Shifts in how operations work: –Best practices propagate automatically across similar assets and sites. –In practice, site leadership typically validates propogation ahead of broader deployment. –Operator actions, overrides and recovery steps are captured at the company level. –AI models are periodically retrained and updated using execution feedback, adjustments and setpoints aggregated across locations. –AI connects signals across factories, warehouses, suppliers and transport to coordinate network-level adaptation. Organizational changes observed: –Human-AI interfaces are redesigned to capture decision rationale and exception handling. –Continuous improvement increasingly harnesses digital tools, with insights embedded directly into execution logic. –Cross-site coordination increasingly incorporates AI-derived performance data into continuous improvement forums. –Workforce development may include efforts on preserving institutional know-how amid turnover. Early vs advanced adopters: –Early: Share benchmarks, key performance indicators (KPIs) and standard operating procedures across sites to reduce process variability and encourage knowledge exchange. –Advanced: Automatically retrain and deploy orchestration logic based on execution outcomes.2.4 From one-speed execution to outcome-driven, continuous and network-wide improvement CASE STUDY 9 Scaling learning and performance with agentic AI Essity embeds agentic AI into high-volume procurement and finance workflows to move beyond static automation towards continuous learning. Agents learn from exceptions, feedback and human judgement, turning individual decisions, such as pricing dispute resolution, into training signals that drive double-digit productivity gains and enterprise-wide improvement.17 CASE STUDY 10 Translate local learning into network-wide knowledge Every warehouse or production site has its own operational DNA – layouts, workflows and constraints that make it unique. Claryo’s AI agents adapt to each facility, learning its specific patterns while carrying transferable insights across sites. This creates a balance between local precision and global scalability, ensuring that each implementation reflects on-the-ground realities while benefitting from enterprise-wide learning.18 Organizational Transformation in the Age of AI: How Organizations Maximize AI’s Potential 18

Organizational Transformation in the Age of AI How Organizations Maximize AI%27s Potential 2026