OCTOBER 20199for an enterprise will be realized using AI. Deep learning systems can learn from iterative data com-putations. They just don't follow explicitly programmed instruc-tions. Deep learning algorithms get more intelligent and context aware with use and experience, mak-ing them a key enabler of artificial intelligence platforms.AI in the form of Facebook tar-geted content feed, Google Alpha-Go, and smart assistant like Alexa have grabbed our attention. The enterprise inflection point would be when AI is incorporated into strategic business applications or business domains. This will help to raise employee productivity, im-prove, and automate business pro-cesses, detect fraud, build smarter factories, and connected vehicles, make better recommendations, an-ticipate customer sentiment, and even address cyber security. The digitally transformed businesses would be "algorithm driven busi-ness" that would use machine learn-ing to drive process automation and improve decision making. These will be the businesses that would reap the benefits of modern day "gold rush" where deep understanding of their data paves the way for inno-vative business models, products, and services.There are major enterprise soft-ware providers that are already working on bringing AI into their core applications. They already have the advantage of having vast amount of digital data and inter-actions in the form of consumer profiles, transactions, and business outcomes. Once the data is ano-nymized; they can make current ap-plications AI adaptive by continually capturing and learning from new data and tapping transactional and behavioral history.One example is in the HR are-na in finding the right talent match for open positions. The talent management solution driven with natural language processing and understanding and predictive lan-guage analysis will help speed up recruitment by allowing you to fo-cus on just not on keywords but the general sentiment of the re-sume and social media profiles. The whole recruitment process can be done with fewer mistakes and be more equitable, accountable, and compliant.Another enterprise application of AI is processing the enormous volume of data and information flows generated by the new gener-ation of connected IoT networks. Because machine learning algo-rithms get smarter as they are ex-posed to more data, these deep learning platforms are key to find-ing insights in the data flows gen-erated by Industrial IoT networks. Machine learning systems can de-tect the anomalies or patterns out-side the norm and create self-heal-ing behavior or alert a human for corrective action. Augmented Reality is another area that is fueled by the advance-ment in AI where, the way we inter-act with everything will be rewrit-ten and new business models will be created. The Tesla Autopilot enabled automobiles are collecting data from millions of miles driven by their drivers in real life situations. These videos and data are fed to a deep learning engine on the cloud to create a terminology of auton-omous driving. As per Elon Musk, the whole Tesla fleet operates as a network. When one car learns some-thing, they all learn it. The same model can be applied to enterprise software. The algorithms can be continuously enhanced and made to reflect on edge devices by constant over the air upgrades. They can help in building conversational interfac-es into any applications using voice and text and create highly engag-ing user experience by constantly learning from the network.For an enterprise, AI can enable better outcomes by eliminating hu-man error and faster decision making that is adaptable to changing busi-ness conditions by simply tweaking an algorithm. AI can enable better outcomes by eliminating human error and faster decision making that is adaptable to changing business conditions by simply tweaking an algorithmRaman Mehta, SVP & CIO
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