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.

AI is becoming a personal C-suite tool. Vasant Narasimhan uses an AI agent trained on Novartis's historical R&D decisions. This allows him to query past contexts and biases when facing a new decision, leading to more informed, data-driven leadership rather than relying solely on memory.

A Coinbase engineering director reports that after scaling AI adoption, his calendar is "almost empty." The massive reduction in coordination overhead—fewer prioritization meetings, status updates, and roadmap discussions—is a primary benefit, allowing leaders to spend more time writing code themselves.

CEO Brad Jacobs uses AI to automatically take notes and generate summaries from important meetings across his company. This technology provides him with near-instantaneous, unfiltered insights into operations and challenges that previously would have taken months to surface through the corporate hierarchy.

A powerful, practical application of AI for leaders is to treat it as a multidisciplinary advisor or "Co-CEO." This framing allows for high-level collaboration on strategic planning, tapping into AI's expertise across finance, legal, HR, and operations.

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.

Use AI agent platforms to build a digital chief of staff that manages priorities, filters messages, and tracks projects. This automates the administrative and strategic legwork traditionally handled by a human assistant, freeing up executive time for high-value decisions.

Time saved from AI-driven efficiencies must be consciously reallocated to strategic tasks that AI can't do, like deeper customer research or improving sales enablement. This compounds the value of the initial time saving, but only if that time is actively protected and reinvested.

While AI for designing novel molecules gets the hype, its practical, near-term impact is in streamlining operational tasks like summarizing medical charts, preparing SEC filings, and analyzing contracts, which are a better fit for current LLM capabilities.