Enterprises have invested, experimented, and launched pilots. The result? Most have started — but only a minority has truly scaled.

Our new benchmark study, based on 500 IT decision-makers at german companies with 2,000+ employees, shows precisely where the transformation stands, what's really blocking scale — and what AI Champions do differently.

Four questions that are crucial to market share today:

• Why do 35% of companies put at most one quarter of their AI pilots into production — and what's really behind that?
• What separates the 21% AI Champions from the 62% AI Mainstream — and can the gap be closed?
• Why do 74% have an AI strategy, but only 34% are actually able to use it to drive results?
• How do you move from "agents in pilots" to "agents in core processes" — and who's already there?
  

• The Type Check: 21% of AI Champions are already scaling. 62% AI Mainstream is still searching for the foundation. 
  *Focus on key finding; see the study for supporting data. 

• The Governance Gap: 74% have a documented AI strategy. Only 34% have tied it to KPIs — making it truly actionable.

• The Real Brake: IT infrastructure complexity (38%), legacy system integration (30%), lack of know-how (34%). Budget and ROI uncertainty? Ranked far lower.
  

• Agentic AI. Ignited, Not Yet in Gear: 76% are testing AI agents. In production across core processes: 19%. Productive depth concentrates at companies with more mature governance — no coincidence.
  *Focus on key finding; see the study for supporting data.

• ROI: Positive — but Selective: 75% of companies that measure report a positive return. Excluding failed projects means counting only the survivors. The picture is good, but it's not complete.
  

AI Champions didn't excel in just one thing. They moved all eight dimensions simultaneously — and that's precisely what sets them apart.

The correlation between organizational breadth and AI impact is strong (ρ = 0.72) and holds stable across industries. No single lever explains the lead. The complete package does.

The Eight Readiness Dimensions: AI Strategy · Data Governance · Cloud & Platform Maturity · Data Domain Coverage · Standardized Processes · Change Management · Budget Stability · Data-Driven Decision Culture

The Three Effectiveness Metrics: PoC-to-Production Rate · ROI Assessment · Day-to-Day Anchoring

We are Business-Focused Techies. We make sure enterprise AI doesn't just shine in the lab — it frees up teams and delivers productive, measurable value. With over a decade of cloud-native expertise, we turn technical debt into productive assets.

• Industrialization of Operations: Replacing manual, ad-hoc processes with automated MLOps pipelines establishes a stable framework for scalable and reproducible AI outcomes.
• Activating the Organization: Technology alone doesn't scale — people do. We anchor adoption as a business management metric, not a communications task.
• Governance for What Comes Next: AI agents don't need a better model. They need an organization mature enough to carry them. We build the control model before operational autonomy increases.
  

Academic precision meets industrial reality. Prof. Dr. Jan Kirenz teaches and researches at Stuttgart Media University — a recognized expert in AI transformation, data-driven business management, and MLOps frameworks.

As the lead author of this study, he ensures that data doesn't become just slides — but defensible, evidence-based strategies for the enterprise sector.

AI impact is not created by a single magic factor, but by the breadth of organizational maturity.

DESIREE BREIDENBACH

Change Management Expert

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JOACHIM ROSSKOPF

Partner Zoi

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