siliconindia | | June 201919AI can enable better outcomes by eliminating human error and faster decision making that is adaptable to changing business conditions by simply tweaking an algorithmlow 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 AlphaGo, and smart assistant like Alexa have grabbed our attention. The enterprise inflection point would be when AI is incorporated into strategic busi-ness applications or business do-mains. This will help to raise em-ployee productivity, improve, and automate business processes, detect fraud, build smarter factories, and connected vehicles, make better rec-ommendations, anticipate customer sentiment, and even address cyber security. The digitally transformed businesses would be "algorithm driv-en business" that would use machine learning 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 work-ing on bringing AI into their core applications. They already have the advantage of having vast amount of digital data and interactions in the form of consumer profiles, transac-tions, and business outcomes. Once the data is anonymized; they can make current applications AI adap-tive by continually capturing and learning from new data and tapping transactional and behavioral history.One example is in the HR arena in finding the right talent match for open positions. The talent manage-ment solution driven with natural language processing and understand-ing and predictive language analysis will help speed up recruitment by allowing you to focus on just not on keywords but the general sentiment of the resume and social media pro-files. 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 vol-ume of data and information flows generated by the new generation of connected IoT networks. Because machine learning algorithms get smarter as they are exposed to more data, these deep learning platforms are key to finding insights in the data flows generated by Industrial IoT networks. Machine learning systems can detect the anomalies or patterns outside the norm and create self-healing 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 interact with everything will be rewritten 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 autono-mous 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 interfaces into any applications using voice and text and create highly engaging 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. Raman Mehta
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