The Great AI Wars

The Great AI Wars

Last week saw one of the last big players make their position in machine learning and AI clearer at Apple's WWDC event with the launch of their machine learning options. These days you're not a credible large cloud provider if you don't provide some interesting APIs around machine learning and AI with the likes of Google, IBM, Amazon, Microsoft (Azure), and Alibaba (Aliyun) to name but a few. Apple's discussion focuses on being able to embed these technologies on the device with the Apps rather than perhaps the building of the models and the execution – much less focus on pushing data into the cloud.

The war I speak of in the title however, is not some dystopian future where humanity fights for survival but rather the current war over talent that enables the use of these technologies. Insurers going through digital transformations and looking deeply at their analytics are finding they are competing with ever more unlikely companies for talent including rising InsurTech firms as observed in previous blogs. The good news is that basic machine learning capability and training is increasingly available as the democratisation of machine learning continues apace – in fact if you look at Apple's documentation this discusses the ease downloading and converting models and integrating them to Apps rather than the nuances of various training algorithms.

Machine learning isn't new to insurance with coverage in our predictive analytics reports courtesy of Nicolas Michellod and case studies. It is clear however that these tools and techniques are increasingly being embedded into solutions throughout the insurance eco-system and beyond – and they are raising customer expectations. A discussion on what this means for core systems is given in my recent report here, as well as a discussion on what this means for new front end opportunities with the rise of chat bots in our discussion on conversational systems and a broader discussion on the differences in designing intelligent systems versus programmed ones is discussed in designing the aware machine.

While AI is a battleground for the big players for insurers it is becoming an increasingly accessible source of new approaches and automation – both an opportunity to better serve customers as well as cut costs. The ease with which machine learning and AI can be embedded into simple applications now will only increase adoption and there are small things any insurer can do. Of course if you want to go much deeper, as pointed out in this Harvard Business Review article, if your company isn't good at analytics, it's not ready for AI. I disagree a little with the authors perhaps, we're in a world where anyone can do something – one can just download and convert a model and incorporate it into our systems as pitched by Apple.

For those looking to go further, the good news is there are many vendors that can help, and many partners too of all shapes and sizes. I'm happy to say the InsurTech investments in the industry are only increasing this number and the opportunities for applied AI in insurance. Further, there are many conferences discussing both analytics and the rise of AI – if you're attending or looking for them do get in touch, I or my colleagues would love to discuss.

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