Vertex Pharmaceuticals

Lina Parra Cartagena

Lina Parra Cartagena is a seasoned FP&A professional with over 14 years of experience in corporate, commercial and operations finance across France, Mexico, Colombia, Spain and the U.S. She has worked in Consumer Goods and Pharmaceuticals, speaks four languages and excels in building FP&A teams by streamlining processes and fostering automation.

At Novartis, Lina implemented AI forecasting for the Spanish affiliate's cash flow with the Global Digital Finance Team. She pursued an Executive MBA at MIT to deepen her knowledge of statistical tools. Currently based in Boston, Lina works for Vertex Pharmaceuticals in the FP&A Operations Cell & Genes Department and is involved in launching the first global CRISPR therapy for Sickle Cell Disease.

Through this article, Lina Parra Cartagena highlights the transformative impact of AI on FP&A in the Life Sciences sector. She emphasizes AI's role in enhancing forecast accuracy, managing risks and improving resource allocation. Lina advises starting with data structuring and process architecture, especially for smaller companies and underscores the importance of human oversight and continuous learning in AI implementation.

Financial forecasts and scenarios inform decision-making. They predict the future, influencing project plans and company budgets. Throughout history, humanity has sought to forecast and improve the future. Financial Planning and Analysis (FP&A) teams are critical in providing accurate forecasts to guide future business plans. The integration of AI in FP&A enhances the accuracy and efficiency of financial forecasts, reducing the risks associated with financial instability and increasing the opportunities for revenue generation.

During the COVID-19 pandemic, the demand for forecasts increased as uncertainty rose. FP&A teams were in high demand, creating multiple scenarios to address diverse business options. AI's predictive analytics can revolutionize financial forecasting in the Life Science Finance sector.

Predictive Analytics Applied to FP&A in Life Sciences.

The possibilities for predicting variables with AI are vast in the FP&A field. We use historical data to detect patterns and forecast the future, often utilizing machine learning algorithms. In addition to financial variables, AI can be used in life sciences to forecast clinical trials and expenses. This efficient process can yield highly accurate results in just a few hours.

We need to forecast and build scenarios to provide higher value to the business and manage risks. For example, we need to consider what happens if a product launch is delayed, if the European access strategy results in a lower patient base, or if clinical trials require the inclusion of more countries. These scenarios are crucial for managing risk and allocating resources, with FP&A playing a crucial role in the process.

FP&A teams often use traditional historical methods without AI, resulting in simplistic projections. Incorporating AI through Machine Learning enables rapid analysis, reducing manual work and generating valuable insights more efficiently. Despite AI limitations, firsthand experience shows it reduces tasks and workload and increases accuracy. Accurate forecasting in Life Sciences can lead to broader medication access, improved resource allocation and better risk management. Accelerating and improving financial forecasting allows for rapid, well-informed decisions, crucial in the Life Sciences sector due to long product life cycles and substantial investments. In addition, AI can help identify potential risks and opportunities more quickly, allowing companies to respond proactively and effectively. This can improve financial stability, increase revenue opportunities and reduce costs for life sciences companies, which results in larger access for patients and lower costs for healthcare systems.

What can we do when AI appears unattainable? Begin with small steps, emphasizing data structure and process architecture.

While discussions about AI may seem daunting, small and medium-sized companies need to recognize their potential to implement AI in FP&A. Several large companies have already paved the way, but this technology is not out of reach for smaller organizations. These companies can position themselves for future success and growth by taking the first steps on their AI journey.

Before embarking on the AI journey, companies must ensure that data is harmonized, real-time and integrated to be valuable. High-quality data is crucial for accurate analysis and successful outcomes. Companies should map their processes, identify the indicators they want to track and define their strategy.

Small and medium-sized companies should focus on structuring their data and establishing the proper process architecture. Small, periodic steps aligned with these objectives will create a secure foundation for AI implementation. Their size allows them to make progress more swiftly than larger organizations.

AI implementation in the FP&A field represents a significant digital transformation project that entails strategic organizational change.

When AI is implemented in FP&A, it brings about organizational changes. AI predictions influence financial KPIs and investor reactions. However, the organization needs to fully understand and trust the results produced by the AI model. Applying appropriate frameworks for organizational change is necessary for effective implementation. It's important to note that AI is a tool that augments human capabilities, not a replacement for human judgment. Human oversight is crucial to ensure the accuracy and fairness of AI predictions and to address any potential biases in the data or algorithms used.

Will AI eat FP&A for breakfast?

AI is expected to reduce workload and improve productivity in FP&A. However, its implementation may create a surge in the need for a new type of FP&A professional who can collaborate with data scientists to develop accurate algorithms. FP&A professionals will need to adapt to the integration of AI and effectively respond to business requirements by integrating business and data analytics. It's essential to consider the potential challenges of AI implementation, such as data privacy and security, the cost of AI tools and technologies and the need for continuous learning and upskilling. These challenges can be overcome with proper planning and preparation, but they should not be underestimated.