Like every industry, the insurance space has been caught up in the digital transformation surge of the last two years.
From delivering completely paperless claims processes to introducing more expansive app-based experiences, the insurance industry has done an admirable job of keeping up with the digital demands that have arisen since the start of the COVID-19 pandemic.
However, with the established industry continuing to face pressure from upstarts like Lemonade, and customers continuing to expect more from their insurance experiences than just the better functionality, insurers are now pushing the envelope even more with technology adoption and experimentation. In particular, artificial intelligence (AI) is taking center stage.
AI adoption across the business world is at an all-time high. The AI market is set to grow from $327 billion in 2022 to $1.3 trillion by 2030.
It can be easy for insurers to get caught up in the hype and rush out to adopt the shiniest AI tools available. In addition, insurers continue to grapple with shifting consumer expectations, particularly from Gen Z and millennials. These consumers want personalized insurance options, so insurers are increasingly eager to jump into AI — and quickly.
The problem is, given that many insurers are still in the early days of their technology revolutions, many do not have the necessary know-how or support structures in place to build an effective, fully functioning AI environment.
It is imperative that insurers have the proper strategy to make sure that they are able to drive as much value and success as possible from this new toolset.
With that in mind, here are a few things that insurers need to keep in mind as they look to build up their AI capabilities.
One of the biggest faults in the software industry over the past several decades is that the technology required clients had to adapt to it as opposed to the other way around.
This simply cannot be the case if insurers are going to access the tools that are designed to tackle their specific needs and goals. Insurers need to take an active role in the development of their projects, and more importantly, find partners that will empower them and view them as equals in the building and maintenance processes.
No one knows a business’s needs and goals better than those inside that business. Insurers need to be their own technology advocates and push for tools to be built around them from the outset.
The insurance industry is changing. It’s becoming more digital, frictionless and social than it has ever been. It no longer takes days to file claims and weeks to generate new policies. The business is becoming increasingly agile, intuitive and behavior-driven.
In many instances, it isn’t the traditional companies that are leading the way in this cutting-edge functionality; it is newly minted startups that are just arriving on the scene. Legacy insurers are beginning to feel the squeeze as investment in insurtech continues to boom. (Insurtech investment hit $14 billion in 2021 alone).
This state of play has resulted in many insurers asking the same two questions:
This is where AI typically enters the equation. The problem is that many insurers are stuck in the pattern of using AI to handle process oriented and administrative tasks like turning paper records into digital ones. That just isn’t going to move the needle.
What can be done? Simply put: It is time for insurers to think big when it comes to how they deploy AI.
With how rapidly AI is able to synthesize data, insurers are now able to dynamically price like they have never before and can turn traditional processes completely on their head.
For example, using image analysis software and merging it with existing datasets can create a new level of consistency and objectivity when it comes to the claims process. Or, by synthesizing driving data, insurers can uncover and push new products in key areas in real-time to capitalize on evolving circumstances. Or, insurers can use social and media monitoring data to strike new partnerships with influencers or catalyze more social commerce functionality.
These are just a few of the creative ways and end goals that insurers should keep in mind.
One of the biggest problems that insurers — and businesses more broadly — have when they adopt AI is that they view it as an add-on or plug-in to their existing technology operations. But in order to really be successful, insurers need to think in terms of building a strong, AI-specific industrialized “data estate” to power their digital processes.
Each organization and industry is different. Simply bolting on AI and flipping a switch isn’t going to provide insurers with the intuitive and specific results that they need and want. And given how highly regulated insurance is, strapping on “any old” AI can be a nightmare when it comes to governance.
AI functionality is incredibly sophisticated and interconnected, so it needs a dedicated support structure in place that can enable both performance as well as explain-ability and reporting. In addition, to get the most out of AI tools, they need to be built in a manner that enables collaboration between the tool and human operators. Otherwise, what good are the insights?
This all depends on having a robust infrastructure of technology and talent in place. Therefore, before diving in, it is imperative that insurers take a step back and assess the maturity curve of their AI adoption or risk spending a huge amount of money and time on technology that won’t live up to expectations.
Sagar Shah is a client partner and Shashidhar Ramakrishnaiah is head of the Cloud Practice at Fractal Analytics. These opinions are the authors’ own.
