Independent methodology by Dušica Živanović

Define what AI may learn, decide and execute.

Prediction estimates what may happen. Execution creates institutional consequence. Decision Architecture determines what an AI system may influence, decide and cause — and under which controls.

Explore the method

01ObjectiveWhat outcome is sought?

02LearningWhat may influence it?

03AuthorityWhat may AI decide?

04ExecutionWhat may it cause?

01 / Method

Technical capability does not establish institutional permission.

Decision Architecture separates AI used to predict, AI used to assist employees, and AI authorized to make or execute decisions. The distinction matters when an output can create financial, regulatory, operational or reputational consequences.

PredictionProduces an estimate or signal.

AssistanceSupports a person who retains decision authority.

Decision and executionDetermines or causes an institutional outcome.

02 / Assessments

One decision architecture.
Three assessment lenses.

The engagement depends on both the system’s role and the project stage: adoption decision, pilot support or production audit.

AI Adoption Readiness & Authority

Can this process support the authority the organization intends to give AI?

Readiness · Materiality · Supported Authority · Authority Gap

For internal assessmentExecutive Guide + Assessment WorkbookAccess the assessment

GDPR by Architecture

Which data, relationships and inferred patterns may influence this decision?

Data Eligibility · Purpose Boundary · Influence Map · Control Gaps

For internal assessmentExecutive Guide + Assessment Workbook + Worked ExamplesAccess the assessment

AI Third-Party Exposure

What exposure is created by the vendor’s position in the decision process?

Access · Decision Influence · Dependency · Architectural Exposure

Third-party exposure principleThe vendor’s characteristics do not determine the organization’s exposure on their own. Exposure is created by the data, access, authority and architectural position the organization gives the vendor.

03 / Research foundations

Published research.
Operationalized in practice.

The assessment system is grounded in a connected body of published work on permissible learning, delegated authority and controlled execution.

From Prediction to Decision ArchitectureA Framework for Trustworthy AI Implementation

Permissible Learning SpaceA Condition for Autonomous AI Decision-Making

From Fiduciary Judgment to Decision ArchitectureStructuring AI for Trustworthy Investment Management

Governing the Path from AI Objective to ExecutionAuthorized Solution Space for Autonomous AI

04 / Independent methodology

Dušica Živanović

Dušica Živanović holds an MA in Economics and has more than 17 years of experience in banking and financial services, spanning business processes, compliance, risk and regulatory implementation. Her current work focuses on translating business, legal, risk, data and process requirements into executable decision boundaries for AI systems.

LinkedIn profile

Start with the process before assessing the AI.

Describe how the decision is currently produced, which information supports it and which consequences it can create. This provides the basis for targeted follow-up questions and risk mapping.