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The five essentials with Abhiram Sripathy, CEO of Mysuru Consulting Group

IT Budgets & Strategy

February 7, 2020
·
3
min read
An image of Abhiram Sripathy, CEO of Mysuru Consulting Group

AI spend isn’t equal to the importance that companies are giving it. In this brief interview, CEO Abhiram Sripathy of Mysuru Consulting Group makes the business case for why organizations should adopt AI at their organization.

Abhiram Sripathy is the CEO of Mysuru Consulting Group

What’s the best way of making the business case as a vendor in AI to an IT exec who doesn’t want to be sold to?

The most important aspect is to first try and understand what the pain point of the person is rather than direct selling. Once we know the pain point, then we should understand what sort of approach they’re taking to resolve that pain point. Is it repetitive in nature? Is it non-repetitive? Once we have a fair idea with regular conversation, rather than a selling or a sales-oriented conversation, you can tell them how you can solve this because you have some data that can address their pain point and that’s a way for us to be on the same page.

In what new ways will enterprises consume AI in 2020?

Everybody is waking up to the fact that AI is important for the company. I think the most important aspect in 2020 is that we will see people trying to infuse the AI first within their processes. And if they are unable to do it for the processes, what they would want to understand is how it will impact their company in such a way that you know it can solve their challenges.

What are some examples of business value creation through AI?

We were working with a telecom company and it is dependent on their partners to give them a phone quote in terms of trying to understand whether there’s a product availability as per the customer requirement and whether all the features that the customers want are there.

There are several dynamic factors here, which the partner has to meet before the telecom company reaches the end customer. Whenever there’s a change in requirement, the iteration has to happen. Despite the fact that the telecom company has a forward-looking contract with their partner, they’re not sure whether the partner is still going to adhere to the price, which may have been concluded six months ago or longer.

So this is the fun part. What they will do is take all the prices and all the different quotes and iterations which have happened in the past, all the while they are trying to understand different patterns across different partners in different geographical areas.

If that information is given to the telecom company, then what they do with the rest of the fixed costs and rest of the overheads, they’re able to give a clean quote to the customer. And when it comes back to the partner for negotiation, they know what range they have to negotiate. That’s the insight which we are offering based on the past data.

Are you seeing interest in AI being reflected in their increases in spend for the year?

People are waking up to AI but it is not entirely equal to the spend. I would say there is definitely an increase because the people have started budgeting for AI. They know that they need to deal with automation. They don’t know how to get the talent as that volume of work is increasing continuously on databases. The value of data is also increasing so they want to handle data explosion through AI and they want to give a better customer experience through AI.

How do you account for this discrepancy between interest and spend?

It depends on different markets. So some of the developed countries are definitely far ahead. They want to spend as they understand the value it is going to bring in over a period of time. But people are just waking up here and we will have to go about educating them as to how it’s going to add value. They will not be able to spend immediately. But over time definitely that spend is going to increase.

In the latter case, they do not have any clear budgets, don’t know how to discover use cases or they don’t know what sort of data is required for implementing. These are all three different aspects which we have seen as primary reasons as to why they do not want to implement AI in their organization.

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