Life sciences must grab the agentic AI lifeline to survive and thrive

Life sciences has long been delivering comfort, cure, and preventive solutions. Yet the next decade will pose unprecedented headwinds (US CMS negotiations, patent cliff, and blockbusters), with potential losses of $1–$1.5 trillion for the industry. Agentic AI must be the primary collaborator to tackle the revenue headwinds. Without it, life sciences enterprise leaders have already lost the battle.

Agentic AI is rewriting the life sciences value chain; make it your partner

The three key drivers to the future success of life sciences enterprises are speed to market, price to consumers, and access to markets. While not unique or new to life sciences, they will become critical to overcoming the industry’s unprecedented challenges.

Agentic AI has the attributes to collaborate with life sciences to affect the three key drivers. Contemporary examples of agentic AI point to its growing role across the life sciences value chain (see Exhibit 1) in research and development, driving accelerated drug discovery, faster and more efficient clinical trials, strategic sourcing, smart manufacturing, effective commercialization, and enhanced patient safety.

Exhibit 1: Agentic AI is already playing an influential role across the life sciences value chain

Source: HFS Research, 2025

Building on those advances, agentic AI enables innovation in ways considered science fiction in the past. It will shape a new competitive landscape anchored in the ability to address more rare diseases, personalize cures, and deliver therapies with higher efficacy. While the cost to enable AI is still in its infancy and hence evolving, it will drive down the cost to create medicine and deliver it at a much lower price, potentially making treatment affordable. The speed and cost elements will also free up resources for life sciences enterprises to explore newer markets and expand access to more populations. This trifecta of value that agentic AI can deliver is significant to the progress of science, benefiting humankind and supporting the right financial incentives.

If agentic is not already in a production-ready state, you just got passed

Life sciences enterprises remain cagey about how, where, and when they deploy agentic AI, given that it is becoming their ultimate market differentiator. However, there is increasing evidence that it is being used at scale across the value chain (see Exhibit 2). Medical device companies are leading the charge with consumer-facing devices that leverage agentic AI and are gaining public trust and acceptance. Biopharma and biotech are also leveraging them, but they are mainly in stealth mode now. Notably, there is increasing evidence that regulators support agentic AI, which must be considered a tailwind for enterprises sitting on the fence.

Exhibit 2: Agentic AI deployments in the real world have shown the ability to speed up healing

Source: HFS Research, 2025

Understand these five hard truths that are blocking scaling agentic AI

The potential for unprecedented and positive transformation through the power of agentic AI is here. However, it is challenging to go from imagination to ideation to operational. Five key barriers to scale must be addressed urgently, or competitors will pass by.

1. Technology: The combination of unintegrated legacy systems and modern platforms, infrastructure not fit for agentic AI, and high upfront capital to prepare for the age of AI remains challenging for life sciences enterprises. CIOs have no choice but to upgrade to interoperable, cloud-based systems that support real-time data exchange and AI integration.
2. Security: Leveraging agentic AI for positive impacts also poses challenges in securing and protecting data silos in a clinical setting. If not well protected, breaches can lead to catastrophic impacts on patients and enterprises. CISOs should step up by enabling strong data governance, supported by regular audits and diverse datasets to ensure intrinsic protection against bad actors while instituting an agentic AI cybersecurity operating model.
3. Regulatory: The lack of a consistent set of AI standards based on legislative action in the US and other parts of the world (excluding EU) complicates AI-driven decisions, which must be otherwise transparent and explainable. To overcome this, general counsels in the industry must create working groups to partner with scientists, technologists, and compliance teams to proactively manage evolving legislation while aggressively ensuring that agentic deployments comply with ethical standards and legal precedents where applicable.
4. Operation readiness: Despite the enthusiasm, enterprises remain resistant at all levels to adopt AI due to literacy gaps, concerns over job security, and a lack of clear executive sponsorship. To address this, the C-suite must be clear and decisive, engaging (empowering through participation on agentic projects) and right-skilling staff for the future instead of merely doing the same job.
5. Governance: When not required by law, enterprises struggle with governance (particularly with data), AI protocol oversight, and human-in-the-loop processes. Strict protocols with monitoring capabilities must be in place to ensure agentic performance is regularly tracked and issues are addressed rapidly and frequently while maintaining human oversight for escalations and rapid decision-making.

The Bottom Line: Hesitation means surrender. Embrace agentic AI as the catalyst to ignite innovation and reverse looming revenue decline.

Moving deeper into the 21st century, global enterprises are facing various risks (geopolitics, conflicts) and uncertainties (policy, climate change impacts). The impact on life sciences enterprises is more acute. While the C-suite tries to navigate the uncontrollable, they must focus on what they can control by making agentic AI a strong ally for survival and growth.