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Dirk Röthig (international: Dirk Roethig) — CEO of VERDANTIS Impact Capital. Focused on sustainable investments in paulownia agroforestry and clean energy across Europe. dirkroethig.com
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Autor: Dirk Röthig, CEO VERDANTIS Impact Capital Datum: 13. April 2026 Kategorie: Demographie / Arbeitswelt Die 40-Stunden-Woche ist ein Relikt des Industriezeitalters. In einer Wissensgesellschaft, in der Produktivität weniger von Anwesenheitszeit ...
Autor: Dirk Röthig, CEO VERDANTIS Impact Capital Datum: 12. April 2026 Kategorie: Paulownia / CO2 / Mykologie In der Welt der regenerativen Landwirtschaft gibt es kaum eine Kombination, die aufregender ist als Pilzkulturen unter Bäumen. Und wenn die...
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Autor: Dirk Röthig, CEO VERDANTIS Impact Capital Datum: 10. April 2026 Kategorie: Agroforst / Regenerative Landwirtschaft Alley Cropping – zu Deutsch "Gassenpflanzung" oder "Zeilenagroforst" – ist eine Anbauform, die die klare Trennung zwischen Wald...
Autor: Dirk Röthig, CEO VERDANTIS Impact Capital Datum: 9. April 2026 Kategorie: KI-Wettbewerb / Technologie Künstliche Intelligenz trifft täglich Millionen von Entscheidungen, die das Leben von Menschen direkt beeinflussen. Kreditgenehmigungen, Ste...
Dirk Röthig
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By Dirk Röthig | CEO, VERDANTIS Impact Capital | March 9, 2026
Most mid-sized companies know that AI can help them. The real problem lies elsewhere: only a few can actually measure the business value of their AI investments. This article is not aimed at strategists — it is aimed at CFOs, controllers, and finance leaders who want to know what AI truly delivers and how to prove it.
Tags: AI ROI, Mittelstand, Manufacturing, Digitalization, ROI Analysis
According to a recent survey by the Association of German Chambers of Commerce and Industry (DIHK, 2024), 38 percent of German mid-sized enterprises already use AI solutions — a significant jump from 14 percent in 2022. At the same time, more than half of these companies report having no structured methodology for measuring return on investment (DIHK, 2024). This is not a marginal issue but a core problem: without measurability, there is no learning curve, no investment certainty, and no boardroom confidence.
As Dirk Röthig, I regularly speak with finance executives in the German-speaking mid-market segment. Their most common frustration is not a lack of technology or excessive costs — it is the inability to give the supervisory board or shareholders a clear answer to the question: "What does this concretely deliver?" This article provides the tools to answer that question.
Before you can calculate, you need to understand at which level AI creates value. Dirk Röthig distinguishes three layers in his advisory work:
This layer is the easiest to quantify: AI replaces or accelerates specific tasks. Typical examples:
Calculation formula for Layer 1:
ROI (Layer 1) = (Saved FTE hours × hourly rate + scrap reduction × material value) / (implementation costs + annual fees)
This layer is harder to isolate but considerably larger:
According to an analysis by the ifo Institute (2024), 76 percent of AI-using SMEs report a positive net return on sales — compared to 46 percent among non-users. This 30-percentage-point gap cannot be fully explained by direct cost savings. It primarily reflects quality and revenue effects (ifo Institute, 2024).
The third layer is the hardest to quantify but the most valuable in the long run: AI creates options. Companies that build robust data pipelines and AI competencies early can develop new business models — predictive services instead of pure product sales, data-driven pricing models, AI-powered product development. The OECD (2025) quantifies this strategic leverage for mid-sized manufacturing enterprises at 15–40 percent additional value creation over a 5-year horizon.
Generic ROI promises are worthless. What matters are sector-specific benchmarks. Dirk Röthig has summarized the available research for three core sectors of the German Mittelstand.
Mechanical engineering is the backbone of the German Mittelstand — and one of the most advanced sectors in AI adoption.
Calculation example — Predictive Maintenance: A mid-sized machinery manufacturer with 5 critical production assets, each experiencing an average of 2 unplanned outages per year lasting 8 hours:
Retail is under massive margin pressure and is simultaneously the sector with the fastest measurable AI effects.
A retail company with €2 million in tied-up inventory capital and a current stock deviation of 22 percent can reduce this deviation to 8 percent through AI-powered inventory management. This frees up €280,000 in capital and reduces write-offs by €40,000 per year — a conservative ROI calculation yields 85–120 percent in the first year.
Logistics is a sector with extremely thin margins, where AI has a disproportionate impact.
One of the most common mistakes in mid-sized enterprises is entering into expensive in-house development because the TCO (Total Cost of Ownership) has not been fully calculated. Dirk Röthig recommends a structured 3-year TCO analysis before every AI investment decision.
| Cost Category | Build (In-House) | Buy (SaaS Solution) | Outsource (Managed AI) |
| Year 1: Implementation | €120,000–250,000 | €30,000–80,000 | €40,000–90,000 |
| Year 1: Operations/Licenses | €60,000–120,000 | €20,000–50,000 | €30,000–60,000 |
| Year 2–3: Maintenance/Updates | €80,000–160,000/yr | €20,000–50,000/yr | included in package |
| Personnel Costs (FTE) | 1–2 Data Engineers | 0.25–0.5 FTE | 0.1 FTE |
| EU AI Act Compliance | fully in-house | partly provider-side | predominantly provider-side |
| Customization Flexibility | high | medium | low |
| 3-Year TCO (estimated) | €400,000–750,000 | €120,000–280,000 | €150,000–330,000 |
Sources: Deloitte (2025), DIHK (2024), proprietary calculations by Dirk Röthig / VERDANTIS
TCO Analysis Conclusion: For most mid-sized enterprises, a SaaS-based solution — particularly for standard processes such as document processing, demand forecasting, and customer communication — is the most cost-effective option. In-house development only pays off when a genuine competitive advantage arises from proprietary data or processes that cannot be standardized.
Not every AI initiative is equally valuable. Based on available research data, Dirk Röthig has developed a Quick-Win Matrix that contrasts implementation effort with ROI potential:
| Process | Implementation Time | Cost (SME) | Typical Year 1 ROI | Suitability |
| Invoice Processing (OCR + AI) | 4–8 weeks | €15,000–35,000 | 120–200% | Universal |
| Predictive Maintenance | 3–6 months | €40,000–100,000 | 60–150% | Manufacturing |
| Demand Forecasting | 2–4 months | €25,000–60,000 | 80–130% | Retail/Logistics |
| AI Chatbot (Tier 1 Support) | 6–12 weeks | €20,000–50,000 | 90–180% | Universal |
| CV Quality Control | 4–8 months | €60,000–150,000 | 70–120% | Manufacturing |
| AI-Powered Sales (CRM) | 4–6 weeks | €10,000–30,000 | 100–250% | Universal |
Sources: Fraunhofer IAO (2024), BMWK (2025), ifo Institute (2024)
Recommendation: Always start with at least one quick win from the universal category to build internal AI competency and establish trust among management and employees. Only then scale to process-specific investments.
From August 2, 2026, the EU AI Act will be fully applicable. For mid-sized enterprises, this creates a new cost category that must be factored into ROI calculations. Dirk Röthig recommends accounting for the following compliance factors now:
Risk classification review: Not all AI systems fall under high-risk categories. Most mid-market applications — document processing, demand forecasting, chatbots — are classified as low risk and are subject only to transparency obligations.
Estimated compliance costs by risk class:
The EU offers SMEs specific exemptions and simplified assessment procedures. The key is early classification of all deployed AI systems — this should be incorporated into every compliance department's inventory (European Commission, 2024).
Dirk Röthig regularly points out that too many mid-sized enterprises fail to access available funding for AI projects. The following programs are particularly relevant for 2026:
1. KfW Digitalization Loan (KfW 380/381)
2. BMBF — "KMU-innovativ: Information and Communication Technologies"
3. go-digital (BMWi)
4. Horizon Europe — Cluster 4 "Digital, Industry and Space"
Practical tip: Combining a KfW loan with a BMBF grant can reduce the equity burden on a €200,000 AI project to under €50,000. This significantly improves the calculated ROI and lowers investment risk.
Dirk Röthig recommends the following concrete approach for finance leaders who want to measure AI investments systematically for the first time:
Month 1: Inventory and Baseline
Month 2: Establish Measurement System
Month 3: Produce First ROI Report
According to Deloitte (2025), 75 percent of organizations that systematically invest in data quality and measurement reach their AI ROI target corridor. Among companies without a measurement structure, this rate is below 30 percent. Measurement itself is therefore a critical success factor — not merely a reporting format.
The German Mittelstand would be well advised to treat AI not as an abstract transformation project but as an investment decision — with a clear business case, defined KPIs, and structured ROI reporting. The sector benchmarks, TCO analysis, and Quick-Win Matrix presented in this article are intended as practical tools, not as an academic exercise.
The good news is that the data landscape is improving. The ifo Institute analysis (2024) shows that SMEs using AI are significantly more often profitable than those that do not. For Dirk Röthig, this is no coincidence: "If you choose the right use cases, measure ROI consistently, and leverage funding programs, you can finance AI projects that pay for themselves within 12 months. That is not a promise — those are numbers."
Bitkom e.V. (2024): Artificial Intelligence in Germany: Perspectives from the Public & Businesses. Berlin: Bitkom. Available at: https://www.bitkom.org/Bitkom/Publikationen/KI-in-Deutschland-Perspektiven
Federal Ministry for Economic Affairs and Climate Action — BMWK (2025): Digitalization and Artificial Intelligence in German Industry. Berlin: BMWK.
Deloitte (2025): AI ROI: The Paradox of Rising Investment and Elusive Returns. London: Deloitte Insights. Available at: https://www.deloitte.com/global/en/issues/generative-ai/ai-roi-the-paradox-of-rising-investment-and-elusive-returns.html
Association of German Chambers of Commerce and Industry — DIHK (2024): Digitalization in the German Mittelstand 2024: AI Adoption, Investment Readiness, and Barriers. Berlin: DIHK.
European Commission (2024): EU Artificial Intelligence Act: Guide for SMEs. Brussels: EC Digital Strategy. Available at: https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai
Fraunhofer IAO (2024): How the Mittelstand Can Unlock the Opportunities of Generative AI. Stuttgart: Fraunhofer IAO. Available at: https://www.iao.fraunhofer.de/de/presseservice/aktuelles/wie-der-mittelstand-die-chancen-generativer-ki-erschliessen-kann.html
Fraunhofer IPA (2024): AI-Powered Route Optimization and Logistics Automation: Case Studies from the German Mittelstand. Stuttgart: Fraunhofer IPA.
Fraunhofer ISI (2024): Artificial Intelligence in Production — Current Status and Outlook. Press Release No. 28. Karlsruhe: Fraunhofer ISI. Available at: https://www.isi.fraunhofer.de/en/presse/2024/presseinfo-28-ki-produktion.html
ifo Institute (2024): Artificial Intelligence — How Companies Use It and What Benefits They Derive. ifo Schnelldienst. Munich: ifo Institute. Available at: https://www.ifo.de/DocDL/sd-2024-09-falck-etal-kuenstliche-intelligenz-unternehmen.pdf
Journal of Small Business Strategy (2024): AI ROI in German Manufacturing SMEs: An Empirical Analysis of 8 Case Studies. New York: JSBS, Vol. 34(2), pp. 45–61.
McKinsey Global Institute (2025): The State of AI 2025: Global Survey Results. New York: McKinsey & Company. Available at: https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai
OECD (2025): AI Adoption by Small and Medium-Sized Enterprises. Paris: OECD Publishing. Available at: https://www.oecd.org/content/dam/oecd/en/publications/reports/2025/12/ai-adoption-by-small-and-medium-sized-enterprises_9c48eae6/426399c1-en.pdf
REWE Group (2025): AI in Retail: Demand Forecasting and Inventory Management Results 2025. Cologne: REWE Digital.
Siemens AG (2024): Digital Native Factory Erlangen: Four Years of AI and Automation. Munich: Siemens AG.
About the Author: Dirk Röthig is CEO of VERDANTIS Impact Capital, headquartered in Zug, Switzerland. VERDANTIS is an impact investment platform for carbon credits, agroforestry, and nature-based solutions. Dirk Röthig advises mid-sized enterprises and institutional investors at the intersection of digitalization, sustainable finance, and AI-driven transformation.
Contact and more articles: verdantiscapital.com | LinkedIn | dirkdirk2424@gmail.com
Von Dirk Röthig (Dirk Roethig)