What decisions should AI be trusted to make, and when should a person remain in charge? A framework from MIT’s Center for Information Systems Research (MIT CISR) offers a practical way to think about that question.
The matrix uses two dimensions: ambiguity—how clearly the available information points to an answer—and risk—the consequences if the decision is wrong. It sorts decisions into four broad types:
- Routine: low ambiguity and low risk. Defined steps may be automated, with a human process owner monitoring results.
- Consequential: low ambiguity but high risk. Set guardrails; have people monitor execution and handle exceptions.
- Exploratory: high ambiguity but low risk. Use AI to explore possibilities while people guide the work and review outcomes.
- Strategic: high ambiguity and high risk. People lead the framing and learning; AI supports analysis and action.
The framework also breaks decision-making into three stages: frame the problem, act, and learn from the outcome. The same AI capability may be appropriate in one context and risky in another. Oversight should therefore depend on the decision being shaped, not simply on the tool being used.
What might this mean for lawyers?
Applied to legal practice, the matrix suggests asking about the decision a task supports—not just whether the task is labelled “AI-assisted”. AI might help organise documents or generate research leads. But those outputs can feed into advice, evidence, pleadings or submissions, where the consequences of error may be serious.
A lawyer still needs to define the question, check the material, decide what to rely on and remain accountable for the work. That is an application of the framework to legal practice; it is not a legal rule stated by MIT.
The authors say the framework draws on 30 interviews with 27 executives at nine global companies, including organisations in the legal sector. It is a management framework informed by interviews, not a validated legal risk test or a prescription of professional duties. Its value is as a prompt for careful questions: What decision is this tool helping to shape? What happens if it is wrong? Who checks the result, and who remains accountable?
Sources:
MIT CISR, “Designing Decision Rights for AI”
MIT Sloan, “A framework for determining when AI can make decisions”