AI Decision Matrix

Same decision, every time. How much can you hand to AI?

Place one business decision on the matrix of ambiguity and risk. Each of the four types splits the work between people and AI differently.

Based on research by

  • Massachusetts Institute of Technology (MIT)
  • MIT Center for Information Systems Research (CISR)
  • MIT Sloan School of Management
  • University of Münster
  • European Research Center for Information Systems (ERCIS)
MIT CISR Research Briefing
  1. Consent
  2. Your decision
  3. Report
About your organization (all optional)

Helps the AI interpret your decision. Leave out anything you prefer not to share.

Heavily regulated industry
LowAmbiguityHigh
Exploratory
Strategic
Routine
Consequential
The matrix updates with every answer.
LowRiskHigh

The framework

Four types of decisions

Ambiguity describes how clearly data determines the answer. Risk describes the consequences of getting the decision wrong. Together they give four decision types: routine, consequential, exploratory and strategic.

Each type splits framing, acting and learning differently between people and AI.

Graphic: MIT Sloan School of Management. Read the article

The AI Decision Matrix. Ambiguity on the vertical axis, risk on the horizontal axis. Routine (low ambiguity, low risk): automate and monitor; AI frames, acts and learns. Consequential (low ambiguity, high risk): manage to ensure reliability; AI frames and acts, humans learn. Exploratory (high ambiguity, low risk): enable experimentation to learn fast; humans with AI frame, AI acts, humans learn. Strategic (high ambiguity, high risk): lead with continuous oversight; humans with AI frame and act, humans learn.

Designing Decision Rights for AI

Leaders have to decide how people and autonomous AI share decisions. The briefing recommends managing AI as a portfolio of business decisions rather than a collection of use cases.

Read the briefing
  • AuthorsIna M. Sebastian, Peter Weill, Thomas Haskamp, Jan vom Brocke
  • SeriesMIT CISR Research Briefing No. XXVI-6
  • PublishedJune 18, 2026
  • Basis30 interviews with 27 executives at nine global companies in 2025 and 2026

Cite as

Sebastian, I. M., Weill, P., Haskamp, T., & vom Brocke, J. (2026). Designing Decision Rights for AI. MIT CISR Research Briefing, XXVI(6).

The authors

  • Portrait of Ina M. Sebastian

    Ina M. Sebastian

    Research Scientist, MIT CISR

  • Portrait of Peter Weill

    Peter Weill

    Chairman and Senior Research Scientist, MIT CISR

  • Portrait of Thomas Haskamp

    Thomas Haskamp

    Assistant Professor, University of Münster · Academic Research Fellow, MIT CISR

  • Portrait of Jan vom Brocke

    Jan vom Brocke

    Professor and Chair of Information Systems, University of Münster · Academic Research Fellow, MIT CISR