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AI in auditFrom chat-based use to AI-native operations
How firms move from individual AI use to integrated solutions built into the audit process.
Foundations
What is AI, really?
At its core, AI is a model plus compute.
01
Model providers
They supply the underlying AI models and infrastructure.
The interface is not the model:
- ChatGPT = user interface + OpenAI models
- Claude = user interface + Anthropic models
- Copilot = a Microsoft interface that can use several models
02
Models are also available via API
The same AI capacity can be wired directly into other systems.
03
AI apps / “wrappers” are built on top
They wrap the model in a ready-made application for one specific problem.
04
The same can be built into your own processes
Data protection and compliance
For business / enterprise use, providers give contractual and technical data-protection guarantees; under these plans, business data is not used to train their general models.
Data handling and infrastructure can then be tailored to your needs:
The same AI capacity can be used in a chat interface, in a ready-made AI app, or fully integrated into a custom process.
Maturity levels
Levels of AI adoption
AI can be used at several levels: from simple chat to operations integrated across the whole workflow.
Level 1
Chat-based use
- Simple questions and answers
- Summaries, drafting, analysis
- Individual use
Level 2
Internal, controlled AI environment
- Shared workspace
- Uses internal knowledge and files
- Skills, shared instructions and organizational context
- More controlled data handling
Level 3
Custom workflows and agentic solutions
- AI built directly into the workflow
- Systems and data sources connected
- Automated and semi-automated steps
- Custom build and operator interface
Chat → shared AI environment → integrated AI operations
Comparison
AI-assisted platforms vs. AI-native operations
Specialized AI tools support individual tasks well. AI-native operations connect the entire process.
Point solution
AI-assisted platforms
Example: DataSnipper or another audit AI platform
- Optimized for one specific problem
- Limited functionality, closed solution
- Standard features
- Typically covers one part of the process
Point solution – supports one well-defined stage of the work
Operating layer
AI-native operations
The audit process itself is at the center — not a single AI tool.
Fully customizable, freely extensible, ready to test within 2 weeks.
AI-native operations are not another tool in the stack, but a connecting operating layer above your existing systems.
Audit Intelligence – demo
The foundation layer of AI-native audit
Audit Intelligence builds a shared, continuously updated knowledge base from all relevant client material, on which custom working papers and processes can be built.
How it works
- Automatic document processing
- Content chunking and indexing
- Vector representation
- Segmentation by metadata
- Retrieval of relevant content with recall / retrieval logic
- Answers generated from the original sources
Knowledge base features
- All client material in one place
- Handles many file formats
- Updates automatically
- Email and storage integration
- Can draw on audit working papers too
- Segmented by client and audit period
- Separated by access rights
One client – one continuously updated audit knowledge base
Demo
Curious how this works in practice?
In a 30-minute online demo we walk through how AI-native audit operations are built around your own processes.
In the demo we show
- 01
Audit Intelligence
How the per-client audit knowledge base is built – with unlimited users and unlimited documents.
- 02
Audit planning
A pre-built methodology connecting closed internal and open external information sources.
- 03
Vouching
Vouching and audit testing, from cost analysis to revenue testing.
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