Operational processes
AI Process Optimization
Agentic AI workflows that connect systems, data and decisions - across operations, factories and back-office.
Where it’s used
Cross-department decisions where silos hide context and slow action.
Where traditional automation breaks
- Decisions stall at handovers
- Context gets lost between systems
- Delays compound across teams
- Context is preserved end to end
- Decisions happen where work happens
- Actions follow a single logic layer
Example operational workflows
Concrete patterns you can apply to your systems and operations — and extend to your own cases.
Incident triage with full context
Classify incidents, add context and route to the right team.
Live operator decision support
Combine procedures with live context and suggest next actions.
Supply chain exception handling
Detect delays, pull data and propose actions with human approval.
Finance & case triage automation
Extract key fields and route cases; humans handle exceptions.
Why AI Process Optimization
Context-aware
Decisions consider history, live context and rules - not just static thresholds.
Cross-system
Workflows follow how work actually flows across ERP, MES, CRM, ticketing and documents.
Evolvable
Logic, rules and models can change without rebuilding integrations or code.
From siloed systems to value streams
How we start — and how it scales
Scope & prioritize
Pick 1–2 workflows, define success and required data.
Build & connect
Connect systems, implement logic, and validate outcomes with humans in the loop.
Operate & improve
Monitor outcomes, adjust rules and models, and expand as value proves out.
Shall we grab a coffee?
A short conversation to assess where agentic AI makes sense, and where it doesn’t.
