In brief
- Traditional software is strong when rules, data fields and expected results are unambiguous.
- Generative AI helps with language, images and variable tasks whose every formulation cannot be programmed in advance.
- The same input can produce different plausible outputs from a generative model.
- Production systems often combine fixed process logic with narrowly bounded AI steps.
How are results produced?
In traditional software, developers define rules and calculations. If input data and system state are the same, the result is generally reproducible. A payroll system calculates a deduction from stored rates; a database returns a stored value.
A generative model has instead learned patterns from many examples. It calculates a likely fitting output for the current context. This enables free language and flexible processing, but it also means an answer can sound plausible without being correct.
- Rule-based software: defined logic and expected outputs
- Generative AI: probabilistic output from model, input and context
- Business application: additional permissions, data, checks and process rules
Which tasks fit each approach?
The task should determine the approach, not how new a technology is. Exact calculations, bookings and mandatory validation belong in deterministic software. Generative AI fits work where language or other unstructured content must be understood, condensed or reformulated. Many real solutions are hybrid: AI handles the variable part, while traditional software validates the result and controls the binding action.
| Assessment question | Traditional software | Generative AI | Hybrid solution |
|---|---|---|---|
| Result | A defined value, status or process step | A variable draft, classification or summary | AI proposes; software accepts only validated fields or actions |
| Predictability | Generally reproducible for the same state | May phrase or weigh the same input differently | Variation is allowed while binding limits remain deterministic |
| Testing | Expected value for each rule, state and edge case | Quality across representative cases, error types and ranges | End-to-end test from input to the effect that is actually executed |
| Error handling | Exception, error code or defined fallback path | Detect uncertainty, unsupported claims or unsuitable output | Software blocks, asks a question or routes to an accountable person |
| Typical task | Calculate a price, validate a field, enforce a permission | Summarise text, classify a request, draft a response | Read an invoice, validate values and post only after approval |
Why does generative AI need different quality controls?
Traditional software tests known rules and states. Generative AI must also be assessed across differently worded, incomplete and contradictory cases. One successful demo says little about reliability in day-to-day use.
- Use representative test cases rather than one expected input
- Measure false, unsupported and disallowed outputs separately
- Determine the impact of an error and add proportionate approvals
- Rerun the same tests after model, prompt or data changes
What does a robust combination look like?
A robust architecture lets AI decide only where linguistic flexibility is needed. Identity, permissions, thresholds, calculations and irreversible actions stay in controlled software. A model can then formulate flexibly without replacing binding business rules.
- Step 1
Decompose the task
Separate language interpretation from exact rules and actions.
- Step 2
Bound the AI
Define permitted inputs, sources and output formats.
- Step 3
Let software control
Validate permissions, numbers, approvals and system changes deterministically.
- Step 4
Test the whole system
Evaluate the complete workflow, not only the model.
Example: processing a supplier invoice
Generative AI reads an invoice with a variable layout and proposes how to map its line items. Traditional software validates supplier number, tax, purchase-order reference and amount limits. A discrepancy does not trigger an automatic booking but a defined approval process. AI handles variable document interpretation; binding financial logic remains deterministic.
What to remember
Use generative AI for variation and traditional software for certainty. The greatest value often comes from the carefully controlled interface between the two.
Sources and further reading
These primary sources provide further detail on definitions, technical foundations or responsible use.
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