AI in eastern Germany: how SMEs can close the gap
5 min read
More than half of German companies now use AI actively. In eastern Germany, ifo says it is only about two fifths. The east is thus roughly where the west was a year ago. For managing directors that means the productivity lever of AI is distributed unevenly and becomes a planning issue for 2026 and 2027.
Key takeaways
- About 40% vs majority: Eastern German firms use AI less often than western ones. According to ifo the gap runs across sectors and eastern states.
- SMEs decide the gap: ifo researchers see smaller and mid-sized firms as the key to whether the gap shrinks or sticks.
- Five practice levers: Use-case priority, data base, budget cap, partner choice and measurable KPIs separate demo pilots from real productivity gains.
Related:When a German AI model pays off / Shadow AI in the mid-market
What the ifo numbers actually say
The regionalised ifo business surveys of July 2026 paint a clear picture. Nationwide more than half of companies use AI actively. In eastern Germany it is about two fifths, roughly 40 percent. According to ifo the eastern economy is thus about one year behind the west.
Joachim Ragnitz of ifo Dresden stresses that the gap shows up across all economic sectors and all eastern German states. Productivity upside remains unused in many places. Robert Lehmann adds that use has also risen strongly in the east between 2023 and 2026. At the same time the share of firms that still leave AI aside stays higher than in the west.
~40%
of eastern German companies use AI actively
ifo business surveys, July 2026 (about two fifths)
Why the gap matters in the executive suite
In many firms AI is already an operational lever for cycle time, offer quality and staffing. Whoever automates the same process later often risks disadvantages in speed and capacity versus competitors who already run it productively.
For mid-market firms in the east this becomes a local location question. Skills shortages meet productivity pressure. If competitors deliver the same order with less manual rework, price pressure shifts. The ifo finding is therefore a signal for investment planning in 2026 and 2027.
Five practice levers (editorial, not ifo)
ifo finding: whether the gap shrinks depends mainly on smaller and mid-sized companies. From that the editors derive five steering levers that work in practice.
1. Pick a bottleneck process, not a showroom use case. Start where time or errors hurt measurably: quoting, invoice checks, service tickets, quality documentation. A process with 20 repeats per week beats three PowerPoint demos.
2. Data before model. Without clean master data and clear repositories every tool becomes unreliable. First name the two or three systems that feed the process. Then clarify export or API path. Only then choose the model.
3. Budget with a cap and a KPI. Set a fixed monthly budget for the first use case and a metric that decides after 90 days: minutes per case, error rate, cycle time. Without a KPI AI remains a cost line with good gut feeling.
4. Partners with industry and shop-floor proximity. Many SMEs mainly need an implementer who understands ERP, DMS and data protection, not a full in-house ML stack. Check references of similar size and sector. Make sure knowledge stays in-house and reaches operations.
5. Make shadow AI visible. Where staff already use ChatGPT or similar tools privately, data and quality risks already exist. Better: offer an approved, logged path and pull informal use into a formal setup.
What you can do in the next 30 days
- Week 1: List three bottleneck processes, estimate time and error cost, pick one.
- Week 2: Data sources and privacy check (personal data? trade secrets?).
- Week 3: Pilot with a small team, budget cap and weekly KPI review.
- Week 4: Go/no-go: scale, adjust or stop. No third “another pilot” without results.
Anyone who runs these 30 days seriously ends with either a proven productivity gain or a clear view of where the data base is still missing. Both beat another non-binding AI workshop protocol.
FAQ
How large is eastern Germany’s AI gap according to ifo?
Eastern German firms use AI actively at about two fifths (around 40 percent); nationwide active use is above half. The east is thus roughly at the west’s level of one year ago.
Does the gap only affect a few sectors?
According to ifo the gap appears across all economic sectors and all eastern German states. It is a structural adoption pattern across industries.
Why are SMEs decisive?
According to ifo it mainly depends on smaller and mid-sized companies whether the gap narrows. That is where many productivity reserves sit and where the first solid use cases are often still missing.
Do you need your own AI team for this?
For the first use case rarely. What matters more is a clear process, clean data and a partner or tool that connects to ERP and privacy. A fixed KPI and budget cap often replace an expensive shadow organisation.
What is the most common mistake at the start?
Starting too broad and measuring too little. One bottleneck process with a 90-day KPI beats five parallel pilots without a target metric. Prove first, then scale.
Editor’s picks
- When a German AI model really pays off
- Shadow AI in the mid-market: what informal use reveals
- AI in the mid-market: the bottleneck sits in legacy systems
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