The Inflection Point of AI

I spent a flight yesterday reading a report called The State of AI 2017: Inflection Point, produced by the research-driven VC firm MMC Ventures. The title is really what grabbed my eye first… 

Oxford Dictionary defines ‘inflection point’ as:

a time of significant change in a situation; a turning point.

A turning point. This is where we stand now. I selected three of my favourite quotes/stats from the report to share with you.

1. “Buyers feel there’s value, but are nervous around making bets.” 

While positive about the potential for AI, many executives express nervousness around undertaking AI initiatives given suppliers’ failure to articulate solutions to specific business problems, difficulty demonstrating ROI, over-promising by suppliers and the failure of some high-profile projects. 41% of firms say they are uncertain about the benefits of AI (McKinsey Global Institute).

This was super fascinating and a familiar issue I have noticed when meeting with prospective clients and investors. The language we use can sometimes be convoluted when describing our product and technology. Henceforth we aim to make things a bit simpler. Limited jargon & terminology. Crystal clear messaging, explanations of specific business value, and a bulletproof ROI timeline. Tech is a space where buzzy terms and hype is built up to sell a product. But we want to limit the BS and let the product do the talking.

2. The back-end really is king

I have noticed that at events especially related to finance/insurtech, people generally think of AI in relation to front end tasks, namely chatbots, automated first point of contact technology, etc. While these are no doubt powerful tools and solid use cases, it can be disappointing to realize that many are still unaware of the capabilities of AI to really tackle those sprawling back-end tasks. It was encouraging then to read that:

...more companies are deploying AI to improve their ability to make decisions or enable process automation (fig. 31). While there is a novelty to innovations such as chatbots, organisations are more pragmatic, with 74% saying they want to apply AI to improve decision making and offer more personalised recommendations, especially in relation to customers (Gartner*). And, nearly two-thirds of surveyed organisations plan to use AI to automate business processes, especially in areas that are manually intensive (ibid). All other AI use cases, combined, account for fewer than half of deployment goals.

3.     “You become big by specialising.” - Specialisation is becoming a key success factor

Large buyers value a supplier that offers specialisation. A specialised supplier offers: expertise in the customer’s vertical or business challenge; large cross-customer data sets to optimise AI algorithms; and peer referenceability in the customer’s vertical. In the short term, specialisation is becoming a success factor for companies to win market share among large enterprise customers.

This has been a major theme in our lives at omni:us over the last year, as we moved confidently into the insurance space. Whereas before we had a more horizontal outlook, excited to jump into multiple industries, we soon realized that it made sense to refocus our energies more closely one vertical: insurance. Within that vertical, we are attacking multiple business sub-functions like claims management and policy comparison. As we master these problems, we will move to new ones, as soon as we have comprehensive expertise in all insurance functions, we will springboard to another vertical entirely and expand our scope.