Why most AI projects fail

Why most AI projects fail

Verticals determining the success of any project vary even within the same field, or even within the very same project down the timeline. This is the case especially in AI, a relatively new field fueling business worldwide and shifting the attention of investors to more technologically powered, sustainable and cost-effective solutions. AI is a business superpower on the rise; the Kingdom of Saudi Arabia hosted the Global AI Summit in October 2020, where an elaborate, market-wide AI strategy was revealed. The strategy aims to train up to 20,000 data and AI experts and set up 300 AI-focused startups. This is predicted to generate up to $20bn in investment by the year 2030.

If AI can be used to develop whole nations, it's then a tool to shift the way the world works, and a lot of decision-makers are noticing this as more than a trend, but also as a gateway into a healthier, more sustainable future.

The enthusiasm to jump on the AI train is understandable and encouraged, but the success of AI depends on the complete universe of data being captured and analyzed through a large-scale database with continuous analysis of the convergence between predictive and real-time data.