0834 GMT September 24, 2019
Artificial Intelligence (AI) is arguably the most revolutionary technology that is seen in several decades having the potential to completely turn the world upside down and then reshape it with new contours.
In the coming years, we will continue to witness the disruption what deep learning and AI-related technologies can bring to create an impact not only to the software and the Internet industry but also to other verticals such as manufacturing, automobile, agriculture and healthcare and so on.
AI will reinvent everything from the nature of work to the way we communicate. The disruptive destruction unleashed by AI would make a turbulent impact on the current skills making jobs redundant while opening avenues for new skills.
With the rise of AI-enabled chips, convergence of the Internet of things (IoT) and AI at the edge, and interoperability among neural networks, automated machine learning will gain prominence. It will automate DevOps (development and operations) through AIOps (Artificial Intelligence for IT Operations) such as speech recognition, virtual agents, AI-optimized hardware and biometrics.
Over the next three to five years, augmented analytics, continuous intelligence and explainable AI will be the toast of data and analytics technology having significant disruptive potential. Augmented analytics deploying machine learning and AI techniques will transform how analytics content is developed, consumed and shared. By 2020, augmented analytics will be a dominant factor pushing new purchases of analytics, BI and embedded analytics with data science and machine learning platforms.
Metadata is changing from being a passive spectator to an active disrupter becoming the primary driver for all AI/ML (Machine Learning) algorithms. As industry experts believe, by 2020, 50 percent of analytical queries will be generated via natural language processing (NLP) or voice or by atomically generated search. The need to analyze complex data combinations and to make analytics accessible to everyone in the organization will drive organizations toward broader adoption, allowing analytics tools to be easily accessible as a search interface or a conversation with a virtual assistant.
In the years to come, tech decision makers should keep looking for ways to effectively implement artificial intelligence into their businesses to drive values both at the stakeholder and the customer end.
As the story of ML and AI keeps evolving, its role keeps on growing, from supporting internal decision making to information products appointing chief data officers and powering enterprises with continuous intelligence. The deployment of automation and AI to mission-critical business processes will more than triple by 2019, which reflects growing confidence among the businesses in these technologies.
This rapid increase into AI and ML adoption suggests that business leaders are becoming more confident that the current proof-of-concept and pilot projects will move into production with technologies making a decisive disruption.
* The article by Kamalika Some, an NCFM level 1 certified professional with previous professional stints at Axis Bank and ICICI Bank, was first published by analyticsinsight.net.