Artificial Intelligence: What leaders need to know

Published on July 2, 2018

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Industrial automation solutions ━ In today’s fast-moving business environment, ignoring digital disruption and the impact of technological change is done at one’s own peril.

The integration of machine learning into operations, especially in the financial services industry, is accelerating at lightning speed. Against the backdrop, there are many ways AI and machine learning are set to positively transform the customer experience.

From streamlining services, industrial automation solutions, to personalising the customer experience, AI is here to stay. So, what should businesses be keeping front of mind during this rapid change and concurrent rise of AI?

We recently spoke to Dr. Catriona Wallace, CEO and founder of Flamingo AI, for our upcoming Banking Industry Outlook Report where she shared in-depth insights on the many factors at play regarding the rise of AI. While you can read her in-depth commentary in the report, below are some of Catriona’s top recommendations for businesses considering and assessing an AI strategy.


  • General Intelligence machines Vs Narrow Intelligence machines: To derive the most value from Artificial Intelligence, businesses need to distinguish between general and narrow intelligence. According to Catriona, general AI machines are commonly being tested and deployed by banks and telcos but are failing to generate the desired results. An example of an artificial general intelligence engine is IBM Watson or the Apple HomePod, whereby the machines are expected to know information on a range of subject matters. However, general intelligence is still a few years away from complete maturity. To effectively enhance the customer experience and industrial automation solutions with AI, companies in the testing phase should look to artificial narrow intelligence. Narrow AI limits the domain of learning, so the machine only needs to learn about one specific product or one process or function. Narrow AI can be both narrow and deep, learning all the questions a customer might have on a specific Use Case, for example Life Insurance quotations, in a short amount of time, often in just weeks.


  • View AI a strategic asset: According to Catriona, AI is mature enough to deliver businesses measurable commercial value. While some companies are grappling with making a return on investment, Catriona believes the unique data that is produced from the AI machines should be able to provide a competitive advantage to firms, especially banks. Clever firms are now trying to understand how AI can not only be leveraged but go one better and become a built-in asset to the business.


  • Understand AI is the future of the workforce: According to a 2017 Gartner report, AI is set to become a positive net job motivator. In 2020, AI will result in the creation of 2.3 million jobs, overtaking the number of jobs poised to be eliminated by AI. Against this backdrop, leaders can benefit from viewing AI as a positive workforce game changer. In this sense, AI is adopted not just as a digital strategy, but more importantly, as a business strategy. Integrating AI into the business model can free up workers from different parts of the businesses so they are in a better position to undertake higher order tasks, such as solving complex problems for the customer.


AI presents exciting opportunities for organisations that embrace it. For example, the future of the financial services sector will see employees and machines working in tandem to create a superior customer experience. Catriona calls this HAVA mode (Human Assisted Virtual Assistants). More importantly, customers will start to use machine learning tools to streamline and improve interactions with an organisation. Some progressive companies have already started experimenting with this aspect of AI, such as training Amazon’s Alexa on banking skills, as a way of delivering greater communication and engagement with the customer.

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