Can AI Help Pharma Overcome Its Formidable Growth Gap?

By Arda Ural, PhD

In the post-pandemic era, the pharmaceutical industry is facing some unfavorable secular trends. This complex environment has multiple forces putting pressure on the industry:

• Loss of patent exclusivity for many of the most profitable biologics, potentially resulting in more than $300 billion in lost revenue between 2023 and 2028, according to Evaluate Pharma and EY analysis.  
• The selling, general and administrative (SG&A) expenses of the industry is stuck at 29% of the revenues for the last decade, and the R&D costs increased from 14% to 18%, according to Capital IQ.
• Regulatory pressures imposed by the Inflation Reduction Act (IRA), leading to tectonic changes in how new products will be developed, including what sequence products are brought to market or whether they are commercially feasible to develop at all.  
• Aggressive oversight from the Federal Trade Commission, which has been using unprecedented legal arguments to regulate the marketplace.
• Uncertainty in the capital markets caused by steep short-term interest rates as a result of the Federal Reserve’s efforts to ease inflation.

With a bulk of pharma revenues at risk over the next five years and a strained dealmaking environment, financial and operational resilience are likely the key to growth for the sector, and emerging technologies like generative artificial intelligence (GenAI) could help bring much-needed efficiencies. But is the sector ready to embrace new technology, and what are the best use cases?

Between now and 2028, more than $360 billion in revenue are at risk for the top 25 largest pharmaceutical companies as many of the blockbuster biologics of the last decade lose patent protection and face competition from biosimilars. Unlike the patent cliff that the industry faced in the early 2010s – which saw a precipitous drop in revenues as generics flooded the market – the current loss-of-exclusivity period is likely to be more of a slow bleed as patients migrate to lower-cost alternatives. Pharma companies are in a race to close the growth gap before the flood of revenue from the current swath of biologics becomes a trickle. The industry will likely also have to rethink its approach to developing small molecules – changes to exclusivity periods in the IRA could reduce incentives for development.

Adding to the difficulties, new product launches have been inconsistent. EY research shows that 70% of drug launches in recent years have not met analysts’ expectations. When companies cannot grow their portfolios organically, they have to revert to inorganic means to buy growth.

Yet, pharmaceutical companies, while boasting more than $1.8 trillion in firepower, have been reluctant to pull the trigger on deals over the last 18 months as macroeconomic pressures and regulatory concerns weigh on the dealmaking environment.

A 40-year high in interest rates and uncertainty about when the Federal Reserve could begin cutting rates again have kept M&A suppressed across all sectors. There have been only a handful of pharmaceutical megadeals over the last year.

Companies are facing difficult dealmaking conditions on multiple fronts. Beyond interest rates, increased antitrust action from the FTC has pharma companies questioning whether larger acquisitions can survive the regulator’s scrutiny. This has meant that most deals conducted over the last 12 to 18 months have been bolt-on or tuck-in acquisitions in therapeutic areas of focus. New modalities – like antibody drug conjugates or cell and gene therapies – accounted for ~30% of new drug approvals in 2022, pushing biologics approvals ahead of small molecules for the first time ever. Biologics are projected to account for 55% of pharmaceutical sales by 2027 and remain attractive acquisition targets.

There is also increased uncertainty around the impact of the Inflation Reduction Act on drug pricing. A number of industry lawsuits are currently pending against the implementation of the law, and it is unlikely we will see a clear outcome around this in the near future.

Desperately looking for a solution

Higher operating costs due to the complexity of development and manufacturing for advanced personalized therapies and intermingled global supply chains make it imperative that pharmaceutical companies create financial and operational efficiencies. The cost of development in the future is only going to grow as pharma companies face higher input costs and increased competition for talent. For instance, the cost of active pharmaceutical ingredient (API) has increased 30%-50% over the last five years, while energy prices have increased 30%-65% depending on location. EY analysis shows that life sciences companies that build financial resiliency are less impacted by downturns and achieve faster and better recovery.

With all of these obstacles to filling the growth gap, pharmaceutical executives are better off looking for solutions of parameters that they can control. This means creating efficiencies within their own business – whether that’s in the form of financial or workforce resilience or within the operational structure of the company. Artificial intelligence (AI) and machine learning (ML) can help deliver on those efficiencies. Many companies are tapping into AI to streamline repetitive back-office functions or are already using it in the drug discovery process to cut down on drug development timelines. There has also been significant exploration of GenAI for content generation in the commercialization process.

However, most companies have yet to explore how these emerging technologies can create efficiencies within their manufacturing, supply chain or quality and compliance functions. For instance, machine learning models can be trained to detect and correct errors in temperature during the manufacturing process that currently need to be monitored by hand. This not only would eliminate most human error but would allow companies to fix a problem before it ruins a batch, cutting down on the amount of product wasted.

Pharma companies produce an exorbitant amount of data throughout the value chain, much of which goes unanalyzed. GenAI models have the ability to sift through all of this data; for instance, when patients or doctors report issues with the product – some saying they cannot open the bottle, others saying the cap was stuck. While these reports may be written in different ways, the technology can detect patterns when certain words or phrases are used, enabling the company to determine that there may be a problem with the packaging and allowing them to address the issue.

Use cases for emerging technologies abound, but pharmaceutical companies have a long way to go in applying these technologies on a broad scale and vary in maturity around AI implementation. The AI revolution does not come without its own issues such as hallucinations, data bias due to poor training or lack of controls for data privacy and master data governance. Everything from a lack of skilled workers to uncertainty around regulation to hesitation around investment are creating challenges to implementation. To succeed in a technologically advanced future, pharmaceutical companies will need to:

• Rethink hiring practices or upskill current employees to create the right mix of workers that can oversee or leverage GenAI or ML processes and execute on the use cases.
• Put a responsible and trusted AI governance framework in place that designates who can access data and where it is stored.
• Consider tax and legal implications of new data flows in order to stay in compliance with current regulation as well as get ahead of any evolving country regulations.
• Revisit cybersecurity protocols to make sure patient data is secure and privacy is protected.

Despite the industry showing resilience in the face of high costs, increased regulation and macroeconomic turmoil, the future of the pharmaceutical sector depends on creating greater efficiency through technology integration. While emerging technologies will not be the panacea to solve all of pharma’s problems, deploying technology in the right way can create more efficient, cost-conscious pharmaceutical companies that can better focus on the business of saving lives.

Arda Ural, Ph.D., is the EY Americas Industry Markets leader for EY’s Health Sciences and Wellness Practice. He has nearly 30 years’ experience in pharma, biotech, and medtech, including general management, new product development, corporate strategy, and M&A. He holds a Ph.D. in general management and finance and an MBA from Marmara University in Istanbul, as well as an MSc and BSc in mechanical engineering from Boğaziçi University.

The views reflected in this article are the views of the author(s) and do not necessarily reflect the views of Ernst & Young LLP or other members of the global EY organization.