RPA and AI are often mentioned in the same breath, but those who confuse them are deploying the wrong tool. After 15 years of implementations in customer contact environments, we at Pegamento know exactly where each technology proves its value – and where it doesn’t. This article gives you an honest picture.
What does RPA actually do?
RPA (Robotic Process Automation) works like a digital employee who does exactly what you teach him. It logs in, copies data, fills out forms and generates reports – every day, error-free and without complaint. As long as the process is predictable and the input always looks the same, RPA is in its element.
A classic example from our practice: processing thousands of invoices per day. The bot reads the invoice number, amount and VAT, and transfers everything to the accounting system. Fast, reliable and scalable.
What RPA cannot do is deal with change. Does an invoice deviate from the expected format? Then the bot stops, or issues an error message. That’s not a shortcoming – it’s just how it works. And naming that honestly is part of a good implementation.
And if we work with AI in our processes?
AI goes a step further. Instead of following fixed instructions, AI learns from data and adapts. It understands unstructured information such as free text, images or speech, and makes connections that are difficult for humans to see.
Consider analyzing customer reviews: AI understands whether feedback is positive or negative, recognizes recurring complaints and can estimate which cases are urgent. Or predicting customer behavior in a webshop, based on historical patterns.
What we have learned in recent years: AI is not hype, but it only works when applied from the right perspective. Many organizations start too big, with too little data or unclear goals. That’s exactly where it goes wrong.
When do you choose RPA and when do you choose AI?
The choice is less complicated than it seems.
Choose RPA if the process recurs regularly, the steps are fixed and the inputs are structured. Consider data entry, system integrations or administrative operations. RPA is relatively quick to implement – often within weeks – and delivers immediate measurable time savings.
Choose AI if you are dealing with variable or unstructured input, complex decision-making, or if you want to discover patterns in large amounts of data. Expect a longer lead time and the deployment of specialists, but also insights that structurally move your organization forward.
Together they are stronger
We see the best results when RPA and AI work together. AI understands and decides, RPA executes. In customer service environments – our area of expertise – AI analyzes incoming messages, determines urgency and categorizes them. RPA then provides the right routing: urgent questions to a specialist, standard questions to the knowledge base, automatic confirmations to the customer.
At Pegamento, we call this approach Agentic AI: an evolution from executive bots to self-thinking assistants that not only follow instructions but take initiative independently. Not a buzzword, but a working approach that we put into practice every day.
What does the deployment of RPA and Agentic AI provide?
RPA typically delivers measurable results within 3 to 6 months. Cost savings of 25 to 80% on automated processes are realistic – depending on the process and baseline. AI has a longer payback period of 12 to 24 months, but can fundamentally improve decision making and processes. To get an idea for yourself of what the use of AI brings to implementation in customer contact, try our AI calculator.
Our approach: start small, with a sharply defined process and a working bot. Grow from there. No grand promises, but step by step to a smarter organization.
Frequently Asked Questions
How soon can I have an initial RPA bot live?
A simple process is often automated within 2 to 4 weeks. If multiple systems or exceptions are involved, count on 6 to 12 weeks. Our experience: start with one clearly defined process. Quick results create internal support for everything that comes after that.
What does my team need to be able to do to get started with this?
You don't need a technical background for RPA. Process thinking and logical reasoning are a good start - the rest can be learned. AI requires specialized knowledge in data and machine learning. Many organizations therefore work together with external experts in the initial phase; we regularly advise this as well.
Where does it go wrong when combining RPA and AI?
Almost always at the basics: poor data quality, processes that have not been properly mapped, or expectations that do not match reality. The pitfall is wanting to go too fast. Start with RPA for the quick wins, make sure your data is in order, and only then add AI where it really adds value. That sounds simple, but in practice many organizations skip these steps.
How do I convince my management?
With figures as well as a concrete story. Calculate what a process now costs in terms of time and people, what automation yields, and what the investment is. Preferably start with a small pilot - without a large pre-investment, but with measurable results. That's a much stronger business case than a presentation full of promises.
Does RPA also work with old systems that don't have an API?
Yes, and that's exactly what RPA is so handy at. A bot works through the user interface, just as an employee would: log in, click, type, copy. No technical modifications required. In practice, we regularly deploy RPA as a bridge while an organization works to modernize its IT environment.
What happens to my employees when AI is implemented?
RPA and AI take over repetitive work - not people. What you see in practice is that employees are freed up for work that requires more attention and insight: customer contact, analysis, process improvement. Therefore, also invest in retraining. Teams that learn to work with automation grow with it. We see that time and again with our customers.
Our experiences with RPA and AI in customer contact environments
For the past 15 years, Pegamento has been active as a developer and implementation partner of RPA solutions for customer contact environments. This has resulted in a lot of knowledge and experience with such systems. So we also know where RPA (Agentic AI) will add something to the work process and where it will not.
Serge Poppes, CEO Pegamento – “Since the inception of AI in customer experience, we have been involved in this. This is how we know that AI is not hype, but can really add something, if applied properly. Our experience did show that there is a lot of talk about it, but that it is not always applied from the right perspective.”
That Pegamento knows what it’s about when implementing AI in the work process , our references show.
