Seemingly every month, another foundational AI model launches with impressive benchmark scores and claims of game-changing capabilities. Enterprises across various industries watch the announcements, scramble to update their systems, and expect better results. Instead they’re discovering something uncomfortable: for specialized tasks, newer models often show very little improvement or even perform worse than their predecessors.
This isn’t a temporary glitch. It’s a fundamental mismatch between how general-purpose AI models are built and trained, and what specialized domains actually require.
The Parameter Budget Problem
Foundational models face a constraint that most enterprises don’t appreciate; namely, every parameter is shared across various tasks, so the model can only allocate limited representation capability to individual domains. When OpenAI spent over $100 million training GPT-4, the model had to learn legal reasoning, medical diagnosis, creative writing, code generation, translation and dozens of other capabilities simultaneously.
This creates an unavoidable trade-off. Parameters optimized for creative fiction writing may work against precision in technical documentation. Adding colloquial training data that improves casual conversation can, at the same time, degrade formal business communication. When a model needs to be adequate at everything, it struggles to excel at the specific tasks that enterprises care most about.
The companies succeeding with AI understand this limitation. They’re not waiting for better models, but instead building AI ecosystems where domain-specific knowledge takes priority, using foundation models as one component rather than the complete solution.
Where General Purpose Breaks Down
Evidence of the shortcomings of generic LLMs appears across industries. Legal AI startup Harvey reached $100 million in annual recurring revenue within three years not by using the latest generation of models, but by building and fine-tuning systems that understand legal precedent, jurisdiction-specific requirements, and law firm workflows. The company now serves 42% of AmLaw 100 firms because it solves problems that general-purpose models alone can’t address.
Healthcare systems face similar challenges. Foundational models trained on publicly available general medical literature (among other things) miss the nuances of specific hospital protocols, patient population characteristics, and regulatory requirements that vary by region. Meanwhile, financial services firms discover that fraud detection models need training on their specific transaction patterns, not generic examples from public datasets.
MIT’s finding that 95% of enterprise AI projects fail reflects this gap. Companies assume the capabilities of the latest OpenAI GPT, Anthropic Claude, or Google Gemini models will transfer to their sector without significant work, and discover otherwise only after months of effort and substantial investment.
Three Requirements for Purpose-Built AI
The systems that work in production share three characteristics that general-purpose models lack:
Curated datasets. Foundation models train on whatever public data is available, but effective fine-tuned systems curate datasets that reflect actual use cases and specific domains. In healthcare, this means electronic health records and clinical trial results. In finance, transaction histories and fraud patterns. In legal work, jurisdiction-specific case law and regulatory documents. Crucially, the data must be continuously updated as regulations and standards evolve, and carefully curated to protect personally identifiable information, especially protected health information.
Specialized evaluation criteria. Standard benchmarks, like Humanity’s Last Exam (HLE), measure general capability, but real enterprise systems need metrics that reflect business requirements. For example, legal AI needs to understand which past cases matter most and how different courts’ decisions rank in importance. Financial systems don’t need that knowledge, but they do need to balance fraud detection against false positives that alienate customers. None of these niche requirements appear in general training.
Production infrastructure. While generic LLMs offer raw capability, enterprise systems need quality assurance, hallucination mitigation, error detection, workflow integration, and monitoring, all specific to how the technology gets used in real workflows. This infrastructure represents the majority of implementation effort, which is why directly integrating LLMs via APIs consistently underperforms trade-specific solutions.
The Real Cost Calculation
The per-token pricing of foundation model APIs looks attractive until you account for actual implementation costs. Without techniques adapting them to a specific industry, models require extensive prompt engineering for each use case, and even then still have a high rate of inaccuracies, some potentially detrimental. Error rates that seem acceptable in demos and POCs become expensive when humans must review and correct every output. Worst of all, operational overhead (building pipelines, mitigating model inference latency, managing quality, handling compliance) often exceeds what custom systems would cost in the first place.
When to Build
Not every company should invest in domain-specific AI, but luckily, the decision usually depends on just a few clear factors:
Task specificity. If GPT-5 or Gemini 3 already handles your use case well, customization rarely justifies its cost. Purpose-built AI pays off when your workflows involve complex, nuanced tasks normally handled by people with deep subject-matter expertise. The threshold is measurable: if your team spends more time correcting AI outputs than doing the work manually, you need systems designed for your field.
Data advantage. Effective AI requires substantial proprietary data. Companies with years of tagged customer interactions, resolved support cases, transaction histories, and internal documentation have the raw material for real differentiation. Those without it face a choice: partner with vendors who’ve already built robust, focused datasets, hire vendors to build custom datasets, or accept that competitors with richer data will maintain an advantage.
Strategic importance. If domain expertise defines your business—as it does for law firms, healthcare providers, and focused consultancies—AI that captures that expertise becomes strategic. If the capability is commodity, general-purpose tools likely suffice.
Most enterprises won’t build everything custom. The most effective approach is to identify which capabilities are critical and complex enough to justify specialization, and which can run on general infrastructure. Application-layer companies (like Harvey, Intercom, and Cursor) create value by handling the nuances of each sector so internal teams don’t have to build from scratch.
What This Means Moving Forward
Foundational models will keep improving, but at a decelerating rate. Sustainable value is moving to companies that combine general capabilities with tailored expertise. This doesn’t mean frontier labs stop developing models—they just become commodity infrastructure. The competitive advantage then flows to organizations who spend time and resources to build specialized systems, and to vendors who package that effort into products that “just work.”
For technical leaders evaluating AI investments, the lesson is clear: stop assuming newer models will automatically perform better on your business’s problems, and start asking whether the AI tools you’re using are actually equipped with the knowledge and infrastructure your use case requires. Anyone can plug in the newest models; the companies who extract meaningful value from AI will be those who understand their own needs deeply enough to build (or buy) something better.
