If you've ever felt overwhelmed by the prospect of implementing Generative AI (Gen AI) while safeguarding sensitive patient data and adhering to regulations, you're not alone. Many professionals in the life sciences industry often struggle to balance innovation with compliance.

SoftServe has held several Gen AI roundtables, both virtual and in-person, to explore this issue. The discussions offer valuable perspectives from industry leaders as they look to adopt Gen AI. Despite challenges in prioritizing use cases, evaluating data readiness, and establishing guardrails for data security and regulatory alignment, life science organizations are finding innovative applications for the technology — from enhancing productivity to groundbreaking drug discovery. Read on to learn more about our Gen AI discussion.

1. How To Find Valuable, Low-risk Use Cases That Deliver Measurable Business Results

The life science industry is actively exploring AI solutions to transform their business models and fulfill their mission of developing innovative treatments to better patient lives. For this reason, we share some of the most promising use cases for AI applications during our discussions:

Early adopters, such as large pharma, use AI for intelligent search and question answering, tapping into the vast medical research they have captured over many years.
Other organizations use AI as a “co-pilot” to support field-based personnel, such as sales representatives and medical science liaisons, for quickly responding to questions from healthcare professionals (HCPs).
Some use Gen AI to automate labor-intensive processes, like data entry and document generation for clinical trial submissions.
Others use large language models (LLMs) to accelerate the discovery of new drugs.

Life science organizations are also using Gen AI to create highly personalized content tailored to the unique needs of HCPs. This development is exciting, especially since it has been increasingly more difficult to engage with HCPs due to the growing demands on their time. Gen AI can “hyper-personalize” omnichannel programs, allowing organizations to create a “segment of one.” This means delivering the most relevant message to each doctor at the right moment along their patients' treatment journey.

Real-world Success Stories

During the roundtables, it's clear that participants are not just talking about Gen AI — they're actively using it to tackle business challenges and learning more about it to become more competitive. From content creation to genetic analysis, the innovative applications of Gen AI across organizations are inspiring and instructive.

User productivity: One standout story comes from a digital experience initiative within a biopharmaceutical company. They employed Gen AI to revitalize website content, tailoring its message to distinct voices and regional preferences. Content is so authentic that readers engage with it like never before. This approach saves time and strengthens the company's market position.

Process automation: Gen AI won’t eliminate jobs; it reshapes them. By automating routine tasks that address administrative, and often repetitive tasks, Gen AI frees up professionals to focus on higher-value work. This change underscores the importance of domain expertise. Our experiments with AI have shown its value, particularly in speeding up processes and reducing costs. In one study, AI-enabled teams completed tasks 31% faster than those through traditional methods, achieving 45% more with less time and resources. This increase in efficiency leads to real economic benefits.

Research innovation: Another example comes from a biotech company that uses AI to advance genetic research. By analyzing incomplete gene data, its team finds new genetic variations linked to diseases, which accelerates the creation of targeted treatments.

Drug discovery: The impact of Gen AI on drug discovery is another area ripe with potential. One participant discussed how LLMs are accelerating drug development, making it more cost-effective and efficient. This approach may reduce the time and investment needed to bring new drugs to market.

2. How To Ensure Data And Organizations Are Prepared

The success of these innovations hinges on one factor: data readiness. All participants agreed on the importance of robust data governance and quality. Here's what we've learned: A surprising 81% of organizations lack the infrastructure needed to make the most of AI tools. Data governance strategy initiatives and a focus on high-quality data selection are important steps towards successful implementation.

Proof of Concept as a Steppingstone

We learned firsthand the importance of proof of concept (PoC) by initiating 60 pilot projects. The experiments showed us that without quality data, even the best Gen AI solutions stumble. Often data comes from different sources, in different formats, and lacks the standardization of concepts, making it extremely challenging to integrate it in a meaningful and actionable way. For that reason, we recommend the following:

Assess data quality: It’s important to pick the right use cases and data that align with your business goals. Also, decide on the best business questions and metrics to guide your project to success.

Implement governance strategies: After completing PoC projects, we suggest a closer look at the data governance strategy, focusing on enhancing data quality.

Proof-of-concept projects: You should test the waters with a PoC. It allows you to see the benefits of Gen AI in action without fully committing right away. Plus, it brings to light any needed adjustments in your approach to data and organizational readiness.

One participant, a data science expert, wholeheartedly agreed. The participant shared how the effectiveness of retraining an AI model depends heavily on the quality of data the company uses. Having the right knowledge and fine-tuning the data selection process are key steps.

Another participant in R&D shared a different concern — the importance of data security and safety in clinical data amidst operational challenges. Despite the difficulties in sharing and collecting data, the participant believes that controlling data is vital for advancements in fields such as digital pathology.

3. How To Implement Gen AI with a Focus on Security and Compliance

Once high-quality data is identified, you must focus on another critical area: security. When organizations introduce Gen AI into their operations, two questions always arise. First, everyone wonders how Gen AI will transform their business. Then, the pressing question is how to ensure everything meets regulatory, privacy, security, and ethics considerations with no negative bias or detrimental impact on clinical care. Here's the consensus on best practices:

Operational guardrails: We recommend that clients use safety measures like operational guardrails, operate in closed systems, and require experts (human-in-the-loop) to oversee the project. But there’s still a risk. It’s a major concern that often keeps people awake at night.
Compliance with executive order: The White House Executive Order on Artificial Intelligence should make things easier for companies. It's designed to promote AI's growth while ensuring its safety. In the end, it’s a helpful framework to ensure Gen AI implementations safeguard intellectual property, bolster security, and protect patient safety.
Encourage responsible use: Participants agree that it’s not just about having the technology; it’s also to ensure it’s used responsibly. With many employees likely to experiment with AI for work or personal tasks, clear guidelines are needed to prevent accidental leaks of confidential information. Life science organizations need to develop comprehensive policies and guidelines, in addition to providing staff training on AI’s appropriate use. As one attendee shared, “We know our employees will use Gen AI. We want to help them get the most from it without introducing risk to our company, as well as the HCPs and patients we serve.” SoftServe believes this is sage advice.

Join the conversation

The adoption of Gen AI is not just happening; it's accelerating. By the end of the year, more organizations will realize they need to adopt AI to remain competitive. When that happens, many will look to an experienced and trusted AI consulting and technology partner to simplify where and how to make Gen AI a reality for better business value, while mitigating risks.

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