Decision Architecture for AI-Enabled Systems

A Methodology

Decision Architecture for AI-Enabled Systems is an original methodology developed by Foresight Navigator to design how authority, judgment, coordination, escalation, and reversibility operate when AI systems influence real decisions.

As AI accelerates sensing, synthesis, and action, the primary risk is no longer technical performance.
It is decisions outrunning authority where systems shape outcomes before ownership, escalation, and exit paths are explicit.

This methodology intervenes upstream of tools, platforms, and automation before commitments become difficult to reverse.

What “Decision Architecture” Means

In this context, architecture is not technology.

It refers to the structural conditions that govern how decisions are made when humans and machines interact, including:

• who owns judgment when systems act

• who has authority to override or halt action

• when escalation is required

• how coordination works across organizational seams

• which decisions must remain reversible

Put simply:

Decision architecture defines how machine-generated judgment interacts with human authority.

When these conditions are implicit, AI amplifies ambiguity, coordination failure, and institutional risk rather than capability.

The Methodology

Decision Architecture for AI-Enabled Systems follows a repeatable logic that is stable across contexts but precise in application.

It consists of five stages:

1. Decision Identification
   Identify the repeated, high-consequence decisions that must be owned and governed before any AI use cases, workflows, or automation are defined.

2. Judgment Anchoring
   Make authority explicit: who owns judgment, who can override, and what conditions force escalation.

3. Coordination Design
   Surface where decisions cross teams, functions, classifications, vendors, or alliance boundaries and where incentives misalign.

4. Optionality Protection
   Preserve reversibility by identifying where commitments, dependencies, or authority loss would become hard to unwind.

5. Stress Testing
   Test decision architecture under real conditions: ambiguity, time pressure, degraded inputs, disagreement, and scale.

The methodology is designed to expose unowned decisions, authority drop-outs, coordination fractures, and premature lock-in before they become operational or political liabilities.

Core Method Instruments

The methodology is applied using a small, disciplined set of decision-level instruments. Architectural tools used to structure high-consequence decision conversations.

Decision Architecture Map
Maps a single consequential decision to make authority, escalation, AI influence, and reversibility explicit before automation.

Judgment Ownership Matrix
Replaces RACI for AI-enabled systems by naming who owns judgment, who can override, and how AI influence is bounded.

Coordination Stress Map
Identifies where decisions will break trust, authority, or performance when systems scale across organizational or alliance seams.

Optionality Guardrail
Prevents premature lock-in by forcing explicit consideration of reversibility, exit cost, and authority loss before commitment.

Together, these instruments form a decision architecture system that sits upstream of platforms, workflows, vendors, and procurement.

How the Methodology Is Used

This methodology is applied through facilitated decision-architecture engagements, including:

• senior leader decision discussions

• command-level tabletop exercises

• wargame and scenario design

• pre-procurement and pre-scaling reviews

• concept and doctrine development

• alliance and coalition coordination workshops

The instruments provide grounding; value emerges through guided interpretation, disciplined questioning, and structured stress-testing.

Defence & Security Contexts

In defence and security environments, Decision Architecture for AI-Enabled Systems focuses on how machine-generated judgment interacts with command authority.

It is particularly relevant to:

• Command and Control (C2)

• Intelligence, Surveillance, and Reconnaissance (ISR)

• Autonomous and semi-autonomous systems

• Coalition and alliance operations

In these contexts, escalation control, optionality, and clarity of authority are operational necessities not governance abstractions.

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Authorship & Use

Decision Architecture for AI-Enabled Systems™ is an original methodology developed by Foresight Navigator.

© 2026 Foresight Navigator. All rights reserved.

The methodology and its instruments may be referenced with attribution.
Application, facilitation, and adaptation are conducted by the author.