RPA: rules and repetition
Robotic process automation is excellent at structured, predictable, high-volume tasks. It follows a fixed script, often clicking through a user interface, which makes it useful for older systems that have no API.
Its weakness is change: when a screen layout or input format shifts, a rules-based bot breaks, and it cannot handle ambiguity on its own.
- Best for structured, predictable inputs
- Automates UI steps in systems without an API
- Deterministic and auditable
- Handles high-volume repetitive work
- Brittle when layouts or formats change
- No judgment for ambiguous cases
AI automation: variability and judgment
AI shines exactly where RPA struggles: unstructured inputs like emails, documents, and images, and tasks that need classification, extraction, or summarization. It tolerates variation and handles exceptions RPA cannot.
The tradeoff is that AI is probabilistic, so it needs confidence thresholds and human review rather than being trusted blindly.
- Handles unstructured emails, docs, and images
- Classifies, extracts, and summarizes
- Copes with variation and exceptions
- Probabilistic, so needs confidence gates
- Improves with more examples
- Not deterministic by nature
Better together
The strongest workflows combine both: AI reads and decides, then RPA or a direct integration executes. AI extracts the fields from a messy invoice, and RPA types them into the legacy system that has no API.
A confidence gate routes edge cases to a person. We pick the right tool per step, not per project.
- AI extracts, RPA or an API enters the data
- Confidence gates route edge cases to humans
- API-first where possible, RPA where necessary
- Monitoring across both approaches
- Chosen per step, not one-size-fits-all
More on ai automation & workflows
Frequently asked questions
Which is right for me?
It depends on how structured and how variable your inputs are. Stable, predictable, high-volume tasks lean toward RPA; messy or judgment-heavy inputs lean toward AI. Many real workflows use both, and we design per step.
Is RPA obsolete now that AI exists?
No. RPA is still the best fit for stable, structured tasks and for driving legacy systems that have no API. AI complements it rather than replacing it everywhere.
Does adding AI make automation unreliable?
AI is probabilistic, so we build in confidence thresholds and human review for anything the model is unsure about. That keeps it controlled, and the deterministic steps stay deterministic.