5 Key Insights From Life Sciences Executives on AI Implementation

The life sciences industry stands at a pivotal moment in its adoption of artificial intelligence (AI). As companies focus significant resources on navigating and implementing this transformative technology, understanding how industry leaders approach the implementation of AI in life sciences implementation provides valuable guidance for those at various stages of their AI journey.

After speaking with several executives from leading life sciences organizations about their AI experiences, we've identified five critical insights that highlight both the opportunities and challenges facing the industry today.

1. AI Has Evolved from "Nice-to-Have" to Strategic Imperative

Life sciences executives unanimously agree that AI has undergone a dramatic shift in strategic importance over the past few years. As generative AI use cases have accelerated, this technology is revolutionizing the industry and processes across the product life cycle. When implemented correctly, companies can create a significant competitive advantage in getting treatments to market.

As one executive explained: "The prioritization of AI has accelerated. We're at a point where most CEOs and business area leaders have some pressure on them—even if they're not initiating it themselves—from the board and external forces to really think about how AI can bring value to their organizations. They're actively thinking about it, as opposed to maybe three or four years ago where it was nice to have, not quite sure what the value will be, experimental in nature."

Another industry leader put it more bluntly: "It will become adopt or perish. I don't think any life sciences company can thrive without adopting AI in its processes because that's going to be a huge difference maker."

This organizational mindset reflects a maturing understanding that AI isn't just another technology trend but a fundamental capability that will determine competitive advantage in the coming years.

2. Effective Governance Is the Foundation of Successful AI Implementation

The interviews made it clear that establishing proper governance frameworks is critical to successful AI adoption, particularly in heavily regulated industries like life sciences.

Leading organizations are implementing multi-tiered governance models for AI in life sciences that balance centralized control with oversight across use cases but with some business unit autonomy. One approach that seems to be gaining traction is the creation of enterprise-wide AI councils with cross-functional representation. This provides visibility into proposed use cases across departments to identify common themes or common risks across potential usage.

"We have an AI council with senior leaders representing all different business functions and sectors," explained an executive from a global pharmaceutical company. "We also established a Generative AI Governance Office as a sub-committee that reviews all use cases to ensure compliance with regulatory requirements, data privacy standards, and IP (intellectual property) protection measures."

Effective governance models appear to be hybrid structures that provide organizational guardrails while allowing flexibility for business units to pursue initiatives aligned with their specific needs.

3. Data Quality and Security Remain Paramount

AI's value in life sciences is intrinsically tied to the quality, availability, and security of data. With that, executives consistently highlighted this as both a challenge and a critical success factor. Whether they are building the technology themselves or partnering with software vendors, these life science vendors recognize the need for vigilance in data security, and avoiding IP leakage or a black box where data usage and reasoning lacks transparency within models.

"The assurances we would need is that our data is maintained by us. It's not available to the general public or the rest of the model," explained one biotech executive. "Some models are open source where they say it's not a big deal, but others are proprietary. There's no way you're going to have a segmented area unless you take the product, disconnect it from the internet, and put it within your four walls."

Companies are addressing these concerns by:

• Keeping sensitive data within company "four walls."
• Creating clear data classification guidelines.
• Training employees on appropriate data usage with AI tools.
• Implementing robust review processes for AI-generated outputs.

As one technology leader explained: "There's always a human in the loop in any of these systems if we have any type of AI component. The AI component isn't making these decisions in our biotech world."

4. Value-Driven Use Case Selection Is Critical

The interviews revealed a pragmatic approach to prioritizing AI initiatives based on business value and technical feasibility. They also share that successful implementation is much more likely to result from doing fewer, more strategic proofs of concept (POCs) with a clear path for adoption, versus attempting to insert AI in life sciences everywhere all at once.

"We've got to be able to identify use cases that have two characteristics," explained a former chief data officer. "One, there is potential to create and generate a lot of value. And two, it's feasible to be solved with relatively mature AI methodologies."

Executives commonly highlighted several high-value applications gaining traction:

• Drug Discovery and Development: "Instead of months to design drugs and find indications, you can do it in sometimes minutes," one executive noted, identifying this as the area with the highest return on investment (ROI) potential.
• Manufacturing Excellence: Multiple examples emerged in this area, including predictive maintenance for equipment ("We can reduce the time maintenance technicians spend on equipment checks based on AI analysis of failure patterns") and exception management in manufacturing execution systems.
• Quality and Compliance: One executive described how AI is streamlining validation document generation: "You free up the validation person from that work and allow them to do the review and execution, resulting in a much smoother process with fewer mistakes."
• Clinical Trials: Several executives mentioned using AI to optimize patient recruitment, site selection, data analysis, and report compilation with one noting it has "significantly shortened clinical trial timelines."

5. AI Will Transform Workforce Skills and Job Functions

Finally, life sciences executives provided thoughtful perspectives on how AI will reshape roles and responsibilities within their organizations.

Rather than wholesale job elimination, they described a more nuanced evolution: "Every job is a collection of tasks, and some of those tasks will be automated. Some you're going to perform with the help of an AI tool," explained one leader. "The big fundamental shift will be that every job will change by effectively leveraging AI to elevate the kind of work that people are doing."

In addition to a shift in roles, there is a need to upskill and educate employees on safe and effective AI usage. Companies are investing in AI literacy programs to help employees adapt. One executive described their approach: "Before you gain access to the (AI) environment, there's a training module that walks people through what AI is, what it isn't, and how you can use it."

The consensus is unanimous— successful organizations will empower employees to transition from routine tasks to higher-value activities where human judgment, creativity, and domain expertise remain essential.

A Balanced Path Forward

What emerges clearly from these conversations is that life sciences organizations that will benefit most from successful AI adoption are those organizations taking a thoughtful, measured approach to AI implementation—balancing innovation with appropriate caution.

As regulatory frameworks continue to evolve and AI capabilities of AI in life sciences advance, the companies that will thrive are those that establish robust governance structures, prioritize data quality and security, focus on high-value use cases, and help their workforce adapt to new ways of working.

The journey from experimental AI projects to enterprise-wide transformation requires patience, persistence, and pragmatism. But as these executives made clear, the potential rewards—in terms of accelerated drug development, manufacturing efficiency, quality improvements, and ultimately patient outcomes—make this journey not just worthwhile, but essential.

This blog post is based on interviews with multiple life sciences executives conducted as part of MasterControl's ongoing research into AI adoption in regulated industries.