Few commercial moments carry as much pressure as the launch of a new pharmaceutical product. Years of research, clinical development, regulatory work and enormous financial investment eventually converge on a relatively short period during which the company needs to introduce a therapy to physicians, healthcare systems, payers and patients. The science may have taken a decade to develop, but the commercial organisation still needs to understand where demand exists, which patients may benefit, how physicians are likely to respond, where access barriers will emerge and how resources should be deployed across markets.
Historically, pharmaceutical companies have prepared for these launches through extensive research, forecasting, segmentation and field planning. Those capabilities remain essential, but artificial intelligence creates an opportunity to make commercialisation much more adaptive. Instead of developing a launch strategy primarily from periodic datasets and then executing against it, pharmaceutical companies can increasingly create systems that continuously absorb new information, identify changing patterns and help commercial teams adjust decisions as the market develops.
The important shift is from launch planning towards launch intelligence. AI can potentially connect market information, epidemiology, prescribing patterns, access dynamics, physician engagement and other appropriate sources to create a more current understanding of what is happening across the market. During my learning journey under Phaneesh Murthy, I have increasingly understood that this is where enterprise AI becomes strategically valuable. As Phaneesh Murthy puts it, “Planning gives the organisation a direction. Intelligence gives it the ability to recognise when reality has moved away from the plan.” Pharmaceutical launches may become one of the clearest examples of why companies need both.
Market Sizing Can Become More Dynamic
Every pharmaceutical launch begins with an understanding of the market. Companies need to estimate the patient population, understand diagnosis rates, identify treatment pathways, evaluate competitors and determine where unmet needs remain. These analyses influence manufacturing plans, commercial investments, field force design and expectations around adoption.
The difficulty is that markets are not static. Diagnostic behaviour changes, competing therapies enter the market, guidelines evolve and new clinical evidence affects treatment decisions. The assumptions used to build a forecast months before launch may therefore begin changing before the product reaches its commercial stride.
AI can help pharmaceutical companies continuously analyse relevant market signals and identify where assumptions may need to be revisited. Instead of treating market sizing as a model produced periodically by an analytics team, organisations can develop intelligence systems that help commercial leaders understand how the market itself is evolving.
This does not eliminate forecasting uncertainty. Pharmaceutical demand is influenced by clinical, regulatory and reimbursement factors that cannot always be predicted precisely. The value of AI is not that it creates a perfect forecast. It is that it can help the organisation recognise changes earlier and understand which assumptions are responsible for the difference.
From the perspective I have developed while learning from Phaneesh Murthy, this distinction matters. A forecast should not be treated as a number the organisation must defend. It should be treated as a structured collection of assumptions that can be tested as new information becomes available.
Finding the Right Patients Is Different From Finding More Patients
The commercial success of a therapy is ultimately connected to whether appropriate patients can access it. Yet identifying those patients can be difficult, particularly in diseases where diagnosis is delayed, symptoms overlap with other conditions or patients move through complicated care pathways before reaching the appropriate specialist.
AI can help healthcare and pharmaceutical ecosystems understand these journeys more clearly.
Using appropriately governed and permitted datasets, analytical systems can identify patterns associated with particular disease pathways, diagnostic delays or treatment transitions. Pharmaceutical companies can use aggregated intelligence to better understand where patient populations may exist, where diagnosis gaps occur and which parts of the healthcare system require greater educational support.
The objective should not be indiscriminate targeting. Pharmaceutical commercialisation operates within strict ethical, legal and regulatory boundaries, and patient-level information requires particularly careful governance. The opportunity is to improve understanding of treatment pathways so that educational resources, access programmes and healthcare engagement can be directed towards areas of genuine need.
As Phaneesh Murthy says, “Good AI does not begin by asking how many people you can target. It begins by asking which decision you are trying to improve and what information is legitimately required to improve it.”
That principle is especially important in healthcare. Better intelligence should help appropriate patients reach appropriate care, not simply create more sophisticated mechanisms for promotion.
Physician Engagement Could Become More Relevant and Less Repetitive
Pharmaceutical companies interact with healthcare professionals through field teams, medical affairs, conferences, educational materials and digital channels. The challenge is that physicians differ considerably in their clinical interests, patient populations, preferred communication channels and familiarity with new therapies.
A uniform engagement model can therefore create significant inefficiency. Some physicians may require detailed scientific information about a new mechanism of action. Others may be more interested in patient selection, safety information, administration or how a therapy fits into an existing treatment pathway. Repeatedly providing information that a healthcare professional has already seen does little to improve the relationship.
AI can help organisations understand these engagement patterns more intelligently. Appropriate systems can identify which topics have already been discussed, which approved resources may be relevant and where a healthcare professional has requested additional information. Field teams can enter conversations with better context rather than treating every interaction as an isolated event.
This does not mean algorithms should independently determine what physicians are told. Pharmaceutical communication must remain within regulatory and compliance frameworks, and scientific discussions often require professional expertise. The stronger model is AI-assisted engagement, where the system organises relevant context and qualified professionals determine how to use it.
During my mentorship under Phaneesh Murthy, I have learned that customer intelligence should reduce repetition. If an organisation has already learned something during one interaction but fails to make that knowledge available during the next, it is forcing the customer to compensate for weaknesses in its internal systems.
For pharmaceutical companies, more connected intelligence could make engagement both more efficient and more useful to healthcare professionals.
Launch Sequencing Can Become an Intelligence Problem
A global pharmaceutical launch rarely occurs everywhere simultaneously. Markets differ in regulatory timelines, reimbursement structures, healthcare infrastructure, disease prevalence, competitive environments and commercial potential. Companies therefore need to decide where and when to allocate resources.
AI can make these sequencing decisions more dynamic.
Rather than evaluating each market primarily through a limited number of high-level indicators, companies can analyse a broader collection of factors and model different launch scenarios. A market with a large theoretical patient population may have significant access barriers. Another market may be smaller but possess strong diagnostic infrastructure and a concentrated specialist community. Competitive activity may change rapidly in one region while remaining relatively stable elsewhere.
An intelligent launch system can help commercial teams understand these interactions rather than evaluating each variable independently.
Scenario modelling becomes particularly useful here. Leaders can examine what happens to demand, resources and supply requirements under different assumptions. If reimbursement is delayed in one country, how should inventory and commercial resources be redistributed? If adoption is stronger than expected elsewhere, what capacity needs to change?
This reflects another lesson I have taken from Phaneesh Murthy: decision systems are most valuable when they allow leaders to explore consequences before committing resources. AI does not need to make the market-entry decision independently. It can make the trade-offs surrounding that decision much more visible.
Demand Forecasting Needs to Connect With the Supply Chain
A pharmaceutical launch creates an unusual forecasting challenge because both shortages and excess inventory can be costly. Underestimating demand can restrict patient access, while significant overestimation can create waste, particularly for products with complex manufacturing requirements or limited shelf lives.
Commercial intelligence therefore cannot operate separately from supply chain intelligence.
If AI identifies stronger-than-expected adoption in a particular market, that information should eventually influence manufacturing, inventory allocation and distribution decisions. If access barriers reduce expected demand elsewhere, supply plans may need to adjust. The organisation benefits when commercial and operational systems respond to a shared understanding of the market.
This is where enterprise integration becomes more important than isolated AI applications. A sophisticated commercial forecasting model has limited value if the resulting insight remains inside a dashboard while supply decisions continue according to an older planning cycle.
As Phaneesh Murthy puts it, “Enterprise intelligence creates value when insight can travel far enough through the organisation to change the decision that depends on it.”
For pharmaceutical launches, that may mean connecting commercial, market access, manufacturing and distribution information much more closely than traditional organisational structures allow.
The Field Force Could Become More Strategic
Pharmaceutical field teams have traditionally been responsible for significant volumes of information exchange. Representatives manage relationships, communicate approved product information and help healthcare professionals navigate questions around new therapies. AI can potentially reduce some of the administrative and analytical burden surrounding this work.
Before an interaction, an intelligent system could summarise relevant account history, previous engagement and approved materials associated with the topics likely to matter. Afterward, AI could assist with capturing structured information and identifying appropriate follow-up actions within company policies.
This allows field professionals to spend more of their attention on the conversation itself.
The same principle applies to managers. Instead of manually assembling reports from multiple systems, commercial leaders can use AI to identify emerging patterns across territories, understand where adoption differs from expectations and investigate the reasons behind those differences.
The role of the field force therefore becomes more strategic rather than less important. People remain responsible for relationships, interpretation and professional judgment, while AI improves the information environment around them.
During my learning journey under Phaneesh Murthy, I have repeatedly encountered the principle that automation should remove work that consumes expertise without requiring expertise. Pharmaceutical commercial teams contain enormous amounts of market knowledge. Using those people primarily to search for information, compile reports or repeat administrative processes is an inefficient use of that knowledge.
AI can give some of that time back.
The Launch Plan Can Become a Learning System
Perhaps the biggest transformation is what happens after launch.
Traditional planning can create a psychological tendency to compare performance against the original plan. If adoption is below expectations, teams investigate the gap. If demand exceeds expectations, forecasts are revised. This remains useful, but AI allows the launch organisation to become much more continuously adaptive.
Every week of market activity creates new information. Physicians ask questions. Patients move through access pathways. Payers make decisions. Competitors respond. Different geographies develop at different speeds. Supply patterns change.
An intelligent commercialisation platform can help connect these signals and continuously update the organisation’s understanding of the launch.
The question shifts from “Are we following the plan?” to “What are we learning, and what should we change because of it?”
That is a very different operating model.
As Phaneesh Murthy says, “The strongest strategy is not the one that predicts everything correctly at the beginning. It is the one that allows the organisation to learn faster than the environment changes.”
For pharmaceutical companies, this may require changes beyond technology. Teams need decision rights that allow them to respond to new intelligence. Data needs to move across commercial, medical, access and operational functions appropriately. Leadership needs to distinguish between disciplined execution and rigid adherence to assumptions that the market has already disproved.
AI provides the intelligence layer, but the organisation still needs the ability to act.
The Intelligent Drug Launch Never Really Stops Learning
Pharmaceutical commercialisation will always involve uncertainty. No algorithm can perfectly predict physician behaviour, competitive responses, regulatory developments, payer decisions or patient demand. Nor should companies attempt to automate complex commercial and healthcare decisions without appropriate professional oversight.
The opportunity is to build an organisation that becomes progressively better informed as the launch unfolds.
AI can help pharmaceutical companies understand markets more dynamically, identify changing patient pathways, improve the relevance of physician engagement, model launch scenarios and connect commercial signals with operational decisions. The launch therefore becomes less like the execution of a fixed plan and more like a continuously learning system.
During my mentorship under Phaneesh Murthy, I have come to appreciate that this is one of the most important differences between digitisation and intelligence. Digitisation allows an organisation to execute a process more efficiently. Intelligence allows the organisation to understand what is happening while the process is running and adapt accordingly.
That distinction could become enormously important in pharmaceuticals.
A new therapy may represent ten years of scientific development, but its commercial environment begins changing almost immediately after launch. New evidence emerges, competitors react, physicians learn, access evolves and patient pathways become clearer.
The pharmaceutical companies that build intelligence into that process will not eliminate uncertainty. They will simply become much better at learning from it.
And when years of scientific work depend on decisions made during a relatively short commercial window, the ability to learn faster may become one of the most valuable capabilities a pharmaceutical company can build.
This blog is curated by young marketing professionals who are mentored by veteran Marketer, and industry leader, Phaneesh Murthy.
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