The Great IT-Divide: Why AI-Adoption in Enterprises Is Failing (its.promp.td)

Enterprises are struggling to turn AI hype into compliant, repeatable business value because the dominant generation of AI was born in “Social‑IT,” not “Business‑IT.” The article argues the post‑2010 democratization of computing (smartphones, social networks, pandemic‑driven digital adoption) shifted innovation away from corporate requirements toward consumer‑centric models that treat personal data and attention as currency. That creates a fundamental mismatch: Social‑IT tools are trained and optimized on open, user‑driven data flows with funding models that don’t align with corporate procurement, privacy, auditability, or regulatory obligations—so plug‑and‑play adoption often fails or creates legal and security risks. For the AI/ML community this is a call to refactor priorities: enterprise adoption demands “Business‑Native AI” designed for data minimization, provenance, access controls, audit trails, explainability, and deployment modes (on‑prem, VPC, federated) compatible with procurement and compliance. Practically, that means engineering choices such as differential privacy, federated learning, robust model governance, reproducible training pipelines, and enterprise SLAs. Business leaders should ask not just “what can it do?” but “which world was it built for?” and developers must bake corporate constraints into model design if AI is to bridge the IT divide and deliver real, safe ROI.