So, who is leading the way in the adoption of AI to service Customer needs? The best in class create customer experiences that are using advanced technology to disrupt the market.
For example, Lemonade in the USA have disrupted the insurance market with a combination of AI and Behavioural Economics. Lemonade take a “human-oriented technology” approach to maximise customer satisfaction. Maya, its own chatbot, uses AI to create and deliver personalised insurance policies and handle claims, without requiring insurance brokers. Speed is a key advantage of using AI, Maya can offer a policy in a matter of seconds without any paperwork or phone calls and with full transparency. In its first two years in business, Lemonade has grown from 96 customers to 250,000. And these customers are happy: 98 percent would recommend Lemonade to a friend. In the case of Lemonade, their technology defines the customer the experience.
Similarly, Bulb and Ocado in the UK have aligned their platform for customer interaction behind a fully orchestrated digital offering, ensuring customers interact primarily digitally, but ultimately have recourse to a human, whether through digital channels like Chat or through traditional voice when needed. It is essential to get the balance right between embedding analytics in customer service but making humans available when needed.
Outside of fintech, insurance and retail, AI is proving to have benefits for the public sector too. Improved planning, personalisation, and tracking of investment are delivering a much-needed step change in both the efficiency and effectiveness of government services. AI is helping by automating repetitive and mundane tasks, enabling staff to take on higher value work. For example, the UK’s Department of Work and Pensions have deployed AI to process incoming correspondence. By tackling labour intensive tasks, AI can positively transform the public service workforce and the desirability of government jobs. Health care is also a promising market for AI, where there is enormous potential in its ability to draw inferences and recognize patterns in large volumes of patient histories, medical images, and other data.