How not to fail your AI projects
In today's world, stories about organizations achieving remarkable success with AI solutions dominate our news feeds, social media platforms, and marketing campaigns. These seemingly magical AI systems promise to revolutionize our lives by solving complex problems. However, the reality is far more nuanced. Amid the success stories, numerous companies find themselves trapped in nightmarish situations, struggling to make AI work for them. What separates the winners from the losers? The answer lies in one key factor: data quality.