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

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 organization’s 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 the business world that AI adoption is key to remaining competitive in 2020. Simple machine learning models have the ability to produce greater more efficient outcomes that pose as a major advantage to your business. Organizations need and want to modernize their data systems and build a flawless data science strategy that will blow their competition out of the water. The problem is, enterprises often don’t know where to start and aren’t able to scale. That’s where data scientists, data engineers and machine learning platforms can step in to overhaul and streamline processes. AI is changing the technology landscape whether companies realize it or not. As the landscape continues to evolve, companies need to adapt alongside it to stay ahead of the curve and competition. We are making some predictions as to how different industries will utilize AI to fuel their growth and innovation. 

The Evolution of Enterprise AI 

There is a reason that the most successful companies today have massive data science teams and in-house data science platforms. This success was recognized by other industry players, which lead to the “race for AI”. Since 2019, enterprises across industries have quickly built data science teams that are just now beginning to perform. As we step into 2020, we’ll see the focus go towards optimization of models in production to both improve production and prove their worth to business leaders. 

Retail

AI has a variety of real world applications to retail. This technology will transform the retail experience for shoppers and is likely to be the most customer facing evolution. As many have likely already noticed, advancements in recommendation engines and search now move across platforms. That means the opportunity for retail companies to give a better overall shopping experience, connecting both in store and online experiences to one. 

Cybersecurity

2019 has seen its fair share of cybersecurity 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 safeguard themselves and their information. Some of the biggest challenges cybersecurity faces today can be combated with AI. Digital risk management and network anomaly detection being some of the greatest threats to today’s business can be solved using predictive models and more accurately measure risk.   

Healthcare

According to a Gartner study, 65% of all automated healthcare delivery processes will involve some form of AI by 2025. Through process standardization facilitated by AI technology, healthcare functions will become more precise for both patients and caregivers, and likely less expensive. In the field, healthcare practitioners are getting more informed in how to utilize and compliment doctors from diagnosing pneumonia to detecting cardiovascular disease. In addition, we’re seeing emerging evidence that the expected potential of AI to help decrease medical error and improve diagnostic accuracy and outcomes is being realized through public medical journals and professionals. 

Financial Services

The financial services industry will likely be influenced the most by machine learning. ML and AI are most effective in automating manual tasks. In an industry like finance, there are a lot of tedious and outdated systems which means that there is a lot of room for improvement. With the quick adoption of ML and AI in finance, we’ll begin to see a rapid change in the efficiency of financial services. Technologies such as robo-advisors for wealth management and fraud detection are critical in staying 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. We’ll 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. You’re sure to see a very intentional AI strategy, and high investment in AI development and management. 

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