Creating value in artificial intelligence in the age of digitization

This is the age of digitization and investment. Companies are investing in artificial intelligence technology to transform their companies.

However, despite efforts to digitize and investments in artificial intelligence technology, companies are still dealing with new challenges nearly three years after the spread of the Corona virus pandemic. From changes in consumer buying patterns to employee turnover to supply chain problemsIn the age of digital transformation, companies are looking for ways to tackle these challenges while remaining relevant.

Sateesh Seethramiah, CEO of Infosys subsidiary EdgeVerve, a vendor of robotic process automation technology, said companies are not well-prepared to deal with these challenges. He is also a member of the MIT Auto-ID Lab, a research group on the Internet of Things.

In this Q&A, Seethramiah discusses what prevents organizations from effectively applying AI technology to their business problems.

What is one of the biggest challenges that companies face in applying artificial intelligence technology on the path of digitization?

The real deal with AI and all the value it can create is about having contextual information and data.

Sittarame SatishEdgeVerve CEO

Satish Setarami: While everyone is immersed in AI, I think the real deal with AI and all the value it can create is about having contextual information and data. And if one did not have it, the ability of AI to influence cognitive processes and decision-making would be limited.

for example, underwriting in the insurance industry. Underwriters need to accurately assess risks, approve or reject insurance claims, and approve the insurance itself.

The amount of information they need to gather to make this decision is enormous: huge critical and historical information and claims. Many of them are in digital contracts; Many of them have paper contracts and video files. The amount of time they invest in making a decision is a tiny fraction compared to the amount of time these people spend gathering data and organizing the information they need.

There is a great deal of opportunity to use technology to digitize and fetch information from documents and build the data layer.

Second, if you have that kind of contract data, the AI ​​sitting with it can make relatively smarter recommendations to these underwriters, as to what kind of risk is an acceptable risk. But today’s artificial intelligence is rarely used in this process because they do not have Related data that can be entered into artificial intelligence technology to make relevant decisions. So, the very basic insurance process…is completely manual in human decision-making.

There are many cases where we see that unless everything is digitized and until everything is digitized, unless all of that data is contextual for that organization…it’s really, really hard The application of artificial intelligence at a very operational level.

What types of operations are needed for the application Artificial intelligence in the digitization process?

Seethramiah: There are many policies that govern every process in an organization. AI not only needs to have access to basic data, it must also have access to key decision-making policies – and how policy has historically been interpreted by humans and applied to their own decision-making.

The first is politics, and the second is the interpretation of politics and how this translates into decision-making. Should come together if AI is really making recommendations Organizations can already use it.

Most companies do not have the knowledge of how to implement processes, let alone automation. Experts in those organizations feel it, but they don’t know how these processes are implemented across multiple organizations. Many of them are even done in IT shadow – It’s not even part of the main enterprise landscape. So, that led us down the path of saying we need technology to decode how processes are carried out.

We need to apply technology intelligently to digitize as much as possible. We need to make sure that digital information can be translated so that companies can take advantage of that at the end of the day. It should serve a larger business cause.

What is stopping companies from doing this kind of AI digitization?

Sitramei: I think the challenge is really to have the right kind of governance mechanism between business and technology, because at the end of the day, it’s where the real ideas happen, the real applications, and the translation of technology into business benefits.

It is not just a technical issue. It has a lot to do with internal governance, internal structuring, and having the right kind of mindset in dealing with this technology. I don’t think any organization wants to apply even a dollar of technology for technology’s sake at the end of the day. They all have to show a lot of business needs.

Obviously, there are concerns about some of the things AI can take advantage of, like, ‘Can I hand this decision-making over? Will it affect my job? There are a lot of other concerns out there.

There’s a bigger problem to solve, and it’s beyond technology, which has to do with the mindset, which has to do with embracing this technology from the new generation and convincing people how it can not only affect businesses but also their own technology. individual performance.

Editor’s note: This interview has been edited for brevity and clarity.

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