The Safest Path to AI Automation
Is the One You Control.
Most AI platforms offer a binary choice: manual process or full automation. ioNova offers a graduated ladder. Start with AI that explains. Graduate to AI that recommends. Then prepare. Then execute. Each step gated by governance.
The All-or-Nothing Automation Trap
Enterprise AI adoption stalls at the governance gate. Not because the technology can't automate, but because organizations can't govern what they can't control. When an AI agent makes a wrong decision in a regulated workflow — approving a suspicious transaction, misrouting a payment — the consequences range from fines to reputational damage to real human harm.
Progressive autonomy resolves this tension. Instead of asking "Should we automate?" it asks "At what level of autonomy should this workflow operate today, and what evidence would justify advancing it?"
Governance Gates Between Each Level. Evidence at Every Step.
Advancing Requires Proof, Not Permission
| Gate | Transition | Required Evidence |
|---|---|---|
| Gate 1 | Level 0 → 1 | Demonstrated accuracy; analyst satisfaction; model validation report |
| Gate 2 | Level 1 → 2 | Acceptance rate >85%; zero critical errors in 90 days; MRM review |
| Gate 3 | Level 2 → 3 | Edit rate <15%; 6 months sustained quality; regulatory review; rollback tested |
Progressive Autonomy — Your Questions Answered
What is progressive autonomy in AI and how is it different from full automation?
Progressive autonomy is a graduated approach to AI automation where AI systems advance through clearly defined levels of independence — from explanation to recommendation to preparation to execution — with governance gates between each level. Unlike full automation (which presents a binary "manual vs. automated" choice), progressive autonomy lets organizations start with AI that answers questions (Level 0: Explain), then advances to suggesting actions (Level 1: Recommend), drafting outputs (Level 2: Prepare), and finally executing autonomously (Level 3: Execute). Each advancement requires documented evidence of accuracy, committee approval, and defined rollback procedures. This resolves the fundamental tension in enterprise AI adoption: teams get AI capabilities immediately while governance teams maintain complete control.
What are the four levels of AI autonomy in ioNova?
ioNova defines four autonomy levels: Level 0 (Explain) — AI answers questions with entity intelligence and cited evidence, while humans make all decisions; ideal for initial deployment and high-risk domains. Level 1 (Recommend) — AI analyzes entity data and suggests specific actions like case routing or risk classifications, while humans approve every recommendation. Level 2 (Prepare) — AI pre-fills forms, drafts SAR narratives, and structures regulatory filings, presented in approval queues for human review. Level 3 (Execute) — AI acts autonomously for pre-approved, low-risk, reversible operations with strict allowlists, anomaly self-monitoring, and full audit trails.
What evidence is required to advance between autonomy levels?
Each governance gate requires specific evidence thresholds: Gate 1 (Level 0→1) requires demonstrated accuracy, analyst satisfaction surveys, and a model validation report. Gate 2 (Level 1→2) requires an acceptance rate above 85%, zero critical errors over 90 days, and a completed Model Risk Management (MRM) review. Gate 3 (Level 2→3) is the most stringent, requiring an edit rate below 15%, six months of sustained quality metrics, regulatory review, and tested rollback procedures. Autonomy can be dialed back at any time — for the entire platform, a specific workflow, or a single entity type.
How does agentic AI governance work in regulated environments?
Agentic AI governance ensures that autonomous AI operations remain auditable, controllable, and compliant with regulatory requirements. In ioNova, even at Level 3 (Execute), the AI operates within strict allowlists defining exactly which actions it can take, continuously monitors its own operations for anomalies, and self-escalates to human review when confidence drops below threshold. Every autonomous action generates an immutable audit trail. This approach aligns with the Federal Reserve's SR 11-7 guidance on model risk management and the EU AI Act's requirements for human oversight of high-risk AI systems — making it possible to deploy agentic AI in regulated workflows without compromising compliance.
Can autonomy levels be set differently for different workflows?
Yes. Autonomy is configurable per workflow, per entity type, and per risk category. An organization might operate at Level 3 (Execute) for low-risk payment reconciliation, Level 2 (Prepare) for SAR narrative drafting, Level 1 (Recommend) for KYC risk classification, and Level 0 (Explain) for novel sanctions scenarios — all simultaneously within the same platform. This granularity means high-volume, well-understood tasks can be automated for 60–90% average handling time reduction, while sensitive or novel scenarios retain full human oversight.