Integration of Al in automation:
From challenge to real business value

If you look at it in practice:

• Automated tasks exist, but no one sees the entire end-to-end flow – so bottlenecks go unnoticed.
• Bots fix minor bugs, while 20% critical variations are still done manually.
• The AI pilot exists, but no one really measures how much impact it has on process, quality, and cash flow.

Data from the industry says the same: genAI is growing, the benefits are there, but the "high performers" are leading the way - those who not only introduce AI, but combine it with data, good risk control and changing the way of working.

Three layers of automation

1. Transparency - process/task mining as a basis
   Without clear logs and controls, automation is guesswork. Process/task mining extracts real flows from ERP, CRM and other systems, shows variations and measures bottlenecks. In practice, it is often the first "aha-moment" when you realize that a process you thought was simple, is not at all.
2. Execution - from RPA to task-centric automation
   It's no longer just about RPA typing data into a system. Task-centric automation focuses on the stability and management of task automation, but also on flexible integration and orchestration. In short, the bot works smarter, not harder.
3. Intelligence – LLM and AI Agents
   Now comes the fun part. AI agents can read emails, PDFs, attached documents, suggest the next step and work together with humans. We can already see that this is the future – agents instead of just working, they actually understand the process and help people be faster and more accurate.