Enterprise AI Readiness: Perspectives from New York Life Investments and Infosys

What are the biggest opportunities for AI to enhance personalization and client engagement in investment services?

Wei Wang:

I think the AI is actually in the iPhone moment right now. So everyone can use AI, especially the ChatGPT moment. The gen of AI can actually change the way we deal with data, do larger amount of information. So now everyone can actually improve their efficiency. They have more time to do the more complicated work and then they really actually free up a lot of time and then we can focus on more strategy work.

What are the most pressing challenges enterprises face for scaling AI and how is Infosys addressing this through the Topaz offering?

Sunil Senan:

Absolutely, I think as Dr. Wang mentioned, the world got into the ChatGPT moment and we started to really experiment and see what this technology can do for us. But this also caught many of our enterprises in experimentation cycle. Thinking about, you know, another company that can test this technology is not going to necessarily push the envelope on what this AI journey can do for us. And hence scaling became a very big challenge because it requires companies to think through why AI, right? Just because there is a technology, how do you use this for the purposes that you have as an enterprise and promote that? So not having a clear strategy and a purpose behind this is one of the single biggest reasons why many of these initiatives don't scale because there is no ROI led business case supporting it. It was built to run some experimentation, did not really understand what all it takes for us to be able to scale it. The other big challenge around this is not having data readiness in the enterprise. Over the years, enterprises have worked on data that was governed, even though there are still work to be done. This is your customer data, product data, order data. But what ChatGPT is going after is the data that is not governed as yet. This is the information and data that's sitting in our emails. It's sitting in our documents, videos that are circulated by customers to contact center agents and so on. And unless you can fingerprint all of this and create a way for you to be able to trust that data, we believe that, you will not be able to necessarily scale AI. So getting enterprises data ready for AI and the lack of it is one of the biggest challenges there. The third is that this is unlike any other transmission wave that we have seen in the industry so far. This requires the reskilling of talent. There’s a lot of fear about losing my work to an AI agent, whereas we believe that this actually amplifies human potential. And because I explore and do newer things, it increases AI potential as well. So reskilling people and then finally managing change and bringing very clear direction in terms of how we can orchestrate the entire organization and the teams about how to embrace this change, not be afraid of this change, but be able to engage it and create value for the enterprise. So these are four big reasons why we don't see AI scaling in the enterprises today.

What are some of the key risks or concerns you have around AI scalability and sustainability and how is New York Life preparing to address them?

Wei Wang:

Yeah, I think from the enterprise perspective, I think the data privacy is the key. So at New York Life, we really care about data privacy. We actually have a very good risk management, governance framework around how employees use AI. And then all the tools we actually use, we start from very careful thought and then try to pay OC and then test and learn from scratch and then gradually actually launch to every employee. And I think another challenge is from different use case. Right now, AI is not able to handle all the complicated especially financial analyst type of task. It's getting, the large language model is getting smarter but we are still learning and then do a lot of iterations as we just mentioned is more like the more iteration we try and then the more we learn and how we utilize AI. So that's actually give us a very good foundation work and then from there we can actually scale the AI to be able to achieve the maximum from the AI.

How do you see academic partnerships shaping Infosys’ AI roadmap and what role do you envision the center to play in this?

Sunil Senan:

I think many of our customers are partnering with Infosys in order to not only bring the client knowledge and the knowledge of their systems, but also to bring new possibilities, new opportunities, and the understanding of how we can harness what's happening in the environment to act to their success or to create that success that they're looking for. Through this partnership, we are looking to build industry blueprints through harnessing newer technologies. Like you said, there was AI, then there was generative AI, and now we are agentic AI. It is not going to stop here, right? Quantum is around the corner already. We are already seeing certain use cases that are coming out of that. How do we create a foundation that is kind of future-proofed? It can evolve with the technology changes that are happening and make investments in solutions, accelerators, frameworks that can be leveraged by our clients in order to drive their enterprise AI journey at scale, right? So we believe that through this center, we now have a kind of an ecosystem where our clients, Infosys and Columbia's best expertise can be all put together to explore not only new possibilities but also create solutions that can help them scale those within the enterprise.