I recently saw a tweet from Mat Velloso - “If it is written in Python, it’s probably machine learning. If it is written in PowerPoint, it’s probably AI.” This quote is probably the most accurate summarization of what has happened in AI over the past couple of years.
A few months back, The Economist shared the chart below that shows the number of CEOs who mentioned AI in their Earnings calls.
Towards the end of 2017, even Vladimir Putin said: “The nation that leads in AI ‘will be the ruler of the world.” Beyond all this hype, there is a lot of real technology that is being built.
So how is 2019 going to look for all of us in the insurance world?
2019 is going to be the year when we see a lot of real-life implementations (large and small) coming together and showing visible impact. Much of the hype will fade away, and solutions that can achieve quantifiable business benefits will see the light of day.
Image recognition, speech recognition, language translation and sentiment analysis have already been deployed in the consumer world by Google, Facebook, Amazon, and others. These technologies will move into the insurance world to solve sharp, focused problem areas such as:
While some of these have already been deployed or piloted by some carriers, the technology will now be available “at scale.” Thus, enabling not just the larger carriers, but carriers of all sizes.
AI is nowhere yet close to the level where it can entirely replace humans…except in movies. However, AI has now reached a level where it can be the best tool that humans can use to deliver their services better.
Insurance has the unique challenge of very low customer engagement and customer loyalty. AI can be a great asset to enable insurers to engage with every single customer at a personalized level and create the much needed connection – financially and emotionally.
Much of AI is a black box and therefore is not totally understood by the business users.This leads to skepticism on their part towards adoption. When making predictions, AI will start to share the ‘influencers’ that led to those predictions. This transparency will clarify the ‘how,’ thereby instilling more confidence for the users towards adoption.
AI will continue to solve problems with increasing complexity. The datasets will become larger and more complex resulting in more opportunities to leverage AI. NGL will enable the translation of complex results into formats that can be easily utilized by its users. This will promote more confidence and broader promotion of the technology.
This is inevitable. The more AI is used in ever-increasing aspects of business, the more discussions there will be around privacy and security. There will be a need for laws and regulations to define how we use all this data. This will surely be a complex task; however, once the framework is established, it will hopefully provide some boundaries within the technology that can be deployed easily.
2019 will be the turning point where real-life implementations achieving quantified business benefits will replace the marketing hype around AI. There is still much work to be done around this, especially since the technology is always improving and we are finding more ways in which to use it.