Intelligent automation
Robotic process automation is good at exactly one thing: repeating a fixed set of rule-based steps, clicking through a system, copying data from one place to another, the same way every time. That's reliable for structured, predictable work, and it falls apart the moment it hits something unstructured, a scanned document, a free-text email, an exception the rule never accounted for. Intelligent automation exists to close that gap, layering AI on top of that execution so the system can also read unstructured input and decide what to do with it.
In practice, that combination usually looks like RPA handling the repetitive execution, machine learning models classifying or scoring something based on patterns in the data, and natural language processing reading and interpreting text that doesn't come in a fixed format. Sitting on top of all of it is decisioning logic that routes something to a person only when the system genuinely can't resolve it on its own, rather than every time it hits anything outside a narrow rule.
The term is closely related to, and often used almost interchangeably with, AI workflow automation. Intelligent automation tends to be the older, broader term for combining RPA with AI at the enterprise level, while AI workflow automation more specifically describes a model deciding the path through a particular multi-step process. In practice, the two overlap enough that the distinction rarely matters much.
What it typically combines
- RPA, for executing repetitive, rule-based steps like data entry or copying information between systems.
- Machine learning, for classifying, predicting, or scoring based on patterns already present in the data.
- Natural language processing, for reading and interpreting unstructured text, like an email or a document.
- Decisioning logic that routes an exception to a person only when the system genuinely can't resolve it itself.
Where it shows up
- Processing an insurance claim, extracting details from a submitted document instead of asking someone to key them in by hand.
- Reading and routing a customer email based on its actual content, rather than a fixed keyword rule.
- Reconciling financial records, flagging only the mismatches that genuinely need a person's judgment.
- Onboarding a new employee, combining scripted account setup with automated document verification.
Benefits and challenges
| Benefit | Challenge |
|---|---|
| Cuts the number of exceptions that get kicked to a person, since the system can judge more of them itself. | Costs more to build than plain RPA, since it needs trained models and integration work, not just a recorded set of clicks. |
| Handles unstructured input, like a scanned document or free-text email, that rule-based automation alone can't. | An AI-driven decision inside the process is less predictable than a fixed rule, and needs its own monitoring and review. |
| Scales a process without scaling headcount at the same rate as volume grows. | A poorly trained model can make a confidently wrong call, quietly, unless a team is actively watching for it. |
Frequently asked questions
How is intelligent automation different from RPA on its own?
RPA executes fixed, rule-based steps and has no way to handle input that doesn't match what the rule expected. Intelligent automation adds AI capabilities, like machine learning or natural language processing, on top of that execution layer, so the system can also interpret unstructured input and make a judgment call rather than stopping and waiting for a person.
Is intelligent automation the same as AI workflow automation?
They're closely related. Intelligent automation is often used as the broader, more established term for combining RPA with AI, while AI workflow automation more specifically describes a model deciding the path through a multi-step process. In practice, the two overlap heavily.
Does intelligent automation remove the need for people?
Not typically. Most implementations aim to remove the repetitive, low-judgment work and the exceptions a rule clearly can't handle, while routing anything genuinely ambiguous, or anything with real consequences if it's wrong, to a person to review.
How Klarent helps
Klarent applies the same idea to testing, pairing automated execution with AI agents that read an application's behavior and make a judgment call, like whether a failure is a real regression or a stale assertion, rather than treating every failed check the same way.