Instead of manual reviews for all AI-generated content, use a 'guardian agent' to assign a quality score based on brand and style compliance. This score can then act as an automated trigger: high-scoring content is published automatically, while low-scoring content is routed for human review.

The most effective way to accelerate the MLR (Medical, Legal, Regulatory) approval process is not by focusing on the review stage itself. The primary leverage point is improving the quality and compliance of the content *before* it is submitted, which dramatically simplifies and speeds up all downstream steps.

AI delivers the most value when applied to mature, well-understood processes, not chaotic ones. Pharma's MLR (Medical, Legal, Regulatory) review is a prime candidate for AI disruption precisely because its established, structured nature provides the necessary guardrails and historical data for AI to be effective.

Large pharma companies are discovering that implementing AI to solve one part of the drug development workflow, like target discovery, creates new bottlenecks downstream. The subsequent, non-optimized stages become overwhelmed, highlighting the need for a holistic, fully choreographed approach to AI adoption across the entire R&D pipeline.

A successful AI strategy isn't about replacing humans but smart integration. Marketing leaders should have their teams audit all workflows and categorize them into three buckets: fully automated by AI (AI-driven), enhanced by AI tools (AI-assisted), or requiring human expertise (human-driven). This creates a practical roadmap for adoption.

Medical Affairs is shifting from a downstream compliance checkpoint to a strategic, upstream function. Using modern platforms, they now architect the core scientific narrative early in the product lifecycle, ensuring all subsequent commercial content is built on a consistent and compliant foundation.

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 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.

An AI agent can track due dates for medication prior authorizations and pre-populate submissions. This transforms a manual, time-consuming letter-writing process into a simple, two-click approval, freeing up significant clinician time to focus on patient care instead of administrative burdens.