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

Shifts in how R&D operates: –AI expands the number and diversity of ideas, designs and hypotheses explored early. –AI integrates signals from customers, operations and regulators to improve problem framing and prioritization. –AI will expand the option space by automating early search and screening while scientists and engineers remain central and become stewards of judgement, focused on problem framing, evidence quality and strategic trade-off.Organization changes observed: –Capturing this opportunity requires shifting how decisions are made, who owns them and how teams collaborate early in the R&D process. –Establish dedicated AI-enabled discovery teams combining domain experts, data scientists and product owners. –Redefine scientist and engineer roles to reduce manual searches and increase evaluation and prioritization. –Introduce shared front-end standards (data formats, ontologies, model validation criteria). –Clarify ownership for early-stage down-selection decisions. Early vs advanced adopters: –Early: Use AI to accelerate search and screening and improve protocol drafting. –Advanced: Combine generative design, simulation and lab automation to run high-throughput discovery.3.1 From narrow exploration to expanded option space Example use cases: –Scanning target and patent landscapes –Designing and prioritizing candidate molecules –Automating the setup of assay and experiment protocols –Interpretating results with evidence packaged for the next “decision gate” CASE STUDY 11 Using AI to expand the chemical search and compress the hypothesis to viable molecule process Merck KGaA is accelerating early-stage pharmaceutical R&D by using AI to digitalize and automate compound optimization. Through an AI-augmented in-silico platform, generative models can virtually screen over 60 billion potential chemical targets in minutes, narrowing options to a shortlist for human review and lab testing. This integrated workflow significantly compresses the journey from hypothesis to viable molecule, saving up to 70% in time and cost, while enhancing accuracy, efficiency and novelty in molecular discovery.26 CASE STUDY 12 AI accelerates early drug discovery by expanding the pool of viable candidates Insilico Medicine uses a generative-AI drug discovery platform to rapidly generate and explore large numbers of candidate molecules. Early predictive models and assays are used to prioritize which options merit further investigation, allowing teams to focus downstream experimentation on the most promising candidates. Across its portfolio, Insilico reports an average of approximately 13 months to nominate a preclinical candidate, with multiple programmes progressing towards clinical trials.27 Organizational Transformation in the Age of AI: How Organizations Maximize AI’s Potential 21