For decades, one of the defining ambitions of the pharmaceutical industry was the blockbuster drug. The commercial logic was compelling: develop a therapy capable of addressing a large patient population, achieve broad adoption across major markets and build the scale necessary to recover enormous research and development costs while generating substantial returns. Many of the industry’s commercial structures were consequently designed around scale, from large field forces and mass physician engagement to forecasting models built around sizeable patient populations.
That model is not disappearing, but another model is becoming increasingly important. Advances in genomics, diagnostics, biomarkers and precision medicine are allowing healthcare systems to identify smaller groups of patients whose diseases may share specific biological characteristics. A condition that was once treated as a single large category can increasingly be divided into multiple subgroups, each of which may respond differently to particular therapies. The addressable market for an individual treatment may therefore become smaller even as the treatment itself becomes more precisely matched to the patients most likely to benefit.
Artificial intelligence can become an important part of making this model commercially and operationally viable. AI can help pharmaceutical companies identify patient cohorts, understand complex treatment journeys, connect multiple forms of evidence and determine where small but clinically important populations may exist. During my learning journey under Phaneesh Murthy, I have increasingly understood that technological transformation often changes the economics of scale itself. As Phaneesh Murthy puts it, “Technology does not always create value by making the market bigger. Sometimes it creates value by allowing the organisation to understand a smaller market with extraordinary precision.”
For pharmaceutical strategy, that distinction could become fundamental.
One Disease Can Become Many Different Patient Populations
Traditional disease categories are useful because they allow healthcare systems to organise diagnosis, research and treatment. But patients carrying the same broad diagnosis can sometimes have very different underlying biological characteristics, disease progression and responses to treatment.
Precision medicine attempts to understand those differences.
As scientific understanding improves, researchers can identify biomarkers, genetic characteristics and other factors associated with treatment response. Pharmaceutical companies may consequently develop therapies intended for increasingly specific patient populations rather than everyone carrying a broad diagnosis.
This changes the commercial question. The company is no longer simply asking how many people have a particular disease. It needs to understand how many patients possess the characteristics relevant to the therapy, how frequently those characteristics are identified, where appropriate diagnostic capabilities exist and how those patients currently move through the healthcare system.
AI is particularly useful when the answer depends on relationships across many variables. Machine learning systems can help researchers and analysts identify patterns within complex datasets that would be difficult to detect through conventional segmentation alone. The objective is not to allow an algorithm to independently determine who should receive a treatment. It is to create better intelligence around patient populations that may previously have been difficult to recognise.
From the enterprise perspective I have learned from Phaneesh Murthy, this is an important shift. A market is not simply a number in a forecast. It is a system of patients, physicians, diagnostics, treatment pathways and access conditions. Understanding the relationships between those components can be more valuable than knowing the theoretical size of the population.
The Diagnostic Pathway Becomes Part of the Commercial Strategy
A precision therapy has limited value if healthcare systems cannot reliably identify the patients for whom it is appropriate.
This makes diagnostics increasingly important to pharmaceutical strategy. A company may develop an effective therapy for a biomarker-defined population, but commercial adoption depends partly on whether relevant patients are actually tested for that biomarker. If testing is unavailable, inconsistently used or introduced too late in the treatment journey, eligible patients may never be identified.
The pharmaceutical company therefore needs to understand the diagnostic ecosystem surrounding the therapy.
AI can help analyse treatment pathways and identify where diagnostic gaps may exist. Aggregated and appropriately governed healthcare information may reveal where patients experience delays, which parts of the healthcare system are performing relevant tests and where education or infrastructure could improve identification.
This does not mean pharmaceutical companies should control clinical diagnosis. Physicians and healthcare systems remain responsible for clinical decisions. The strategic opportunity is to understand whether the infrastructure required for precision medicine actually exists around the product.
As Phaneesh Murthy puts it, “A product can only create value when the ecosystem around it is capable of using it. Sometimes the most important part of product strategy sits outside the product itself.”
For precision therapies, the diagnostic pathway may therefore become almost as important commercially as the treatment pathway.
Small Patient Populations Require Better Intelligence
A large market can absorb a certain amount of commercial inefficiency. When millions of potential customers exist, companies can use relatively broad segmentation and still reach meaningful portions of the market.
Precision medicine offers less room for that approach.
If a therapy is appropriate for a relatively small patient population, every part of the commercial system needs to become more accurate. Companies need to understand where relevant specialists operate, how patients reach those specialists, what diagnostic steps occur before treatment and which access barriers prevent eligible patients from receiving therapy.
AI can help connect these signals.
Instead of deploying resources primarily according to geographic size or historical prescription volume, pharmaceutical companies can build a more detailed understanding of where relevant patient journeys are occurring. Field teams, medical affairs and patient support programmes can then be organised around the structure of the actual treatment ecosystem.
During my mentorship under Phaneesh Murthy, I have learned that AI becomes particularly valuable when the cost of being imprecise is high. In a broad market, an inefficient allocation may reduce productivity. In a highly specialised market, the same inefficiency can mean missing a substantial portion of the entire opportunity.
Precision markets therefore require precision operations.
Patient Journeys Become More Important Than Market Segments
Traditional commercial segmentation often groups physicians, institutions or patients according to characteristics that make them easier to manage operationally. Precision therapies may require pharmaceutical companies to understand something more dynamic: the journey a patient takes before reaching treatment.
A patient may begin with relatively common symptoms, move through primary care, undergo multiple tests, visit different specialists and receive an initial diagnosis before a specific biomarker or clinical characteristic is identified. Each stage can introduce delay or loss from the pathway.
AI can help organisations model these journeys at a much more detailed level.
Patterns across appropriately governed data can reveal common sequences of care, points where diagnosis tends to slow and differences between healthcare systems or regions. Pharmaceutical companies can use this intelligence to better understand where educational initiatives, diagnostic support or patient services may create legitimate value.
The strategic shift is important because the commercial organisation stops seeing the market simply as a collection of targets. It begins seeing it as a system through which patients move.
Phaneesh Murthy says, “If you want to understand the customer, do not only study the final transaction. Study the journey that had to happen before the transaction became possible.”
In pharmaceuticals, that journey can be extraordinarily complex. AI gives companies a new set of tools for understanding it.
Precision Medicine Changes the Economics of Commercialisation
Smaller patient populations also challenge traditional assumptions about how pharmaceutical commercial organisations should be structured.
A therapy intended for a broad population may justify large sales teams and extensive promotional infrastructure. A treatment designed for a small population concentrated among specialised clinicians may require a very different model. Medical education, diagnostic support, market access expertise and patient services can become proportionally more important than conventional promotional scale.
AI can help make these specialised commercial models economically viable by reducing the cost of understanding and coordinating a fragmented market.
Commercial teams can identify where relevant expertise exists, prioritise educational resources, understand differences between treatment centres and continuously update their view of the market as new information becomes available. The company may operate with a smaller commercial footprint while possessing much deeper intelligence about the ecosystem.
This reflects a broader principle I have learned from Phaneesh Murthy: digital transformation should not simply automate an operating model designed for a previous era. The more important opportunity is to ask whether the operating model itself should change because technology has altered what is economically possible.
Precision medicine may require exactly that reconsideration.
AI Could Connect Research Strategy With Commercial Strategy Earlier
One of the most significant opportunities may occur long before a therapy reaches the market.
Pharmaceutical companies make major decisions during research and clinical development about which disease areas, mechanisms and patient populations to pursue. Commercial teams later need to understand whether healthcare systems can identify those patients, whether diagnostic infrastructure exists and how the treatment would fit into clinical practice.
AI can help connect these questions earlier.
Scientific information, clinical data, epidemiology, treatment patterns and market information can be analysed together to create a more integrated view of potential patient populations. Researchers can understand whether biological subgroups appear meaningful, while commercial and access teams can begin considering how those populations might eventually be identified and treated.
The purpose is not to allow commercial considerations to dictate scientific decisions. It is to reduce the distance between discovering a promising therapy and understanding the real-world system required to deliver it.
During my learning journey under Phaneesh Murthy, I have come to appreciate that many enterprise problems exist because important functions begin collaborating too late. Technology can help create a shared information environment earlier in the process, allowing different teams to understand the consequences of decisions before those decisions become expensive to reverse.
For pharmaceuticals, connecting research, clinical development, diagnostics, market access and commercial intelligence earlier could create more coherent strategies around precision therapies.
The Ecosystem Around the Drug Becomes Part of the Product
As therapies become more specialised, the treatment itself can become only one component of the value delivered to the patient.
Diagnostic testing may be required before treatment begins. Patients may need specialised administration, monitoring or follow-up. Healthcare professionals may require additional education. Payers may need detailed evidence to understand the appropriate population. Patient support programmes may help individuals navigate complex treatment pathways.
The pharmaceutical company’s challenge therefore becomes increasingly ecosystem-oriented.
AI can support coordination across these components by helping organisations understand where patients encounter friction and which parts of the pathway require intervention. The company can develop services around the therapy that make appropriate treatment easier to access and manage.
This does not mean expanding indiscriminately into every part of healthcare. Pharmaceutical companies need clear boundaries around their role, particularly when dealing with clinical decisions and patient information. But they can design their commercial and support infrastructure around the reality that specialised therapies depend on multiple participants working together.
As Phaneesh Murthy puts it, “The more specialised the product becomes, the more important the system around the product can become. You are no longer delivering a unit. You are enabling an outcome.”
That principle may become increasingly important as precision medicine expands.
Precision Must Include Access, Not Just Biology
There is also an important risk in the movement towards highly specialised medicine. The ability to identify increasingly precise patient populations does not automatically mean those patients will have equal access to diagnosis and treatment.
Advanced diagnostic infrastructure may be concentrated in major hospitals or wealthier regions. Specialist expertise can be geographically uneven. The cost of testing and treatment can create additional barriers. Some patient populations may therefore benefit from advances in precision medicine much earlier than others.
AI should help healthcare ecosystems identify these gaps rather than simply optimise around the populations that are easiest to reach.
Pharmaceutical companies can use market intelligence to understand where diagnostic or access barriers are limiting appropriate treatment. Healthcare systems and policymakers can similarly use data to identify regions where infrastructure needs improvement.
The goal of precision medicine should not be to create extraordinarily sophisticated treatment pathways for a narrow group of easily identifiable patients while leaving everyone else invisible.
From the perspective I have developed while learning from Phaneesh Murthy, technology transformation needs to be evaluated according to the outcome it produces across the entire system. An algorithm can be extremely accurate and still contribute to a poor overall outcome if the surrounding ecosystem prevents people from benefiting from that accuracy.
Precision therefore needs to extend beyond the science of identifying the right patient. It should also include the operational ability to help that patient reach the right care.
The Definition of a Blockbuster Could Change
The blockbuster model was built around scale. Find a large medical need, develop an effective therapy and create broad adoption across a substantial patient population. That model will continue to produce important medicines.
But the economics of AI, diagnostics and precision medicine create another possibility.
A therapy may address a much smaller population while delivering extraordinary value because it is precisely matched to the biology of those patients. Pharmaceutical companies may operate portfolios containing multiple specialised therapies rather than depending as heavily on a handful of products designed for enormous populations.
Success in that environment requires a different kind of intelligence.
Companies need to understand patient populations with greater precision, identify diagnostic pathways, coordinate specialised ecosystems and deploy resources according to the actual structure of care. AI can make this complexity more manageable by connecting information that traditional commercial models often analyse separately.
During my mentorship under Phaneesh Murthy, I have learned that technology frequently changes not only how companies compete but also what scale means. The industrial era rewarded organisations capable of serving enormous markets efficiently. The AI era may increasingly reward organisations capable of understanding smaller markets with extraordinary depth.
For pharmaceutical companies, that could change the definition of a blockbuster.
The next generation of important therapies may not always be designed for tens of millions of patients. Some may be intended for relatively small populations whose biology can finally be understood well enough to treat them differently.
The market becomes smaller.
The intelligence required to serve it becomes much greater.
And the value created for the right patient could become greater still.
This blog is curated by young marketing professionals who are mentored by veteran Marketer, and industry leader, Phaneesh Murthy.
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