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

Shifts in how R&D operates: –Go/no-go decisions move earlier in the R&D process, reducing mid- and late-stage resets, increasing the share of projects that reach launch and boosting confidence that the concept matches customer needs before major spending. –Partial but richer evidence is used to assess technical, performance, manufacturability and compliance risk. –As a result, weak options are stopped sooner; stronger options receive resources earlier. Organizational changes observed: –Redesign portfolio governance with earlier, lighter-weight decision gates and explicit authority to stop work. –Improving R&D success rates requires stronger early-stage governance, with shared evaluation criteria and a shift in how teams view validation and failure. –Reframe early project termination as a success, not a failure. –Establish cross-functional risk review forums spanning R&D, regulatory, quality and manufacturing. Early vs advanced adopters: –Early: Implement structured early gates and screening to trim low-value options. –Advanced: Use continuous portfolio management systems that dynamically rebalance in near real time based on model outputs and scenario simulations.3.2 From late failure to early risk calibration CASE STUDY 13 Applying AI knowledge graphs to identify drug targets Lundbeck built a domain-specific knowledge graph for headaches and migraines, integrating 54 million electronic medical records with biological, genetic and disease data. Machine learning models were applied to predict the probability of gene–disease links in the knowledge graph and infer new connections that were not explicitly stated by the graph, allowing potential novel drug targets to be identified 80% more quickly.28 CASE STUDY 14 AI diagnoses toxicity early, enabling safe redevelopment of abandoned drug candidates Ignota Labs acquires drug candidates abandoned late in development due to safety or toxicity issues. Its AI platform identifies the molecular causes of toxicity and redesigns compounds for a second development attempt. By narrowing the number of possible biological pathways, the platform provides a clearer, safer way forward. This can return a redesigned compound to clinical trials in under two years and for less than $1 million – versus the $10 million and 7–8 years typically required.29,30 Organizational Transformation in the Age of AI: How Organizations Maximize AI’s Potential 22