Course Objective & Scope
Routing every item to a person defeats the purpose of automation, while letting AI execute high-stakes actions unchecked creates unacceptable risk. This course teaches how to place Human-in-the-Loop (HITL) and Human-on-the-Loop controls at the right decision points with minimal friction.
Participants design risk- and confidence-based routing matrices, build context-rich approval cards in Slack, Teams or review portals, configure async wait/resume states with SLA escalations, and capture human edits as training and evaluation data.
Who Should Attend?
- Automation architects, product managers and business analysts
- Risk, compliance, internal control and operations leads
- Builders designing AI-assisted decision and transaction workflows
- Process owners for customer communications, financial approvals and document review
Key Takeaways
- Define a three-tier routing policy (straight-through, human review, block/escalate) by risk and confidence
- Design context-rich approval cards that let reviewers decide in seconds without switching tools
- Implement async wait/resume webhooks, reminders, SLA timers and delegation rules
- Prevent rubber-stamping and alert fatigue with smart sampling and UI design
- Log human corrections to continuously improve prompts and golden evaluation sets
Curriculum
HITL, HOTL and Risk-Based Thresholds
- Distinguishing Human-in-the-Loop, Human-on-the-Loop and Human-in-Command
- Routing matrices combining financial impact, regulatory risk and model confidence
Designing Low-Friction Approval and Review UIs
- Presenting source evidence, AI proposal and diffs in Slack/Teams cards or review queues
- Design patterns that counter reviewer fatigue and rubber-stamping
Async State Management, SLAs and Escalation
- Pausing and resuming long-running workflows with state persistence
- Reminder schedules, out-of-office delegation and SLA breach escalation
Audit Trails and Segregation of Duties
- Logging who approved or edited which AI recommendation and why
- Maker-checker controls and EU AI Act human-oversight alignment
Closing the Loop: Learning from Human Edits
- Capturing diffs between AI drafts and final human-approved outputs
- Turning recurring corrections into few-shot examples and eval test cases
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