Artificial intelligence: definition, value and limits
Artificial intelligence Updated
Definition
Artificial intelligence describes software that solves tasks which usually need human judgement, such as understanding language, spotting patterns or proposing decisions. In process automation, AI is the building block that handles unstructured inputs, where fixed rules reach their limit. It does not replace rule-based automation but extends it with understanding.
Why the Term Matters
Many mid-market flows fail not at execution but at reading an email, a receipt or a free-text note. That is exactly where AI helps. Who places the term correctly sees that AI is only one part of the whole. Provable ROI appears only in the interplay of AI, RPA and the integration of existing systems.
When It Is Relevant
- Inputs arrive as free text, image or document.
- A flow needs a judgement that fixed rules cannot capture.
- Classification, extraction or summarisation is the bottleneck.
- The rest of the flow is already structured and rule-friendly.
When Not
When a flow is fully structured and fixed rules suffice, AI is unnecessary and costlier than needed. At very low case volumes, the effort for training, checking and operating rarely carries the value. Then pure RPA or a lean integration is the more honest choice.
Industries That Use It
- Machinery and plant engineering: classify technical enquiries and supplier documents before the structured flow starts.
- Insurance: read claims from free text and propose the right handling path.
- Logistics and freight forwarding: extract shipping papers and advices and hand them to the transport system.
- Human resources: pre-sort applications and certificates so review runs faster.
Typical Use Cases
- Read receipts and documents and extract the relevant fields.
- Classify incoming requests and route them automatically.
- Turn free text into structured data for an ERP or CRM.
- Produce a proposal that a human only needs to approve.
- Flag anomalies in a standard flow for review.
Decision Guide
| Signal | Artificial intelligence fits | Alternative |
|---|---|---|
| Unstructured inputs | an AI step in the flow | pure RPA is not enough |
| Fixed, clear rules | no AI needed | rule-based automation |
| Few cases per month | check effort first | manual stays cheaper |
| A decision is required | AI proposes, human approves | full automation is risky |
Example
A mid-market company receives orders as PDF and email in varying formats. An AI step reads the line items, RPA creates the order in the ERP, and an integration mirrors it to the CRM. Unclear cases go to a person with approval. Value is proven against the baseline measured beforehand. This describes the method, not an asserted client metric.
Service Connection
The entry is usually process automation with AI or an RPA build, and the framing the process automation consulting.
Sources
This framing describes common mid-market practice for using AI in process automation. Models, integrations and data protection depend on the case, the systems and the tools and must be checked case by case.