Which tasks should be delegated to artificial intelligence, and when should humans remain involved? For Beth Stackpole, writing in MIT Sloan Ideas Made to Matter about work from the MIT Center for Information Systems Research, designing these decision rights is one of the major challenges facing companies. Ina Sebastian, Peter Weill, Thomas Haskamp and Jan vom Brocke built the AI Decision Matrix from interviews with 30 executives, on one premise: not all decisions are equal.
The matrix crosses two dimensions. Ambiguity measures the extent to which data determines the answer and the extent to which people agree on it. Risk measures the consequences of an error and the difficulty of correcting it. Each decision has three components: framing (problem, assumptions, constraints, success criteria), acting (gathering, evaluating, recommending, authorizing, executing) and learning (tracking outcomes, responding to them, evolving the system).
Designing Decision Rights for AI
The four resulting types call for different roles for AI, with human oversight throughout. Routine decisions lend themselves to automation: at telecom operator One New Zealand, an agent translates marketers' requests into SQL queries to create segments, cutting time spent by 60%, with human review before launch. Consequential decisions require balancing automation and intervention: in customer service, any decision touching pricing keeps a human in the loop, since a pricing error is deemed unacceptable. Exploratory decisions let framing evolve between humans and AI, as with marketing content creation, with governance monitored as experimentation moves toward riskier cases. Strategic decisions require human leadership: fifteen agents analyze network data and orchestration agents coordinate them, but trade-offs between reliability, customer experience and cost remain human.
The article draws four practices from One New Zealand: giving business units ownership of framing for each use case, creating an AI and data center of excellence (unified platform, orchestration layer, responsible AI policy), naming a human owner for each deployed agent, and managing AI as a portfolio of business decisions. Routine and consequential decisions deliver quick efficiency gains, while exploratory and strategic ones deliver longer-term value. The authors summarize the shift: moving from the question "where should AI be used?" to managing the decisions it shapes.