How AI can keep life sciences innovation moving despite funding and staffing cuts

AI-driven efficiency boosts and smarter research bets may prevent these twin pressures from choking the flow of innovation.

The life sciences sector is facing a storm that could reshape its future trajectory. On one hand, research budgets are tightening, putting pressure on the very early-stage scientific exploration that fuels the discovery of tomorrow’s therapies. On the other hand, drug regulators are facing staff reductions even as the complexity of pharmaceutical submissions continues to rise.

The equation is not hard to follow: Fewer funded projects + fewer reviewers = fewer treatments making it to market. That is a scenario no one in this industry, from research labs to commercial organizations, can afford to ignore.

For those focused on commercialization, this isn’t just a theoretical concern. If the pace of biomedical innovation slows, the downstream effects will be felt in fewer launches, tougher competition for formulary access and, ultimately, fewer patients getting the treatments they need.

Why this doesn’t have to become a crisis

The silver lining is that both the Food and Drug Administration and the National Institutes of Health – the two agencies that stand to be impacted most by the above structural constraints – are taking steps to ensure progress doesn’t grind to a halt. And at the center of those efforts is artificial intelligence (AI). 

Notwithstanding the thousands of articles that regularly toe the “AI as savior” line, this is not about “AI replacing regulators” or “algorithms running the NIH.” It’s about practical augmentation. In other words, using AI to handle the repetitive, data-heavy work so that scarce human expertise can be applied where it matters most.

At the FDA, tools are being piloted to help reviewers manage enormous volumes of documentation, extract insights from safety data, and surface red flags earlier in the process. At the NIH, AI models are being used to identify high-potential areas of science, detect emerging trends, and guide limited grant dollars toward proposals with the greatest likelihood of impact.

This is how AI directly addresses the question at hand: By increasing efficiency, ensuring fewer staff can still process a growing workload, and by helping decision-makers allocate scarce resources more wisely. Without these interventions, the twin pressures of budget cuts and staff reductions could choke the flow of innovation. With AI, the system can continue moving forward.

Life sciences’ own inflection point

The same dynamic is playing out across the life sciences industry. For the past few years, we’ve seen an explosion of pilots: a generative AI tool for medical writing here, a chatbot for medical scientific liaison (MSL) support there. These were useful experiments, but they didn’t move the needle on enterprise productivity.

Now the industry is entering a new phase. The organizations that will succeed are those that integrate AI into the core machinery of their operations. This is where agentic AI, which refers to systems of coordinated agents that not only plan but can execute and adapt within defined workflows, is starting to make a real difference.

Some examples already underway include the following:

• Field force optimization: AI agents that analyze prescribing behavior, patient demographics, and access restrictions to dynamically reprioritize outreach.

• Evidence generation at scale: Automating the analysis of real-world data to produce payer-ready dossiers and health economics studies, cutting weeks or months from timelines.

• Omnichannel orchestration: Systems that monitor physician engagement across channels and adjust campaign strategy in real time, reducing wasted spend and improving relevance.

These are not hypothetical use cases. They’re live initiatives that show how AI can do more than patch holes. It can reshape operations for greater resilience and efficiency.

What will separate leaders from laggards

Of course, not every AI initiative will succeed. Some will stall at proof-of-concept; others will fail under the weight of regulatory scrutiny or integration complexity. From my perspective, three conditions are non-negotiable if AI is going to prevent productivity choke points in life sciences:

• Trust and transparency
  AI systems must be explainable and auditable. Regulators, payers, and even internal compliance teams will not tolerate black-box models when patient safety or drug approvals are at stake.

1. Domain-specific design
   Generic AI tools have limited value here. Life sciences operates under unique data, regulatory, and ethical constraints. Success will come from models and workflows tailored to these realities, not one-size-fits-all platforms.

• Operational embedding
  AI cannot live on the margins. It has to be embedded into core processes, such as regulatory submissions, evidence generation, and commercial planning. And it must work in a way that delivers measurable ROI and withstands compliance checks.

Organizations that meet these criteria will move beyond pilots and deliver results. Those that don’t will find themselves in the same trap many industries face: big promises, little impact.

Looking ahead

Cuts to research funding and regulatory staffing are not abstract projections. They are happening, and their effects will ripple through the industry in the years ahead. The question is whether we allow those constraints to choke off innovation or deploy the right tools to adapt.

The reality is that the life sciences industry is in a “do more with less” period. But rather than creating a downward spiral, this has the potential to inspire greater efficiency and continued innovation through these and other emerging applications of AI.

AI is not a silver bullet. It won’t solve funding shortages or magically expand regulatory teams. But if applied thoughtfully, it can ensure that the resources we do have are used far more effectively. That is how we maintain momentum in biomedical innovation even when the external environment grows tougher.

The real risk isn’t that AI will fail to live up to its promise. The risk is that we don’t act quickly enough to put it to work in the areas that matter most. Those who make that leap — regulators, research agencies, and life sciences companies alike — will not just survive this storm. They will define the next era of biomedical progress.