HOW AI IS SHAPING BUSINESS

RAMPRAKASH RAMAMOORTHY, DIRECTOR OF AI RESEARCH AT MANAGEENGINE, ON CURRENT TRENDS FOR AI.

How do you see the democratisation of AI impacting enterprise IT?

From a ManageEngine perspective, the Middle East region was the fastest adopter of AI last year. Even from a product perspective, every single ManageEngine product is AI-enabled now. We talked about the democratisation of AI. Now it is all about commoditisation of AI. Artificial intelligence was seen as a niche technology a couple of years back, but now the technology has achieved escape velocity. So, it is everywhere. And now, with things like ChatGPT and new language models coming in, there is a sustained interest in AI – it is no longer going through hype cycles. It has become mainstream, and we will see more of that in the future.

How are you helping your users adopt AI?

Like always, we’re adding more Explainable AI wherever possible. We’ve seen that wherever we add an explanation, the confidence grows, and people start returning to using that feature. And we have added more AI-enabled reports. Last year, AI was more of a pull thing. Now it is a push thing. And users are also getting confident with the results. So that’s why we see more adoption. We’ve been making improvements to ensure the accuracy goes up - what was around 85 percent is now, on average, at 90 percent. This means the model has stayed in production for a long time, has seen a lot of past data, and it’s getting better. So it’s more like a natural evolution.

Are you seeing any new use cases?

The recent trend has been on generative AI, which will be a game changer. But we will have to wait and watch how it will impact enterprises, especially IT. These are more consumer-focused tools. For example, six months back, we had DALL.E, which generates images and art from a text prompt, making a lot of ripples. And now we are seeing chat GPT making a lot of noise. These are natural language models; I see them as an enhanced version of Siri or Alexa. But the impact Siri or Alexa made on IT was very minimal.

However, where we see use cases coming up with generative AI is writing some firewall rules. For example, by looking at past usage patterns and if there is heavy social media traffic after 5 pm, you can amend your firewall scripts in such a way that it is reduced. So there will be very niche areas where generative AI can be used. But the challenge is these models are very large today. So you need a ton of training data to train these models. At ManageEngine, we have always focused on building models with a lower footprint, meaning less data is required to train the models, but at the same time, it should be deployable at the endpoint. Though language models are important, we aren’t sure how large language models will work because the use case is very niche in service delivery.

Are you leveraging AIOps in IT management?

We eat our own dog food. We use ManageEngine in Zoho. Most of our beta releases are being tested in our production environment, and that knowledge seeps into our customers.

So we are definitely using AIOps in production.

When do you expect AI-enabled hardware to come to the market?

In the consumer world, we already see a lot of headphones with powerful noise cancellation technologies and in-built voice-to-text features, etc. On the hardware side, now we have things like FPGAs (Field Programmable Gate Arrays), where you can customise your chipset for workloads you have. We are working on bringing AI models to FPGAs so that you can remove the clutter from the CPU. The CPU does what it does, and then all of your AI is done on this FPGA platform. For example, Intel has QSV chips. What it does basically is when you receive a video stream or on a Zoom call, it does not tax the CPU; it goes into the QSV chip. Now we see such specific hardware-enabled AI chips coming to the market. Everybody’s working on it. Intel is working on it. Nvidia is working on it. Even from the inference perspective, the training can be done on your cloud without taxing the CPU on FPGAs. We don’t make hardware but work with many of these hardware companies to ensure our models are FPGA compatible. Again, we are looking at this from a cloud perspective, not hardware.

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