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cognitive/artificial intelligence

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For companies that are committed to creating Digital Transformation (DX) within their organizations, artificial intelligence (AI) is a critical component. The data that is created in DX initiatives has limited value if an organization can’t extract valuable, accurate, and timely insights from it. That’s why enterprise organizations are using AI technologies to pull actionable value from its data; in fact, by the end of 2019, 40% of all DX initiatives will be related to AI.

AI is at the heart of digital disruption; By end of 2019, 40% of DX initiatives will use AI services and AI will be the technology that will propel DX. By 2021, 75% of commercial applications will use AI; By 2022, 75% of IT Ops will be enabled by AI; by 2024, By 2024, AI-enabled interfaces will replace 30% of today’s screen-based applications; By 2024, 7% rise in AI-based automation will drive new wave of business processes.

Face it, you are paying attention to robotics. Maybe it’s as a curiosity, maybe it’s out of due diligence, or perhaps your interest stems from a realization that robotic technology has become useful in ways well beyond the expected use. Regardless of your interest in robots, the fact is, robotic technology is quickly expanding beyond the realm of industrial automation and has steadily been making its way into new industries and use cases. As this technology expands into new areas, it is important for companies developing business applications, IoT and analytics platforms, and systems integration to pay attention and look for opportunities to capitalize on a new and growing market.

In October 2018, a Reuters article informed the world that Amazon had scrapped an AI–based recruitment application that turned out to be biased against women. Most headlines about this story highlighted the company’s failure in developing an actionable and fair solution for one of the most important processes of the HR team.

However, what this and similar examples of today’s AI “failures” neglect to acknowledge is the complexity of end-to-end process automation based on AI technology. This complexity stems not only from current technical limitations but also from the immaturity of corporate policies, government regulations, and legal systems to deal with machines that automatically analyze, decide, and act.

There are two primary ways to buy or trade data in the Data as a Service (DaaS) market – direct sales from a data provider to end users, or via a data marketplace. While large, established information services businesses continue to make direct sales to their customers, many are also participating in data marketplaces. For smaller and emerging providers of DaaS, the rise in data marketplaces has made it simpler for them to package and sell their offerings, and for potential customers to find them. Marketplaces simplify the searching process, providing a variety of sources and types of data, along with a ready group of potential buyers.

It’s been said that all businesses are technology companies in the age of digital transformation. It’s also true that many are becoming information businesses as the amount and value of data they produce and consume continues to increase. In fact, business leaders and CIOs will find themselves not only missing opportunities but also at a competitive disadvantage if they don’t leverage data assets before markets are crowded with competitors.

What if you discovered oil in your backyard? How would you get it extracted from your property and deliver it to customers? Where would you sell it and what would you charge? If the world’s most valuable resource is no longer oil, but data, how does your enterprise leverage the data it already creates and manages to turn what is ‘in your backyard’ into a sustainable revenue stream?