For years, progress in AI followed a simple logic: Bigger models produced better results. More parameters, more data, more compute. But recently, the limits of scale have become harder to ignore. While benchmark scores still inch upward, the practical impact of new models feels increasingly incremental.
As gains from scale have slowed, leading AI companies have begun improving systems in other ways. One of the most significant has been the introduction of long-term memory. Instead of responding to each prompt in isolation, AI systems can now retain context across interactions and adapt over time, enabling more continuous, personalized interactions.
But scale and memory alone are not enough for real-world deployment. Today, the key challenges in applied AI center on trust, domain understanding, evaluation, and integration into existing workflows. Addressing those challenges increasingly requires focusing on data, constraints, and workflows specific to a given domain.
The Shift is Already Beginning
As returns from scale have slowed, leading AI companies have begun moving downstream, shaping their systems around specific workflows rather than positioning them as general-purpose assistants.
OpenAI’s recent moves illustrate this shift. The company hired 100 former investment bankers to automate tasks traditionally performed by junior staff; launched ChatGPT Health, a sandboxed environment for users to ask their health-related questions; and agreed to acquire Torch, an AI healthcare app, for $100 million in equity. In education, OpenAI launched Study Mode, a ChatGPT spinoff for students.
Anthropic has taken a similar approach. Its expansions into financial services, life sciences, and healthcare, reflect a growing focus on reliability, domain context, and usability in high-stakes environments.
Google’s recent activity points in the same direction. In January 2026, Google announced that it was partnering with companies like the Princeton Review and focusing its AI education efforts on standardized testing with free SAT practice exams powered by Gemini. Google also added full-length practice tests in Gemini for the Joint Entrance Exam (JEE), India’s nationwide engineering exam used to shortlist candidates for the country’s top technical institutes.
This shift is also changing how enterprises connect AI to their own data. OpenAI’s release of ChatGPT company knowledge, along with similar moves from Google and Anthropic, reflects a broader expectation that AI systems operate on proprietary data inside real workflows. Industry-specific intelligence will increasingly be grounded in internal context, not just larger models.
Alongside these moves by model providers, a growing class of applied AI systems is emerging on top of foundational models. In research-intensive environments, platforms like AlphaSense demonstrate how AI becomes most valuable when paired with proprietary content, internal knowledge, and domain-specific workflows. Rather than competing with general models, these platforms shape and constrain them to support real decision-making.
Accelerating Trust, ROI, and Adoption
Instead of only pouring resources into ever-larger general-purpose systems, leading companies are investing in specialized AI systems. Beyond improving accuracy, these tailored systems promise stronger trust, faster ROI, and closer alignment with industry regulation. In 2026, this shift will be reinforced by a more disciplined approach to AI investment, as enterprises increasingly demand clear proof of value rather than experimental promise.
And, as AI systems become more proactive, the cost of errors rises, making trust and domain constraints even more critical. A general-purpose model making a mistake in a draft email is a minor nuisance; a proactive agent preparing flawed materials for a patient’s diagnosis or an investment decision has real consequences.
Specialization helps manage that risk in three concrete ways:
- Faster integration. With narrower scopes and clearer success criteria, specialized systems are easier to evaluate and integrate. Organizations can move from pilot to production more quickly.
- Reduced hallucinations. When models are grounded in well-defined data sources and tasks, hallucinations become less likely. By limiting the scope of what the system is expected to handle, developers can apply stronger controls and validation mechanisms.
- Streamlined compliance and governance. In specialized systems, regulation and compliance requirements are built in from the start. This makes it easier for legal, risk, and compliance teams to sign off on deployment, removing one of the most common barriers to enterprise adoption.
2026: When Vertical Intelligence Becomes the Norm
General-purpose models are not disappearing. They will continue to underpin the AI ecosystem, providing broad capabilities that support a wide range of tasks. They remain highly effective for ideation, drafting, and accelerating routine communication tasks.
But their role is changing.
In enterprise settings, general models increasingly function as infrastructure rather than differentiation. Specialization built on top of foundational models allows organizations to benefit from continued advances while addressing the trust, governance, and workflow requirements that scale alone cannot solve.
2026 is likely to mark a turning point. Strong foundational models will continue to matter, but for the enterprise, differentiation will increasingly come from how AI is applied. Advantage will depend on deep domain expertise, workflow-aware design, and measurable ROI.
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