First to the Finish: How AI in Market Access Will Determine the Future Market Leaders in the Pharmaceutical Industry
By Dr. Barbara Walter
To finish first, first you must finish. A molecule that once took four to six years to identify can now be surfaced in much less time. Clinical trial feasibility assessments that once consumed weeks are now completed in days. But market access, contributing to a drug’s success from day one, involuntarily slows things down. The field itself is structurally complex, with regulations, payer landscapes, and reference pricing dynamics that shift constantly - what Menasheh Fogel calls “a data-heavy moving target.” Automating this field is not as straightforward as other sectors. In EU27 countries, the average time between marketing authorisation and a product appearing on a public reimbursement list stands at 531 days, this estimate is currently growing. Pharma has built a race car. But speed itself means nothing if you can't cross the finish line before the competition does.
Market access is a strategic domain active throughout a drug's entire life cycle. It shapes a drug’s commercial fate long before it reaches a patient. National health authorities, payers, and reimbursement bodies determine whether a drug will be covered. The clinical endpoints defined during study design are the same ones market access teams will defend in pricing negotiations years later. Get that wrong early, and no amount of speed at launch recovers it. In Europe alone, this means navigating Germany's G-BA (Gemeinsamer Bundesausschuss), France's HAS (Haute Autorité de santé), the UK's NICE (National Institute for Health and Care Excellence), and dozens more. Each body has their own evidence requirements and negotiation logic. Pricing, HEOR (Health Economics and Outcomes Research), regulatory affairs, and local affiliates must all move in concert. Like an F1 team operating under constantly shifting regulations, except the frameworks here are stricter, and the stakeholders more numerous. The question is no longer whether these functions need to work in concert. It is how.
In 2027 the pharmaceutical AI budget is estimated to be $22 billion. But today technological progress is still blocked by legacy data silos and organisational structures built for a pre-digital era. Nowhere is this gap more visible than in PRMA (Pricing, Reimbursement and Market Access), with only a small fraction of HTA (Health Technology Assessment) submissions incorporating AI tools in any meaningful way. The tools exist and the pilots are already running. Automated reimbursement dossier drafts are reducing HTA preparation time. Value pricing models are incorporating Real-World Evidence dynamically, simulating payer reactions before a negotiation room is ever entered. Predictive tender analytics are giving commercial teams probabilistic visibility into markets previously managed on intuition and institutional memory, the list goes on. Despite the availability, a live poll of 246 pricing professionals at ISPOR Europe 2025 found that only 15% have actually adopted AI tools in their pricing work. There is room for improvement and significant advantages held for first movers.
The 15% who have moved are already pulling ahead. And it might not always be the largest players, as smaller biotechs, unburdened by legacy infrastructure, might be able to close market access gaps that once seemed structural. The organisations that will define this next era are not waiting for consensus. They think ahead and they are building.
Imagine AI agents that monitor HTA guidance updates in real time across Germany, France, the UK, and Italy - autonomously recalibrating launch sequencing the same day a policy shifts. Imagine pricing corridors adjusting dynamically as reference country data flows in, months before a negotiation begins. Imagine a system that doesn't just generate a dossier, but stress-tests it against predicted payer objections before a human signs off.
For many, this still sounds like Zukunftsmusik. “Having an agent recommend an optimal sequence, by constantly monitoring how HTAs are referring to one another makes a lot of sense. Doing this manually is non-trivial and highly complex, but I for one would be curious if modern AI can cut through that complexity,” says Menasheh Fogel, Fractional CIO with over 20 years at the intersection of IT and life sciences. What the industry needs now are decisive leaders willing to build this infrastructure and bring that race car across the line. Because behind all the potential monetary gains, there is something far more precious at stake. A human life.
If you have any further questions, please feel free to contact Bertram Weiss.
LinkedIn:https://www.linkedin.com/in/drbertramweiss/
Email: bertram.weiss@merantix-momentum.com
Menasheh Fogel brings a rare combination of skills: decades of IT leadership experience at multinational pharmaceutical companies, as well as hands-on experience in building digital infrastructure for market access and pricing. We sat down with him to delve deeper into the subject: the structural barriers, the hidden complexities, and the actual requirements for building AI capabilities in this field. [Read the full interview →]
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First to the Finish: How AI in Market Access Will Determine the Future Market Leaders in the Pharmaceutical Industry
By Dr. Barbara Walter
To finish first, first you must finish. A molecule that once took four to six years to identify can now be surfaced in much less time. Clinical trial feasibility assessments that once consumed weeks are now completed in days. But market access, contributing to a drug’s success from day one, involuntarily slows things down. The field itself is structurally complex, with regulations, payer landscapes, and reference pricing dynamics that shift constantly - what Menasheh Fogel calls “a data-heavy moving target.” Automating this field is not as straightforward as other sectors. In EU27 countries, the average time between marketing authorisation and a product appearing on a public reimbursement list stands at 531 days, this estimate is currently growing. Pharma has built a race car. But speed itself means nothing if you can't cross the finish line before the competition does.
Market access is a strategic domain active throughout a drug's entire life cycle. It shapes a drug’s commercial fate long before it reaches a patient. National health authorities, payers, and reimbursement bodies determine whether a drug will be covered. The clinical endpoints defined during study design are the same ones market access teams will defend in pricing negotiations years later. Get that wrong early, and no amount of speed at launch recovers it. In Europe alone, this means navigating Germany's G-BA (Gemeinsamer Bundesausschuss), France's HAS (Haute Autorité de santé), the UK's NICE (National Institute for Health and Care Excellence), and dozens more. Each body has their own evidence requirements and negotiation logic. Pricing, HEOR (Health Economics and Outcomes Research), regulatory affairs, and local affiliates must all move in concert. Like an F1 team operating under constantly shifting regulations, except the frameworks here are stricter, and the stakeholders more numerous. The question is no longer whether these functions need to work in concert. It is how.
In 2027 the pharmaceutical AI budget is estimated to be $22 billion. But today technological progress is still blocked by legacy data silos and organisational structures built for a pre-digital era. Nowhere is this gap more visible than in PRMA (Pricing, Reimbursement and Market Access), with only a small fraction of HTA (Health Technology Assessment) submissions incorporating AI tools in any meaningful way. The tools exist and the pilots are already running. Automated reimbursement dossier drafts are reducing HTA preparation time. Value pricing models are incorporating Real-World Evidence dynamically, simulating payer reactions before a negotiation room is ever entered. Predictive tender analytics are giving commercial teams probabilistic visibility into markets previously managed on intuition and institutional memory, the list goes on. Despite the availability, a live poll of 246 pricing professionals at ISPOR Europe 2025 found that only 15% have actually adopted AI tools in their pricing work. There is room for improvement and significant advantages held for first movers.
The 15% who have moved are already pulling ahead. And it might not always be the largest players, as smaller biotechs, unburdened by legacy infrastructure, might be able to close market access gaps that once seemed structural. The organisations that will define this next era are not waiting for consensus. They think ahead and they are building.
Imagine AI agents that monitor HTA guidance updates in real time across Germany, France, the UK, and Italy - autonomously recalibrating launch sequencing the same day a policy shifts. Imagine pricing corridors adjusting dynamically as reference country data flows in, months before a negotiation begins. Imagine a system that doesn't just generate a dossier, but stress-tests it against predicted payer objections before a human signs off.
For many, this still sounds like Zukunftsmusik. “Having an agent recommend an optimal sequence, by constantly monitoring how HTAs are referring to one another makes a lot of sense. Doing this manually is non-trivial and highly complex, but I for one would be curious if modern AI can cut through that complexity,” says Menasheh Fogel, Fractional CIO with over 20 years at the intersection of IT and life sciences. What the industry needs now are decisive leaders willing to build this infrastructure and bring that race car across the line. Because behind all the potential monetary gains, there is something far more precious at stake. A human life.
If you have any further questions, please feel free to contact Bertram Weiss.
LinkedIn:https://www.linkedin.com/in/drbertramweiss/
Email: bertram.weiss@merantix-momentum.com
Menasheh Fogel brings a rare combination of skills: decades of IT leadership experience at multinational pharmaceutical companies, as well as hands-on experience in building digital infrastructure for market access and pricing. We sat down with him to delve deeper into the subject: the structural barriers, the hidden complexities, and the actual requirements for building AI capabilities in this field. [Read the full interview →]
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