AI's primary value isn't replacing employees, but accelerating the speed and quality of their work. To implement it effectively, companies must first analyze and improve their underlying business processes. AI can then be used to sift through data faster and automate refined workflows, acting as a powerful assistant.

Martin Shkreli argues that the primary bottleneck in drug development isn't finding new molecules, but the immense inefficiency caused by poor communication, irrational decision-making, and misaligned incentives across numerous human departments. He believes AI's greatest contribution will be optimizing this complex organizational process rather than just improving discovery.

The most effective AI strategy focuses on 'micro workflows'—small, discrete tasks like summarizing patient data. By optimizing these countless small steps, AI can make decision-makers 'a hundred-fold more productive,' delivering massive cumulative value without relying on a single, high-risk autonomous solution.

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.

While AI is a universal trend, its application is highly contextual. In drug discovery, it's used for complex, high-science tasks like protein folding. In the CDMO space, its value lies in streamlining less glamorous but critical functions like communication, paperwork, and process optimization.

While AI has vast potential, its most immediate and successful entry point is automating prior authorizations. This administrative bottleneck is considered an 'easy win' because it's non-patient-facing, has a clear ROI, and sits at the front of treatment, leading to natural and rapid adoption.

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.

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.