We decided this Sourcebook was timely, given how enterprise AI has caught mainstream attention and triggered the imaginations of those working in IT, knowledge management, and a score of other areas within the enterprise. AI is dominating headlines, and people want to know how they can best apply the various technologies under the AI banner.
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With the recent AI boom, data is being created and generated at unprecedented rates. Per the latest estimates, 402.74 million terabytes of data are created each day. This increase calls for a paradigm shift in company efficiency—quickly enabling enterprises to uncover quality insights and effective organization of this data. To help organizations progress along their data-driven journeys, each year, DBTA presents the Readers' Choice Awards, which provide the opportunity to recognize companies whose products have been selected by the experts—our readers!
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There is a confluence of activity—including generative AI models, digital twins, and shared ledger capabilities—that are having a profound impact on helping enterprises meet their goal of becoming data driven. Capturing data, converting it into the right insights, and integrating those insights quickly and efficiently into business decisions and processes is generating a significant competitive advantage for those who do it right.
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Despite the overwhelming promise of AI, its rapid proliferation has been accompanied by some unwanted side effects, including a dramatic uptick in cyberattacks— particularly social engineering attacks like phishing, which have grown 1,265% since late 2022. As a result, organizations across industries are faced with an ever-evolving challenge: balancing the positive use cases associated with AI, such as automated monitoring and streamlined workflows, with its very real risks.
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