In Ensemble, the strategy to address this challenge is knowledge distillation. Systematic conversion of expert judgment and operational decisions into machine-readable training signals.
For example, in healthcare revenue cycle management, systems can be combined with explicit domain knowledge and then deepened their coverage through structured daily interactions with operators. In Ensemble’s implementation, the system identifies gaps, formulates targeted questions, and cross-checks answers from multiple experts to capture both consensus and edge-case nuances. It then synthesizes these inputs into a living knowledge base that reflects the situational reasoning behind expert-level performance.
Turning decisions into a learning cycle
Once a system is constrained enough to be reliable, the next question is how it gets better without waiting for annual model upgrades. Every time a skilled operator makes a decision, they create more work than is completed. They generate a potentially labeled instance – a context paired with an expert action (and sometimes an outcome). At scale, across thousands of operators and millions of decisions, that stream can power targeted forms of supervised learning, evaluation, and 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-in-the-loop design places experts in the decision process, so the system learns not only what the correct answer was, but how the ambiguity is resolved. In practice, humans intervene at branch points – selecting among AI-generated options, correcting assumptions, and redirecting operations. 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 concise, structured logic, capturing decision factors without the need for lengthy free-form logic logs.
Building towards specialization
The goal is to permanently embed the accumulated expertise of thousands of domain experts – their knowledge, judgment and reasoning – into an AI platform that amplifies what each operator can achieve. Done well, it produces a quality of performance that neither humans nor AI achieve independently: higher stability, improved throughput, and measurable operational benefits. Operators can focus on more consequential work, supported by AI that has already completed the analytical groundwork in thousands of similar prior cases.
The broader implication for enterprise leaders is straightforward. The gains in AI will not be determined solely by access to general purpose models. This will come from an organization’s ability to capture, refine and combine its information, its data, judgment and operational decisions while building the controls necessary for high-risk environments. As AI shifts from experimentation to infrastructure, the most lasting edge may be those companies that understand the work well and can translate that understanding into systems that improve with use.
This content was produced by Ensemble. It was not written by the editorial staff of MIT Technology Review.
