Modernize or Bust: Will the Ever-Evolving Field of Artificial Intelligence Predict Success? – insideBIGDATA

In this special guest feature, machine learning platform cnvrg.io co-founders Yochay Ettun and Leah Kolben explore how AI/ML are integral to a modern organizations success, alongside predictions, successes and pitfalls they foresee for the technology in 2020 and beyond. Yochay is an experienced tech leader with a background in building and designing products. He received a BSc in Computer Science at the Hebrew University of Jerusalem (HUJI) where he founded the HUJI Innovation Lab. Leah earned a BSc in Computer Science at the Hebrew University of Jerusalem while simultaneously working as a software team leader at WatchDox, which was later acquired by Blackberry. In her last position, she lead the startup, Appoint, as CTO and has followed her career consulting enterprises on AI and Machine Learning.

It has become eminently clear in thebusiness world that AI adoption is key to remaining competitive in 2020. Simplemachine learning models have the ability to produce greater more efficientoutcomes that pose as a major advantage to your business. Organizations needand want to modernize their data systems and build a flawless data sciencestrategy that will blow their competition out of the water. The problem is,enterprises often dont know where to start and arent able to scale. Thatswhere data scientists, data engineers and machine learning platforms can stepin to overhaul and streamline processes. AI is changing the technologylandscape whether companies realize it or not. As the landscape continues toevolve, companies need to adapt alongside it to stay ahead of the curve andcompetition. We are making some predictions as to how different industries willutilize AI to fuel their growth and innovation.

The Evolution of Enterprise AI

There is a reason that the mostsuccessful companies today have massive data science teams and in-house datascience platforms. This success was recognized by other industry players, whichlead to the race for AI. Since 2019, enterprises across industries havequickly built data science teams that are just now beginning to perform. As westep into 2020, well see the focus go towards optimization of models inproduction to both improve production and prove their worth to businessleaders.

Retail

AI has a variety of real worldapplications to retail. This technology will transform the retail experiencefor shoppers and is likely to be the most customer facing evolution. As manyhave likely already noticed, advancements in recommendation engines and searchnow move across platforms. That means the opportunity for retail companies togive a better overall shopping experience, connecting both in store and onlineexperiences to one.

Cybersecurity

2019 has seen its fair share ofcybersecurity scandals, including those with US Customs and Border Protection,American Medical Collection Agency and First American. As businesses grow,their risk of cyberattack increases and they must seek new ways to safeguardthemselves and their information. Some of the biggest challenges cybersecurityfaces today can be combated with AI. Digital risk management and network anomalydetection being some of the greatest threats to todays business can be solvedusing predictive models and more accurately measure risk.

Healthcare

According to a Gartner study, 65% ofall automated healthcare delivery processes will involve some form of AI by2025. Through process standardization facilitated by AI technology, healthcarefunctions will become more precise for both patients and caregivers, and likelyless expensive. In the field, healthcare practitioners are getting moreinformed in how to utilize and compliment doctors from diagnosing pneumonia todetecting cardiovascular disease. In addition, were seeing emerging evidencethat the expected potential of AI to help decrease medical error and improvediagnostic accuracy and outcomes is being realized through public medicaljournals and professionals.

Financial Services

The financial services industry willlikely be influenced the most by machine learning. ML and AI are most effectivein automating manual tasks. In an industry like finance, there are a lot oftedious and outdated systems which means that there is a lot of room forimprovement. With the quick adoption of ML and AI in finance, well begin tosee a rapid change in the efficiency of financial services. Technologies suchas robo-advisors for wealth management and fraud detection are critical instaying competitive amongst the financial services industry.

The bottom line is that companies need to adapt and incorporate AI/ML to increase productivity and ultimately heighten success. As the base for data science teams have already been established, 2020 will be a year of improving customer facing AI. Data professionals will now need to prove the success of their work by focusing on business impact, and showing the results. The companies that are able to focus on the performance of AI in their business will likely succeed. Well see enterprises utilizing the most up and coming data science tools and methods will likely be the most successful in producing high impact AI. Keep an eye out as the top performing companies of 2020 begin to emerge. Youre sure to see a very intentional AI strategy, and high investment in AI development and management.

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Modernize or Bust: Will the Ever-Evolving Field of Artificial Intelligence Predict Success? - insideBIGDATA