Supplied byEnsemble
A division exists in enterprise AI that is less discussed than others. The public discourse often revolves around foundational models and metrics—GPT compared to Gemini, reasoning assessments, and minor improvements. However, the more significant and lasting advantage is structural: who controls the operating layer where intelligence is utilized, regulated, and enhanced. One approach regards AI as an on-demand service; the other integrates it as an operating layer—the amalgamation of operational software, data collection, feedback mechanisms, and governance that interfaces between models and actual work—that accumulates with usage.
Model vendors such as OpenAI and Anthropic offer intelligence as a service: encounter a problem, access an API, receive a solution. This intelligence is general-purpose, predominantly stateless, and only loosely linked to the daily operations where decisions are enacted. It is highly effective and increasingly interchangeable. The key difference is whether intelligence resets with each prompt or builds over time.
Established entities, on the other hand, can apply AI as an operating layer: tools across operations, feedback loops from human decisions, and governance that transforms individual tasks into reusable policies. In this framework, every exception, correction, and authorization serves as an opportunity to learn—and intelligence can advance as the platform absorbs a greater volume of the organization’s tasks. The organizations poised to define the enterprise AI landscape are those able to integrate intelligence directly into operational platforms and equip those platforms so that work produces actionable signals.
The dominant narrative suggests agile startups will surpass established players by creating AI-native solutions from the ground up. If AI is primarily seen as a modeling issue, this narrative is valid. However, in numerous enterprise contexts, AI presents itself as a systems challenge—encompassing integrations, permissions, assessment, and change management—where the advantage lies with those already embedded in high-volume, critical operations and who can turn that position into learning and automation.
The reversal: AI performs, humans judge
An AI-driven platform reverses this model. It takes in a problem, deploys accrued domain expertise, independently executes what it can with high certainty, and assigns specific sub-tasks to human specialists when the situation necessitates discernment that the system cannot yet consistently provide.
However, reversing human-AI interaction goes beyond a simple UI redesign—it necessitates foundational material. It is feasible only when the platform is anchored by a bedrock of domain expertise, behavioral insights, and operational know-how amassed over time.
The three cumulative assets incumbents possess
AI-native startups commence with an unblemished architectural foundation and can act swiftly. What they struggle to replicate is the foundational material that secures domain AI at scale:
- Exclusive operational data
- A sizable workforce of domain specialists whose routine decisions yield training responses
- Accumulated implicit knowledge regarding how complex tasks are actually executed
Service companies already possess all three assets. However, these components do not serve as barriers independently. They provide an edge only when a company can effectively transform chaotic operations into AI-ready insights and organizational knowledge—then reintegrate the outcomes into operations ensuring continuous improvement.
Systematizing expertise into reusable insights
In the majority of service organizations, expertise is implicit and fleeting. The top operators know things they cannot readily express: heuristics evolved over time, intuitions in edge cases, and pattern recognition that functions beneath conscious cognition.
At Ensemble, the approach for tackling this issue is knowledge distillation. The organized transformation of expert judgment and operational choices into machine-readable training signals.
In the realm of healthcare revenue cycle management, for instance, systems can be initiated with explicit domain knowledge and then broaden their scope through structured daily engagement with operators. In Ensemble’s methodology, the system detects gaps, crafts targeted inquiries, and verifies answers across multiple experts to capture both consensus and nuances of edge cases. It subsequently combines these inputs into a dynamic knowledge repository that embodies the situational reasoning underpinning expert-level performance.
Transforming choices into a learning loop
Once a system is sufficiently refined to earn trust, the next question is how it can enhance itself without relying on periodic model updates. Each time a proficient operator makes a decision, they produce more than a finished task. They generate a potential labeled example—context coupled with an expert response (and occasionally an outcome). At scale, across thousands of operators and millions of decisions, that flow can fuel supervised learning, evaluation, and targeted reinforcement—educating systems to function more like experts under real conditions.
For instance, if an organization handles 50,000 cases per week and captures merely three high-quality decision points for every case, that results in 150,000 labeled examples weekly without establishing a separate data-collection initiative.
A more sophisticated human-in-the-loop design incorporates experts within the decision-making process, allowing systems to understand not just what the correct answer was, but also how to navigate ambiguity. Practically, humans step in at decision branches—selecting from AI-suggested options, rectifying assumptions, and redirecting processes. Each involvement becomes a high-value training insight. When the platform identifies an edge case or divergence from the anticipated process, it can request a brief, structured explanation, capturing decision-making factors without the need for extensive free-form reasoning records.
Striving for expertise amplification
The aim is to integrate the extensive expertise of thousands of domain specialists—their knowledge, decisions, and reasoning—into an AI platform that enhances the capabilities of every operator. When executed effectively, this yields a level of performance that neither humans nor AI can achieve alone: increased consistency, enhanced throughput, and measurable operational improvements. Operators can concentrate on more impactful tasks, supported by an AI that has already performed the analytical groundwork across thousands of similar prior cases.
The broader implication for enterprise leaders is clear. Advantages in AI won’t solely hinge on access to general-purpose models. It will arise from an organization’s capacity to capture, refine, and build upon its knowledge, data, decisions, and operational judgment, while establishing the necessary controls for environments that carry substantial stakes. As AI transitions from experimentation to foundational infrastructure, the most enduring advantage may belong to those companies that understand their operations well enough to instrument them and can translate that comprehension into systems that evolve with usage.
This content was produced by Ensemble. It was not authored by the editorial staff of MIT Technology Review.
