Critical Mistakes Companies Make When Integrating AI/ML into Their Processes

Many companies undermine their own AI/ML projects by failing to first establish a solid data foundation and a clear business case. Teams often chase trendy, complex models when a simpler solution would be more effective, creating a disconnect between the ML team and business stakeholders. Critical infrastructure for deployment and monitoring, known as MLOps, is frequently ignored, leading to brittle and unscalable systems. Furthermore, a lack of proper A/B testing and undertrained management can result in launching features with no real impact or even negative consequences.