The Great IT-Divide – Why Is Business-AI Failing? (youtu.be)

The piece argues that after the hype peak, enterprise AI is sliding into a trough of disillusionment not simply because models underperform, but because a deeper IT-divide is emerging: business IT (governed, integrated, compliance-heavy systems) and social IT (fast-moving, user-driven, consumer-grade tools) follow different logics. Organizations expected the same rapid gains seen in consumer apps, but AI’s success in the enterprise depends on far more than model quality — it hinges on data readiness, integration with legacy systems, governance, change management, and aligning AI outputs with existing business processes. For the AI/ML community this matters because technical fixes alone won’t close the gap. The article highlights practical implications: invest in robust data pipelines, domain adaptation/fine-tuning, MLOps for deployment and monitoring, explainability and auditing for compliance, and secure, observable model serving. It also flags shadow/social IT adoption as both a risk and an opportunity — informal use can surface value quickly but creates governance headaches. Bridging the divide means engineering for reliability and compliance while designing for real user workflows and measurable ROI, shifting priorities from novelty to production maturity and organizational integration.