The initial use of AI in life sciences is a passive copilot, like a smarter search bar. The next leap is to 'agentic AI' which proactively closes knowledge gaps, simulates conversations, and provides real-time visibility. This shift is about preparing teams, not just arming them with information.

After a year of extensive experimentation, major pharmaceutical companies are now adopting AI at scale, marked by large-scale deals with AI tooling companies. This signals a market inflection point where pharma is moving beyond testing and is actively deploying AI across R&D and commercial functions after seeing demonstrable ROI.

The core philosophy of innovation—deeply understanding customer problems—remains unchanged by AI. However, modern AI tools dramatically accelerate the pre-development phases. Teams can now use AI to quickly conduct market research, define user segments, and validate hypotheses, reducing weeks of manual 'grunt work' and allowing more time for strategic decision-making and validation.

The relationship between AI startups and pharma is evolving rapidly. Previously, pharma engaged AI firms on a project-by-project, consulting-style basis. Now, as AI models for drug discovery become more robust, pharma giants are seeking to license them as enterprise-wide software suites for internal deployment, signaling a major inflection point in AI integration.

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.

Long-term competitive advantage will belong not to firms with the best algorithms, but to those that build the most intelligent organizations *around* AI. The key is developing the ability to absorb, direct, and compound AI's power in service of coherent strategic goals.

Similar to how the rise of the internet forced every retail company to adopt e-commerce, the advancement of AI will mandate that every surviving pharmaceutical company becomes 'AI-native.' This isn't an optional upgrade but a fundamental business model shift necessary for survival in the coming years.

AI is transforming Product Portfolio Management (PPM) from a function reliant on periodic, presentation-heavy reviews into a real-time intelligence capability. Leaders can move beyond quarterly business reviews and use AI to query portfolio status, surface risks, and gain continuous visibility, enabling proactive decision-making.

Novartis's CEO views AI not as a single breakthrough technology but as an enabler that creates small efficiencies across the entire R&D value chain. The real impact comes from compounding these small gains to shorten drug development timelines by years and improve overall success rates.