See how the work is structured
Learn how we assess the process, business case, data security, and quality before development begins.
Explore the methodology →Expert guide
How an AI process audit works
A strong AI process audit maps the current workflow, data, business case, risks, and the smallest pilot that can produce useful evidence.
Map the actual process
The audit follows the real path from input to outcome, including exceptions, manual workarounds, roles, and systems. A written procedure is not enough when the day-to-day process works differently.
- Trigger and input
- Participants and systems
- Decision points
- Exceptions and rework
Separate process problems from AI opportunities
Unclear ownership, duplicate data, poor CRM configuration, and unstable rules should not be hidden behind an AI model. The audit identifies what must be fixed first.
- Process redesign
- Integration
- Rule-based automation
- AI-assisted step
Assess data and controls
The audit checks whether representative examples exist, who owns the data, what may be sent to a model, and how outputs can be verified.
- Data availability
- Access boundaries
- Source of truth
- Human review
Define the pilot
The result is a focused scenario with a business case, architecture options, risks, success metrics, and clear stop criteria.
- One end-to-end scenario
- Baseline metric
- Acceptance test set
- Rollback plan
Choose your next step
Start at the stage that matches your situation
You do not need a detailed brief to begin.
Assess one business process
Determine whether the task calls for AI, rule-based automation, or a process redesign.
Assess a process →Share a defined task
Send the current process, constraints, and expected outcome, and we will suggest a practical first step.
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