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.

Just this past month, an article was shared that showed that over 30% of the data used by Google for one of their shared machine learning models was mislabeled with the wrong data. Not only was the model itself full of errors, but the actual training data used by that model itself was full of mistakes. How could anyone using Google's model ever hope to trust the results if it's full of human-induced errors that computers can't fix. And Google isn't alone with major data mislabeling, an MIT study in 2021 found that almost 6% of the images in the industry-standard ImageNet database are mislabeled, and furthermore, found "label errors in the test sets of 10 of the most commonly-used computer vision, natural language, and audio datasets". How can we hope to trust or use these models if the data used to train those models is so bad?

Anyone who has been following the news on AI in 2022 knows of the high rate of AI project failures. Somewhere between 60-80% of AI projects are failing according to different news sources, analysts, experts, and pundits. However, hidden among all that doom and gloom are the organizations who are succeeding. What are those 20% of organizations doing that are setting themselves apart from the failures, leading their projects to success? Surprisingly, it has nothing to do with the people they hire or the technology or products they use.

Are you curious about what could go wrong with AI projects? If you've heard about some of the artificial trends of 2022 and are possibly thinking about incorporating AI into your workflow, you may be cautious of AI project failures. These have left countless companies facing huge losses and a compromised workflow. Unfortunately, it is the case today that the majority of AI initiatives fail. A Pactera study established that 85% of all AI projects end up not meeting objectives.

Artificial intelligence is changing industries and how businesses function with hundreds of use cases. Artificial intelligence is a powerful technology but implementing it without a clear business strategy is not enough to achieve success. Instead of starting from the solution of an indefinite business problem, companies should start by determining business problems and then decide whether an AI tool would help solve them. There may be other cost-effective tools and methods to solve the specified problem. Asking these questions would enable companies to have a clear vision and measure the costs, benefits, and business impact of AI projects.

A clear majority of employees (87%) peg data quality issues as the reason their organizations failed to successfully implement AI and machine learning. That's according to Alation's latest quarterly State of Data Culture Report, produced in partnership with Wakefield Research, which also found that only 8% of data professionals believe AI is being used across their organizations. For the report, Wakefield conducted a quantitative research study of 300 data and analytics leaders at enterprises with more than 2,500 employees in the U.S., U.K., Germany, Denmark, Sweden, and Norway. The enterprises were polled regarding their progress in establishing a culture of data-driven decision-making and the challenges they continue to face. According to Alation, 87% of professionals say inherent biases in the data being used in their AI systems produce discriminatory results that create compliance risks for their organizations.