Just a few decades ago shopping was done by physically going into retail stores or shopping through mail order magazines. Then, the shopping experience was revolutionized with the internet, and always connected mobile phones. Artificial Intelligence is now making significant changes in the way people buy and sell online, from creating more personalized experiences to targeted marketing, crafting tailored messages to be delivered at the right time and through the right channel or AI enabled chatbots to interact with customers at any time of the day.
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The pace of adoption for AI and machine learning continues unabated with global, widespread, adoption and usage. It’s not just companies that are taking note of the tremendous value AI can provide them. Countries and governments around the world are also seeking competitive advantages by harnessing the power of AI. Governments that can take advantage of the tremendous transformation presented by AI and cognitive technologies can position themselves for global competitiveness in the future. As a result, countries around the world are adopting AI strategies to provide roadmaps, funding, education, and strategies needed to differentiate themselves and become leaders in different areas related to AI and cognitive technology.
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AI systems are constantly evolving. Machine learning models learn from data and experience, and once they are released into the real world, they need to continually be monitored, tested, and retrained on an ongoing basis. It also needs to be created with ethical and responsible frameworks in place.
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Many enterprises, vendors, and startups often confuse the role of data scientist and data engineers. While the overlap of these roles is substantial they’re not particularly interchangeable.
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AI brings mixed emotions and opinions when referenced in the context of jobs. AI will eliminate the need for many different kinds of jobs in many different categories. But at the same time, AI will create new jobs in many categories. Is AI an overall job killer or job creator?
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As AI continues to become a focus for an increasing number of enterprises, these organizations are realizing how important it is to have the right people and skills in place. In particular, there has recently been a significant increase in demand for data scientists in organizations as AI, various applications of machine learning (ML), non-ML predictive analytics, and other so-called “big data” approaches continue to gain traction in the enterprise. In fact, the significant demand for data scientists has led to the talent crunch that we’re seeing across many enterprises and organizations. However, given that 80% of an AI project has to do with data preparation and data engineering activities, perhaps organizations should really be searching for data engineers even more than data scientists?
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