No shared execution path
AI is used ad hoc, outside the workflow and without a clear approval point.
Controlled AI use inside a process
AI Insight
A dashboard for controlling data, AI outputs, cost, response quality and the decision workflow.
Quality
Cost
History
Approval
Reporting
Process control
Process active
Current stage
Quality review
OK
- 01Data validationDone
- 02AI scenarioDone
- 03Output reviewIn progress
- 04ApprovalPending
One view connects the result, status, cost and decision.
This material shows an anonymised example of a solution. It does not contain client data or production information.
01 / Business challenge
AI may work, but it is still difficult to govern.
Single experiments quickly become part of everyday work. Without common rules, cost, risk and manual coordination all increase.
Incomplete history
It becomes difficult to reconstruct which data was processed, what the status was and who made the decision.
Cost without context
Analysis spending is not linked to the scenario, the output or the business value of the task.
Quality without a criterion
There is no shared review method or rule that defines when a human decision is required.
Consequence: the organisation cannot easily tell which AI use cases are stable, cost-aware and ready to scale.
02 / Who it is for
A shared operating view for business, operations and approvers.
B
Process owner
Visibility into stages, exceptions and places where the workflow needs a decision.
M
Manager
A comparison of cost, quality and volume without going into technical details.
O
Operations team
One queue of tasks, readable statuses and clear rules for manual approval.
A
Solution administrator
Configuration of sources, scenarios, permissions and quality thresholds.
03 / How the process works
From data to decision, with control at every stage.
01Input dataA standardised source
02ValidationCompleteness and format
03AI scenarioA defined analysis goal
04AI modelA controlled invocation
05OutputStored with history
06Quality reviewOne criterion or scoring method
07ApprovalAutomatic or human-reviewed
08CostAssigned to the analysis
09Report / decisionExport or next step
D
Core principle: the AI output does not end the workflow. It still needs review, a decision and a recorded cost context.
04 / Solution modules
Capabilities described through the tasks they solve.
Input structuring
Data sources, validation and completeness rules reduce errors before AI is even used.
- one standard format
- error status
- scope control
Work scenarios
Each AI use case has a defined goal, input, quality criterion and expected output.
Run history
A list of analyses shows execution path, status, decision and cost without searching across many tools.
Quality and approval
The result can be reviewed, rejected or routed to manual verification according to a rule.
Cost and dashboard
Analysis cost becomes visible in the context of volume, scenario and quality.
Roles, export and administration
Permissions structure accountability, while export supports reporting and downstream work.
05 / Before and after
The change is not about simply "adding AI". It is about embedding it into the process.
Area
Before
After the dashboard is introduced
Data
Different formats and manual clean-up
Validation before analysis
Output
Scattered and without history
Stored with context and status
Quality
Subjective review after the fact
A defined metric and approval threshold
Cost
One total bill without a reason
Cost assigned to the analysis
Decision
Unclear ownership
A visible owner and status
06 / What the user sees
Exceptions and decisions first. Details second.
Process overview
Demonstration view
Control centre
Awaiting approval-Need a decision
Quality-Selected metric
Analysis cost-Average for the scenario
Analysis queueRecent statuses
Demonstration analysis AAwaiting approval-
Demonstration analysis BApproved-
Demonstration analysis CValidation-
Current decision
Manual verification
The output did not meet the defined quality threshold.
Illustrative view. Names and values are fictional and do not represent production data.
07 / Optional metrics
Scale and efficiency of the solution.
This section stays hidden by default and should only be shown after credible data is added.
08 / Minimal pilot
A small scope, but a concrete answer.
The pilot should verify whether one selected AI scenario produces a repeatable result, what it costs and where a human decision is genuinely required.
Pilot question
Is this process worth scaling?
- 01One data typePreferably repetitive and easy to review
- 02One AI scenarioOne clear business objective
- 03One quality metricA simple acceptance criterion
- 04Cost per analysisComparable across every run
- 05Manual approvalNo decision automation at the start
- 06Dashboard and weekly reportStatuses, quality, cost and conclusions
09 / Possible extensions
Expand the solution only after the basics are confirmed.
Each extension should answer a specific issue revealed by the pilot.
Stable
process
process
Quality scoring
Cost alerts
Model comparison
Scenario versioning
Human-in-the-loop
Management reports
Business integrations
Local model and anomaly checks
10 / Project type and related services
A demonstrator from the AI and quality-control area
The project structures input data, the AI scenario, the output, cost, quality, human approval and decision history inside one workflow.
Describe a similar processIO
AI InsightOps Console
Do you want to introduce AI into a process while still controlling cost, quality and risk?
Start with one scenario, one measurable criterion and one clear decision point.
Discuss a pilot
[TO COMPLETE: full name]
[TO COMPLETE: email address]
[TO COMPLETE: phone number or website]
This material shows an anonymised example of a solution. It does not contain client data or production information.