The adoption of AI tools by enterprises has reached an important inflexion point, one that will define the AI industry throughout 2026.
In short, enterprises don’t need more AI tools. They need better AI tools, ones with intelligence that’s built around the functions where their most valuable data lives.
As companies move beyond pilot projects and begin their transition into adoption of AI tools for real day-to-day operations, an unmistakable pattern is emerging. Broad, general-purpose AI platforms simply cannot unlock and use the deep, complex data that drives critical business functions.
This means that the next important breakthroughs are coming from domain intelligence. The market seeks AI that is designed to understand the nuance, context, and decision-making embedded in a function’s data. Domain intelligence is equipped with the specialized capabilities that each domain demands.
Most critically, domain intelligence taps into the deep pools of experienced wisdom that senior employees have learned over their years at the company.. For example, the knowledge that an experienced service technician carries around in his head is invaluable. The processes that a purchasing team has developed over years are likely to be a key element of a thriving enterprise.
In the next year, leading C-level executives increasingly will demand that every data-rich function relies on its own domain intelligence layer. Corporate leaders will ask their AI teams and consultants to create dedicated platforms that understand their organizations’ specialized language and workflows. They want AI that can turn existing expertise into actionable intelligence for the entire enterprise.
Untapped advantages hidden in domain data
Across industries, organizations are sitting on massive, underutilized datasets. By ingesting that data into a domain-specific AI platform, you can make it truly useful—and accessible to everyone across the company.
Just look at a few examples:
- Among equipment manufacturers, service teams hold decades of technician notes, failure histories, repair strategies, and tacit knowledge stuck in the heads of the most senior technicians. But much of it remains buried.
- In the insurance industry, teams rely heavily on adjuster notes, claims narratives, and underwriting commentary that reveal risk patterns. Often, the information remains siloed.
- Banking teams manage suspicious activity reports, fraud investigation notes, and compliance findings. These narratives are full of behavioral signals, but existing systems cannot interpret them.
- Healthcare operations, meanwhile, generate triage logs, clinical shorthand, and care-coordination notes. Each provides critically important information, but each also demands extremely precise contextual understanding to be useful.
All this data is incredibly valuable — not only to the function itself but to every connected department. Creation of AI tools that allow this data to be unlocked, shared and used is the challenge faced today by enterprises large and small.
In one industry after another, executives today understand the need for well-integrated AI tools.
Among manufacturing companies, for example, the latest IFS and Accenture State of Service report finds that manufacturers are breaking down silos and embedding AI across the full operational lifecycle. Many pay particularly close attention to domain intelligence in their service operations. Given that service operations often are viewed as a key competitive advantage, it’s no wonder that executives demand integrated AI tools that allow teams to deliver smart and fast service.
Although the recent IFS and Accenture report focuses on service, its broader insight applies across industries. Organizations no longer seek mere isolated AI assistants. They demand platforms that connect insights across functions and make domain intelligence accessible to teams ranging from product development and engineering to finance and risk-management.
General-purpose AI platforms fall short
General-purpose AI platforms play an important role, but they are designed for breadth rather than depth. Their shortcomings become evident in several ways in a marketplace that increasingly seeks tools that can tap deeply into domain expertise.
First, today’s general-purpose platforms lack domain-tuned comprehension. Complex terminology, shorthand, and expert reasoning are common across fields such as service, insurance, banking, and healthcare. But generic models simply cannot interpret these nuances with the level of precision that enterprises require.
Second, general-purpose platforms struggle with messy, narrative-heavy data. Case notes, claims narratives, and investigation logs all demand the deep contextual understanding that broad AI systems were never optimized for.
Third, generic platforms cannot provide domain-specific capabilities. Domain intelligence platforms, on the other hand, come with specialized functionality that reflects the realities of the environment. Service teams, for instance, need voice-based diagnostics and offline mode. Insurance organizations require claims-grade reasoning and consistency checks. Banking requires audit-ready output and alignment with often complex regulatory standards. Healthcare requires workflows that respect protected health information while also providing interpretation of clinical shorthand.
These capabilities are not add-ons. They are fundamental to real adoption.
Every function needs a domain intelligence layer
A domain intelligence layer, however, does what general-purpose AI platforms cannot. It understands the function’s vocabulary, logic, and workflows. It unifies scattered data sources into a domain-specific intelligence backbone. It powers agents capable of expert-level decisions. Most importantly, it makes a function’s intelligence usable across every department that depends on it.
When service insights inform product design, when underwriting data strengthens fraud detection, and when compliance findings improve onboarding, domain intelligence becomes enterprise intelligence.
The year of domain intelligence
The next 12 months will be the year of domain intelligence. The functions with the richest and most complex data will drive the most transformative AI outcomes during 2026. Each will adopt its own domain intelligence layer, not as another tool, but as the foundation for cross-functional intelligence.
To be sure, general-purpose AI will remain useful. But the future belongs to AI that understands the domain deeply enough to elevate the entire business.
