Why has AI failed to fulfill its promise?

These days, it seems as though AI is omnipresent, but the reality is that many businesses are having difficulty implementing AI successfully. Over 70–80% of AI projects are expected to fail. 85% of machine learning (ML) projects fail, according to Gartner. 85% of AI projects, according to TechRepublic, ultimately fail to produce the desired results for the organization. The failure rate shouldn’t be as high with all the intelligent people, resources, and effort poured into these projects. The issue is failing to adhere to best practices for managing AI initiatives, not bad technology or people. There are several reasons why AI initiatives fail after studying thousands of AI projects. By using what you can learn from others, you may avoid becoming another AI failure statistic.

Almost ten years ago, research on AI blossomed, promising to bring a wide range of science fantasy solutions. From the advent of the singularity to autonomous vehicles and business-oriented perfect prediction machines (where machine intelligence accelerates past human intelligence). Analysts expected a shakeup in the system as AI made human labor unnecessary in many effort sectors. Some believe educating people on repetitive business actions is useless. For example, AI became advanced and can replace human radiographers.

Despite this potential, AI adoption is not as widespread as many had hoped or anticipated. The recent creation of both Mid journey and Stable Diffusion is a case in point of how research is improving the underlying AI technology, and businesses continue to invest in AI. In fact, investment increased during the first several years of the pandemic. The majority of AI efforts, however, fail. Intense demonstrations don’t translate into solutions that add value. Despite early success and enormous investment, commercial, mass-market versions of autonomous cars consistently seem to be a decade away. Similar tales are shared by AI experts working in companies striving to use AI, where painstakingly constructed models and solutions are abandoned because they are either unattractive enough or too fragile to replace current ones. There have been significant accomplishments, like machine translation, but there seem to be more failures.

Its promise was tarnished with tragedy today even before it began. Uber’s technical breakthrough earned notoriety for taking a pedestrian’s life. What transpired, then? Human error and equipment malfunction? Design errors? Flaws in risk assessment? All of the above, indeed.

The creation of technology meant to mitigate people’s inherent unreliability is evidence that, for some reason, we have evolved to assume that technology is always superior to humans. Robots, computers, GPS, and other devices are designed to reduce mistakes and simplify our lives. That assertion is not true at this time. So, it is incorrect, but it might change in the future.