Pharma 2025: AI, GLP-1 competition, and the future of clinical trials

I’ve worked in clinical trials for twenty years. For most of that time, the core processes and challenges have been fairly consistent, with incremental improvements along the way. That’s all about to change.
The pace of exploration and innovation has accelerated. Artificial intelligence (AI) is hitting its stride in R&D. Pharmaceutical companies are cautiously stepping outside of tried-and-true playbooks. In 2025, we’ll see the beginnings of a new era in drug development, brought on by key technological advances. I’ve got my eye on four trends – the maturation of AI-powered drug development, fierce competition in the GLP-1 space, the rise of community-based trials, and a more grounded perspective on decentralised clinical trials – to define the next twelve months in clinical trials.
AI will deliver beyond drug discovery
There’s a lot of buzz about AI in the pharmaceutical industry, but it’s largely been focused on discovery. I understand the promise and allure of this: it’s exciting, it’s linked directly to the science, it could lead to the next big medical breakthrough.
Yet… The biggest barrier to bringing new drugs to market isn’t discovery; it’s clinical trials. It takes hundreds of millions of dollars (or more) and a decade of work to bring a new drug to market. Clinical trials eat about half of that cost and time, and they’re becoming even larger, longer, and more complex. Accelerating drug discovery without addressing the real bottleneck won’t help us bring game-changing therapies to patients faster. It will only kick the delays down the line.
This year, we’ll see AI have a more decisive impact on clinical development. For example, researchers can now use AI to analyse vast amounts of patient data to quickly find the right participants for an upcoming trial. This requires sophisticated analysis and medical reasoning that wouldn’t have been possible with previous models, but can be a game changer for site and patient experiences. Elsewhere, AI can be used to monitor safety indicators and enhance pharmacovigilance. AI models can identify and flag unusual patient data, initiate follow-up from researchers, and catch potential incidents early. I anticipate significant growth in these applications and others across the value chain in 2025.
In a pivotal year, GLP-1 competitors will vie for sites, patients, and a seat at the table
All eyes were on GLP-1 therapies in 2024, and there’s certainly no signs of a slowdown. One in eight US adults say they’ve taken a GLP-1. The global market is projected to reach $100 billion by 2030. The sector presents a massive opportunity for drug developers. The challenge: Lilly and Novo Nordisk have a clear first-mover advantage. They could capture the market before others have a chance to catch up, creating a much higher bar to be relevant to patients and payers.
This is a pivotal, high-pressure year for others pursuing GLP-1s. I expect we’ll see a hum of clinical trial activity to bring these alternatives to market. This, in turn, will create stiff competition at sites and for patients. The current way of conducting clinical trials won’t be efficient enough to keep pace. Instead, companies will need to embrace innovation in the process to accelerate their drug development and secure a seat at the GLP-1 table.
Sponsors will make a concerted effort to engage community research sites
Historically, large urban academic centres conducted the majority of clinical trials. This created a number of challenges for the industry, including inaccessibility, lack of representation, and intense competition for participants. With growing industry pressure for greater trial access, 2025 will bring a more focused effort to bring research into communities. We’re starting to see momentum in this vein. Major pharmaceutical players like Lilly have pledged to bring research to smaller communities, and the NIH is piloting new programmes to improve access to research. This shift will help sponsors reduce enrolment bottlenecks and increase participation from underrepresented populations served by community sites; an EY survey of patients shows a multifaceted, community-based approach may bring more underrepresented populations into clinical research.
Incremental digital improvements will fade into the background as AI advances steal the spotlight
A few years ago, you couldn’t talk to anyone in the clinical trials space without hearing about the belle of the ball: decentralised trials. But, while the pandemic sparked industry-wide interest and investment in this approach, its adoption has been limited to point solutions like e-consent and electronic patient-reported outcomes — both of which predate the decentralised trial movement. In fact, a survey by the Clinical Trials Transformation Initiative found that while 75% of trials incorporate some decentralised technology, only 10% are fully decentralised. Hybrid trials aren’t going anywhere, but expect any remaining buzz about decentralised trials to simmer down as buzz about AI’s potential kicks up. AI has significantly raised the bar and the expectations for disruption, pushing companies to think about how they can implement the latest models to really change the face of drug development, rather than digitising existing workflows.
Our industry has always had an important opportunity: find and develop new therapies to address our most pressing healthcare needs. Pressure test them, prove their efficacy, and bring them to market so patients can benefit.
Now, we have another opportunity: do all of that faster and more effectively. Maximise advances in technology to reimagine processes that no longer serve us. Use AI to zero in on the right drug targets, to modernise trials, to find the right participants. Ensure that all patients, regardless of where they live, get a seat at the table. Accelerate access to breakthrough therapies. It’s time to move beyond incremental tweaks and changes and address the root challenges within trials to deliver patients access to new treatments where and when they need them.