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FUNDAMENTAL Gaps in AI
2017 is probably the year AI has reached its height or rather the peak of its hype. Whichever though, there is no doubt it is making waves in the digital age that will differentiate much of the way business is done - on the internet, for one. Major e- commerce, social media platforms and tech giants continue to invest in this technology and raise the bar for a seamless experience and create an impact on the lives of consumers. Despite progress and milestones in AI technology, experts identify some gaps in the development and untapped source of insights that could be of huge value in the future.
Despite the abundance of data being generated in E- commerce, there is lack of automated or more sophisticated ways of interpreting data at speed and scale. Before companies start exploring around AI for personalisation or predictive analysis, they should focus on exploring the role of AI and machine learning in transforming this aspect of e- commerce. The ability to gain useful insight at speed and scale from existing data, E-commerce teams will be able to deliver improvements in customer experience and revenue. Lack of useful insight In an article by Duncan Keene, he cites an example scenario where one out of four mobile users abandons a site if they get an error. If this scenario is known to exist, it is likely to be addressed in a more efficient manner. Improving the customer has a direct impact on conversion and indirectly on revenue. He noted that much of the testing today is hinged on best practices and what competitors are doing rather than being business-specific. If AI can be utilised to identify a problem and its location, for example (a page that does not convert well), an immediate test can be performed to resolve it, and there is no need to rely on best practices or copy what competitors are doing which does not offer guaranteed results.
The online market is a highly-competitive battlefield where time spells money. With its fast-paced environment, being unable to quickly identify disconnects in the process for days, or even hours could mean a loss of revenue. This may include, for example, Lack of Speed to insight knowing how a new product is performing in the market or the effectiveness of a new strategy, or high cart abandonment rate. If AI can transform the speed at which insight is delivered, it can bring tangible and immediate value to the industry.
One area that will reap significant benefits from Artificial Intelligence is customer care. Even before visitors become customers, roughly 45% already rely on chatbots for price information and service options but only 30% of providers on the average use this technology to deliver these demands. Chatbots are relatively successful in engaging customers at par with expectations. Talk about 24/7 assistance, real-time support and advice and convenient communication delivered in the most convenient form. Chat apps are slowly taking the place of social media as the preferred method of communication. Lack of involvement from According to Gartner,85% of customer interactions will largely depend on emerging technologies such as machine learning by 2020. If this is the direction, there is a very significant perspective from customer care stakeholders that AI developers should be harnessing at this early stage. Research by Forrester found that 39% of decisions on AI technology are being managed primarily by IT Teams. Only 26% say that customer executives were involved and only 6% involved the marketing department in the AI roadmaps of their companies. Ironically, these companies are investing heavily in AI with focus on customer support. Where customer experience is a crucial in driving engagement and sales, the development of AI should be overseen by those who understand consumer needs the most. Customer Engagement executives
In a panel discussion at the World Economic Forum last year, MIT (Massachusetts Institute of Technology) Media Labs Director Joi Ito said that many computer engineers of AI come from the same background and that there is a need to widen the skills pool of those involved in the development of the technology. This means that were running a risk of a potential gap in knowledge. An example of a risk he cited was the discovery of their African American female researcher about the core libraries for face recognition where dark faces don’t show up. This means that if an African American is subject to facial recognition, AI will not be able to identify the face of the person. He referred to this as an oversight due to the lack of diversity in the location where AI is being tested. Lack of Inclusivity and Diversity Further to focusing the development of AI tools, Ito urged companies developing AI systems to involve lawyers and ethicists in understanding the tools being developed for a more comprehensive approach. Here, we can see that AI has much more to cover than it already does and there is a wide application than what developers already perceive. With inclusivity and diversity at the forefront, more people will benefit from AI technology. If loopholes can be studied carefully, fundamental gaps can be closed, and consumers and businesses can maximise the effectiveness of AI in all aspects.
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