Human-in-the-loop is not evidence.
The next AI compliance failure will not be a bad model. It will be a defensible policy paired with operators who clicked approve without reading the recommendation.
The Proof Gap
Every serious AI governance framework points to the same requirement: prove that humans are exercising judgment over AI-assisted decisions. Most enterprise AI programs can produce policy documents, model review records, vendor questionnaires, and approval committee minutes. Almost none of them can produce evidence that the human reviewing the AI was actually thinking.
That is the Human Oversight Proof Gap.
Three numbers that name the gap
The market is not missing another model-monitoring tool. It is missing proof that the human in the loop is still a human in the loop.
Why the gap exists
AI governance was built around the model. Tools watch model behavior, drift, fairness, security, prompt risk, output risk, and data lineage. Almost none of them watch the operator.
The operator is the part of the system that the law actually points at. The Colorado AI Act puts deployer obligations on the operator side. NIST AI Risk Management Framework expects oversight as a measurable function. OMB M-24-10 names oversight as a minimum practice for rights- and safety-impacting AI in federal use. NYC Local Law 144, FDA AI/ML guidance, FAA AI guidance, SEC AI disclosure, FTC, ISO/IEC 42001, and EU AI Act Articles 14 and 26 all converge on the same person. None of them tell you how to measure whether that person is doing the job.
That is the gap COHESION measures.
Ten pain points the proof gap creates
Policy without proof.
Companies write that humans review AI output. They cannot show the review happened.
No owner.
AI governance is split across legal, compliance, security, data, AI, product, risk, and HR. No single owner can answer the oversight question.
Audit weakness.
Audit, board, and regulator conversations require evidence. Logs are fragmented across tools and prove activity, not judgment.
Over-reliance.
Oversight frameworks explicitly name automation bias. Human approval becomes a checkbox. Operators accept high-confidence wrong answers.
Wrong logs.
A click does not prove review. An approval event does not prove understanding. A timestamp does not prove deliberation.
Agent accountability.
As AI agents act across systems, the question gets sharper: who approved, monitored, corrected, or stopped the agent?
Vendor proof.
AI vendors selling to regulated buyers need to show customers can use the tool responsibly. Vendor risk questionnaires slow deals.
Training does not prove competence in the moment.
Records show training. Behavior shows judgment. The frameworks expect both.
Board and reputation risk.
AI failures are reputational events, not just technical incidents. Boards need a clear oversight signal they can read.
Legal trust gap.
Legal AI adoption is moving from experiment to infrastructure. Lawyers remain accountable. Firms cannot easily prove AI delivered value safely.
Each pain has the same root: oversight is claimed but not measured.
How COHESION measures it
COHESION is a hosted measurement service for human oversight of AI. It does not watch models. It measures the operator behavior around AI-assisted decisions. The output is a Judgment Independence Score (0–100) across seven dimensions: Deferral Resistance, Error Detection Capability, Independent Performance, Deliberation Depth, Post-Error Recalibration, Domain Confidence, Decision Autonomy.
The score feeds into one buyer artifact: the AI Oversight Evidence Pack. The pack maps the score to the Colorado AI Act, NIST AI RMF, OMB M-24-10, NYC Local Law 144, FDA, FAA, SEC, FTC, ISO/IEC 42001, and EU AI Act. It includes a signed JSON receipt, audit-log export, dimension breakdown, operator distribution, remediation plan, and seal status (Self-Reported or Audited).
Self-Reported is the entry wedge. The customer connects one workflow, attests the data feed, and uses COHESION to generate the evidence. Audited is the trust layer. A Big-4 firm or accredited conformity-assessment body verifies the data feed and assurance process. Same JIS. Different verification rigor.
Your AI governance program can prove the model was reviewed. Can it prove the human exercised judgment?
If the answer is no, COHESION measures it.
The offer
90-Minute Oversight Proof Test. Bring one AI-assisted workflow. In 90 minutes, COHESION will instrument one decision point and show whether the human oversight is evidence-grade. The visit is no-charge.
If the evidence is useful, the 60-day Self-Reported pilot starts at the $25K floor, scoped to one workflow with 10 to 25 operators. The output is one AI Oversight Evidence Pack: board, audit, risk, insurer, or partner-ready.
This is not company-wide certification on day one. It is one workflow, one decision point, one evidence artifact. If that artifact matters, the account expands.
Who this is for
- Regulated AI deployers with high-frequency human approval workflows.
- Vertical AI vendors selling into regulated industries.
- Big-4 assurance partners and accreditation bodies needing a measurement primitive.
- Boards, audit committees, risk officers, insurers, and legal teams asking for evidence.
What COHESION is not
COHESION is not a model-governance tool. It is not a vendor-risk platform. It is not a SOC 2 product. It is not a chatbot.
COHESION is the measurement layer underneath every claim that a human is exercising oversight of AI.
Bring one workflow.
In 90 minutes, COHESION will show whether your human oversight is evidence-grade.
Sources
- Grant Thornton 2026 AI Impact Survey
- Deloitte 2026 State of Generative AI in the Enterprise
- Gartner AI governance market projections
- KPMG Q1 2026 AI Pulse on AI agent accountability
- JPMorgan Chase: Securing Agentic AI
- Colorado AI Act (SB24-205)
- NIST AI Risk Management Framework 1.0
- OMB M-24-10
- NYC Local Law 144
- ISO/IEC 42001
- EU AI Act Articles 14 and 26