Debunking The Myths And Reality Of Artificial Intelligence, a few years ago, it was hard to find anyone to have a serious discussion about Artificial Intelligence (AI) outside academic institutions. Today, nearly everyone talks about AI. Like any new major technology trend, the new wave of making AI and intelligent systems a reality is creating curiosity and enthusiasm. People are jumping on its bandwagon adding not only great ideas but also in many cases a lot of false promises and sometimes misleading opinions.

Built by giant thinkers and academic researchers, AI adoption by industries and further development in academia around the globe is progressing at a faster rate than anyone had excepted. Accelerated by the strong belief that our biological limitations are increasingly becoming a major obstacle towards creating smart systems and machines that work with us to better use our biological cognitive capabilities to achieve higher goals. This is driving an overwhelming wave of demands and investments across industries to apply AI technologies to solve real-world problems and create smarter machines and new businesses.

AI overcame many obstacles over the last decades mainly on the academic side. However, it is facing now one of its major challenges so far, that is the adoption in real-world industry scenarios and the myths and misunderstanding surrounding it. Unfortunately, with confusing and conflicting messages about what AI can and can’t do, it is challenging for industry leaders to distinguish between facts and fiction in the rapidly crowded and noisy ecosystem of enthusiasts, platform vendors, and service providers. However, once the dust settles down and things get clear, the truth of AI will endure, eventually losers and winners will be declared.

The challenge is how industry leaders would have a realistic opinion about what AI can and can’t do for their business and continuously update it so that they can lead their organizations to apply AI in the right way in solving real-world problems and transform their businesses. Also, academics and AI practitioners have the responsibility to get out of their bubble and engage with industry experts to be able to further develop the academic foundations of AI in a way that would make its real-world adoption faster, more rewarding and responsible.


The current “messy” state of AI adoption in industries


Over the last few years, business leaders from nearly every industry have been trying to understand the new magical technology called Artificial Intelligence (AI) and how their businesses can benefit from it. Unfortunately, until now most of the implementations of AI-powered solutions haven’t gone beyond Proof of Concepts (PoCs) in the form of scattered Machine Learning (ML) algorithms with a limited scope. While this level and approach of AI adoption is wasting many opportunities and resources for companies, it has helped to convince business and IT leaders that Artificial Intelligence can drive transformative and relevant innovation.

Many PoC projects today are using basically simple statistical methods to add some simple prediction or classification capabilities to their analytics solutions and call it AI solutions. This is still defined as analytics or possibly advanced analytics which still needs extensive human intervention in understanding the outcome and make a decision or take an action.

As the business processes and operational conditions continuously change, the newly generated data and the continual changes in different business factors are reducing the level of precision and the value such algorithms can offer rendering them over time to be useless or even lead to dangerous decisions.

Such an approach and its outcome are just another part of the frustrating reality that is confusing business leaders and hindering the right adoption of sophisticated AI technologies in the appropriate way to gain valuable results.

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