Agentic AI and the Future of Insurance

Agentic AI will reshape the insurance industry

Kannan Amaresh:

There is only one thing that's occupying everybody's mind, it's AI, and obviously, agentic AI to be more specific. Insurance is going to have a lot of support to implement, adopt agentic AI. It's very clear to me that insurance is rich with data, rich with processes, rich with physical and let's just say digital interaction that has started from COVID, we're going to see a huge adoption in agentic AI. I'm actually pleasantly surprised that Anthropic is being bought licenses directly by a lot of these insurance companies. So you are going to see a lot of adoption when we look at agentic AI.

Better decisions, faster operations, and trusted AI

Kannan Amaresh:

Specifically, when you look at what is it going to give it to the ultimate user or to the insurance company? Let's talk about the insurance company first.

You're going to have a better underwriting capability. You're going to have better risk management. You're going to have better way of analyzing all data as one source. So that's the first. So you're going to do better decision making.

The second is all about speed. If you are going to adopt agentic AI, one of the coolest things that's going to happen is the speed. It's 24-7 available. Obviously, you have to have the right guardrails as well as responsible AI focus, but essentially you want to open up the door to make sure that the speed is going to be an important factor in doing business.

The last is of course the trust part. You got to make sure that all folks in the value chain can trust your processes when you say there is agentic AI involved.

AI meets a world of escalating global risk

Kannan Amaresh:

As I've said before, insurance industry is the guardians of the global economic risk and I'm really glad that this year's World Economic Forum Global Risk Report. If you look at the top 10 risks that they've highlighted, the first one is of course the geopolitical situation.

The other two risks which caught my attention was of course climate risk, we continue to invest in making sure that we are able to manage, mitigate, prevent, possibly all things that we can do with AI solution to address the climate risk.

The last one is about cyber risk. Now that most of the stuff is moving into digital and AI native, the cyber risk is going to be of extremely important focus for enterprises, but also for the insurer. And I think most of the insurance, especially on the PNC side, they have been rolling out a lot of cyber risk focus policies. But now with AI adoption by most of the enterprise, you will have to have a better view of cyber risk, both mitigation, management, prevention, and all of that together. So I'm quite excited to see how the enterprises adopt all of this as we move forward.

AI scales fastest inside the enterprise first

Kannan Amaresh:

If I look at scaling the AI in an enterprise, especially in the context of the insurance business, it's going to be interesting to see how far they want to first do it within the enterprise? How far they want to extend beyond the enterprise? I'm actually quite feeling good about how they will use it within the enterprise first, because within the four walls of the enterprise as an insurance carrier, you could go from pilot to scale much faster.

For example, if you look at claims administration, if you're doing it yourself, how fast can you implement, let's say, persona-based digital twin? Or if you say underwriting, if I can create some underwriting assistance, digital twins faster within the enterprise, I think the benefits would be much more if you could do it within the enterprise first. And obviously, you control the security. You have all the explainability that you need to talk to your regulator much easier.

Internal adoption will outpace full value-chain transformation

Kannan Amaresh:

My bet is probably in the next 18 months, the intra-agentic AI adoption would be far greater than going the full value chain because every obviously insurance carriers are risk averse. So they first want to prove it to themselves that it works within their own four walls before you extend beyond the value chain.

But having said that, there are a lot of boutique companies coming in on the Silicon Valley or otherwise, focused on AI to solve point solution, which might be even extending the enterprise. So I guess in some sense, the CXO community or the business leader needs to watch out both while doing intra, but also keep an eye on what's coming from outside so that you don't lose out on the market efficiency.

AI transformation demands workforce reskilling and leadership discipline

Kannan Amaresh:

As I look at unfolding of 2026 going into 2027, I think I gave an 18-month view what's going to happen, there are plenty of opportunity for an insurance carrier to do a lot of stuff within the firm. Adopting agent AI should be the top of the agenda for business leaders, CIOs, you name it, everybody.

One thing to probably watch out is when you look at your talent planning, you have to think about upskill, reskill pretty seriously. It's not about what they do today. It's about what they're going to do maybe 18 months from now or 24 months from now. So as a leader, you have responsibility to look at your talent, upscale, rescale, and talent management as a whole, then craft your adoption.

Don't do adoption and then look for what happens to the talent. I think that would be one thing I would probably request all of the CXO to look at. How does it go hand in hand with the talent management? I think that's a very, very important aspect. And take advantage of all the reskilling, upskilling opportunity available already. Put that in the plan as you go from pilot to scaling.