Artificial Intelligence (AI) is the talk of the world and it features prominently in predictions for 2019 (see here and here) and recent surveys by consulting firms and other observers of the tech scene. Here are the key findings:
Consumer adoption: “Smart speakers” lead the way to the AI-infused home of the future
Smart speakers (e.g., Amazon Echo and Google Home) will become the fastest-growing connected device category in history, with an installed base projected to surpass 250 million units by the end of 2019. With sales of 164 million units at an average selling price of $43 per unit, total smart speakers’ revenues will reach $7 billion, up 63% from 2018. (Deloitte)
Enterprise adoption: Timid first steps
47% of business executives say their companies have embedded at least one AI capability in their business processes and just 21% say their organizations have embedded AI in several parts of the business. 30% say they are piloting AI. (McKinsey)
20% of business executives say their companies will deploy AI across the business in 2019. (PwC)
POST WRITTEN BY Derek Schoettle, Chief Business Officer for IBM Cloud.
(Click here for the full article from Forbes – Five Data Trends – IBM – Forbes)
“As we move further into the new year, companies may need to harness greater amounts of their data for competition and innovation. This will not only help to solve the challenges surrounding dark data and upcoming data regulations but will also open the door to uncovering new ways to innovate with data and AI.
Data Preparation Will Shift To Support Data Science And Fuel AI
Previously, data scientists worked by the 80/20 rule, with 80% of their days spent finding and preparing data and just 20% on actual data analysis. Thanks to advancements over the last year in cloud-powered data cataloging and data refining, this equation has been flipped. By using technologies that can automatically ingest, classify and organize all of a company’s data sources, data scientists can now spend more time creating and exploring new models and projects with ready-to-go data sets instead of spending long hours priming them manually first.”