AI is generating tangible innovation across the entire healthcare value chain, including R&D, clinical trials, manufacturing, and sales & marketing. AI agents are not replacing human roles but are central to the 'Bionic Operations' model, which learns the work methods of high-performers to standardize and elevate productivity across the organization. In the R&D phase, AI significantly shortens the time required to analyze vast amounts of research data and derive insights. In the clinical and regulatory approval stages, it reduces the time spent on complex regulatory document preparation by over 70%. In manufacturing, AI has been reported to proactively detect anomalies, reducing equipment downtime by over 30%, and optimizing process conditions to improve yield by more than 10%. In sales and marketing, AI agents are standardizing sales productivity, which was once considered an area of individual skill, enabling all sales representatives to leverage the strategies of top salespeople. AI-First companies are redefining the speed and methods of drug development, overturning existing paradigms. Ignota Labs in the UK developed a method to quickly resolve toxicity issues of already effective substances using AI, based on the unconventional idea, "What if we could reuse substances discarded due to toxicity problems?" As a result, they secured a new drug candidate in just one and a half years, which is one-seventh of the typical development period. A European pharmaceutical company, in collaboration with BCG, developed a genomics data-based foundation model, enabling AI to recognize human cell gene data as 'language,' similar to how ChatGPT recognizes words. This allows for the identification of genes involved in specific diseases and the prediction of whether a particular drug will be effective for a specific patient, thereby accelerating personalized medication. Common threads among successful companies include clearly defining business problems, designing organic roles for AI and humans, and managing changes in organizational work methods. This demonstrates that BCG's '10:20:70 rule' (10% algorithms, 20% data, 70% people and organization) is effectively at play.