By Toby McClean – Countless use cases exist today proving that artificial intelligence (AI) systems are capable of transforming businesses, society and the economy. Examples of AI behaving appropriately in a growing number of scenarios across industries — from autonomous driving for the elderly and disabled and detecting tumors in medical scans to AI-driven education in the classroom — have crystallized the ever-growing value of putting AI to work at scale. 

Despite all of AI’s glorious promises, organizations have yet to reach economies of scale in AI. This goal of scaling AI has been the biggest and most elusive challenge for businesses in their digital transformation efforts and it remains a work in progress. A recent PwC survey underscored the severity of this problem by revealing that although 90% of executives believe AI offers more opportunities than risks, only 4% plan to deploy AI enterprise-wide this year. That’s a significant drop from the 20% who said they intended to deploy AI in 2019.

The survey suggests that companies need to get the basics of the technology right before they can scale it. AI introduces operational, managerial and job skill training challenges, as it affects every level of a company’s organization and radically changes workflows and business models.

At its core, AI is about productivity, so it’s safe to say that AI is about economics. AI systems are predictive machines that automate and augment tasks and predictions in the economy, allowing organizations and people to be more productive and make more informed decisions. AI mimics human cognitive functions, like learning and problem-solving, without human shortcomings, such as fatigue, emotion and limited time. Some experts predict that AI could add $15.7 trillion to the global economy by 2030. Read On: