Anil Kaul, Chief AI Officer, EVP AI and Analytics at Infogain

The story titled “AI Will Do a Lot More Good Than Harm” highlights the transformative potential of Artificial Intelligence (AI) and its promising prospects for transmuting human lives and business. The story talks about how AI is going to change the world and generative AI has been a big step in that direction.

The story further mentions Infogain’s perspective of integrating generative AI into NAVIK due to the ready infrastructure. The story also talks about Infogain's plans in exploring the closed and open-source models available in the market along with the use of leveraging generative AI to address enterprise security issues and provide a relatively secure environment. Anil Kaul, Chief AI Officer, Infogain, is an AI optimist. He believes the technology will revolutionize the way people interact with machines and data. Read the original article here.

‘AI Will Do a Lot More Good Than Harm’

However, there is also need for caution as the technology in the hands of bad actors can pose significant threats or risks.

The emergence of generative AI has sparked intense discussions and debates about its potential threat to humanity. Influential figures like Eliezer Yudkowsky have raised concerns about AI going rogue. Not so long ago, Geoffrey Hinton, one of the Godfathers of AI, left Google to address the growing dangers in the field.

However, not everyone is scared of AI. Anil Kaul, chief AI officer & chief executive, AbolutData, Infogain, is an AI optimist. He believes the technology will do more good than harm. “I have been in the AI space since 1993-94. At that time, not many people were working on AI. I believe AI is absolutely going to change the world and generative AI has actually been a big step in that direction,” he told AIM.

However, Kaul emphasises the need for caution. Drawing comparisons between nuclear energy and generative AI, he warns that in the wrong hands, this technology could become a threat. Nonetheless, he believes that AI is designed to augment human capabilities. “Besides, I don’t want to underestimate the intelligence of humans, because a lot of times we tend to think singularity is happening and AI systems will be smarter and more intelligent, and I think they will be in certain ways. But humans will be able to control and manage it.”

Generative AI

“We’ve used Microsoft Azure environment to bring OpenAI models in an enterprise acceptable manner. What we did was replace the NLP model that we had built with OpenAI’s GPT models ( So today, there is a generative AI module built into Navik,” Kaul said. However, even though they are leveraging GPT models for now, Infogain is also exploring the potential of leveraging other closed and open-source models available in the market.

GenAI: Intrigue vs Apprehension

What generative AI has done compared to conventional AI is that it has caught everyone’s attention, Kaul said. Infogain’s customers are eager to leverage generative AI capabilities, but they are also a bit apprehensive. “Despite considerable interest, there is a prevailing sense of caution and a desire to understand the technology better before committing to any particular approach from an enterprise client
perspective.” Kaul continued, “Hence, our recommendation to clients is to proceed systematically and thoughtfully. Avoid rushing into decisions due to the unknowns, but don’t stay out of the AI landscape. Embrace AI and generative AI ( to stay competitive in a market with numerous startups vying to replace those who don’t adapt.”

Building proprietary generative AI capabilities

One of the major causes of apprehension among enterprises for leveraging generative AI is security risks. Kaul believes ChatGPT did a lot of good things, like bringing generative AI to the limelight. “But it also did one bad thing — it scared the enterprise folks because it was actually never built for the enterprise,” Kaul said.

Hence, one way to minimise the security risks is leveraging generative AI capabilities through the hyperscalers whether its Microsoft, AWS ( or Google. “By leveraging generative AI through their system, many enterprise security issues are already addressed, providing us with a relatively secure environment.” Another way for enterprises to
tackle security issues are to build their own generative AI models, Kaul said. This eliminates the risks associated with LLM APIs ( Moreover, in the coming years, almost everybody is going to be using the same generative AI models, according to Kaul.

“If three years from now, everybody is going to be using the same generative AI models, how are we going to be better from the others? Our advantage lies in combining generative AI with our superior code, empowering our developers to produce higher-quality code than others. This competitive edge necessitates creating unique intellectual property internally, possibly by refining open-source models and building our own solutions.”

Navik AI

Through Navik, Infogain is helping enterprises leverage AI and help them make better business decisions. What Navik does is create a recommendation on what actions to take. Kaul says Navik’s design allows not just analysts but the business team themselves to utilise its capabilities effectively. Navik AI helps businesses make data-driven decisions, and can be utilised by sales, marketing, technology, and
operations leaders. It blends AI, data, and analytics to serve as an intelligence layer for forward-thinking businesses.

“With Navik, we have helped the sales team of a US-based beverage company to decide which particular drink or beverage should be put in a particular restaurant.” Similarly, for one of its insurance clients, AI is employed for accident assessments. Customers simply send a picture of the damage, and AI determines whether it’s significant or minor. “For substantial damages, a physical inspection occurs, while smaller claims are assessed and settled based on the AI’s analysis of the image, estimating repair costs and more.

“Furthermore, for another client, Navik AI performs volume forecasts for 500,000 restaurants across 200 brands weekly. This extensive forecasting process is supported by a deep learning model and a neural network that generates precise forecasts,” Kaul concluded.