The pharma launch market has settled the question of which tools to use. It still hasn't answered the only question that decides the launch: who is in the room. The short version: The platforms are real and the value is there, but the edge was never in owning the software. The gap nobody sells into is the person who runs it against your molecule, with authority to act. The 2027 launches that look effortless will belong to whoever put control back in the room. Six posts ago I

Every launch platform on the market makes the same silent assumption: that your data is ready for it. Most of the time it isn't, and that gap is where launches go wrong. The short version: 68% of pharma CDIOs told ZS that neglecting data quality and governance early is why AI initiatives fail. The post-mortem on a stalled launch-AI project almost never blames the algorithm. Everyone will sell you the agent. Almost no one will make your launch ready to use it. This isn't a hun

AI patient-finding is the most transformative capability in a launch. It's also the most oversold, and the difference between the two is everything. The short version: In rare and specialty disease, surfacing miscoded patients isn't incremental. It's the launch. The same pitch aimed at primary care targets a bottleneck that was never the constraint. Either way, a patient the model surfaces still needs a human and a system ready to act. Start with where it's real. In rare and

I have pulled together launch dashboards. Most tell you what happened, and by the time the room agrees on why, the window has moved. Lou Pillai calls the fix the orchestration layer, and is blunt that it is not a program management exercise. The capability worth sitting with: AI can surface the heuristics your 30-year super specialist carries in their head, while the decision is still live. One line stays above the dashboard. AI cannot take accountability. Having launched mul

"AI predicts launch success" is the line everyone is selling right now. It also happens to be pointed at the easier half of the problem. The short version: Predictive launch tracking is real, and the people running pharma's biggest AI programs are still careful about the claim. Prediction works when a problem holds still, and a launch doesn't. The ROI lives in response time, not in the forecast, so buy the sensor and build the reflexes. IQVIA describes active launch tracking

Your best launch plan has a shelf life of about three months. That is how long a single tactic has traditionally taken to turn around, inside a window that runs six months to a year. Lou Pillai on what changes when a launch runs as a closed loop rather than a static map. The stakes show up at patient access, where a two-week wait starts costing you prescribers. That three months is the honest number: rework the content, re-run the review, get it back into the field. Teams can

The entire industry agrees on where AI belongs first in a launch. Pfizer already built it, and named it after the man who founded the company. The short version: • Pfizer's Charlie is the clearest real-world proof of the MLR consensus, and it works because it routes risk to humans faster rather than deciding anything itself. • It's the lowest-regret AI win in launch, and worth building. • But the pitch assumes the review was the bottleneck, and the real delay usually sits ups

That gap, between knowing what good looks like and being able to act while the market is still moving, is what I wanted to put to someone who has lived it. Lou has spent three decades in commercialization and has since sat on the other side of the table, advising the teams making these calls. Not a vendor with a platform to sell. An operator who has watched launches drift in real time and can tell you where and why. My thanks to him for a genuinely candid hour, and for being

Every big firm is now selling the same pharma launch. Agentic. Sense-and-respond. "Launch as a learning system." They are all describing the same thing, and none of them is in the room when it fails. The short version: The big-firm launch point of view has converged, and the platforms behind it are real. Launches still drift anyway, because the gap was never structure. It was control. The question isn't which agentic platform to buy. It's who holds the loop closed when the ma

From Ivy Tree Advisors Pharma's commercial and customer experience landscape is undergoing its most significant transformation in a generation. "Better CX" in pharma means less friction, more trustworthy information, and faster time to therapy not better marketing. The imperative has shifted from "should we use AI?" to "are we using it to solve the right problems, in the right workflows, for the right people?" EXECUTIVE SUMMARY 1. AI driven Next Best Action is delivering me

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