Connect your process repository to automated and AI-enabled work
In your words: “Our process repository is not connected to how automated and AI-enabled work actually runs”
- Process-to-workflow integration
- Governed AI route
- Process-to-execution
Approved processes are modelled and governed in the process architecture, but workflow automation and AI initiatives are designed in separate environments. This becomes most visible when work crosses teams, approvals and systems while execution is coordinated outside the approved process model.
When the design system of record informs how automated work runs, logic is shared and controls stay consistent.
Automation evolves safely toward agents, with one source of intent and less duplicated logic.
A governed path from process model to workflow execution preserves intent while allowing increasing autonomy. In practical terms, the organisation can automate priority work now while retaining governance, evidence and a deliberate path toward greater autonomy.
- Reduced design-execution drift and a reusable, governed path from process models to workflow and agentic capability
- Visible work status and accountability
- Reduced manual coordination and rework
- Governed reuse of workflows and controls
- A safer path from automation to agents
- Which approved processes are candidates for executable workflow?
- Where is automation logic currently governed?
- How will today’s workflow support tomorrow’s supervised or governed agents?
- Which hand-off or approval creates the greatest avoidable waiting time?
- What runtime evidence would demonstrate that the approved process is actually being followed?
McKinsey’s findings underline that AI value depends on redesigning workflows, not simply adding technology alongside existing work.
McKinsey: The State of AI: Rewiring to Capture Value
NIST provides a use-case-and risk-based framework for governing, mapping, measuring and managing AI risk across the system lifecycle.
NIST: AI Risk Management Framework