Launching into an AI-powered future: a prescription for success

Launching prescription medicines excellently – achieving optimal commercial performance that reflects a medicine’s value and the clinical, commercial and financial investment made in it – has always been one of the most important activities an innovative pharmaceutical company undertakes.

In many cases, the future growth of the company depends on excellent launch. What it takes to achieve launch excellence has always evolved, as the types of innovative medicines coming to market and the environments they face have changed. In the next ten years, however, the stakes are higher, and the environmental challenges more transformative.

According to IQVIA figures, the global patent cliff for innovative medicines to 2030 equates to more than $230bn of current list price value exposed to lower-cost competition. The risk is concentrated among leading innovative pharmaceutical companies, with up to 65% of big pharma’s current revenues at risk. A further $200-250bn of industry revenue is at risk by 2035. Some of the world’s most valuable pharmaceutical brands, including Keytruda, Wegovy and Dupixent, will lose exclusivity in these periods.

Replacing this value will not be easy. Pharmaceutical companies are launching more innovative products, but many of these products are individually smaller products facing faster, fiercer competition. In fact, half of launches in the top eight countries make less than $5m in their first year per country. Multibillion-dollar brands still exist, often driven by strong multi-indication strategies (eg, Keytruda, Humira) or exceptional new market creation (such as modern obesity medicines). But they are the exception, not the norm.

Launches are also facing competition faster: think of the Wegovy pill, launched for obesity in the US in January 2026 and already facing competition from Lilly’s oral obesity agent Foundayo, launched in April 2026. This matters because IQVIA’s launch excellence research has consistently identified the first six months as a critical window of opportunity for most launches to establish long-term success.

This launch dilemma was already difficult: the need to replace unprecedented value loss through a larger number of often individually smaller launches facing fast competition.

Since the beginning of 2025, that dilemma has become a trilemma. Two fast-moving curveballs are rapidly changing the launch environment. The first is policy: the US administration’s goals on international reference pricing through Most Favoured Nation, aimed squarely at the key ex-US countries for global launch success in Europe and Japan. The degree to which the policy will affect ex-US launches in terms of sequence and timing has yet to be seen, but the potential for impact has been widely discussed, and what is absolutely certain is that the policy has created uncertainty for ex-US launch strategy and, along with it, risks on levels and timings of investment and activity.

The second is how rapidly generative AI tools are becoming the front door for healthcare professionals (HCPs) to access medical information. As of December 2025, an IQVIA multi-country study showed that 85% of surveyed HCPs already utilise generative AI tools, with 41% using them daily among oncologists and GPs across France, Germany and the UK. Importantly, the most widely reported use cases include guideline review and support with treatment decisions. As a result, pharmaceutical companies risk seeing their traditional engagement and role as a source of medical information and support diminish, with less opportunity to shape early understanding of newly launched brands.

The implications of the trilemma are clear: future launches will need to be more effective, more precise and cost-effective, and more agile:

• Launch must be more effective: replacing, and ideally exceeding, nearly one-third of the global pharmaceutical industry’s value in a decade demands consistently strong launch performance. Success cannot depend only on a handful of exceptional blockbusters; it must come from improving performance across the portfolio
• Launch must be more precise and cost-effective: as launches become more numerous and often individually smaller, companies cannot invest in every product, geography and customer segment in the same way. They will need to focus capital and resources on the highest-value opportunities, while still nurturing smaller launches to their full potential
• Launch must be more agile and resilient: companies still need global launch success, but the path to that success has become more volatile and less predictable.

Established country sequences may change, with some less familiar country markets moving earlier in launch plans, while more familiar markets may face delay.

Geopolitical tensions, policy shifts and changing commercial opportunities mean companies must be able to pause, accelerate or redirect launch activity quickly.

These requirements require a new launch model that is more dynamic than the launch playbooks of the past, which are dependent on static assumptions made years before launch.

Making launch more effective, precise, cost-effective and agile demands new thinking and new technologies. This is where well-governed AI, built on high-quality data, can create real advantage: connecting diverse inputs to enable launch teams to make sense of an increasingly complex environment and act with greater speed, precision and agility at scale.

• AI agents to augment launch processes and teams: AI agents can support teams from early planning through execution, accelerating and transforming activities that are complex, resource-intensive and repetitive. Think agents that provide on-demand insight to support data-driven launch planning, agents that support value dossier development or field force agents that improve HCP targeting, call preparation and insight capture

• AI to enable flexible and scalable operating models: as launch pathways become less predictable and geographic priorities shift, companies need to scale capabilities quickly and cost-effectively. AI enables this by embedding intelligence directly into core workflows, allowing activities to be executed more consistently and reused. Starting from a clean slate, emerging biopharma companies could seize the benefits of a leaner commercial model by using technology-enabled partners to access modular capabilities, specialist expertise and local market presence, scaling without recreating the cost and complexity of traditional in-country affiliates. Larger pharmaceutical companies with legacy commercial models should take note: they may face smaller competitors with state-of-the-art, more flexible capabilities, able to out-compete them in the new environment, if they themselves do not change

• AI as a decision engine for complex data environments: launch teams now have access to more data than ever before, from market and customer insight to performance management. The challenge is making sense of these inputs quickly enough to inform better decisions. AI of the right quality and sophistication is built to handle this, and to grow rather than become redundant as data sources evolve.

As well as transforming how pharmaceutical companies plan, execute and adapt their launches, AI is also central because it is reshaping the external information environment into which medicines launch.

‘AI agents can support teams from early planning through execution, accelerating and transforming activities’

As HCPs increasingly use AI tools to summarise guidelines, compare treatment options and support clinical decision-making, traditional approaches to pharmaceutical engagement will need to evolve to remain relevant. In-field teams will need to upskill to support deeper, more consultative engagements focused on evidence interpretation, identifying addressable care gaps and overcoming practical barriers to adoption. The strategic battleground for new launches will also shift away from share of voice, supported by pharma-owned media, towards share of algorithmic trust. In practice, this means launch teams must think about what evidence they create, and how quickly, credibly and consistently that evidence enters the trusted information ecosystems from which AI tools draw (eg, clinical guidelines, medical society recommendations, highly rated peer-reviewed journals). This new environment also requires new measures of launch success, moving beyond activity-based metrics such as the number of interactions with HCPs, towards indicators of influence, such as visibility in AI-mediated search and contribution to guideline-aligned pathways.

Launch success in the future will not come from fixed launch playbooks, but from building new launch processes and dynamic capabilities that are fit to face future challenges: more value to replace, more launches to support, more volatile global launch commercial environment and an information environment (for HCPs, patients and pharmaceutical companies) increasingly shaped by AI.

Used well, AI can be embedded in the launch engine as an enterprise capability to elevate human decision-making: surfacing the right insight earlier, reducing manual effort, helping teams focus resources where they matter most, and supporting faster course correction when assumptions change. Launch excellence will remain a human, scientific and commercial discipline, but it will increasingly be enabled by embedding AI across core data and workflows. The companies that succeed must combine the speed, scale and precision AI enables with the scientific credibility, local insight and human judgement that successful launch still requires.

References
1. https://www.mckinsey.com/featured-insights/week-in-charts/agentic-ai-advantage-for-pharma
2. https://www.mckinsey.com/industries/life-sciences/our-insights/reimagining-life-science-enterprises-with-agentic-ai
3. https://www.veeva.com/resources/the-role-of-medical-affairs-in-times-of-ai/