DEGGENDORF, GERMANY - In recent years, a number of companies have started to invest in artificial intelligence (AI) to stay competitive. Yet the sad truth is that some 80 percent of AI projects fail or do not add any business value. This surprisingly high ratio has been discussed in various market reports and tutorials.1,2 Let us take a closer look at the some of the reasons for this, and discuss the best practices that enable AI projects to succeed.
Many AI projects start when data scientists or managers are actively looking for ‘AI projects.’ This approach also tends to often already be the start of the end. Given that a solution, e.g., AI, has already been set, stakeholders then start looking for problems or even creating problems that did not actually exist before. Instead, one should ideally start by looking for real business problems and then start solving them in ways that are as simple as possible. In many cases, rethinking and simplifying business processes at the beginning of a project turns out to be the most fruitful and productive way of doing things. AI is likely to be most helpful during later phases of projects where it can substantially improve automated decision-making; this is also less likely to fail.
Another current reason why AI projects fail is the way that AI is taught at universities. This means that graduates’ skills often do not align well with real-world demands, with graduates only able to build prototypes and failing to scale and integrate them into organizations. This topic is further discussed in Bridging the gap between academia and industry. Learn more about challenges when implementing AI in companies here.3
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