Driving Business Results with Artificial Intelligence

By Nora Li, Innovation Actuary, Angie Ng, Innovation Pipeline Manager, Aia Group (Hkg: 1299) and Georgio Mosis, Head of Innovative Technologies

Nora Li, Innovation Actuary, Angie Ng, Innovation Pipeline Manager, Aia Group (Hkg: 1299) and Georgio Mosis, Head of Innovative Technologies

Artificial intelligence is the renewed hype in insurance innovation. The results of this hype are, however, very minimal and usually are stuck in the proof of concept stage. As it is driven by the technology push and urge to show that innovative technology is being explored, it is disconnected from real business impact. Where are we going wrong and how should the leaders in this field drive innovation beyond proof of concepts?

Innovation approach: business pull vs. technology push

At AIA we take a different approach in applying AI to drive business impact. We have observed innovation theatres creating pilots that are published to drive likes on social media and also concluded that what is missing is a structured innovation approach focusing on driving business results.

We, therefore, always start our innovation journey with first identifying the business objectives. Once we have defined the business problem, we turn that into an AI problem. In order to do that, we employ AI consultants who can communicate with both subject matter experts and technical experts.

As a result, we can identify what business problems are most suitable to be solved with AI. AI solutions drive what business people find valuable: solutions that make money, save money, save time, improve customer experience or solutions that give our business units an edge in digital transformation.

As each specific goal is identified, we develop an AI solution that meets the needs of business sponsors to the envisioned targeted state, as opposed to searching for problems for AI technology driven solutions to plug into. Our AI solutions always mimic intelligence that is required to address the business problems. Armed with the knowledge of the multiple intelligences theory from Gardner, we know how far we can drive AI to really address the problems in an economically sensible way without overselling what is technically feasible yet still remaining business attractive.

Beyond data science: employing multidisciplinary AI team

Real digital transformation requires deep technological knowledge of how to architect intelligent solutions and subject matter expertise in the insurance domain. This golden combination prevents falling in to the trap of employing expensive platforms that often invalidate the business case economically. Once the AI solution is designed and implemented, we create a learning environment to initiate and drive the key performance indicators that are important in business transformation which comes after the AI technology innovation.

AIA innovation teams, therefore, consist of insurance subject matter experts, AI experts and IT developers working in close collaboration with problem owners in the business, driven by a solution architect capable of aligning objectives and solution options.

AI marathon vs AI sprint

We learned that meaningful application of AI is more than a “one and done” activity. It is rather a marathon in which innovation teams have to continue to engage business units in the transformation of their business processes and people skills to maximize the benefits of AI products. The AI journey we engage our business in, requires us all to rethink the way we collect, store and utilize data in an ethical way and to stay future proof.

We recognize that the future of AI is in empowering our workforce with the superpower that comes with AI and blending the subject matter expertise as in “Fusion skills”: Man and machine.