An oncology leader views AI's most powerful near-term application as handling tedious logistical and bureaucratic tasks, not discovering novel molecules. By automating paperwork and trial planning, AI can liberate scientists to spend more time on deep, creative thinking that drives breakthroughs.

The traditional drug-centric trial model is failing. The next evolution is trials designed to validate the *decision-making process* itself, using platforms to assign the best therapy to heterogeneous patient groups, rather than testing one drug on a narrow population.

While AI holds long-term promise for molecule discovery, its most significant near-term impact in biotech is operational. The key benefits today are faster clinical trial recruitment and more efficient regulatory submissions. The revolutionary science of AI-driven drug design is still in its earliest stages.

AI's primary value in early-stage drug discovery is not eliminating experimental validation, but drastically compressing the ideation-to-testing cycle. It reduces the in-silico (computer-based) validation of ideas from a multi-month process to a matter of days, massively accelerating the pace of research.

While most focus on AI for drug discovery, Recursion is building an AI stack for clinical development, where 70% of costs lie. By using real-world data to pinpoint patient locations and causal AI to predict responders, they are improving trial enrollment rates by 1.5x. This demonstrates a holistic, end-to-end AI strategy that addresses bottlenecks across the entire value chain, not just the initial stages.

While AI-driven drug discovery is the ultimate goal, Titus argues its most practical value is in improving business efficiency. This includes automating tasks like literature reviews, paper drafting, and procurement, freeing up scientists' time for high-value work like experimental design and interpretation.

AI will create jobs in unexpected places. As AI accelerates the discovery of new drugs and medical treatments, the bottleneck will shift to human-centric validation. This will lead to significant job growth in the biomedical sector, particularly in roles related to managing and conducting clinical trials.

A traditional IT investment ROI model misses the true value of AI in pharma. A proper methodology must account for operational efficiencies (e.g., time saved in clinical trials, where each day costs millions) and intangible benefits like improved data quality, competitive advantage, and institutional learning.

AI tools can be rapidly deployed in areas like regulatory submissions and medical affairs because they augment human work on documents using public data, avoiding the need for massive IT infrastructure projects like data lakes.