This is the fifth report in our Intelligent biopharma series, which explores the current and future potential of artificial intelligence (AI) across the biopharma value chain.1 This report focuses on how companies can use AI to improve drug launches and their commercial models.
In the biopharma value chain, launch and commercial activities enable patients to gain access to new therapies. However, companies are facing increasing challenges in achieving a successful launch, including the escalating costs of drug development, growing competition, mounting pressure to reduce time-to-market, new models of care and ability to pay for new, innovative medicines.
As in any industry focused on meeting market needs, biopharma companies have to plan and execute winning launch and commercial strategies, including optimising marketing, pricing, regulatory, compliance and sales approaches (figure 1). Biopharma companies seek to identify and direct commercial activities towards the right market segment at the right time by leveraging different communication channels based on the needs of each stakeholder (payers, providers, health care professionals (HCPs) and patients). For the past few years, the traditional one-size-fits-all go-to-market strategy, predominantly based on physical channels, has started to shift towards the use of digital channels.2
Over the past 12 months, the unequivocal challenges and disruption caused by COVID-19 has led commercial teams to ask new questions, including which channels are best suited for stakeholder needs, how to address the needs of HCPs and patients more effectively, and what digital technologies can be leveraged to drive successful launches?3 Furthermore, the disruptions caused by COVID-19 are likely to have a long-lasting impact. With the growing pressure to shorten time-to-market, companies increasingly need to understand what components of their sales and marketing operations, as well as other innovations, drive prescribing behaviours and expand patients’ access to new treatments. Consequently, early and efficient engagement with stakeholders is crucial to ensure companies can communicate their product’s value – this is where AI comes in.
Today, biopharma companies have access to data from multiple internal and external sources. AI can enable companies to realise the power of this data, particularly real-world data (RWD), to improve their launch and commercial performance, by managing tailored engagements with different stakeholders and delivering added value that meets their needs more effectively. By effectively implementing the right AI technologies, companies can gain access to comprehensive real-world results and obtain valuable strategic insights to support key decision-making (figure 2).
The adoption of AI technologies is therefore becoming a critical commercial imperative, specifically in the following five areas.
While biopharma companies are already adopting AI applications in a number of areas, such as drug discovery and clinical development, marketing and sales have generally lagged other parts of the pharma value chain in digitalising systems and processes and the use of AI. However, companies have recently accelerated their digital transformation of launch and commercial activities, driven largely by the COVID-19 pandemic. Biopharma leaders should create a culture that promotes commercial innovation with a focus on operational excellence; having a clear view of what they can anticipate from investments in data, AI and other advanced technologies.
Marketing and commercial teams should align their thinking around launch excellence and its execution, and how to integrate advanced digital technologies to foster cross-functional collaboration to enhance engagement and maximise the value from their products. By breaking down data silos and interconnecting the right technologies across the product life cycle, performance can be monitored from end-to-end using key metrics to ensure business activities support product value and lead to commercial success. Biopharma companies should therefore build a robust, end-to-end market access strategy framework to understand what matters most to market access stakeholders and develop products and approaches that meet their priorities.8
This end-to-end visibility across commercialisation will provide significant benefits. Utilising AI technologies, biopharma companies can coordinate product launches better, establish proof of value to support reimbursement models for new curative therapies and services and improve patient engagement. However, before adopting and upscaling AI solutions across their commercial operations, there are a number of questions companies need to consider (figure 3).
Life sciences companies continue to respond to a changing global landscape and strive to pursue innovative solutions to address today’s challenges. Deloitte understands the complexity of these challenges and works with clients worldwide to drive progress and bring discoveries to life.