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Jun 1, 2018 | By Brent | Share this article

Every year, Mary Meeker’s internet trends report sheds light on much more than what you consider the conventional web. Last year, I pointed out parts of the presentation which hinted at the grander adoption of AI in the consumer space. This year, Meeker shows growth of AI in the consumer space continuing, plus where it may head in the enterprise.

First off, data showing Google’s Machine Learning word accuracy meeting the threshold for human accuracy, and Amazon’s increased user base of Amazon Echos (and corresponding skills) sees some correlation with customer satisfaction. The world’s 2 largest companies that focus  on AI-based virtual assistants – and back that focus up with research and development – are resonating with consumers. This time last year there were 12MM Amazon Echos installed in the USA – today that number is above 30MM. Google Home’s numbers are even higher with 44MM units sold. These devices, and their integrations, are increasingly used by consumers to search out customer support, and sometimes link directly to the companies from whom they seek help.

Both the companies have focused on refining and adding to their datasets, improving their precision rates, and scaling the costs of AI-based solutions – both for themselves, and as the following slides show, others who want to take advantage of their AI services and infrastructure. Companies like these two, and other giants like Microsoft, Intel, and IBM are comfortable at this point to tout their AI and machine learning platforms for consumer facing products and beyond.

And in the last slide you see why. AI-based solutions are increasingly taking up space in the budgets of enterprise companies worldwide. While 2016 and 2017 saw proofs of concept and experiments in consumer facing and enterprise solutions based on artificial intelligence and machine learning, industry leaders are now rolling up their sleeves and preparing for mass enterprise adoption of AI. Check out the full presentation, embedded below, for much more data an analysis.