Less PoC wasteland, more impact: AI with a plan – a systematic guide
Companies are investing more in AI than ever before – but many projects fail to make it into production: While over 90% are increasing their AI budgets, around three out of four initiatives fail before reaching production readiness. The gap is less about technology than methodology, business fit, and lack of measurability.
This white paper provides a proven, five-step approach that takes AI projects from impulse to value creation: Systematically identify use cases, prioritize them with a hybrid evaluation framework, refine them into a clear PoC with scope, data, and architecture decisions, develop them iteratively in reality – and make informed decisions about scaling using review and KPI logic. This way, you avoid “solutions looking for a problem,” PoC deserts, and acceptance gaps.
The core elements are the value-effort matrix for transparent go/no-go decisions, multidimensional criteria (strategic fit, ROI, feasibility, organization), user-centered co-creation, and the early and consistent integration of new technologies into existing systems (edge/cloud, ERP/MES) and MLOps pipelines.
The conceptual example of “Maschinenbau AG” shows how the approach works in real-world manufacturing conditions: The white paper accompanies the fictional manufacturing company through all phases of use case management and recreates the individual decisions in a practical manner.
Key questions addressed in the white paper:
- What data and questions align AI use cases with the customer journey, rather than just collecting technology ideas?
- What criteria are used to prioritize use cases fairly, holistically, and transparently?
- What turns an idea into a feasible PoC? (Scope & KPIs, data requirements, architecture, PoC template, structured handover)
- How is the PoC developed agilely under real conditions – including usability tests, drift management, edge vs. cloud, and legacy integration?
- Which KPI and review logic leads to reliable go/no-go decisions and a clear plan for scaling?
- Which roles, skills, and change levers ensure adoption?
- What does a reliable infrastructure/MLOps roadmap look like – from ERP/MES connection to DevOps-based MLOps?