Guides on AI Automation for Companies
Structured, evergreen articles on the questions that come up sooner or later in every SME project: When does an AI pilot pay off? AI agent or classic workflow? What does a realistic ROI model for AI automation in companies actually look like?
No hype. No buzzword collections. Instead: what we've actually learned in DACH consulting and implementation projects.
Available guides
What an AI pilot in companies really needs
Most AI pilots don't fail because of the technology — they fail because of scope, ownership and the hand-over into regular operations. What really matters before the first project starts.
AI agent or classic workflow? When each one pays off
AI agents are one of the most overused terms of the past few years. An honest decision framework: when an agentic approach is the right choice — and when a classic, rule-based workflow remains the more robust tool.
When AI automation in companies really pays off
A realistic ROI model for firms with 20–500 staff: which assumptions hold up, which don't, and which use cases pay for themselves within 4–8 months. With a concrete ROI formula and common mistakes.
Document automation: where it actually works in practice
Which document types can be automated robustly today, which edge cases stubbornly stay manual — and how a pilot is set up in practice. With a three-tier escalation logic.
ERP/CRM integration without breaking your systems
How to embed new AI workflows into grown system landscapes without breaking existing processes. API-first, no platform lock-in, with clear maintenance ownership after go-live.
Managed AI operations: what really comes up after go-live
What actually comes up in the first twelve months of operation — and which tasks companies rarely estimate realistically beforehand. Drift, interface maintenance, escalation shares, honest operations reports.
On-premise or cloud AI? When each direction makes sense
A sober comparison of on-premise, EU-managed cloud and hybrid: data sovereignty, GDPR, three-year costs, latency, operational overhead. No ideology, with a quick-decision matrix.
AI governance in practice: what companies really need
Governance beyond consulting theatre. Five building blocks of pragmatic AI governance, compatible with the EU AI Act, GDPR, ISO 42001 and NIS2 — without 200-page frameworks.
Glossary & fundamentals
Compact definitions of the key terms around AI automation in companies — AI agent, workflow automation, document automation, ERP integration, on-premise, managed AI operations, AI governance, monitoring, rollback, exception routing, drift, confidence threshold, and GDPR in the AI context.
Go to Glossary →In preparation
More guides on topics that keep coming up in practice. Published step by step — no roadmap promises, no quarterly planning. We publish once the content is genuinely substantial.
What companies should clarify before their first automation project
The four operational questions that determine project success — before any tool is even selected.
Customer service automation without brand damage
Where AI carries customer service, where it does damage — and how hand-over to a human works.
Got a specific question instead of a general guide?
In a free initial call, we answer the most important questions for your specific company. Within 48 hours, in writing, at no upfront cost.
Request a free initial call