Why traditional automation alone is no longer sufficient

Hello everyone,

Many companies are already using RPA and SAP to make their business processes more efficient. In practice, however, there is often a central problem:
Automation is carried out without really understanding the processes.

The result:

  • Bots automate inefficient processes

  • SAP processes remain opaque

  • Projects deliver less ROI than expected

This is where a topic that will become increasingly important in 2026 comes into play: process mining as the basis for intelligent automation.

For IT consulting firms with a focus on RPA, SAP and project management, this opens up a decisive competitive advantage.

What is process mining – and why is it currently so relevant?

Process mining analyses real process data from systems such as SAP and visualizes how business processes actually run – not how they are documented.

Instead of making assumptions, the optimization is based on:

  • Log data from SAP

  • System events

  • real process runs

The result: complete transparency regarding throughput times, bottlenecks and manual interventions.

This is a game changer, especially in SAP-driven companies, as SAP as a central process platform provides enormous amounts of data that can be used for optimization.

The new automation strategy: first process mining, then RPA

1. create transparency about end-to-end processes

In many companies, processes have grown historically. Documentation is outdated or incomplete.
Process Mining shows:

  • Media breaks

  • Manual workarounds

  • Inefficient loops in the SAP system

2. identify automation potential in a targeted manner

Not every process is suitable for RPA.
Data-based analysis can be used to identify processes that are ideal for automation, e.g:

  • Invoice processing

  • Master data maintenance in SAP

  • Ordering processes

  • Reporting and data transfers

This prevents typical mistakes in automation projects.

3. use RPA bots strategically instead of in isolation

Without analysis, bots are often implemented selectively.
With process mining, on the other hand:

  • a scalable automation roadmap

  • a clear business case

  • measurable ROI

SAP + RPA + Process Mining: The perfect combination

Companies that use SAP benefit particularly strongly from this interaction.

Central advantages at a glance

1. data-based process optimization
SAP provides structured process data that can be used directly for process mining.

2. higher automation rate
By identifying standard processes, significantly more processes can be automated.

3. reduced error rates
RPA takes on repetitive tasks, while SAP serves as a stable system basis – a combination that significantly reduces errors.

Many automation initiatives are already demonstrating that the combination of SAP and RPA makes processes faster, more efficient and more traceable.

Effects on project management in automation projects

An often underestimated factor: the role of project management.

Modern automation projects are no longer pure IT projects, but transformation projects.
Process mining also changes the project structure:

Classic approach (outdated)

  • Workshops

  • Interviews

  • Process assumptions

  • Implementation

  • Rework

Data-driven approach (best practice 2026)

  • Process analysis via mining

  • Prioritization according to business impact

  • Agile RPA implementation

  • Continuous monitoring

This significantly reduces project risks and ensures greater acceptance in the specialist departments.

Typical challenges – and how consulting firms can solve them

1. unclear process landscapes

Solution: Systemic process analysis based on SAP data

2. lack of automation strategy

Solution: End-to-end automation roadmap instead of individual solutions

3. resistance in specialist departments

Solution: Transparent visualization of actual processes (change management)

A structured consulting approach is crucial here, as technological expertise alone is not enough – process understanding and project methodology are equally necessary.

Concrete practical examples of data-driven automation

There is particularly great potential in:

  • Finance & Controlling (e.g. invoice verification in SAP)

  • Supply Chain Management

  • Purchasing & Logistics

  • Master data management

  • HR processes

Here, the combination of SAP, RPA and process mining not only increases efficiency, but also provides strategic competitive advantages.

Why 2026 is the right time for this approach

Digitalization is evolving from pure automation to intelligent, data-based process control.
While RPA has long been considered an entry-level technology, the next evolutionary stage is now emerging: hyperautomation with a data-driven decision-making basis.

Companies that take this step early:

  • avoid bad investments in the wrong automations

  • increase your ROI sustainably

  • create scalable process structures

Conclusion: From automation to intelligent process optimization

The future of automation no longer lies in the pure use of bots, but in the intelligent combination of:

  • SAP as data and process core

  • RPA as an automation technology

  • Process mining as a strategic basis for decision-making

This represents a clear opportunity for IT consulting firms:
Those who provide strategic support for data-driven automation position themselves as long-term digitalization partners rather than pure implementation service providers.

Now is the ideal time to rethink automation projects – structured, data-based and closely interlinked with SAP and professional project management.

See you soon and good luck!
Your amotIQ solutions team