AI in Action Beyond Experimentation to Transform Industry 2025

of companies report challenges in adopting AI at scale.74%Swiss Federal Railways (SBB), in partnership with a leading software solution and technology company, developed an AI-powered visual inspection solution to minimize unplanned downtime and improve maintenance efficiency. Using advanced machine learning and enhanced model training, the solution enables condition-based and predictive maintenance for critical rolling stock components like pantographs. This approach allows the company to assess wear and tear without requiring the removal of these parts from services. The upshot: inspection times are down by 60%, and inspection errors have fallen by 20-30%.26CASE STUDY 7 Embedded AI in functions Beko has developed a connected system of AI applications designed to overhaul the after-sales process. Using customer interaction insights, this system feeds data into analytics and predictive tools, which helps resolve issues quickly, suggest relevant upsell options and ensure technicians have the correct parts for a successful first visit. Additionally, it offers real-time guidance, providing technicians with AI-driven troubleshooting support and access to translated manuals for enhanced service delivery.CASE STUDY 8 After-sales multi-agent system The depth of AI adoption within organizations is in the early phases To truly transform industries and enable organizations to fully realize its benefits, AI should be adopted in alignment with core business objectives. This involves embedding AI deeply into operations, strategies and decision-making processes, moving beyond isolated or experimental initiatives. Insights from community engagement indicate that the depth of AI adoption remains largely in its early stages. Many organizations are still experimenting with AI or implementing individual use cases rather than achieving end-to-end transformation across the enterprise. While some industries appear more advanced in AI adoption, individual organizations show significant variation in the depth and scale of their efforts. Notably, 74% of companies report challenges in adopting AI at scale, with only 16% of enterprises prepared for AI-enabled reinvention.27,28 When planning AI investment and integration, companies should assess where they stand in their adoption journey, which ranges from initial experimentation to continuous reinvention. Based on feedback from the AI Transformation of Industries community, different phases of adoption are characterized by specific descriptors and metrics (see Table 1). To fully harness the potential of AI, organizations need to advance beyond the experimental phases, embracing deeper integration to realize its collaborative potential alongside potential human collaboration. This journey provides a roadmap for leaders to assess their current position and identify priority steps towards AI-enabled transformation. Early adopters and frontrunners play a crucial role as benchmarks, sharing best practices and insights to guide others on this path. AI Governance Alliance: Transformation of Industries in the Age of AI 13 13 AI Governance Alliance: Transformation of Industries in the Age of AI