Innovation to Impact: Drug Development, AI, and Regulatory Strategy

Click here to watch a video of this episode. Drug development workflows only get heavier, and subtractive change requires predictivity and decision-grade evidence that can survive regulatory science scrutiny. In this episode we talk about how AI and translational science can earn the right to delete steps, not decorate them. The tension is uncomfortable: are you willing to remove something you have always done, or will you just add another layer and call it progress? We introduce the idea of a Predictivity Ledger, a simple way to make miss rates visible and force clarity about what is being claimed, in what context, and with what failure modes. Deletion is not a vibe. It is a governance decision with receipts. Takeaway: pick one legacy step, define deletion criteria, and start logging misses like they cost time and money, because they do. (00:00) - Episode purpose, resolutions, and AI adoption risks (02:00) - Subtractive strategy and replacement opportunities in practice (04:00) - Evidence authority, familiarity, and higher validation bars (07:00) - Add-on endpoints and why adoption stalls (08:00) - Decision bias and the need to challenge interpretation (10:00) - Predictivity ledger components for accountable decisions (12:00) - AI capabilities, limits, and accountability requirements (14:00) - Administrative AI example and subtractive realization questions (18:00) - Process efficiency versus biology, fast-fail critique (25:00) - Breaking silos, expert finding, and organizational knowledge (29:00) - Historical knowledge, foundational literature, and AI leverage (33:00) - Closing remarks and next episode sign-off If you liked this episode, steal the monthly cheat sheet at Innovation2Impact Newsletter (we do the digging, you keep the credit).

Click here to watch a video of this episode. In drug development, ROI debates can drown out decision-grade evidence and the hard work of translational science. This episode asks a blunt question: when AI, digital biomarkers, or new assays change the work, who actually gets the return and who carries the downside? The tension is that finance wants clean numbers, while biology delivers messy truth. We challenge the habit of treating ROI as a single scoreboard and propose a more honest framing: financial return, return on intention for patients, and return on learning for the next decision. When those diverge, teams optimize for optics. When they align, innovation becomes durable and defensible. Takeaway: define your returns explicitly and report them side by side at governance meetings, before you declare a “win.” If you liked this episode, steal the monthly cheat sheet at Innovation2Impact Newsletter (we do the digging, you keep the credit).

Click here to watch a video of this episode. In drug development, translational science can generate endless options, but decision-grade evidence starts with the patient decision. This episode looks at why AI and digital biomarkers often get adopted before we agree what “better” means for patients and for the teams who carry the risk. Here’s the uncomfortable question: are we improving outcomes, or just improving the story we tell ourselves about progress? We talk about how platform-first thinking quietly rewires priorities, shifts incentives, and turns “innovation” into a procurement cycle instead of a decision discipline. The goal is not fewer tools. The goal is a tighter link between what we measure and what we are trying to decide, in a world where biology and regulators do not accept vibes as evidence. Takeaway: before you build or buy anything, write the decision, the patient impact, and the owner in one paragraph. (00:00) - Videocast launch, origin, and purpose (03:00) - Background and roles across multiple hats (04:00) - Toxicologic pathology perspective on drug development (05:00) - Machine learning career and pharma transition (07:00) - Patients first, platforms second framing (08:00) - Defining the problem before chasing solutions (12:00) - Purpose-driven framing and leadership reward signals (19:00) - AI not a miracle button framing (21:00) - Data foundations, modalities, and emerging AI trends (26:00) - Decision support versus dashboard accumulation (28:00) - Breaking silos and improving probability of success (30:00) - Closing takeaway on direction, feedback, and humility If you liked this episode, steal the monthly cheat sheet at Innovation2Impact Newsletter (we do the digging, you keep the credit).