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ONTOFORCE has been supporting life sciences organizations for over a decade and remains dedicated to the growth and evolution of the industry and its key players. Throughout this time, we’ve witnessed trends that fade quickly and others that leave a lasting impact, shaping the future of the industry’s landscape. In this article, we’re covering the five trends we believe will greatly impact how 2025 unfolds for life sciences organizations.
There are currently a lot of moving parts making up the backdrop of the life sciences industry contributing a sense of uncertainty about what the future will bring:
Possible ban on DTC pharmaceutical advertising
As a new American president is ushered in in 2025, the pharmaceutical industry's direct-to-consumer (DTC) advertising practices face significant challenges. The appointment of Robert F. Kennedy Jr. as HHS Secretary under the Trump administration poses a major threat. Kennedy has openly advocated for a ban on DTC pharmaceutical advertising. This considerably threatens pharma companies’ bottom lines, as return on investment for DTC ads can reach as high as 100-500% according to a recent report from Intron Health.
Patents and prices fall
Many large pharma companies are bracing themselves for the inevitable patent cliffs. EY estimates as much as $300 billion in revenues will be wiped out by patent expirations by 2028. In addition to this, under the US’s Inflation Reduction Act, prices for selected drugs will be lowered for Medicare starting in 2026. Ten drugs have already been selected for price drops in 2026, and an additional 15 drugs will be selected in 2025 for price reductions starting in 2027. More drugs will be selected in the coming years. However, these pricing negotiations may be delayed under the Trump administration.
Looking for efficiencies
Companies are investing more and more in technology. Some of this investment is driven by a desire for the increased efficiency that GenAI (generative artificial intelligence) and other technology can promise. In August of last year, Gartner predicted that IT spending in healthcare and life sciences markets would reach over $300 billion, growing by 9.4% compared to the previous year. This spending is expected to continue into 2025 as organizations look to invest further in GenAI and other tech tools to help, in part, drive efficiencies.
At the same time, it seems that many life science companies, especially biotech and pharma, have been seeking operational efficiencies and leaner approaches via personnel considerations. Fierce Biotech reports that in general, there was a slight raise in biopharma layoffs in 2024 (when compared to 2023) but that for across big pharma companies, the number of layoff rounds jumped by 281%. As budgets grow in certain areas, will leaner approaches continue to prevail in 2025?
There seems to be no end in sight for AI’s stronghold on the life sciences industry. That’s no problem as the expected returns could stand to greatly benefit the industry and ultimately patients. As more and more companies have begun to roll out AI in the organizations, they have perhaps realized that success is not so straightforward. As a recent McKinsey report points out, only 5% of 100 life sciences leaders interviewed say that their company has been able to effectively hone GenAI as “a competitive differentiator that generates consistent and significant financial value.” Despite this, McKinsey suggests that more companies will be growing their GenAI budgets this year, with an increasing number of companies planning to spend over $10 million on GenAI alone.
In fact, innovative AI applications are only one part of the puzzle. For success and tangible value, more is needed: high-quality data and well-established digitalization processes. This year, we can expect to see more investment in all three parts of the puzzle. According to a report from ZS, in 2025, 93% of technology executives at life sciences companies expect to increase investments for data, digital, and AI.
At the intersection of data, digital, and AI, we also find knowledge graphs. Knowledge graphs have seen a recent uptake in the industry as a tool to manage and integrate data for AI applications. Last year, the 2024 Gartner® Hype Cycle™ for Artificial Intelligence placed knowledge graphs on the "Slope of Enlightenment," highlighting their increasing maturity and essential role in enterprise AI approaches.
Knowledge graphs add a semantic layer to enterprise data ecosystems, making them more intuitive by describing the data in human terms. They also establish new logical connections between previously disconnected data sources. As AI models advance and begin to think in more human terms, knowledge graphs allow both the models and business users to better understand the data available and subsequently produce real insights about it.
As investment for AI grows and as our initial hype around GenAI begins to die down, different forms of AI will take center stage. GenAI applications have been evolving over the recent years to drive efficiencies and innovation, and this will certainly continue into the near future, especially as underlying models continue to improve. Building on this maturity, organizations are beginning to experiment with more advanced forms of AI, such as agentic AI.
Agentic AI refers to artificial intelligence systems designed to operate with a degree of autonomy, exhibiting goal-directed behavior and decision-making capabilities. Unlike reactive AI (task-specific systems e.g. machine learning), which responds to inputs in predefined ways, agentic AI can plan, adapt, and take proactive actions to achieve specific objectives within its environment. The concept is rooted in creating systems that can act as independent agents, capable of performing tasks with minimal human intervention while aligning their actions with overarching goals set by their creators.
Agentic AI leverages multiple tools to function as agents that work autonomously to achieve these goals. In drug discovery, agentic AI could look like a system that collects and processes data then independently hypothesizes and experiments to find new targets. These experiments can be executed quickly and at scale through fully automated labs, where in an around-the-clock workflow, AI agents conduct trials continuously.
AI agents go beyond conventional scientific thinking. Connecting diverse domains and exploring unconventional hypotheses. What may seem like counterintuitive experiments at first can in turn uncover hidden patterns or mechanisms, paving the way for new discoveries. This approach could drive breakthroughs for drug discovery by generating knowledge that traditional methods might overlook.
It’s no secret that clinical trials have become increasingly complex. Trials are taking longer and costing more all while success rates remain low. To tackle this complexity, we’ve witnessed innovative approaches such as new trial formats, novel endpoints, inclusion of new data sources like digital device data, and more. In 2025, we can expect to see the shift towards more innovative approaches to grow.
The use of a synthetic control arm (SCA) is one approach that could gain further traction this year. In a randomized control trial, participants are typically distributed into either an intervention group or a control group. However, participants in a control group might be missing out on a receiving a treatment that could cure their sometimes life-threatening disease. In addition to this moral dilemma, patients receiving a placebo might be more likely to drop out of the trial, jeopardizing timelines and the statistical power of a study. The use of a SCA can mitigate these challenges. A synthetic control arm is derived by combining existing data, such as historical real-world evidence and previous clinical trial data and applying statistical techniques to match the characteristics of the treatment group. This ensures that the control group closely mirrors the same population in terms of key factors like demographics, disease characteristics, and treatment history.
A SCA can act as an independent control group or a part/auxiliary of a study’s control group. Using a SCA may reduce the number of patients needed in a control group which can correlate to reduced costs and faster patient recruitment. On top of this, the use of SCAs may increase the feasibility of trials and reduce reporting bias.
SCAs are not universally adopted across the industry, however, this year some companies might begin to utilize some aspects of a SCA for their trials. Despite some regulatory hesitation (rightfully so, as a randomized control trial remains the gold standard for study design), SCAs are especially helpful in trials for rare disease treatments where patients may be limited.
The reuse of existing clinical data presents enormous potential to enhance future clinical development. In addition to SCAs, existing clinical data assets can be used to build virtual of cohorts of patients. These virtual cohorts allow researchers to create patient profiles that mimic real-world patients before entering the study phase. In this way, researchers can explore "what-if" scenarios enabling them to modify variables to observe potential outcomes. Learn more about the advantages of building virtual patient cohorts.
In 2025, life sciences organizations are continuing to strengthen their data strategies as they seek to further harness the potential of robust data management and analytics. ZS reports that 77% of technology executives have either already adjusted or plan to overhaul their data strategies this year. There seems to be a growing understanding that effectively managing and leveraging data is the linchpin to staying competitive in this rapidly evolving industry.
The increasing complexity of data ecosystems, stricter regulatory requirements, and the emphasis on data quality are some of the factors driving this focus on data strategy. Life sciences organizations are increasingly looking to implement platforms that allow them to connect their data across their business. However, caution should be taken, not every data platform or tool eases data complexity, rather a new tool/platform might only create more chaos. Practical tools will be those that not only enhance efficiencies through automation of data cleansing, harmonization, and quality assurance but also accelerate critical processes across the drug development timeline.
Another pivotal development in 2025 underpinning the importance of a strong data strategy is the deepening collaboration between the pharma and healthcare industries. In these types of collaboration, leveraging shared data is essential for the development of more effective treatments and better patient outcomes. Nevertheless, the challenge of data interoperability persists. Organizations should look to their data strategies to ensure there are the proper processes and infrastructure in place to address siloed systems and enable seamless, secure data flow across platforms. A data strategy focused on enabling interoperability is critical to realizing the full potential of this collaboration.
By prioritizing advanced data strategies, life sciences organizations are setting the stage for a more connected, efficient, and patient-focused future, underscoring the role of data as the backbone of innovation and transformation in the industry.
