AI will automate the majority of traditional PM tasks like data analysis and writing PRDs. PMs must embrace this shift, focusing on the core 10% of their craft—the strategic, high-judgment work—whose value will be amplified exponentially by AI-driven leverage and automation.

The future role of a Qualified Person (QP) shifts from repetitive checking to designing automated checks. When a QP identifies a new edge case or potential risk, they can digitize that specific check. This allows their unique expertise to be run automatically and perpetually on all future batches, continuously scaling their impact and enhancing patient safety.

Before creating a new headcount for administrative or repetitive work, conduct a thought experiment: can an AI agent or an automation workflow fulfill these duties? This approach can reduce overhead and force a re-evaluation of how tasks are accomplished.

M&A leaders can feed diligence findings and past deal notes into an enterprise AI tool to quickly generate risk logs and identify key focus areas. This saves significant time that can be reinvested into crucial, high-touch stakeholder alignment and communication.

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.

The MLR process is not a single review step but a six-stage journey: content submission, internal readiness check, the MLR review, final sign-off, health authority submission, and expiration management. Recognizing this granularity reveals distinct automation opportunities at each stage beyond the review itself.

Technical operations teams can waste up to 70% of their time manually collecting data. Deploying specialized AI agents to autonomously parse unstructured engineering logs, financial databases, and project updates automates this process, eliminating this 'operational tax' and freeing up teams for higher-value strategic work.

Pharmaceutical giants are adopting AI not for moonshot "cure cancer" prompts, but to streamline critical, error-prone processes like compiling 10,000-page FDA documents. This mundane application prevents costly delays and accelerates time-to-market for multi-billion dollar drugs.

A study of 100 R&D leaders found teams spend a staggering 70% of their time on communication-related tasks: 30% on information lookup and 40% creating documentation. This administrative burden is a primary bottleneck slowing speed-to-market for new products.