Assemble the decision state.
Bring together the outputs, evidence, workflow conditions, policies, and human observations that bear on one decision.
Decision Control Plane™
Why was AI allowed to make this decision?
Explainability can show how a model produced an answer. DCP addresses a different question: why was AI permitted to determine or materially shape what happened next in this case?
PROPOSED DECISIONAUTHORITY RECORD
Hold and obtain evidence
A required fact remains unresolved, so the action should not proceed automatically.
ALLOW
HOLD
ESCALATE
BLOCK
EvidenceIncomplete
AuthorityConditional
Human reviewPending
RecordCreated
Cross-tool coordination
Specialized tools do not automatically form a coherent decision system.
One system generates an alert, another summarizes the record, another recommends an action, and a person contributes information none of the tools can see. Each may perform well on its own. What is missing is a shared view of the decision they are all acting on.
Surface disagreement and insufficiency.
Identify where systems conflict, where evidence is missing, and where conditions have moved outside what a system was built for.
Establish which system or person may act.
Determine what may inform, recommend, initiate, or execute, and who must intervene.
Control the consequential action.
Allow, hold, escalate, or block based on the case at hand.
DCP does not replace specialized AI tools. It gives them a common decision to operate against.
The accountability gap
An explanation of the output is not an explanation of authority.
A model may be accurate, secure, monitored, and explainable. The organization still needs to show why AI was allowed to make or materially shape the decision in this situation.
What an answer must contain
The five questions form one evidentiary chain.
The organization cannot answer the category-defining question with a confidence score or a human signature alone. It must reconstruct the decision as it existed when the action was allowed to proceed.
- What decision was actually being made?Name the determination, the person or entity affected, and the downstream action.
- What evidence was available, missing, current, and relevant?Show what the workflow knew at that moment and which claims the evidence supported.
- What role and authority had been delegated to AI?Distinguish informing, recommending, ranking, initiating, approving, and executing.
- What could happen if the decision was wrong?Account for severity, reversibility, time sensitivity, and the people or operations exposed.
- Why was proceeding justified?Explain why the workflow allowed action instead of requesting evidence, routing review, holding, or blocking.
The workflow response
DCP converts delegated authority into an operating decision.
The response changes when evidence, authority, case state, consequence, or required review changes.
Allow
Proceed when the evidence and delegated authority support the action in the current case.
Hold
Pause because a required fact, condition, or review remains unresolved.
Escalate
Move the decision to the person or authority qualified to own the consequence.
Block
Stop an action that falls outside approved authority or creates an unacceptable consequence.
Buyer questions
Questions that expose the authority gap.
The questions a model explanation cannot answer by itself.
How is decision control different from explainability?
Explainability describes how a model produced an answer. Decision control reconstructs why AI was permitted to make or materially shape the decision, including the evidence, delegated authority, workflow state, human role, and possible consequence.
Can a human remain in the loop while AI effectively makes the decision?
Yes. AI may set the recommendation, rank the case, determine which evidence is visible, or establish the default action. DCP records the role AI actually played rather than treating a final human signature as proof of independent judgment.
How can DCP begin before runtime enforcement?
Shadow mode records the decision state and shows where the authority, evidence requirement, or review path would have changed. The organization can compare those findings with current practice before DCP is permitted to intervene.
Can DCP contain the effect of a compromised model or agent?
DCP complements cybersecurity controls by governing whether the proposed business action is authorized. A harmful prompt or compromised agent may reach the model, while the resulting action is still held, escalated, or blocked at the decision layer.
Scope
Where DCP applies
DCP is intended for consequential, context-sensitive decisions where an improper action could create material clinical, financial, legal, regulatory, or operational harm. It is not designed for routine, low-consequence content or marketing automation.
- Patient care and clinical operations
- Clinical trials
- Insurance and coverage decisions
- Regulated financial decisions
- Federal determinations
- High-consequence industrial workflows
Start with the current decision
Start with one consequential decision in shadow mode.
GNS-AI is accepting a limited number of design partners for shadow-mode deployment. Bring the workflow, the role AI plays, the evidence available, who currently reviews the case, and what happens if the decision is wrong.
Low-friction entry points: executive briefing, applied workshop, AI pilot and commercial assurance, workflow blueprint, production validation, or DCP shadow mode.