There is no surprise that AI and ML have become the key ingredients of modern technology and cyberspace. From wearables to robotics, AI is almost everywhere and in every sector. Most companies extend their hands to AI vendors to adopt AI into their workflow. They spent lots of time, money, and effort to ensure a successful project. However, Gartner estimated that more than 85 percent of AI projects fail and render errors. Another report says that around 70 percent of companies say that implementing AI has minimal or zero impact on overall workflow efficiency.

Due to the pandemic, most businesses are increasing their investments in AI. Organizations have accelerated their AI efforts to ensure their business is not majorly affected by the current pandemic. Though the implementation is a positive development in terms of AI adoption, organizations need to be aware of the challenges in adopting AI. Building an AI system is not a simple task. It comes with challenges at every stage. Even though you build an AI project, there are high chances of it failing upon deployment, which can be attributed to numerous reasons.

Another classical error at this point is assuming a problem is one kind of machine learning when it may be a multi-step, ensemble approach. Again, returning to the sentiment analysis example, suppose we need to turn a pile of tweets into a prediction of what kind of tweets earn the most engagement. We think we're solving for a prediction, and that may be the last step in the problem, but before we can solve for what makes a tweet engaging, we have to solve for turning text into numbers.

Who do you blame when AI projects fail? The data? Certainly you can put blame on solving the wrong problem with AI, or applying AI when you don't need AI at all. But what happens when you have a very well-suited application for AI and the project still fails? Sometimes it comes down to a simple approach: don't take so long. At a recent Enterprise Data & AI event, a presenter shared that their AI projects take on average 18 to 24 months to go from concept to production.

Who do you blame when AI projects fail? The data? Certainly you can put blame on solving the wrong problem with AI, or applying AI when you don't need AI at all. But what happens when you have a very well-suited application for AI and the project still fails? Sometimes it comes down to a simple approach: don't take so long. At a recent Enterprise Data & AI event, a presenter shared that their AI projects take on average 18 to 24 months to go from concept to production.

Ready to learn Machine Learning? Browse courses like Machine Learning Foundations: Supervised Learning developed by industry thought leaders and Experfy in Harvard Innovation Lab. Nothing is worse than AI project failure. Everyone has AI on their 2017/2018 roadmap these days. Bottom-tier innovation verticals like HR, multi-level marketing, entertainment, fashion, medical, supply chain (anyone else we should throw under the bus?) are even starting to talk about it.

Artificial Intelligence (AI), which is also known as Machine Intelligence (MI), is playing a vital role in reshaping business models. The technological advancement attributed to AI will change the logic of business models, transform the lifestyle and living standards of human beings. AI can lead us to a world that is smarter, innovative, and simplifies the way we live. The speed with which AI projects is entering every sector is forcing companies to adopt AI projects technology and become AI companies. This is also influencing businesses, strategists, pioneers, entrepreneurs, investors, etc, to use AI to design new strategies and create new sources to increase business, grab more people's attention. According to a Forbes report, "Most of the AI projects fail" and TechRepublic reported 85% as the threat.

Why do most AI projects fail? Something which many people do not know, is that up to 90% AI projects lead to failure. And this is not only for AI, this is for IT projects in general. This might sound weird coming from a company specialising in data science and AI. But that's the reality, and this is why the Tesseract Academy was created: to ensure that all organisations can enjoy the benefits of data science and AI,,without the risk of implementation.

Due to the pandemic, most businesses are increasing their investments in AI. Organizations have accelerated their AI efforts to ensure their business is not majorly affected by the current pandemic. Though the implementation is a positive development in terms of AI adoption, organizations need to be aware of the challenges in adopting AI. Building an AI system is not a simple task. It comes with challenges at every stage. Even though you build an AI project, there are high chances of it failing upon deployment, which can be attributed to numerous reasons.

We bandy about the term "artificial intelligence," evoking ideas of creative machines anticipating our every whim, though the reality is more banal: "For the foreseeable future, computers will not be able to match humans in their ability to reason abstractly about real-world situations." This is from Michael I. Jordan, one of the foremost authorities on AI and machine learning, who wants us to get real about AI. "People are getting confused about the meaning of AI in discussions of technology trends--that there is some kind of intelligent thought in computers that is responsible for the progress and which is competing with humans. We don't have that, but people are talking as if we do," he noted in the IEEE Spectrum article. Instead, he wrote in an article for Harvard Data Science Review, we should be talking about ML and its possibilities to augment, not replace, human cognition. Jordan calls this "Intelligence Augmentation," and uses examples like search engines to showcase the possibilities for assisting humans with creative thought.