Exception to Execution AI Loop Governance Software
An AI model flags a bearing temperature trend before anyone on shift would catch it and recommends shutting the line down, and the plant has never written down who signs off. Kissflow puts a governed review, approval, and audit trail between every AI-flagged exception and its execution.
Trusted by energy operators worldwide
A recorded human decision between every AI recommendation and the action it triggers
An AI model watching a production line flags a bearing temperature trend before anyone on shift would catch it, and recommends shutting the line down. A quality model flags a batch as high-risk and recommends a hold. A planning model flags a schedule conflict and recommends a reprioritization. The recommendation is only as good as the governance around whether, and how, it turns into an action, and most plants running early AI pilots never formally defined who reviews it or what gets logged.
Kissflow governs that loop: every AI-flagged exception routes for review, an approval is recorded before execution, execution is tracked, and the loop rolls into a dashboard leadership can audit. Flagged exceptions and recommended actions arrive from connected AI systems, quality, production, or maintenance, and route to the right reviewer with delegation and escalation if a decision stalls. Once approved, the action, a work order, a hold, a schedule change, executes and tracks to completion, and the outcome logs against the recommendation so model owners can review accuracy. Reporting rolls up how often recommendations get approved, declined, or modified, and whether execution resolved it.
An AI recommendation without a governed loop is just an unactioned alert, or an unreviewed one
Recommendations execute without review
An AI-flagged exception triggers an automated action directly, with no recorded human checkpoint before it happens.
Or they sit ignored
A recommendation lands in a dashboard nobody is assigned to monitor, and the exception it flagged goes unaddressed.
No record of who approved what
When an AI-recommended action does get executed, there's no log of who reviewed it, what they decided, or why.
Outcomes never feed back
Nobody tracks whether an executed recommendation actually resolved the exception, so the model's real-world accuracy stays unmeasured.
Six modules. Configurable to your operating model.
Every module ships with default forms, approval logic, integrations, and dashboards. Configure each one to your operating model in the visual builder.
Exception intake from AI systems
Receives flagged exceptions and recommended actions from connected AI models, quality, production, or maintenance.
Review & approval routing
Routes each flagged exception to the right reviewer, with delegation and escalation if a decision stalls.
Execution tracking
Tracks the approved action through to completion, whether it's a work order, a hold, or a schedule change.
Status tracking
Shows every AI-flagged exception in the loop, from flag to review to execution, in one place for oversight.
Reporting & analytics
Rolls up how often recommendations are approved, declined, or modified, and whether execution resolved the exception.
Feedback loop record
Logs the outcome of each executed action against the original recommendation, for model owners to review accuracy.
From request to system of record in four steps
Report
A connected AI model flags an exception and recommends an action, which enters the review queue.
Assess
A qualified reviewer evaluates the recommendation and approves, modifies, or declines it, with escalation where needed.
Resolve
The approved action executes — a work order, a hold, or a schedule change — and is tracked to completion.
Record
The result is logged against the original recommendation and rolled into the reporting dashboard.
What changes when the AI exception-to-execution loop runs on Kissflow
| Process | Before Kissflow | On Kissflow |
|---|---|---|
| AI recommendations | Execute automatically or get ignored, with no consistent middle path | Every flagged exception routes for a recorded human review |
| Approval | No defined reviewer or approval step | Routed to the right reviewer with delegation and escalation |
| Execution | Untracked once an action is taken | Tracked from approval through completion |
| Audit trail | No record of who approved an AI-recommended action, or why | Full log of the recommendation, reviewer, decision, and execution |
| Model accuracy | Never measured against real outcomes | Outcomes logged against recommendations for ongoing accuracy review |
| Reporting | No plant-wide view of how often AI recommendations are acted on | One dashboard covering every flagged exception, decision, and outcome |
Connects to the AI, quality, and production systems already flagging exceptions
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Built for keeping a human in the loop as AI recommendations move toward execution
A human checkpoint before every execution
No AI-recommended action executes without a recorded review, no matter how minor it looks.
Escalation that actually moves
A stalled review escalates automatically instead of sitting in a queue nobody owns.
A real audit trail for AI-driven decisions
Every recommendation, reviewer, decision, and outcome is logged and searchable.
Outcomes feed back to model owners
Executed actions are logged against their original recommendation, so accuracy is measurable, not assumed.
Live in weeks, not a re-implementation
Configure intake, review routing, and reporting in the visual builder without a platform-wide build.
One dashboard across every AI system in the plant
See flagged exceptions from every connected model in one place, not one dashboard per tool.
Related apps
AI Model Governance Management System
Govern the model that generates the recommendation, before its output ever reaches this review loop.
AI Tool Request Intake & Governance Management System
Approve the AI tool itself before it starts generating exceptions for this loop to govern.
IT Change Management Approval & Release Management System
Route the underlying system change if an AI-recommended action requires one.
We help manufacturers keep a human in the loop as AI recommendations move toward execution
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GVP & CIO at McDermott International, Ltd
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“Advanced automation of all processes is easy to set up. I cannot imagine how to manage workflows without this software.”
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Global Head - Digitalization & Business Improvement
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CIO, SN Aboitiz Power Group
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Talk to usGot questions? We're here to help.
Get SupportAny output from a connected AI or analytics system that recommends an action, a shutdown, a hold, a reprioritization, on production, quality, or maintenance data.
It adds a governed review and approval step before execution. Whether the underlying AI system can act autonomously outside this loop is a configuration and integration decision your team makes.
Routing is configurable, typically the process or asset owner closest to what the recommendation affects, with escalation if they don't respond.
It's purpose-built for recommendations originating from AI or analytics systems, with a feedback loop that logs outcomes against the original recommendation for accuracy review.
Yes. Process owners configure intake sources, review routing, and escalation thresholds in the visual builder, and the AI Builder can generate a working app from a plain-language description.
Through APIs and integration connectors, under single sign-on and role-based access, with every action written to an audit log.
Configuration and AI generation move delivery from weeks to days, without a multi-year platform program or an engineering backlog.
Kissflow is certified to SOC 1, SOC 2, SOC 3, ISO/IEC 27001, HIPAA, GDPR, and CCPA, hosted on Google Cloud with data residency in the US, EU, APAC, and Oceania.
