16 . Mai 2017
Posts nach Autoren: Dr Nicola Millard, Head of Customer Insight and Futures, BT
Innovation has a showy, brash side and a quiet, functional one. Many innovations disappear into the fabric of life – like 4G (and 5G to come), cloud, search engines. They are there. They are embraced. But they don’t create newspaper headlines.
And then there are the ‘noisy’ technologies that draw the attention of the media. One of the ‘noisy’ technologies being showcased at Innovation 2017 is artificial intelligence (AI). Beyond all the hype, AI – or, more accurately, machine learning – has a quiet side too. It underpins many of the applications that we use on a daily basis.
Machine learning is integral to search engines like Google and Bing, recommendation engines like Netflix and Amazon, and even helps us take photos on our smartphones. It works behind the scenes to create better customer experiences.
The key is good quality data.
Machine learning can sift through massive data sets to detect patterns that would never be visible to the naked eye. Some good examples of this will be found in our Cyber Security zone at Innovation 2017, where will be showing how machine learning can be used to defend and detect digital and physical assets from cyber-attacks. As cyber-attacks become more sophisticated and malware acquires more intelligence, machine learning can be used in combination with other technologies, such as data visualisation, to proactively identify anomalies and react quickly to them.
In our Future of Customer Experience zone we’ll be demoing other machine learning technologies that enable us to create more personalised, proactive and, potentially, predictive customer experiences.
On the more ‘noisy’ end of the AI/machine learning scale we’ll be demoing chatbots. These can act as “digital triage” for customer service issues – signposting customers to information that is already there, or intelligently ‘speed dating’ them with expert customer service people who can tackle the complex and emotive issues that the bots can’t (of course, this assumes that the customer service people aren’t acting like robots themselves). We also be showing how chatbots can refine their conversational style by learning from their human customer service counterparts.
Finally, as part of our speaker programme in the Dome, I’ll be investigating how machine learning technologies change the skills we need on the customer service front line, particularly in the contact centre. In ‘Botman vs. SuperAgent’ I’ll be looking at how the best attributes of human and machine can combine together to create something more powerful: “augmented intelligence”.