As Artificial intelligence technologies are being largely integrated into large retail, supply chain, legal, financial and IT firms; it has shifted towards becoming a key component of Enterprise Applications (EAs) and a major factor of successful business strategies. Enterprises today are now able to perform more works in less time, create personalized customer experiences and more significantly, it can prognosticate business outcomes to drive greater profitability.
As noted by the research reports, global tech giants like Apple, Amazon, Google, IBM, and Microsoft invested about USD30 billion on AI-powered technologies in 2016, in which 90 percent of the fund spent in research and deployment. So, here are the core factors accountable for accelerating the growth of AI across today’s industries, including affordable, high-performance computing facilities- A range of commodity computing facilities in the Cloud enables easy access to low-cost, and high-performance computing power. Before the innovation in this tech, non-cloud and cost restraining systems were the only options in the computing ecosystem for AI. Huge availability of data- Training AI to make enterprise-level predictions needs more data. Hence, the accessibility of specialized applications for labeling data, and affordability of storing processes, businesses today are able to concoct both defined and undefined data for training AI algorithms in accordance with their explicit scenes.
The progressive utilization of AI-enabled enterprise applications across the globe, enterprises have opened their arms to obtaining a competitive edge through AI enterprise applications. Although, a number of far-reaching applications available in the marketplace, such as process management, computerized billing systems, online shopping and payment processing, IT compliance, sales force automation, office productivity suites, and resource plan. As a result, revenue from AI-powered enterprise applications is predicted to worth over USD 31.2 billion by 2025. While AI shows in several forms, from Machine Learning and NPL (Natural Language Processing) to optimization operations and enterprise applications, many businesses are migrating towards more advanced iteration of AI tools to develop their business strategies and streamline their operations.