Oct 6, 2026 · 16 min · 7 segments
Industry is using AI to surface hidden patterns and accelerate decision-making in high-pressure launch environments. In a new pharmaphorum podcast, host Nicole Raleigh speaks with Jason Smith, CTO of…
Nicole RaleighHost
... and look more closely at those challenges within the life sciences setting.
And you mentioned the accumulated pharma data that you were looking at, looking to distill the noise, so to speak.
So I was wondering if you could, Jason, tell us more about using AI to surface hidden patterns, as you say, and accelerate decision-making in high-pressure launch environments.
Yeah, I think one of the things that, um, we kinda named the core problem first when I talk to customers, right? It's, um, most pharma launch decisions that we've seen kind of run in what I would call a kinda episodic insight models, right? They're gathering feedback at milestones.
They're, they're looking to act on a picture, but they're acting on a picture of market that's already moved over that time, right? So it's very episodic.
I think what we have in the market is an integration problem, um, and how we integrate all that data, so you can look across it, um, and leverage what AI is really good at, which is pattern matching, right? That's where AI really accelerates, um, a- and can help us 'cause it can look across all these fragmented sources, you know, and you're looking at qualitative signals.
So these are things like advisory board notes and sentiment from advisory boards, MSL notes, and then you have these quantitative signals that are stored that are prescribing and claim strengths.
Um, so being able to look across those, um, all those disparate sources to surface those patterns, that's, that's the real key that we see AI.
You know, it compresses that time, um, from months to days, and it removes the manual workload, right? So it's not really just about speed, it's about the, now the companies have the agility with AI to work through, you know, the launch instead of looking, you know, and kind of locking in the strategy at the start.
You can make decisions along the way because AI is now able to look at across all those data sets, discern those patterns, look for signals when those patterns start to change, and then humans in the loop can make why they're there is can look at that information real ti- almost real time and make better decisions along the way.
One is, right, breaking down those silos and looking across those disparate data sets, both qualitative and quantitative, and then the ability to see signals faster and make decisions, so you're no longer locked into one strategy.
You can be a lo- lot more dynamic in the market as the market shifts with you and as you try different things to enter that market with your, you know, your target therapy.
... and look more closely at those challenges within the life sciences setting.
And you mentioned the accumulated pharma data that you were looking at, looking to distill the noise, so to speak.
So I was wondering if you could, Jason, tell us more about using AI to surface hidden patterns, as you say, and accelerate decision-making in high-pressure launch environments.
Yeah, I think one of the things that, um, we kinda named the core problem first when I talk to customers, right? It's, um, most pharma launch decisions that we've seen kind of run in what I would call a kinda episodic insight models, right? They're gathering feedback at milestones.
They're, they're looking to act on a picture, but they're acting on a picture of market that's already moved over that time, right? So it's very episodic.
I think what we have in the market is an integration problem, um, and how we integrate all that data, so you can look across it, um, and leverage what AI is really good at, which is pattern matching, right? That's where AI really accelerates, um, a- and can help us 'cause it can look across all these fragmented sources, you know, and you're looking at qualitative signals.
So these are things like advisory board notes and sentiment from advisory boards, MSL notes, and then you have these quantitative signals that are stored that are prescribing and claim strengths.
Um, so being able to look across those, um, all those disparate sources to surface those patterns, that's, that's the real key that we see AI.
You know, it compresses that time, um, from months to days, and it removes the manual workload, right? So it's not really just about speed, it's about the, now the companies have the agility with AI to work through, you know, the launch instead of looking, you know, and kind of locking in the strategy at the start.
You can make decisions along the way because AI is now able to look at across all those data sets, discern those patterns, look for signals when those patterns start to change, and then humans in the loop can make why they're there is can look at that information real ti- almost real time and make better decisions along the way.
One is, right, breaking down those silos and looking across those disparate data sets, both qualitative and quantitative, and then the ability to see signals faster and make decisions, so you're no longer locked into one strategy.
You can be a lo- lot more dynamic in the market as the market shifts with you and as you try different things to enter that market with your, you know, your target therapy.
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