The cost
Too much review wastes automation. Too little control takes too much risk.
Decision Control Plane™
A model can work correctly and the final action can still be wrong.
Decision Control Plane™ controls what happens when AI-shaped work becomes a consequential action. It helps regulated organizations get more useful automation, keep responsibility selective, and keep a clear record of why an action was allowed. The same approved workflow can contain cases that deserve very different levels of authority and scrutiny.
DCP
Decision Control Plane provides an external decision-level control layer for consequential AI-influenced actions. It is designed to determine how the proposed action should be handled under the conditions that actually exist: proceed, verify, require qualified review, or hold/escalate, with a decision record.
The problem
Correct model output is not the same as a safe final action.
AI can draft, rank, route, or recommend well and still push the wrong action into a live workflow. Review grows. Value leaks. Leaders cannot explain why AI was allowed to act on a hard case.
The better state
Give AI more room where the case supports it. Add control where consequence and uncertainty rise. Keep a clear record.
Human authority stays
Organizational policy and qualified decision-makers keep final decision authority. DCP controls how AI-shaped work is handled before the action lands.
What changes
Outcomes Decision Control is built to create.
Enterprise decision-control for regulated environments.
More useful automation
Let AI finish more work where the case supports it.
Selective responsibility
Expand AI work only where operating evidence supports it.
Stronger control
Add tighter handling when consequence, uncertainty, or policy demand it.
Lower unnecessary review
Stop reviewing every case the same way.
Clearer accountability
Know who owned the handling decision for the action.
Better economics
Count review, delay, exceptions, and failure cost at the unit of work.
Preserved record
Keep why an action was allowed under the conditions that existed.
Public actions
Four ways a case can move.
Same workflow. Different handling by case. A record you can explain.
Proceed where supported. Verify where needed. Review where judgment matters.
Proceed
Let the AI-shaped action move forward when conditions support it.
Verify
Confirm sources or workflow facts before reliance grows.
Review
Require a qualified person to review before the action moves. Qualified review should be substantive: the reviewer receives enough relevant context and real decision authority to exercise independent judgment before the action moves forward.
Hold or escalate
Pause the action, or send it to higher authority, until required conditions are met.
In practice
Why case-appropriate control matters
Case-appropriate AI authority. Selective, substantive human review. The same AI workflow can require different handling from case to case.
FDA / CDRH
Performance after deployment
What happened. FDA/CDRH highlights how changes in populations, sites, protocols, and inputs can alter AI performance after development. Draft lifecycle guidance also stresses ongoing attention across the device lifecycle.
Why it matters. Average performance at development time does not automatically justify the same operating authority under changed conditions.
Where Decision Control could intervene. This is a point where Decision Control is designed to intervene when operating conditions change after deployment.
What different handling could have occurred. Changed conditions could have required verification, qualified review, or a hold before the same AI retained prior operating authority. Proceed · Verify · Qualified Review · Hold or Escalate as the case warrants.
Cigna PxDx
What review means in practice
What happened. ProPublica reported that Cigna's PxDx system supported rapid claim denials with limited clinician time per case. Cigna has stated that physicians review claims and that the reporting mischaracterized its process.
Why it matters. Named human review is not the same as substantive review with enough context and real decision authority.
Where Decision Control could intervene. This is a point where Decision Control is designed to intervene when review is named but may not be substantive for the case at hand.
What different handling could have occurred. Cases that look routine on paper could have been routed to qualified review with enough context and authority before a denial posted.
UnitedHealth / nH Predict
Practical authority over care
What happened. Plaintiffs allege that UnitedHealth and naviHealth used nH Predict to cut off post-acute care in ways that overrode treating clinicians. The company states the tool guides care and clinicians make medical necessity determinations; in February 2025 a federal court allowed some claims to proceed.
Why it matters. Leaders need an operational answer for how much authority an AI-shaped conclusion should receive in the particular case.
Where Decision Control could intervene. This is a point where Decision Control is designed to intervene when an AI-shaped recommendation begins to carry practical authority over care.
What different handling could have occurred. High-consequence recommendations could have been held or escalated for qualified clinical judgment before care was cut off.
CNN military scenario
Verification before action
What happened. CNN reported that an AI-assisted intelligence error moved toward a possible military operation before specialized analysts revisited the underlying evidence.
Why it matters. A fluent model output can still be the wrong basis for a consequential move if verification never enters.
Where Decision Control could intervene. This is a point where Decision Control is designed to intervene before an AI-shaped intelligence conclusion becomes operational action.
What different handling could have occurred. The conclusion could have required verification by specialized analysts before it moved toward a possible military operation.
The common problem is deciding how much authority an AI-shaped conclusion should receive in the particular case, what level of scrutiny belongs before action, and whether human intervention can meaningfully change the outcome.
Buyer questions
Three questions Decision Control answers.
Use these when AI already shapes consequential work.
Where is AI still creating value?
After review, exceptions, delay, and failure cost are counted.
Where can AI take on more work?
Where operating evidence supports more AI responsibility.
Can you explain why AI was allowed to act?
Knowing a model ran is not the same as defending the action.
How to start
Begin with a Decision Control Assessment.
One consequential workflow. Public terms: 3-4 weeks | Starts at $30,000.
Bounded assessment
Find where AI can do more work, and where a person still needs to step in.
Shadow Mode
Observe how Decision Control would evaluate an existing workflow before you change live handling.
Then control
Move to persistent Decision Control when the operating case justifies it.
Illustrative example
AI-shaped action about to post
Hold or escalate
Situation
An AI score and system checks support a high-dollar claim denial. The denial is ready to post. A qualified person has not confirmed it yet.
Handling result
Hold the denial and send it to a qualified reviewer before it posts.
Buyer value. Leaders can show what was stopped, who must review it, and what happens next.
Shadow Mode first.
Shadow Mode shows where Proceed, Verify, Review, or Hold or escalate would have applied, without changing production yet.
Buyer value
Different systems answer different questions.
Use the right control layer for the job.
Policy and business rules establish baseline requirements and permitted operating conditions. Model and agent monitoring show how AI components are behaving. Decision Control determines the appropriate handling of the consequential action in the case at hand.
Two cases can satisfy the same workflow rules and receive similar AI recommendations while still warranting different levels of scrutiny before action. Should both cases receive the same operating authority?
Workflow automation
Moves work through steps and handoffs. It does not decide how much authority AI should receive on a consequential case.
Model monitoring
Tracks model quality and drift. It does not set case-level handling when the final action still has to land safely.
Policy governance
Sets rules, roles, and oversight. It often sits above the runtime moment when AI-shaped work becomes an action.
Decision Control
Controls what happens when AI-shaped work becomes a consequential action: proceed, verify, review, or hold, with a record you can explain.
Operating value
More AI responsibility does not automatically create more operating value.
As AI takes on more work, review, verification, exceptions, rework, delay, and failure exposure can rise with it. The gap between potential value and realized value is value leakage.
Illustrative operating model
X axis: AI responsibility / automation. Y axis: Operating value.
The shaded gap is value leakage. Contributors can include review, verification, exceptions, rework, delay, and failure exposure.
Illustrative operating model. Actual economics depend on workflow volume, review requirements, failure exposure, and operating conditions.
Comparison
Why existing controls are not enough
Useful layers still leave a gap when AI-shaped work becomes a consequential action in the case at hand. Decision Control works alongside policy and rules. It does not replace them.
Fixed rules
Rules are good at enforcing conditions you already know to look for. They work well for known requirements and hard constraints. They become less useful when the combination of conditions that makes an action inappropriate was not anticipated when the rule was written. Rules are not obsolete. Decision Control Plane works alongside policy and rules.
LLM as judge
Another model opinion does not establish decision authority. Using one AI model to judge another still depends on an AI system recognizing when AI-shaped work should not be trusted. A plausible answer, confidence score, or second model opinion is not the same as an external decision-control determination.
DCP does not depend on an AI model knowing when it should not be trusted.Blanket human review
Strong oversight can destroy the economics of automation when it scales with volume. If nearly every AI-shaped case still requires full human review, much of the labor and throughput value disappears. Qualified human judgment remains essential where required. The problem is using the same level of review for every case.
Decision Control
Change the handling of the action as the situation changes. Decision Control Plane is designed to determine whether AI-shaped work should proceed, be verified, receive qualified human review, or be held before the consequential action occurs.
Failure modes
What Decision Control is designed to detect before the action lands
These are operating failure patterns buyers already recognize. Decision Control is designed to detect them before the action lands. That is not a claim that every failure is prevented.
Model output looks normal while the operating situation changed
The AI response can look fluent while population, site, protocol, evidence, or consequence has moved outside what was previously supported.
A small AI error travels farther than expected
A limited mistake in ranking, drafting, or routing can still drive a consequential action if handling does not change with the situation.
The rule never anticipated this combination
Static rules catch conditions you already wrote down. Novel combinations can still make an action inappropriate.
An AI judge agrees confidently
A second model can sound sure and still leave decision authority unresolved. Confidence is not the same as external control of the action.
Human review expands with automation
As AI volume rises, full review can expand with it and erase the labor and throughput value automation was meant to create.
Every action receives the same control treatment
Uniform proceed or uniform review ignores cases that need verification, qualified judgment, or a hold before the action occurs.
Behavioral proof
Same AI output. Different situation. Different handling.
The same AI output does not always deserve the same level of authority. When the operating situation changes, the required handling may change even if the underlying model output does not.
Supported routine case
Evidence is complete, consequence is bounded, and operating conditions match what has been demonstrated. The AI-shaped action can move forward with the normal record.
Source check before reliance
The recommendation looks fluent, but a key fact or source still needs confirmation before the organization should rely on it for the next step.
Judgment required
Consequence or ambiguity rises. A qualified person needs enough relevant context and real decision authority before the action moves forward.
Stop until conditions are met
Required evidence, authority, or operating support is missing. The action is held or escalated rather than allowed to post.
Illustrative synthetic example. It demonstrates control behavior, not a customer deployment or measured outcome.
Decision record
What leaders can retain
Explainable handling
Observable fields
Situation description, handling result (Proceed / Verify / Review / Hold or escalate), who owned the handling decision, and what happens next.
Buyer-visible outcome
Case-appropriate handling with a preserved basis for why the action was allowed, verified, reviewed, held, or escalated.
Buyer-visible behavior
What Decision Control Plane does in the workflow
Decision Control Plane provides an external decision-level control layer for consequential AI-influenced actions. It determines how the proposed action should be handled under the conditions that actually exist: proceed, verify, require qualified review, or hold or escalate, with a decision record leaders can explain.
Selective AI responsibility, substantive human review where judgment matters, and a preserved basis for why an action was allowed.
Prioritize → Design → Build → Prove → Scale remains the delivery method. Decision Control is cross-cutting when AI shapes consequential actions. It does not replace Prove or Scale.
Decision Control Assessment: 3-4 weeks, starting at $30,000, typical $30,000-$45,000. Persistent Decision Control Plane when the operating case warrants it.
Discuss a Decision Control Assessment
Bring one consequential workflow. We will confirm where AI can do more work, where a person still needs to step in, and whether persistent Decision Control belongs.