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

Shifts in how R&D operates: –R&D shifts from sequential phases to short “test–learn–adjust” cycles. –Insights from experiments, production and market use feed back into discovery. –Decisions are updated continuously as new evidence emerges, allowing flexible portfolio rebalancing. Organizational changes observed: –Evolve performance management to reward evidence quality, adaptability and learning speed. –Shift from rigid stage gate models to learning loop operating models with more frequent reviews. –Form persistent, cross-functional teams accountable for outcomes across phases. –Track velocity as a key performance metric. –Introduce data and machine learning operations (MLOps) capabilities to support continuous model updating and monitoring. –Adjust performance management to value learning speed, evidence quality and adaptability. –Consider more regular resource reallocation as a standard. Early vs advanced adopters: –Early: Introduce faster iteration cycles within existing stage–gate structures. –Advanced: Fully integrate learning systems where data continuously informs discovery, development and portfolio decisions. The transition from early to advanced adoption is driven less by technical capability than by leadership confidence in AI-supported evidence justifying real resource reallocation. CASE STUDY 17 Using end-to-end AI enablement to dramatically reduce development JLL piloted an end-to-end AI enablement – including automated gathering of requirements for code generation and testing and streamlined GitHub workflows. This delivered 75–85% time savings for frontend teams and reduced development resource needs by 30%, allowing senior engineers to focus on higher-value problem-solving and experimentation. CASE STUDY 18 Agentic AI co-researcher to orchestrate drug discovery experiments at scale SandboxAQ developed an agentic AI “co-researcher” – a hierarchical network of semi-autonomous virtual scientists that orchestrate multi-step experiments, data analyses and simulations that were once managed by human experts. The system is expected to achieve fourfold increases in project throughput, double its concurrent project capacity and unlock a 50% reduction in competition time. This agentic layer serves as the foundational reasoning engine that unlocks SandboxAQ’s broad set of modular scientific workflows and democratizes access to high-fidelity scientific simulation.3.4 From linear execution to short, evidence-driven learning cycles Organizational Transformation in the Age of AI: How Organizations Maximize AI’s Potential 24