Everyone talks about AI. But who dares to look at what it really takes to make it work?
AI offers opportunities for automation, efficiency, and new ways of working.
But success doesn’t depend on technology alone.
An organization that wants to use AI effectively starts with understanding:
- How work really gets done
- Which systems are in use
- Where the biggest bottlenecks and opportunities lie
Tip: start small, but start with mapping reality. Without this, you’ll quickly hit a dead end.
This isn’t unique to AI. The same applies to ERP projects: without insight into processes, systems, and information flows, you’ll inevitably get stuck.
AI demands that same honesty.
An assessment reveals where friction exists and where there’s room for improvement, automation, and innovation.
To make these bottlenecks and opportunities concrete, a structured look at the organization helps.
What helps to really understand reality?
An objective view of your digital organization forms the foundation. Look at where processes, systems, and information flows intersect, where bottlenecks appear, and where opportunities exist. Important areas to start with include:
- Process insight
- What exists only in procedures, and what only in people’s heads?
- Information flows
- Are data duplicated? Does fragmentation lead to errors or delays?
- Organizational structure
- How do teams collaborate, and how is information shared?
- Application landscape
- Which systems are in use, why, and by whom?
- Shadow IT
- Which unofficial solutions fill gaps, like Excel, Asana, or Mendix?
- Management vision
- How does leadership view digital growth and the role of AI?
This assessment exposes blind spots and shows where AI can immediately add value. It forms the foundation for the next steps toward a smarter, more flexible, and efficient organization.
The approach: from optimization to intelligent orchestration
A successful AI transformation doesn’t happen overnight.
It requires a step-by-step approach: understand → optimize → pilots → orchestrate.
Organizations that try to do too much too quickly inevitably run into disappointment.
Guideline: clean up and strengthen before innovating.
Phase 1 – inventory: understanding processes and systems
Map processes, systems, and information flows.
Where are the bottlenecks? Where are opportunities for improvement?
Phase 2 – optimization: make the most of what exists
Maximize the use of existing systems and processes.
Optimization is a necessary step before introducing new technology.
Phase 3 – identifying gain creators: small, measurable wins
Where can AI add immediate value?
Repetitive, predictable tasks with measurable outcomes are ideal to start small, smart, and visibly.
Phase 4 – AI pilots: learn and scale
Start small with clear goals and measurable results.
Learn quickly what works. Scale only once the value is tangible.
From isolated pilots to real orchestration
When AI solutions are integrated with existing systems and processes, a coherent ecosystem emerges.
Humans and machines reinforce each other, making work more efficient and smarter.
A successful approach requires vision, patience, and step-by-step implementation.
It’s not just about technology, but about an organization’s ability to use AI sustainably.
The real potential of AI
Perhaps the real promise is this: not the technology itself, but the clarity it forces.
AI doesn’t make you work smarter. It shows where you’re not working smart yet. It’s a mirror.
Do you dare to look and act on what you see?