siliconindia | |NOVEMBER 20219ics, not marketing or data networking or sales. Their last charges usually needed the context that could only come from complete knowledge and experience.In an AI-based framework, an algorithm could be trained to under-stand the information it is analyzing and could then consolidate far more data at a much swifter pace to produce highly contextualized results. Eventu-ally, this is anticipated to push these supreme analytics tools to the people who need them so that the analyt-ics experts could devote their time to what they do best ­ crafting the mod-els required to make AI analytics more active and precise.This demand for context is com-pletely illustrated when applied to a common enterprise function like mar-keting. Arguably one of the most data-intensive systems in modern business, marketing is often controlled to com-peting interpretations of the truth de-pending on the context in which data is presented.AI excels at predictive analytics ­ the ability to spot future trends based on past and current data, according to Mike Kaput, a Chief Content Of-ficer at Marketing AI Institute. This capability, of course is like gold to a marketing team. At the same time, AI delivers prescriptive analytics -- the ability to make recommendations based on predictive analyses. In both cases, today's AI engines are capable of sifting through massive amounts of data to ensure these results are being presented within the full context of all available information, and they also have the potential to refine their algo-rithms to enhance themselves using their own previous analyses.UNDERSTANDING THE RULE This capacity to receive is one of the key contrasts between regular auto-mation and AI. An automated system may still be able to get a lot of data. However, it is structured properly and developed to address the particu-lar requirement for which the system was developed. For example, a sim-ple broadcasting tool could be used to update itself with new information over time, but it won't be capable of providing new insight into transform-ing data unless someone creates a dashboard that enables it to do so.Similarly, basic automation can-not respond to the general queries that pertain to diminishing performance and other factors. This typically needs hours and worth of work by a data analyst, who is likely to still sort only a limited amount of data. On the oth-er hand, a completely trained AI en-gine could provide results to multiple questions in a matter of few minutes.Possibly, the ideal way to observe AI's contribution towards analytics is through one of the ancient analyti-cal methods of all, the cost-benefit model. On the cost side, it requires a fairly sizeable upfront investment, provided you are building the under-lying foundation from the initial step. However, this charge would amortize periodically as output measures. On the brighter side, AI can chomp mas-sive data than even a huge batch of analysts couldn't, and it could draw data from an untold number of refer-ences to recognize problems or oppor-tunities that would differently remain hidden. Conclusively, it would drive analytics capabilities into the hands of skilled workers who are the best fit from the insights customized to their unique difficulties, making the entire firm more valuable and prolific.The application of AI and the au-tomation of activities could facilitate productivity growth and other gains not just for corporates, but also for the entire country's economies. At a macroeconomic level, we could esti-mate automation alone to raise prolific growth across the globe by 0.8 to 1.4 percent every year.For marketers working with ana-lytics, business intelligence tools, or an analytics platform, the possibilities are that artificial intelligence could aid them to boost their revenue and cut-down expenses. That means now is the chance to get initiate developing AI capabilities at their companies, ir-respective of their skill or proficiency. This hints that they could build a con-ceivably unbeatable competitive edge. To slow down the process means you risk getting left behind.Data and analytics have been shifting the basis of competition in the years for a long time now. Lead-ing firms are using their potential not only to upgrade their core opera-tions, but also to drive entirely new business models. The network ef-fects of digital platforms are creating a winner-take-most dynamic in some markets. Although the size of avail-able data has increased exponentially in recent times, most organizations are attracting only a fraction of the po-tential value in terms of income and profit gains. The application of AI and the automation of activities could facilitate productivity growth and other gains not just for corporates, but also for the entire country's economies
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