will build a very sustainable, affordable model of technology. So that’s how we exist, coexist.

Swapnil Bhartiya: I want to go back to merge both security and AI topic as you said. You know, you do want to give customers flexibility. You cannot force and that is not the right approach as you know, because in companies, employees bring their own AI tools into the workflow. How are your customers through manage engineering, managing and securing that layer. Because we are not even talking about regulatory industries because you have to be but a small slip. And also when you’re using API, all the data, if you’re not using zdr, even you’re using zdr, nobody knows what the company is actually doing. So how do you ensure that while you give them this flexibility, the layer is secure?

Rajesh Ganesan: Yeah. So I’ll be honest, this is a problem that we are still working on. I don’t think anyone has cracked it yet. You talk about people bringing their own AI or as we call it the shadow AI as we speak today, we do have some controls. You can build an AI gateway and make all traffic go through that gateway, intercept and do certain things. But people are smart, and what kind of endpoints they use and what channels they use. Because AI is so compelling, we are all always under pressure to deliver things. The companies would have policies and procedures on how to use AI. The workforce, the employees are always looking at avenues, opportunities to sort of circumvent other ways of doing things. Also, this is one of the hardest problems. When I said cybersecurity number two, it includes doing AI security properly. So even before deploying AI technology leaders are thinking about how do we secure our environment to leverage AI better. So to that count, what we are thinking inside managed engine, how do we build a very comprehensive, capable AI control plane. So it has to be holistic. It cannot protect only one channel or one avenue. It has to look into every nook and corner of your infrastructure, where data flows, which AI systems it goes to. Because a model could be sitting inside your phone. How do you control one of it could be from an unauthorized phone, unapproved phone. So these gaps would exist. This is what we need to address. So we have started working, we have made a lot of progress. So that is where we are headed in terms of your question of how do companies protect their workforce from all the capabilities that AI provides, all the channels that AI provides, it’s not an easy problem. This is going to be the next, I would say the typical phrase, the next million dollar problem in security would be doing this.

Swapnil Bhartiya: And in the beginning also you said when you talk to customers, they talk about consolidation, security, AI here. What kind of AI feedback are you hearing most from customers that may also influence your product roadmap.

Rajesh Ganesan: Like what I said just now, the way typically companies operate today, it’s very hard to standardize AI. Say for example, you are a software company, you will use AI to generate code. That model is very different from what you use for an analytics function or what you use for say legal function or basic automation. So companies today are in a state where they will pick and choose multiple different language models, some commercial, some open source. And for all collaboration and personal assistance inside, they’ll use something like your teams or copilot. So that’s what they would go with. So there’ll be multiple different AI systems in place, purpose built for each. So managing that is going to be a huge, huge challenge going forward. But this is what we hear, and companies are not like flipping a switch, going 100% AI overnight, they go 2%, 3% at a time. They start with deploying copilot. This is day to day productivity enhancement. Then comes analytics, then comes code generation. Then comes other forms of generative AI. Then comes how do they apply to their deep research. Say for example, if they’re a biotech company. So they go in steps. At every step they need controls, they need governance, they need risk management. That is how they ask. Which is why they give us the roadmap. This is going to be my journey. I want you to map with what you are planning to see how well you will be able to help me in my journey. Because it’s not going to be an overnight switch.

Swapnil Bhartiya: AI has always been a very confusing term from the very beginning. AI, deep learning, machine learning. Because some of these technology we have cameras which can track our face. That is also AI. Now we are talking LLMs, you know, which is like we use natural language too. So sometimes you may actually not need an LLM, you know, you know you can write a lot of scripts that will, you know, go through. And I mean you folks predate AI and you, I mean the AI that we talk about.

Rajesh Ganesan: Generative AI and agent.

Swapnil Bhartiya: Exactly. So my question to you also that are they also, you know, the. Either the customer feedback you hear or you advise them, hey, this is where you should use generative AI. And this is because it’s not like you know, one size fits all and it’s like you know, one silver bullet which is solution to all.

Rajesh Ganesan: I agree. Which is what. So we do offer our suggestions but the conviction should be with the business. They should have the self awareness of who they are, what business they are in, what customers they serve. They know better what kind of AI will serve them better. That is very important part of like being a successful business. We know today where to apply, what kind of AI. So even though we have our own language models for software generation, we go with third party models because quality always has to come first. Even though we write a lot of code for manage Engine, it is managing infrastructure that is priority, that is primary, that is bread and butter, not building a model that generates code. Our models, we focus on managing the infrastructure, managing the cybersecurity better. We don’t focus on building a model that generates code better. So our conviction is to focus all our energies on managing infrastructure. So companies should know be aware of what they exist for and then make their choices.

Swapnil Bhartiya: The reason for that question is also because initially started with just generative AI, then MCP came, it changed. The whole NPC was open source. So now you cannot live without plugging those tools. Now agents are autonomous, they can take action on, I mean we keep hearing a lot of reports these days. So while it is easy and ideal to just work with Zoho, work with management, but they need to do some homework. They need to prepare their own infrastructure. So how should IT teams, what kind of guard is they should put internally? So because they cannot, you know, you cannot.

Rajesh Ganesan: This is what I’ve been talking about all day today, from the morning. So look at manageengine giving you a good set of tools, good set of platform. But all you need to do is the first pondering question you must answer. I am this business operating in this region, on this size, serving this many customers. Answer this question honestly. The digital infrastructure that I have today, is it reliable enough, robust enough if I bring in AI? It is a harness question, how do you do this assessment? You need to be aware of yourself. I gave a blueprint, how do you go and do this assessment? Ask a set of questions. Do you have a common data plane? Do you have workflow systems cutting across all your business functions? How did you do cybersecurity? Did you bolt it on, brought it after. So these are set of questions that you can ask and tell with certain degree of confidence how reliable your digital infrastructure is. Only then you start looking at how can you bring AI into the mix? It’s going to be a third party AI. How do you share data, what do you use it for, what kind of token economics you are looking ahead. All of those questions come after because when you look at AI, it’s not just factors like productivity, your cost is a big factor, security, data protection, compliance, all of those. And then you calculate the roi. So to what I pay. If you cannot forecast what you’re going to pay, how do you even come up with an roi? So are you going to wait for outcomes, hoping that all the dollars I pay today would someday bring me returns back? No, businesses don’t operate that way. So which is why I keep going back to I can offer you suggest you some guiding principles, some blueprints. But end of the day, the business, the leadership should know, be aware of who they are, have build convictions, know about their business and then see how they can bring AI in. And you cannot just rely on one AI system, have systems like ManageEngine to manage those systems. So that’s the approach.

Swapnil Bhartiya: I will latch onto that word forecast. You know, if I ask you over the next year, first of all, this is a question I’m very careful to ask that what do you think? Because we don’t know something will happen next week. But if you look at the patterns and you also work in a space where things may change, but enterprises move slow, they have to move at a snail’s pace, they cannot move at a dry space. What is one AI bet that you feel will matter the most for managing your customers?

Rajesh Ganesan: I mean, this is not a bet. So I am an optimist. I look at AI and I look at what it can do. It is already delivering and we are in Orlando today. Last night we had this announcement from Microsoft and Nvidia. Microsoft is putting native agents in their Windows endpoints. This is the way things would go. It’s not a bet. It’s already happening. So we understand. So we are in the business of managing endpoints. Endpoints came with software deployed, patches being applied. Those were the challenges. How do I keep the configuration of the endpoint gold standard was a challenge. My patch posture was a challenge. I take it across the globe, connect it to multiple different networks, keep it secure was a challenge. But today these endpoints are going to come with pre built agents, native agents. The next step is to go address that challenge because that agent will need to be managed, need to be monitored. So Microsoft could give tools, but you need also a neutral perspective to how you manage technology from multiple vendors. So we have accepted that this is how things are going to be. AI agents will be there everywhere. Today I will make this statement. Even for a company like us, it doesn’t make any economic sense to keep writing code manually. We are already using AI to generate code and this is what most businesses would do, it make economic sense for them to use AI. Let’s face it, we have to accept it. But once you do that, you also need to have good governance, good risk management, good compliance posture. That is where we come in. Our commitment is we don’t deliver the next shiny LLM. But if you’re going to adopt one, we’ll be there to help our customers with using it safely, using it efficiently. That is our part to play and we’ll play it well.

Swapnil Bhartiya: Excellent. That is the right approach. And if I’m not wrong, at Jakarta conference you also said that AI outcome. Amit, I’m kind of paraphrasing. You will not come from the smartest model used. It comes from your readiness. And that is also kind of that we discussed about. What exactly did you mean then? What does this readiness look like? Because customers have to prepare themselves, their infrastructure, their team. So can you quickly talk about what did you mean by AWS and how, as you rightly said, this AI agents, this cat is out of the bag and this is not going back, you know, so what we have to do is to prepare ourselves and instead of complaining or staying back, we should be ready. Otherwise, we’ll be left behind. So what exactly did you mean by that?

Rajesh Ganesan: Let me give you certain examples from our own lives. We are a software company. We sell software products. So today we use AI to generate code. It can spit code out in volumes, you ask, and it delivers, but does that. All right, so you need to still build a product, take the product out. You have to take it to the customers, sort of convince them that product solves their problems. That is when you realize the value. Today, we generate a lot of code at speeds unimaginable, like even two years ago. But there are bottlenecks. You generate the code, you still have to manually verify a lot of portions of the code. How do you test the piece of code generated by AI? So how do you package them? Like, today, the situation is you have a lot of code generated, but you still don’t have a good way to ship them to the market. This is one example. This is what I mean by readiness. So you have a complex business. You can be in healthcare, in finance, could be a geographically distributed educational institution. You bring AI in. But you create hundreds of agents doing tasks autonomously. They will be now doing tasks in such a pace that humans cannot even blink. This is what we mean by readiness. We speak about observability, your ability to know what is happening in your infrastructure. Now, when you throw AI in, it’s going to create blind spots of observability. Like, you won’t even know there are spots causing issues in your network. There are vectors of attack in your network because of how you have deployed AI, the scale at which AI comes in, the pace at which AI comes in. Unless you know your exact situation, you are not ready for it. This is a very important realization. And this was my message. So do a readiness check, do a harness check, and give an answer for yourself. How ready am I? How robust my infrastructure is, how resilient resilience is. Once things fall, how quickly can you come back? Reliability is. The system should behave the way it should, the way you expect it to. It cannot act differently every two hours when you bring a in. Like, if the system behaves differently every three hours, what are you managing? What are you ready for? So this is what I mean by you should know. Like, as professionals, we know. So when are we ready to take on a challenge? It takes a lot of preparation. So you do so much groundwork, and then you know, like, say, for example, you’re going for an interview or taking an exam. How do you know you are ready. How do you know you’re confident about taking that? It’s the same thing. You just don’t get lured into and jump into putting a. That’s all I meant.

Swapnil Bhartiya: Now one thing that Zoho has also created waves is like, you know, when you look at these big players, you know, Microsoft, Google, you know, suddenly there’s an alternative that go, no, the alternative is not just about something is cheap, it’s about. Because sometimes what happens is that some of these big companies, they remind us the monolith of old time. Remember monolith software? We broke down. They come with all that baggage. You know, Microsoft is struggling to tone down Windows. If you look at it at the same time, you’re also, because you are, you’re meeting customers where they are in their journey, nimble journey, solving the problem they face today. At the same time, you’re also kind of turning India into a hub which is not just building the blocks of a big machine, but building the whole machine. So how do you look at that and also what kind of message you will have for other countries to kind of get into, take risk, become entrepreneur and try to bring solutions that the world needs.

Rajesh Ganesan: Absolutely. It’s a deep question that we can reflect on. I can personally reflect on. I started with this company in 1997, January. At that time I didn’t have this realization. But within a year or two, by 1999, I got my mental picture very clear. The first project that we worked on as a company was with IIT Madras. We still have our professor, Professor Jijanwala, he used to tell us that’s the first project we worked on. India should become a superpower. For that to happen, we need revolution in telecommunication. We need to be putting 100 million telephone lines in India. Mind you, this was time. When you apply for a telephone line, land line, you’ll have to wait six to 12 months to get a line. And this person was dreaming about putting 100 million telephone lines in India. We worked on that technology and that inspired me when I was fresh out of college, the founding belief of this company, Zoho and managed engineers, deep tech, global tech can be built from India, from rural India, from any part of the world. That is what we wanted to prove. Not build a specific product or be an alternative to Microsoft. No, that was not the goal. Just attempt something and build something of global standard from India. We are still in our journey doing that. More companies from India should do that. More companies from other regions, other continents should do this. Not just because you want to be an alternative because sovereignty is becoming very important the way geopolitics is taking turn. You don’t want one person, one leader of a company switching off your technology dependencies. Let’s face it, the entire world is dependent on four or five big tech companies supplying technology. This dependency is someday going to be crippling for the world. So this is one important factor. The other factor is today talent is completely democratized. You don’t need to chase one region, one geography. We have to have a model where opportunity goes to where talent is. These are some of the things our leadership inspired us to do. Why did I stay back in India for 30 years? Because that one dream of Professor Jhunjanwala, like we will someday put 100 million telephone lines, inspired me in late 90s and today what? India has 3 billion mobile phone connections, the cheapest 5G Internet across the country. So I could live the dream, see it happening. That inspires you. And we need to serve as inspiration to more companies, more people, be the mentor. That is our approach. So I won’t say we have succeeded, we have completed our journey. Our job is to just stick to our founding beliefs, values and be an inspiration to others and they can do far greater things. As we can see in India, the number of space startups that we see in India today is going up. Extremely heartening to see all that is happening. So we take a little bit of credit. We have been the 90s company from India to be a startup when the word startup didn’t exist. So we have done our part and we expect more such companies to do their part.

Swapnil Bhartiya: Rajesh, once again thank you so much for sitting down with me. Not only share the journey of Zoho managing but most importantly talking about where customers are in journey, solving their problem, meeting in there and then letting them influence the product and roadmap. Because that’s what we are serving. Thank you so much for your time today and I would love to have you back on the show.

Rajesh Ganesan: Thank you. Appreciate you saying all this yourself. Thank you so much. My pleasure. Thank you.