Writing Tools
Part of Evaluating AI tools for business tasks
General-purpose assistants versus specialised AI products
Understand when a flexible assistant or a task-specific AI product fits better, with documented workflow examples and limits.
Choose between a general-purpose assistant and a specialised AI product by identifying the output the work needs and what must happen to it next. An assistant can help with varied drafting, analysis and questions. A specialised product is built around a narrower workflow. Neither guarantees a better result: the useful choice depends on the task, required controls and receiving process.
Compare the routes
ChatGPT Business is an example of a conversational assistant that can work with supported uploaded documents, spreadsheets and presentations.
Microsoft Azure AI Document Intelligence's prebuilt invoice model is an extraction component: it analyses supported invoice-related documents and returns structured fields and line items.
These are different workflows, so their feature lists do not amount to an accuracy comparison.
| Question | General assistant route | Specialised extraction route |
|---|---|---|
| Main job | Ask varied questions or draft and reshape material | Turn a supported document into defined fields |
| What to inspect | The answer, its source meaning and unsupported claims | Each field and line item before it enters a record |
| Work that remains | Edit or approve the response | Map, review and approve extracted data for business use |
General-purpose assistant vs specialised AI extraction: workflow comparison
- Main job
- Ask varied questions or draft and reshape material
- What to inspect
- The answer, its source meaning and unsupported claims
- Work that remains
- Edit or approve the response
- Main job
- Turn a supported document into defined fields
- What to inspect
- Each field and line item before it enters a record
- Work that remains
- Map, review and approve extracted data for business use
Where ChatGPT Business may fit
Consider it when staff have several approved, text-led tasks and need a conversational way to examine or transform supplied material. Check the intended account and files.
Where Document Intelligence may fit
Consider Microsoft's prebuilt invoice model when the job is to extract invoice fields and line items for a defined downstream process. Microsoft documents structured JSON output, an API and a Studio route.
Using that output in a finance system still requires a receiving workflow, field mapping and review.
The prebuilt model accepts supported document formats, rather than arbitrary office files. Check the selected version and document types before testing your files.
Implementing Document Intelligence for invoice processing
- Select prebuilt invoice modelUse Microsoft Azure AI Document Intelligence’s prebuilt invoice model for supported formats
- Upload compliant documentsEnsure files are in supported types (e.g., PDF, JPEG, PNG)
- Retrieve structured JSON outputExtract fields and line items via API or Studio interface
- Map data to finance systemAlign extracted fields with existing business system fields
- Review and approveVerify accuracy before entry into accounting or ERP systems
Decide by the receiving workflow
For varied questions and drafts, test whether an assistant covers the approved tasks without excessive prompting and correction. For repeatable extraction into named fields, examine a specialised route and the review process around it.
If the work includes both interpreting an exception and updating a business system, assess those stages separately.
Test candidates suited to the same defined task against its permitted inputs and acceptance rules. Record accepted results, corrections, staff effort and failures in the intended account.
Assess permissions and data handling before supplying personal information.
Pre-implementation checks for AI tools in Australian organisations
- Confirm permitted input formatsCheck whether files meet requirements (e.g., PDFs only for Document Intelligence)
- Assess data handling and privacy controlsReview ATO and OAIC guidance on personal information use
- Test against acceptance rulesValidate outputs using real-world examples and documented criteria
- Document staff effort and correctionsTrack time spent editing or fixing results in production accounts
- Verify permissions and access controlEnsure only authorised personnel can upload or view sensitive data
