What Life Sciences Gets Right (And Misses) About AI
By Anastasia Christianson, Ph.D.
I’ve spent most of my career in life sciences, and like many in the field, I’ve seen how slow and cautious the industry can be when embracing new technology. It’s understandable. We work in a highly regulated space where patient safety is the highest priority. Still, viewing AI only through the lens of operational efficiency is limiting its full potential.
Other industries with just as much at stake have found ways to make AI work powerfully and transformatively. Industries like manufacturing, aerospace, finance, and healthcare are built on complex systems, high risk, and serious accountability, just like ours.
In manufacturing, companies use AI to predict when machines need maintenance, minimize downtime, track product quality, and detect anomalies early. These applications are improving both operational efficiency and product reliability, and pharma is beginning to explore and, in some cases, use similar approaches in manufacturing medicines. They’re using sensors to track equipment performance in real time. Even in pharma, where some manufacturing environments already utilize real-time sensor tracking, there is an opportunity to extend those capabilities into labs and research settings where instrument performance monitoring is often overlooked.
In finance, AI supports fraud detection, risk assessment, and even investment decisions. These systems handle massive volumes of data, adapt to evolving threats, and meet constant regulatory scrutiny. The parallels to drug safety and supply chain security are striking, and areas like counterfeit drug detection, fraud detection in clinical trials, and early identification of product imperfections remain underutilized by current AI tools.
In healthcare, pockets of innovation — like AI-driven diagnostics and personalized care models — are emerging in response to patient needs. Life sciences could build on this momentum by expanding how we apply AI to accelerate and enhance clinical trial outcomes in similarly responsive ways.
Overcome Fear With Education
So, what limits the full potential of AI?
Fear.
Fear of change, fear of the unknown, especially in the context of evolving regulations and uncertainty about what regulators will accept. Fear of altering something that technically works, even if the success rate is low. After all, candles once worked for lighting homes, and horse-drawn carriages transported people and goods — but embracing the light bulb and the automobile changed how we live. The rules are evolving, and it’s hard to know the limits. But when that fear turns into inertia, progress stalls. Misinformation and anxiety about AI replacing people only deepen the hesitation.
The solution starts with education, based on real information, not hype. When people understand how AI works and where it adds value, they see it differently. One of my former colleagues used to say, "Take the monkey out of the work." Maybe — with the correct use of AI — we can free people to focus on what they do best: critical thinking, collaboration, and breakthrough innovation.
This is the missed opportunity in life sciences. Too often, we use AI to speed up what we already do — essentially trying to make the candle burn brighter or the horse run faster. Instead, we should ask: What is the equivalent of the light bulb or the car for our field? What new capabilities could AI unlock that were never possible before?
Envision The Possibilities
Digital twins offer a compelling example. In aerospace, digital twins are essential for modeling and testing aircraft performance before a single plane leaves the ground. Pharma can adopt that same mindset. Imagine simulating the entire life cycle of a drug—from early discovery through clinical trials and into manufacturing. An end-to-end virtual process, modeled and optimized before it ever touches a patient. Imagine doing this across the portfolio and progressing only the most promising drugs.
Now, imagine integrating that with real-time data from labs, production facilities, and patient outcomes. This would create a dynamic, evolving system that doesn’t just make us faster — it makes us smarter. It would help us avoid the inefficiencies of retrofitting AI into disconnected systems. Instead, we could design processes with intelligence at the core.
We’re starting to see other industries deploy AI agents — autonomous digital systems that automate tasks, guide technicians, flag future risks, and even suggest new commercial opportunities. Sectors like healthcare, finance, manufacturing, and retail are leading the way, using agents to create more adaptive, intelligent operations. For life sciences, biopharma in particular, this represents a major opportunity: embedded intelligence could transform operational moments into strategic ones, enhancing compliance and innovation across the value chain.
Take The Next Steps
Recently, I spoke with a team working to modernize a legacy clinical trial workflow. The initial resistance wasn’t from regulators — it was internal. AI felt like an added risk. But their view shifted when we walked through how predictive modeling could anticipate patient dropout or how digital simulations could streamline protocol design. Not because the technology dazzled them, but because it clearly supported what mattered most: better outcomes, faster timelines, and fewer missed opportunities.
So, what needs to happen next?
- First, we need more open collaboration between industries. Manufacturing has already solved for predictive maintenance. Finance excels at anomaly detection. Healthcare is charting paths in personalized diagnostics. Life sciences can learn by inviting these playbooks in. Even simple partnerships or case study exchanges can go a long way. In fact, some of the challenges we face in drug discovery — like analyzing complex multimodal data — have already been addressed in fields like astrophysics. Tapping into solutions beyond our immediate industry could accelerate breakthroughs in ways we haven’t yet imagined.
- Second, broader education is critical. We need to equip our data scientists, clinicians, trial designers, and regulatory teams. For example, training on how AI explains its outputs or decisions (explainability) can ease concerns among regulators. Showing marketing or clinical teams how AI tools segment audiences or patient populations or optimize campaigns can help bring therapies to market faster and more effectively.
- Third, we need to shift the mindset. The biggest benefits of AI aren’t found in operational efficiency alone. They come from driving innovations and doing what wasn’t previously possible. For instance, imagine using AI to identify disease markers that no human pattern recognition could find — or predicting global supply bottlenecks months in advance. These are not incremental gains. These are transformative leaps.
- Finally, we need to stop the hype and start measuring benefits. We also need new metrics. If we measure only cost savings and speed, we’ll miss the deeper impact. What about the number of new therapeutic targets, medicines, or modalities generated through AI? Or the percentage of clinical trial outcomes improved by simulation? These are the benchmarks that signal true transformation.
We’re not starting from zero. The tools are here. The lessons are available. The use cases exist. What’s needed now is the willingness to think differently, cross-industry partnerships to learn from others, and to act with intention.
The future of AI in life sciences is not just about automation. It’s about imagination. Let’s give ourselves permission to use it that way.
About The Author:
Dr. Anastasia Christianson is a recognized senior executive in Data Science, Artificial Intelligence, and information technology, with nearly three decades of experience driving digital transformation in the pharmaceutical industry, most recently as Head of AI, Data, and Analytics at Pfizer. She is currently Managing Principal at EPAM and Executive in Residence at Columbia Tech Ventures.