Imagine asking a cardiologist to solve a corporate tax case. That’s what using generic AI models in domain-specific tasks feels like.
Organizations are rapidly moving away from “one-size-fits-all” models. JPMorgan Chase, for instance, has invested over $200 million in AI personalization, creating bespoke tools for fraud detection, wealth management, and regulatory reporting. Similarly, Morgan Stanley has built a GPT-powered assistant trained on 100,000+ internal research papers to provide tailored financial advice to its 15,000 advisors.
A McKinsey study finds that AI systems with domain-specific training have 32% higher accuracy and 45% fewer compliance violations than general-purpose models.
In fields like medicine, this could literally be the difference between life and death. Imagine an oncology AI trained on generalized health data versus one trained on millions of anonymized cancer case studies curated by real oncologists.
Expertise isn’t a luxury—it’s a safeguard.
