RPA vs. Generative AI: Which is the best choice for automation?
When it comes to process automation, we often get asked: What's better, RPA (Robotic Process Automation) or the use of generative AI? While it's tempting to see one technology as *the* solution, the real answer lies in a combination of techniques, tailored to the specific challenge.
RPA and Generative AI: What are the differences?
RPA is designed to automate repetitive and structured tasks, such as filling out forms, processing data, and executing workflows. It operates based on clear rules and follows fixed processes.
Generative AI, such as language models, goes a step further. This technology can perform creative and language-related tasks, such as drafting texts, analysing complex datasets, or even communicating with customers. It is particularly powerful in situations where context and variation play a role.
While both technologies have unique strengths, it's not a question of 'either/or', but rather 'both/and'.
The power of combination
Most automation challenges require a mix of technologies:
- RPA for structuring and streamlining repetitive processes.
- Generative AI for tackling language- or context-based tasks.
- Data analysis to recognise patterns and generate insights that support decisions.
By taking a flexible and contextual approach to the challenge, you can deploy exactly the right technologies.
Example: Invoice processing
Imagine an organisation that wants to automate its invoice processing.
- RPA can be used to automatically download invoices and extract the relevant fields.
- Generative AI can help understand exceptions, such as incorrect or unclear data.
- Data analysis can recognise trends in invoices and provide advice on supplier performance.
This example shows that an interplay of technologies often offers the most effective solution.
Solution over technology
At The Automation Group, we believe that technology is a means, not an end in itself. It’s always about the solution: what does the organisation need, and how can technologies contribute to a more efficient process?
That's why we advise organisations to look at technology with a broad perspective and understand its possibilities. By cleverly combining tools and techniques, the most valuable solution emerges.
Conclusion: When it comes to automation, it's not about what's better, RPA or generative AI, but about choosing the right combination of technologies to solve the problem.
Would you like to know how we can help your organisation cleverly deploy technology? Contact us and discover the possibilities!
- Best AI tools
- GEO: discoverable in AI search engines
- Writing AI prompts
Job van den Berg is an AI keynote speaker, tech entrepreneur and author of five books on AI. He ships AI agents into production every week and delivers 150+ keynotes a year on AI agents and agentic commerce.
The short answer: by being able to say, at any moment, what each agent does, which model it runs on, which data it works with and since when. If you don't know that, you can't call an agent safe, however good the technology around it is. You capture that overview in a passport per agent, which you update with every change and review every quarter. Below is how that works and why it is more than paperwork.
Intelligent AI agents need a reliable data foundation. Identity resolution, Customer 360, data orchestration and governance: from a single source of truth to a system of context.
Top brain, workhorse or sprinter? Choose the right AI model for each task and balance quality, speed and cost with smart AI-agent orchestration.
AI can accelerate archive research, but it can also invent missing facts convincingly. How to combine the right model, strict instructions and critical verification for reliable genealogy research.