Since generative AI has become omnipresent among the big tech, the companies have been funnelling money not just to develop AI models but also help their customers ride the tide. In one such attempt, in June, Amazon’s cloud unit invested a hundred million dollars to assist their clientele in developing and implementing solutions through the new Generative AI Innovation Center. 

At the recent AWS re:Invent, AIM caught up with Sri Elaprolu, global head, AWS Generative AI Innovation Center, to learn about the inner workings of the recently launched innovation centre. “From ideating, identifying and building a solution that proves the value and meets requirements, that’s what the team does,” pinpointed Elaprolu.

More than 1,000 customers have come in across all industries since the project started five months ago. Elaborating on AWS clientele, he mentioned that the customer base has both global enterprises and public sectors. For instance, the Singapore ministry of education is a customer. 

Elaprolu also mentioned that AWS is helping its clients understand what to build and how to build it at an enterprise level. “It’s now getting more into the core of the enterprise, not just on the chatbot style application,” he elaborated. 

Diving deeper geographically, Elaprolu mentioned that usually, the US tends to be relatively high on the list. This is a trend usually seen in the past. But generative AI has seen a different beginning.

“The thing that’s different this time”, continued the industry veteran, “is that it has started to be hot across and not just one active geography. We’re supporting customers across all areas, including Latin America, Africa, and the Middle East — locations we normally don’t see a jump right into emerging tech.”

Amazon has been at the forefront of AI, he highlighted by stating the examples of AI in Amazon through retail, AWS, and Alexa. “GenAI is new. But in our view, it’s an extension and not a completely radical thing. It’s just that until now, you’ve been able to predict, but now you can create. Very few companies know what that means for them,” he said. 

A Broad Spectrum 

Speaking about companies moving towards generative AI under industry’s pressure, Elaprolu said, “Maybe early on, it was a lot of experimentation. Now, there are two ways you can think of how companies are leveraging hardware.” One is how a business process can be improved and optimised by bringing generative AI. For example, Bridgewater Associates is the world’s largest hedge fund manager. They have investment analysis, which needs intensive maths crunching.

“The company is leveraging Amazon’s Bedrock and Anthropic’s Claude models to simplify many things that took weeks and now take hours. They’ve also built investment analysts that allow junior analysts to move quickly. That’s more of an internal optimisation of what you already do,” Elaprolu said. 

In terms of cutting down costs time, you can look at customers who are building new capabilities that did not exist earlier. “That’s another area where we see a lot of customers,” the AWS employee stated. He gave the example of Lonely Planet building a solution for personalised itinerary planning so that their customers can get a much better experience than what they perhaps were doing previously with manual clicking and dragging.

“So it’s a wide spectrum of how companies are evolving,” Elaprolu said in conclusion. 

Stepwise Breakdown 

Building and maintaining a client base of over 1,000 customers in less than six months is not a piece of cake. AWS is following a strategy that is helping them work with many enterprises. 

“The first step that we do there is working with the business leadership and technical leadership to understand their vision, pain points, and opportunity areas for their business. Then we work backwards from there to identify what we can do now with AI and methods that are still traditionally valid. Then we do a business impact analysis to check for positive returns,” Elaprolu revealed. 

He further explained that this process is done with the customer at the table, not AWS going off and doing their work. “We then move into understanding their environment, data, technical abilities, and various requirements. If you’re operating in a regulated world, you have compliance regimes to meet,” he added. 

Once Elaprolu’s team knows the details, they build that solution. That usually can go roughly 4-8 weeks, on average. AWS then demonstrates the value of that solution with the customer data with their users in mind. “That gives them a lot more confidence now because now it’s not just theory,” Elaprolu stated.