Several factors, however, could complicate matters. First, the pharma sector continues to grapple with increased regulatory burdens from the Inflation Reduction Act (IRA), which was signed into law in 2022. While the legislation’s negotiation provisions don’t come into effect until 2026, it could cost pharmaceutical companies billions in potential revenue as a result of lowered drug prices and increased rebates. The Congressional Budget Office has projected that the IRA would save more than $287 billion over 10 years.
Boom and bust cycles
Meanwhile, biotech has its own headaches. While the sector boomed in 2021, it wilted in 2022 and 2023 with more than 100 biotech companies pruning employees in 2023 alone. Big Pharma has also not been immune. Prominent examples of companies in restructuring mode in 2023 include Pfizer, which announced a $3.5 billion cost-saving campaign, Bristol Myers Squibb, Johnson & Johnson, Novavax, Amgen, Thermo Fisher Scientific and Sanofi.
At the same time, the biopharma sector is facing a global skills crisis, particularly in scientific disciplines critical to maintaining high standards in quality and risk management. Complicating matters is the rapid evolution of required competencies, especially in emerging fields such as data science, AI, and biotechnology, leaving recruitment as a top headache for life science companies in 2023. Meanwhile, the talent pool isn’t keeping pace with evolving needs of drug discovery and development leaving companies to focus on internal talent development, reskilling initiatives, and strategic recruitment from other sectors.
Skilled workers needed to rethink operations
In response to these challenges, pharma companies are also rethinking their operational models, as McKinsey has noted. While most companies have some type of data science or AI initiative to streamline operations, research and supply chain management, however, many of those projects remain somewhat aspirational, leaving some companies to worry about the near-term return on investment on such projects, and the headache of “pilot purgatory.”
Simultaneously, pharma companies must not only identify and groom employees with new skill sets but adapt their organizational culture to foster agility and new processes, relying on tech firms, startups and academic institutions to bridge the talent gap. Growing pains are common, especially for legacy companies, aiming to shift towards more agile, data-driven decision-making processes and the creation of a robust digital infrastructure that can support current and future demands in a data-intensive field such as drug discovery and development. Additionally, the necessary investment in infrastructure that will support ambitious AI and data science programs for not only operational improvements but also for predictive analytics, enhancing supply chain resilience is not insignificant.
A cautious approach common
Recent research from CRB found that 98% of respondents had a dedicated budget for AI and data science initiatives, but the majority of respondents, 49%, planned to invest between $1 million and $10 million over the next two years, signaling a cautious strategy in launching new digital projects. On the higher end, one-fifth of companies budgeted between $10 million and $50 million while only 3% are venturing into even larger investments of $50 million to $100 million. No companies reported spending more than that upper amount.
Despite economic uncertainties, the pharma industry’s commitment to R&D will likely remain robust in 2024 with AI and real-world data tools providing a boost to companies with sufficiently skilled workforces. Resilient demand for drugs will likely support the sector’s resilience as they have in prior periods of financial instability.
To adapt, pharma companies will have the option of ramping up the adoption of emerging AI technologies aggressively with the hope they can help streamline operations and maintain competitiveness or adopting a more measured approach. More cautious companies are likely to constrain their data science efforts to smaller less high-stakes projects. It’s too early to tell if a tortoise or hare strategy will win out in the long run in terms of AI in pharma, but the lack of sophisticated one-size-fits-all off-the-shelf offerings can translate to significant risk for some players. Outside of the pharma sector, a number of early adopters of emerging technologies such as AI and IoT have encountered high-profile setbacks. The conservative pharma sector is likely to carefully evaluate risk versus reward in the sector’s AI endeavors, ensuring technology serves both innovation and stability. Companies that move too slowly, however, are likely to fall behind just as those who are rush ahead are prone to stumble out of the gate.