How integrating AI across the commercialisation process can drive better outcomes

Early commercialisation is one of the most demanding stages in bringing a therapy to market, and the challenges are only growing. Today’s assets are frequently developed for multiple indications across diverse therapy areas, requiring teams to manage larger volumes of data, greater strategic nuance and deeper layers of analysis than ever before.

In this environment, artificial intelligence (AI) has emerged as a powerful ally. Once seen as a buzzword, it’s now proving to be indispensable. It has the potential to accelerate timelines, improve decision-making, identify risks and opportunities earlier, and streamline operations.

In this article, I share how AI can deliver tangible value across the early commercialisation life cycle, helping teams cut through complexity and boost operational efficiency.

AI is already enabling teams to drive better outcomes, more quickly. Integrating it across the commercialisation process equips organisations with sharper insights and greater confidence at every step.

1. Smarter clinical trial design
Think of AI as a supercharged research assistant, one that’s read virtually every clinical trial ever published and remembers the details. It can sift through bundles of past trial data to recommend smarter endpoints and help you set inclusion and exclusion criteria that align with both regulatory expectations and commercial goals.

And when it comes to selecting study sites and investigators, AI can forecast which ones are most likely to deliver based on past performance and patient availability, saving you valuable time and costly missteps.

2. Forecasting and ‘what if’ scenario planning
AI is reshaping forecasting by validating base-case scenarios, as well as modelling high-risk or unexpected outcomes with remarkable speed. Whether anticipating changes in competitor launch timings, differing clinical outcomes or the entry of generics or biosimilars, AI helps teams test assumptions and adjust plans in a matter of minutes.

It also quantifies an asset’s strategic risks, such as label variations and market access challenges, and simulates their impact on the commercialisation process, empowering teams to make informed decisions before issues arise.

3. Clearer future market and competitive landscape
AI acts as a market intelligence engine, scanning everything from ongoing clinical trials and intellectual property filings to treatment guidelines, analyst reports and real-world evidence. It builds a comprehensive view of the current landscape and projects how it could evolve over time.

Integrating structured data with unstructured sources like medical literature and social media enables AI to uncover dynamic unmet needs, revealing therapeutic gaps that traditional analysis might overlook.

4. Deeper epidemiology and patient insight
Understanding your target population at a granular level is critical, and AI excels at this. Many teams are already using AI to integrate real-world data, including registry data and other clinical sources, to build detailed pictures of disease prevalence and patient subgroups.

AI also analyses features such as comorbidities, treatment response rates, therapy duration and socioeconomic factors to pinpoint the most valuable patient segments for commercial focus.

5. Early pricing and reimbursement strategy
This is where AI turns strategic intent into real-world economics. It can test different pricing strategies in the current market and model cost-effectiveness outcomes based on varying external price benchmarks, healthcare system data and clinical outcomes.

It also synthesises health technology assessment reports and policy updates, helping you anticipate reimbursement challenges and tackle them proactively, before they slow you down.

6. KOL and stakeholder mapping that works
Engaging the right voices early on is critical. AI can scan everything from publication records to conference activity to highlight who’s really shaping the conversation in your space. Better yet, it tracks how that influence shifts over time, helping you tailor your engagement strategy with precision.

These insights power more tailored, effective engagement strategies, ensuring you’re speaking to the right people, with the right message, at the right moment.

For the right AI integration, teams will need to upskill these capabilities and reimagine elements of support from external partners to operate with greater speed and precision. Real progress, however, depends on how teams engage with the technology.  That includes developing the skills to frame meaningful questions and interpret outcomes in context, while upholding strong data governance, especially when working with sensitive or proprietary information.

As adoption deepens, AI is setting a new standard for early commercialisation: faster, smarter and more resilient by design.