AI is Ushering in a New Era for Life Sciences, According to Top Life Sciences Firm

Artificial intelligence is no longer a futuristic concept in life sciences, it’s a present-day force reshaping how pharmaceutical companies innovate, commercialize, and engage patients. From early detection models that anticipate disease to forecasting platforms that predict drug launch outcomes, AI is shifting the sector from static insights to dynamic systems of continuous intelligence.

Jonathan Jenkins, Head of Digital & AI Solutions at Trinity Life Sciences, has a front-row seat to this transformation. He describes an industry that has moved past the hype of generative AI into a phase of practical adoption, albeit one still marked by uneven progress. “When ChatGPT launched in 2022, there was immediate energy in the life science industry to understand generative AI and its impact,” Jenkins says. “Early reactions were divided, with some enthusiasts who saw its transformative potential, while others remained skeptical, likening generative AI’s relevance to that of the Metaverse.”

Today, skepticism has given way to experimentation and acceleration. According to Trinity’s TGaS Advisors, the share of companies using generative AI at an enterprise level has surged from 10% in 2023 to 44% in 2025. That momentum, Jenkins argues, reflects a recognition that AI is now a strategic priority, not a passing fad.

From One-Off Insights to Continuous Intelligence

The first driver of adoption is changing client demand. Pharma companies are no longer satisfied with static, one-time outputs produced by individual experts. Instead, they are seeking intelligent systems that embed expertise, automate decision-making, and provide dynamic forecasts.

“Rather than an individual, expert-driven market forecast, clients now want forecasting systems that combine predictive AI, strong governance of methods and assumptions, the ability to generate clear summaries of what is driving the forecast, and tools to model the impact of different scenarios,” Jenkins explains. “Achieving this level of sophistication isn’t possible without significant AI investment.”

This demand is reshaping commercialization strategies across the industry. In Trinity’s case, AI is being integrated into platforms like CloudCast, its forecasting system, to make revenue predictions more accurate, transparent, and adaptable to shifting market conditions.

Impact Across the Value Chain

The applications of AI extend far beyond forecasting. “Every part of the value chain is being impacted by AI,” Jenkins says. In commercialization, two areas stand out: advancing patient insights and enabling precision customer engagement.

For instance, Trinity recently presented results from a study, supported by Sanofi, showing that an AI and machine learning model could detect type 1 diabetes a full year before a clinical diagnosis, while maintaining greater accuracy than traditional screening methods. Such breakthroughs highlight how AI can accelerate discovery and improve outcomes at scale.

On the engagement front, companies are deploying AI to tailor communication at the individual level, orchestrating digital and in-person interactions across channels. These models draw on vast datasets to optimize timing, sequencing, and messaging, delivering personalization at a depth previously impossible.

Blending Human Expertise with Machine Intelligence

Yet, Jenkins emphasizes, AI is not replacing expertise. Rather, it is amplifying it. “Our focus is combining the deep domain expertise of our team with AI, so that this knowledge is embedded directly in day-to-day decision making and operations,” he says.

One example is Trinity’s work codifying decades of therapeutic expertise into an “analogue agent” that helps clients identify appropriate drug comparators for forecasting and strategic planning. Another is its Patient Services Center of Excellence, which merges AI with patient-centric methodologies to provide trustworthy, well-governed insights into the patient journey.

“Organizations that excel at blending the expertise of their teams with the power of AI,” Jenkins argues, “will shape the next decade.”

The Competitive Imperative

The race to embed AI is not just about innovation, it is about survival. Jenkins sees a looming divide between companies that harness AI holistically and those that lag behind. “It’s hard to predict where AI adoption will be in five years, but I anticipate a divide between organizations that build strong people, business and technology systems, making AI a true differentiator, and those that do not,” he says.

Already, pharma executives are prioritizing AI not only to improve efficiency but also to gain a competitive advantage in engaging patients, navigating regulatory complexities, and forecasting market responses. As Jenkins notes, building empowered teams that can confidently apply AI is becoming a critical leadership skill.

Looking Toward 2030

The pace of adoption suggests AI will be deeply ingrained in life sciences by the end of the decade. Jenkins foresees a future in which AI-powered systems anticipate regulatory and payer responses, unify disparate datasets into shared intelligence, and encode decades of therapeutic expertise into tools that continuously learn and improve.

Trinity’s long-term vision is to lead in shaping those solutions. “Our vision is to shape solutions which apply distinctive data, expertise, and AI to address the most important challenges in life sciences commercialization,” Jenkins says. “We all have a lot to learn to prepare for the AI adoption of 2030, but it’s an incredibly exciting time to be in this field.”

The life sciences industry stands at an inflection point. What began as curiosity around generative AI has evolved into a strategic transformation reshaping research, commercialization, and patient engagement. For companies like Trinity Life Sciences, the challenge — and the opportunity — lies in codifying expertise, embedding AI into core processes, and equipping teams to work confidently with these tools.

The message from Jenkins is clear: the winners of the next decade will be those who combine the best of human expertise with the power of artificial intelligence.