I’m sure you’re all familiar with the ‘fab four’ of digital assistants, Siri, Cortana, Google and Alexa. Love them or hate them, these digital personal assistants are just the tip of the iceberg. The potential for this technology is phenomenal, with the major tech players racing to develop the next generation of personal assistants that move beyond being purely querying tools and instead act as bone-fide real life PAs.
Technology such as that being developed by Viv, an artificial intelligence platform and IBM’s Watson, is looking to take digital assistants to the next level, using artificial intelligence algorithms and cognitive computing learning capability to actively learn and apply that knowledge across devices.
It is certainly big business, with Gartner predicting that by year-end 2016, more complex purchase decisions such as back-to-school equipment made autonomously by digital assistants will reach $2 billion dollars annually. This translates to roughly 2.5% of mobile users trusting assistants with $50 a year.
The role of data
While technology and innovation in terms of A.I, cognitive computing and the IoT are lauded as the key facilitators and drivers of digital assistants, enabling, for example, automatic ordering of groceries when your fridge senses you are running low on essentials or sending gifts to family members for their birthdays unprompted.
What is often overlooked, however, is the hugely important role that data plays here. Every decision a digital assistant makes needs to be based on data. Without access to, and good integration between accurate and relevant data, none of the above is possible.
As digital assistants develop and take more control of day to day purchasing decisions for individuals, questions will be raised about the relationships that brands need to develop directly with these assistants. This will dramatically alter the way brands market themselves and how they share content going forward.
Once again, data will be key here, with an opportunity for brands to ensure they rank highly. The big challenge will be how to use increasingly complex and large amounts of data for the sophisticated and autonomous decision engines that are being developed at a rapid pace.
Written by Dan TellingBack to Insights
Register here to receive the latest NewsletterRegister