Do you know where you can scale AI in your organization?
The AIMRI assessment shows, fact-based, where scalable value emerges and which prerequisites are missing. The white paper is currently available in German, with an English edition coming soon.
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More InformationSix clusters where AI makes the difference.
Cluster 1: Scaling stalls without strategy & portfolio steering
Without a strategy, even the best technology won't help. In many companies, use cases emerge where energy and enthusiasm are present, not where the greatest value contribution for the company lies.
Cluster 2: Missing operating models keep AI stuck in pilots
AI stays in the lab as long as the organization isn't built up alongside it. Many AI programs produce impressive concepts and prototypes but then fail at the transition into productive operations. The reason rarely lies in technology, but in a structural gap.
Cluster 3: Data foundations are often better than their AI usage
Paradoxically, the data foundations of many industrial companies are better than their actual AI usage suggests. Although data warehouses, cloud platforms, and structured data models exist, AI adoption remains low despite substantial investments in these platforms.
Cluster 4: Focus on quick wins instead of core transformation
GenAI applications for knowledge work, search, summarization, and translation often ramp up quickly and gain rapid visibility. But this entry point clouds the view of the real goal: deep end-to-end optimization of business processes.
Cluster 5: Operations lead, while PLM & supply chain lag behind
AI initiatives in industrial companies frequently concentrate on the shop floor and immediate production. AIMRI analyses show despite solid digital foundations, PLM is often the least AI-developed area – even though considerable potential exists here.
Questions on the path to AI Readiness.
We have already invested in cloud, a data lake, and initial AI tools. Doesn't that already make us “AI-ready”?
Not necessarily. This is precisely one of the most common misconceptions: the decisive difference lies not in the technology, but in five dimensions: strategy, organization, data governance, governance/responsible AI, and productive implementation. The data foundations of many industrial companies are in fact often better than their actual AI usage; the blind spot lies between existing infrastructure and real scaling.
Our pilots are running well so why aren't we rolling them out at scale?
Because pilots take place in a protected environment: a motivated team, limited scope, permitted extra effort. Scaling then fails not because of the AI itself, but because of missing industrialization, unclear ownership, missing business KPIs, undefined operating processes, and patchy governance. AI stays in the lab as long as the operating model isn’t built up alongside it.
Where exactly should we start if we want to see quickly effective results?
Not necessarily where everyone is looking – the shop floor. Supply chain and PLM typically offer more structured data, clearer standards, and shorter scaling paths. In the supply chain, levers such as supplier scanning, demand forecasting, and faster procurement decisions act directly on capital tied up, so not only efficiency, but a measurable cash effect. The build-up path toward production is planned in parallel and with a long-term perspective.
Why is production so demanding, when it is after all the heart of value creation?
Because it is structurally the hardest to scale historically grown, customer-specific environments with heterogeneous machine fleets, varying data depth, and locally optimized processes make reproducible solutions difficult. Not every use case – e.g. predictive maintenance – pays off in every context. This requires a multi-year build-up path with data, standardized interfaces, and clear responsibilities.
Should we continue to rely on digital twins, or is there a better way?
The white paper sees a paradigm shift. Complex digital-twin constructs, with their high modeling, integration, and maintenance costs, are increasingly out of economic proportion to their benefit, especially in heterogeneous production environments. Flexible AI agents can be integrated faster, improved iteratively, and put into productive use earlier. The prerequisite remains: governance and operating model must be clarified from the start.
What are the first steps we as a leadership team now need to decide?
Three decisions are on the CEO agenda: first, understand AI as a business and define three to five KPIs (cost, cash, quality, risk, time-to-market). Anyone who cannot answer this clearly should not yet invest. Second, manage AI as a portfolio, with clarity on the starting points. Third, industrialize scaling through operating models, data, governance, and operations. The first step here is not a tool, but clarity on your own maturity level.
