At Ensemble, the strategy to address this challenge is intuitive. Systematically transforming expert judgment and operational decisions into machine-readable training signals.
In healthcare revenue cycle management, for example, systems can be seeded with clear domain knowledge and then their coverage can be deepened through structured daily interactions with operators. In Ensemble’s implementation, the system identifies gaps, formulates targeted questions, and examines responses from multiple experts to achieve both consensus and edge-case nuance. It then synthesizes this information into a living knowledge base that reflects the situational reasoning behind expert-level performance.
Turning decisions into a learning flywheel
Once the system is compelling enough to trust, the next question is how to improve it without waiting for annual model upgrades. Whenever a skilled operator makes a decision, he produces more than a completed task. They generate a possible labeled instance—the context is associated with the expert action (and sometimes the outcome). At scale, across thousands of operators and millions of decisions, that chain can power supervised learning, evaluation, and targeted forms of reinforcement—learning systems to behave like experts in real situations.
For example, if an organization processes 50,000 cases a week and captures only three high-quality decision points per case, that’s 150,000 labeled instances each week without creating a separate data collection program.
A more advanced human-inside design puts experts inside the decision process, so systems learn not only what the correct answer was, but how ambiguity is resolved. In practice, humans intervene at branch points—selecting among AI-generated options, correcting assumptions, and redirecting workflows. Each intervention becomes a high-value training signal. When the platform detects an edge case or deviation from the expected process, it can prompt for a short, structured argument, capturing the decision factors without the need for lengthy free-form reasoning logs.
Building towards skill development
The goal is to permanently embed the accumulated expertise of thousands of domain experts—their knowledge, judgment, and reasoning—into an AI platform that can cater to every operator. Ideally, this creates a standard of execution that neither humans nor AI can achieve independently: higher consistency, better throughput, and measurable operational benefits. Operators can focus on more productive work, supported by an AI that has already completed the analytical groundwork on thousands of similar previous cases.
The broader implication for enterprise leaders is straightforward. Gains in AI will not be determined by access to general-purpose models alone. This will come from an organization’s ability to capture, refine, and synthesize what it knows, its data, judgments, and operational judgment, while building the controls required for a high-stakes environment. As AI moves from experimentation to infrastructure, the most sustainable edge belongs to companies that understand this work well and can transform it into systems that improve with use.
This content was produced by Ensemble. It was not written by the MIT Technology Review editorial staff.