When discussing business process automation, we repeatedly encounter this scenario: a company hears about AI and wants to "implement AI automation," but in reality has processes where classic RPA (Robotic Process Automation) would work better, cheaper and more reliably. Conversely, other companies run legacy RPA solutions that constantly break down because their processes are too variable. This is precisely where AI would make a significant difference.
This article is a practical decision-making guide. We will explain, What is RPA and what is AI automation, when each approach works better, how to combine them and how to proceed with migration from legacy RPA to modern AI solutions.
What is RPA and how does it work
RPA (Robotic Process Automation) is a technology that automates processes by simulates human mouse clicks on a computer. The robot "remembers" the exact sequence of steps, where to click, what to enter, where to look, and repeats this sequence precisely. The market leaders are UiPath, Automation Anywhere and Blue Prism.
RPA works well if the process is always the same. Always the same forms, always the same fields, always the same data format. The robot needs no understanding, only precise instructions. That is why RPA is most powerful where people perform repetitive, precisely defined work without exceptions.
Processes ideal for classic RPA
- Data are always the same structure - forms, spreadsheets, database outputs
- Process steps are precisely defined and do not change
- Exceptions are very rare (less than 5-10% of cases).
- Do you need auditability - deterministic process flow, every step logged
- You integrate systems that do not have an API the only way is via the UI
- Transaction volumes are high and margins for error are low (regulatory reporting).
Examples: Transferring data from one system to another, generating regular reports from templates, processing standardised forms, regulatory reports.
What AI automation adds extra
AI automation: typically a combination of large language models (LLMs) such as GPT-4 with orchestration tools like Make.com or n8n, works differently. Instead of a precise script, understands context. It can read an email and understand what the customer is requesting, even if they have phrased it in four different ways. It can process an invoice, even if the supplier has used a different format than usual. It can decide what to do when a situation arises that was not covered by the rules.
This flexibility is the key difference. AI does not follow a script - achieves the objective. If it encounters an obstacle, it seeks an alternative path. This makes it an ideal tool for processes where variability and exceptions are not marginal occurrences but the norm.
Processes ideal for AI automation
- Input data are unstructured - emails, PDFs in various formats, chat messages
- The process requires language understanding - query classification, summarisation, information extraction
- Exceptions are common (more than 20-30% of cases require a different approach).
- Rules change or are vague AI adapts; an RPA robot breaks down.
- Do you need decision-making - not only executing the step but also assessing the situation
- Targeting customer interactions, conversations, responses, personalisation
Examples: Email sorting and response, processing invoices in various formats, customer chatbot, intelligent ticket routing, feedback analysis.
Direct comparison: RPA vs. AI automation
| Criterion | Classic RPA | AI process automation |
|---|---|---|
| Input data | Structured and predictable | Structured and unstructured |
| Exception handling | Weak: the robot stops or fails | Strong, seeking alternative solutions |
| Text comprehension | None, works with patterns and positions | Excellent, understands context and intent. |
| Auditability | High, every step deterministic | Medium, requires explicit logging |
| Maintenance during changes | High risk, changing the UI will break the bot. | Low, AI adapts to format changes |
| Deployment speed | Weeks to months (complex processes) | Days to weeks |
| Best for | High-volume, structured, stable processes | Variable, language-specific, and exceptional processes |
Hybrid approaches: The best of both worlds
In practice, the most powerful solutions combine RPA and AI according to what each approach does best. Typical pattern: RPA ensures reliable integration with systems, while AI provides intelligence for data processing..
Example: Processing invoices from various suppliers
Invoices arrive by email in various PDF formats, each supplier has a different layout.
- AI layer: Reads PDF invoices (including non-standard formats) and extracts key fields, IČO, amount, VAT, due date. Handles handwritten notes or unusual layouts.
- RPA layer: Structured data from AI is entered into accounting software (Pohoda, Money S3) via a precisely defined sequence of steps. Reliably, deterministically, with full logging.
Result: AI handles input variability, while RPA ensures reliable data entry into the system. Overall accuracy exceeds 97% without manual intervention.
Example: Customer service with escalation
An AI chatbot or email agent handles 70% of inquiries automatically, answering routine questions about order status, availability and prices. Complex or sensitive cases are handed over to a live operator with a conversation summary and context. After resolving the issue, the operator records the result back into the CRM, this part is automated by an RPA robot.
Result: The customer receives a quick response 24/7; the operator deals only with cases where it is truly necessary.
Migration from RPA to AI: When and how
If you operate an older RPA solution that constantly breaks or requires extensive maintenance, it may be time to migrate. However, migration does not have to be "all at once" - we recommend a gradual approach:
- Audit existing robots - how many hours per month does your team spend on repairs? Where is exception handling highest? These processes are candidates for migration.
- Start with the exceptions - Let an RPA robot handle standard cases and add an AI layer for exceptions. Minimal risk, immediate benefit.
- Replace unstable robots Replace processes with high failure rates or UI dependencies with an AI solution built on API integrations.
- Maintain stable robots - Leave regulatory reporting and other deterministic processes where the robot performs reliably untouched. Do not replace functional systems simply to follow a trend.
Key findings from practice: Companies that replaced unstable RPA bots with an AI solution report an average reduction in maintenance costs of 60-70%. At the same time, they increased automation coverage, AI handled cases that the bot could not manage.
Decision guide: Which path is right for you?
5 questions before choosing an approach
- How structured are your input data? Consistent formats → RPA. Variable formats → AI.
- What percentage of cases are exceptions? Under 10% → RPA is sufficient. Over 20% → AI is required.
- Do your systems have an API? Yes → AI solutions are more cost-effective. No → RPA may be the only option.
- How critical is auditability? Regulatory requirements → RPA. Operational automation → AI.
- How often do processes or forms change? Often → AI is more resilient. Never → RPA is more stable.
If you still don't know where to begin, flylight.ai offers a free analysis of your processes and recommends a specific approach for your situation. You do not have to decide on your own.
Tip: Do you want to prepare your team for working with AI tools before implementation? BeeSkills.cz offers practical AI workshops for companies: from basics to advanced use in automation.
AI or RPA? We will help you decide.
flylight.ai will carry out a free analysis of your processes and recommend the optimal approach, including estimated costs and savings for your specific situation.
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