How AI and Data Science are Poised to Drive Significant Changes for Enterprises in 2019
With more and more enterprises undergoing a digital transformation, business intelligence strategies and tools are a critical necessity to derive real value through such a transition
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As 2018 draws to a close, companies are looking back on the year, which saw a string of changes across the business landscape. It was the year of intelligent systems, as research, development, and adoption of such systems continue to influence both enterprise performance of early adopters in the present, as well as their outlook for the future. On the other hand, the late adopters of intelligent and intuitive technologies were driven by a growing need to manage an unabated flow of data from all quarters, while leveraging it to build on their efficiencies. Consequently, with more and more enterprises undergoing a digital transformation, business intelligence strategies and tools are a critical necessity to derive real value through such a transition.
Data Science has, without a doubt, been among the most valuable additions to the business ecosystem in recent years. The role of Big Data and data science will only become more prominent on an enterprise level in the coming years. Data and data science technologies are transforming both front-end and back-end processes within organizations that are increasingly being powered by Artificial Intelligence, Machine Learning, and Deep Learning.
The following are some of the key trends witnessed in the year gone by, and those that are poised to influence the enterprise ecosystem in 2019, and beyond.
Artificial Intelligence (AI) Will Drive Smoother Workflows and Seamless Customer Interactions
The self-learning, intuitive, and proactive capabilities of AI and ML are powering new enterprise systems that enable enterprises to achieve unprecedented levels of operational efficiency and drive successful sales and marketing efforts. Businesses are increasingly developing and using intelligent customer-facing applications to deliver a distinct, value-driven experience to customers in order to retain and build stronger relationships with them.
Furthermore, the conversation around AI has been dominated by automation, prescriptive analytics, and business process automation so far. In 2019, however, businesses are expected to leverage AI and data science to make their interactions with customers much smarter and insightful.
Virtual agents or chatbots drove by Machine Learning are delivering substantial benefits to players across various industries such as banking, finance, e-commerce, retail, healthcare, etc. in a way. Chatbots are capable of carrying out seamless and highly contextual conversations with customers, all the while analyzing a trove of data in the background to deliver personalized solutions to their queries and demands. The abundance of data created from both internal and external channels will be a major advantage, as well as the key driving force behind more businesses using this tool for more seamless interactions with better outcomes.
AI as a Tool for Decision Management Will Gain Ground
There's not much that needs to be said in this regard since the capabilities of enterprise AI platforms are well-known and have been appreciated all over the globe. AI has already been recognized for its ability to help decision-makers function more effectively and efficiently; as the results from AI's integration become apparent, we can expect to see AI-driven technologies become more integrated into the decision-making process. The dynamic, and ever-evolving nature of these technologies provides a strong foundation for enterprise decision-making engines, improving workflows, and delivering a superior customer experience.
AI Will Play a Crucial Role in the Hunt for Talent
AI and ML have already begun to make their way into the HR industry, and it has already shown its usefulness in finding and screening job candidates, learning how a company hires, and ranking potential candidates to ensure the right hiring decisions. However, organizations find it especially difficult to identify and hire AI experts; in addition to skills pertaining to the sciences, incorporating AI requires expertise in social and legal issues, marketing, customer relationships, and operational management. This is because the proper training and management of AI systems are essential to getting good results; put in the wrong data or omit the business context, and poor results are almost a certainty.
Companies are looking for people who can creatively problem solve, who possess relevant business experience, and who can tackle the complex technical issues of AI. If that sounds like a challenging search, it is; that's why traditional recruiting practices will need to be supplemented with AI's processing brilliance.
Machine Learning (ML) has already been getting a lot of attention, and we can expect this to continue in 2019 as well. The idea of a self-learning machine may still be "shiny and new' for some, but companies are starting to see its practical and far-reaching applications in their business processes. ML's primary application is currently in the field of analytics and Big Data, but we can expect to see further advancements in this field as its business applications expand.
Natural Language Generation
Natural Language Generation (NLG) is a discipline within Natural Language Processing (NLP), which processes language as humans use it. While NLP studies language and extracts data (i.e. speech recognition, voice-to-text), NLG studies data and produces language. For example, an NLG-enhanced BI tool could analyze data and produce a written summary. Clearly, there is a lot of potential for NLG in the business world.
As AI continues to become part of our everyday lives – both personal and professional – we can expect to see the hardware we use become more AI-friendly too. Probably one of the first places where the transformation will begin is in the architecture of GPUs and CPUs that will be specifically designed to support AI, allowing for smoother integration and operation of much more intense machine learning and deep learning platforms which require higher processing power. Further, AI-optimized mobile tech is fast turning into reality, which will ultimately make everyday interactions and data collection even simpler for businesses on a consumer level.
Looking back on 2018, it's easy to see that we're in the midst of an AI-led Fourth Industrial Revolution. In 2019, then, one can anticipate great things in the realm of data science. As the adoption of advanced AI- and ML-driven applications is set to gather significant momentum, their role will only be more marked in both enhancing organizational efficiencies, as well as in enabling humans to add much more value to their job roles with the help of more meaningful insights.