In many ways, this year will come to be remembered as the one when artificial intelligence (AI) and machine learning (ML) finally broke through the hype, delivering consumer-focused products that amazed millions of people. Generative AI, including DALL·E and ChatGPT, manifested what many people already knew: AI and ML will transform the way we connect and communicate, especially online. This has profound repercussions, especially for startup companies looking to quickly find how to optimize and enhance customer engagement following a global pandemic that changed how consumers purchase products. As startups navigate a uniquely disruptive season that also includes inflationary pressures, shifting economic uncertainty, and other factors, they will need to innovate to remain competitive. AI and ML may finally be capable of making that a reality.

What data makes possible today is really much more than what data made possible just a few years ago. In this era of digital everything, data now informs and empowers transformative things all around us. This includes data science organizational culture, early warning detection of important phenomena, predictive (forecasting) of outcomes yet to come, prescriptive (optimization) modeling for better outcomes, robotic automation of complex and/or redundant processes, and deep machine learning for enterprise intelligence from rich data assets. In this first post in a series exclusive to Data Makes Possible, Dr. Kirk Borne, Principal Data Scientist and Executive Advisor for Booz Allen Hamilton, outlines the top 10 trends in Data Science and AI from 2017 that are carrying over into the new year of 2018. In future posts, he will explain the real power of AI and Data Science to augment and assist data-driven decisions and actions in all organizations, regardless of size, industry, or application domain.

By Raju Vegesna The past decade has been a true testament to the success of many inventive technologies. The biggest wave we witnessed was the advent of artificial intelligence and machine learning. NLP & Conversational AI In the early 2010s, consumer natural language processing (NLP) allowed us to talk to our phones and control smart-home appliances reliably. Many people expected NLP to explode in other domains, but it never really materialized, because of either poor implementations or a focus on other types of development. Over the next decade, NLP will be put to use in complex software to lower the barrier to entry.

In the words of Malcolm Gladwell, marketers in 2020 have finally reached the'tipping point' where scalable hyper-personalization of marketing activities is not only possible, but is rapidly becoming a requirement in order to stay up with evolving consumer trends. The shift to more towards personalized, targeted shopping experiences is largely due to the advancements in marketing technology, with elements of machine learning, artificial intelligence and biometric identification all becoming more integrated with one another in order to deliver customized promotional opportunities. An example of this can be found at the Westfield shopping complex in Shepherd's Bush, London - the complex now has cameras in and around the mall which use facial recognition technology to determine the age, sex, and even the mood of the shoppers as they move through the buildings. Based on what the system learns, it can then display different ads on the various digital billboards around the mall in order to maximize consumer response. In today's digital era, with the advent of such engagement technologies, we are now at a stage where account-based marketing (ABM) and personalization have become more practical and scalable than ever before.

There are two Kirana stores in my neighborhood market. Their square footage is almost the same and they both offer similar products. The only differentiating factor between the two is the customer experience they both offer; one is really lousy at it, whereas the other excels at it. Guess which of these stores gets my business? It's obviously the one where I – the customer – feels valued and gets a personalized experience.

What data makes possible today is really much more than what data made possible just a few years ago. In this era of digital everything, data now informs and empowers transformative things all around us. This includes data science organizational culture, early warning detection of important phenomena, predictive (forecasting) of outcomes yet to come, prescriptive (optimization) modeling for better outcomes, robotic automation of complex and/or redundant processes, and deep machine learning for enterprise intelligence from rich data assets. In this first post in a series exclusive to Data Makes Possible, Dr. Kirk Borne, Principal Data Scientist and Executive Advisor for Booz Allen Hamilton, outlines the top 10 trends in Data Science and AI from 2017 that are carrying over into the new year of 2018. In future posts, he will explain the real power of AI and Data Science to augment and assist data-driven decisions and actions in all organizations, regardless of size, industry, or application domain.