General-purpose AI models struggle with domain-specific nuance. We explain why vertical AI solutions built for specific industries deliver superior results and faster ROI.
The first wave of enterprise AI adoption centred on general-purpose models applied broadly across organisations. The emerging second wave is defined by vertical AI solutions: purpose-built systems designed for specific industries that deliver dramatically better performance on domain-specific tasks. This shift from horizontal to vertical AI is reshaping the competitive landscape and creating opportunities for companies that combine deep industry expertise with AI engineering capability.
Why General AI Falls Short
General-purpose models like GPT and Claude are trained on broad internet data, giving them wide but shallow knowledge across domains. When applied to specialised tasks in healthcare, legal, financial services, or manufacturing, they frequently miss domain-specific nuances, apply incorrect reasoning frameworks, or produce outputs that technically sound plausible but are substantively wrong to domain experts. A general model asked to interpret a financial covenant clause or analyse a medical imaging scan will produce a response, but its accuracy and reliability fall well below what the domain demands.
The Vertical AI Advantage
Vertical AI solutions address these limitations through domain-specific training data, specialised model architectures, and built-in validation rules that reflect industry standards and regulations. Harvey, built for legal work, understands contractual language and legal reasoning in ways that general models cannot match. Tempus, designed for precision medicine, processes genomic data with clinical-grade accuracy. At QverLabs, our compliance platform is trained specifically on Indian data protection regulations and enforcement patterns, enabling it to identify DPDPA compliance gaps that a general AI would overlook.
The economics strongly favour vertical solutions. While building a vertical AI product requires significant upfront investment in domain expertise and specialised data, the resulting system delivers higher accuracy, requires less human oversight, and generates faster return on investment for customers. Industry-specific models typically achieve 85-95% accuracy on domain tasks where general models score 60-75%.
Building a Vertical AI Strategy
For AI companies and startups, the vertical approach requires a fundamentally different team composition. You need domain experts who understand the industry as deeply as your engineers understand AI. You need access to high-quality, industry-specific training data, which is often proprietary and difficult to obtain. And you need feedback loops with actual practitioners who can validate outputs against real-world standards. The companies that assemble these ingredients first in underserved verticals will build durable competitive advantages that are extremely difficult to replicate.
