Intelligent CIO Middle East Issue 104 | Page 78

DISRUPTIVE TECH

Rajesh Ganesan , President , ManageEngine

AI works best without any type of silos . For the usage of ManageEngine products the key is to have an equal window into data generated from monitoring , security , service delivery , and analytics . According to Ramprakash Ramamoorthy , Director of AI Research , ManageEngine , what is required is to streamline processes , streamline data , and increase the usage of automation . “ These three can be the bedrock for your AI implementation ,” he says . “ If you get these three pieces right , your AI is going to be more effective .”

If the AI engine does not have complete visibility into how enterprise processes are operating ; if it does not have complete access to real time data generated by users , applications , networks and devices ; and if automation has not been integrated , enterprises will struggle to demonstrate any meaningful benefits from AI .

The better the visibility of data for the AI-engine across the enterprise , the higher is the confidence around that specific insight . This allows AI to raise its business value from predictive to prescriptive . In other words , not just predicting an incident is going to happen , but also how to fix it .

Continuing , the level of Roi that an enterprise can generate from AI depends on the maturity level of process streamlining , data streamlining , and automation . An organisation that is still using manual

processes and has not implemented digital platforms , will not see a big bang even if they have adopted AI enabled products from ManageEngine .

“ It is about how digitally mature you are ; how well rounded is your process streamlining , data streamlining , and automation ; that that would have a direct impact on the Roi that your AI is going to give to you ,” emphasizes Ramamoorthy .

Large language AI models

By default most of the current AI use cases , offered by IT vendors , emerge from narrow language AI models . The limitation of these models is that they can only do one thing at a time . If you have a model for invoice data extraction , you can only do invoices . If you give it any other document , it will not be able to extract the required data , as it is programmed for invoice data extraction . Small language models are more effective , when the context is narrowed down .

ManageEngine has seen improvements in agent productivity and reducing mean time to respond at the help desk .

“ One thing that we realized is bigger models provide more emergent behaviour . The model is able to answer

things that it has not seen before . And then there are some use cases where you will need that emergent behaviour ,” points out Ramamoorthy .

For reference , small language models require 3 to 5 billion parameters ; medium language models require 5 to 20 billion parameters ; and large language models operate above 20 billion parameters . ManageEngine plans to customise its large language models to specific domains .

“ We have been building our own foundational models and we are tweaking it to a domain . For example , large language model for security , monitoring , service delivery . This is easier because it is all natural language . The only place where we see OpenAI coming in is in service delivery . But even then , when we build our own large language model that will become more contextual . The first party data access is what is going to differentiate us , and we want to play to our strengths ,” summarizes Ramamoorthy .

This is part of Explainable AI , which is a set of tools and frameworks to help you understand and interpret predictions made by your machine learning models , that are natively integrated into products and services . This is where ManageEngine is looking at a selfhealing and prescriptive approach into integrating AI into IT operations . p

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