Generative AI in life sciences: How to move from concept to impact

Generative AI (GenAI) has the potential to transform the life sciences sector, driving immense value across R&D, medical affairs and commercial functions. But despite the excitement, many organizations remain stuck in the proof-of-concept (POC) stage, with many of life sciences POCs never making it to production. 

In our work with life sciences companies, we’ve seen first-hand what makes the difference between a stalled POC and a scalable, high-impact GenAI initiative. Organizations that successfully transition from concept to production overcome challenges such as: 

• Tool overload. Reps struggle with disjointed systems, juggling CRMs, dashboards, and next-best-action tools. 
• ROI skepticism. Leadership often questions the tangible business value of generative AI initiatives. 
• Trust issues. The ‘Blackbox’ algorithms make stakeholders wary of adoption. 
• Privacy concerns with public tools: The use of tools like ChatGPT raises concerns about data security and regulatory compliance. 
• Resource-intensive in-house development. Building an internal GenAI solution requires significant time, talent, and computing power. 

Organizations that act now stand to gain a competitive edge. This guide is built on real-world lessons from our work helping life sciences companies move from POC to production. It brings together insights from live case studies, broader experiences, and proven strategies to help organizations unlock the full potential of GenAI. We’ll provide a roadmap that shows you how to: 

• Build the foundation: Establish a powerful infrastructure of data, technology, and governance to ensure long-term scalability. 
• Prioritize the right meta use cases: Select high-impact, scalable use case clusters that drive early wins and deliver measurable value. 
• Drive organizational traction: Address organizational challenges and drive stakeholder adoption for broad generative AI integration. 

Whether you are an emerging biopharma company or an established pharmaceutical leader, this guide equips you with the insights you need to maximize generative AI’s transformative potential in your organization. 

Building the foundation 

Building a strong foundation for your generative AI project is the first and most important step in ensuring its success. This hinges on aligning with business goals and being adaptable to the rapidly evolving nature of this nascent technology. 

Defining a maturity roadmap for generative AI implementation 

We’ve seen firsthand how tempting it is for organizations to dive headfirst into a full-scale generative AI rollout. But without the right foundation, many of these efforts can falter. On the other hand, some companies aim too high from the start, trying to build fully embedded AI systems without first testing what works. 

From our experience working with life sciences organizations, the most successful AI implementations follow a structured, phased approach. One that balances immediate impact with long-term scalability without overwhelming resources. 

Here’s how we’ve seen companies successfully evolve their generative AI programs, moving from early experimentation to full-scale integration: 

• Stage one: Proof of concept (POC) 

Start with small use cases to validate feasibility and demonstrate early value. These pilot projects build confidence and highlight areas for improvement. 

• Stage two: Scaled implementation 

Introduce broader (adjacent) meta-use cases that drive measurable value across specific teams, such as medical affairs or R&D. This phase solidifies processes and demonstrates tangible benefits. 

• Stage three: Optimized deployment 

Expand use cases across multiple departments, creating synergies and reinforcing impact. This stage integrates generative AI into a cohesive and workable system. 

• Stage four: Fully embedded 

This is where generative AI becomes intrinsic to operations, driving transformative change and enabling new capabilities. 

Lessons learned: While it may be tempting to play it safe with smaller proofs of concept (level one) or dive straight into fully embedded systems (level four), these extremes can prove to be an enticing trap. Level one initiatives risk being too narrow to demonstrate meaningful impact, causing you to lose momentum, while Level four deployments can overwhelm teams and fail to align with your current capabilities. 

Instead, level two or level three can provide an ideal starting point for most companies. These stages allow you to build valuable confidence through observable impact and drive better actions across targeted teams or functions. By focusing on meta-use cases and addressing measurable business needs, you can solidify processes and showcase tangible benefits before investing further time and resources. 

The power of an upward compatible tech stack 

Defining your roadmap is just the beginning. Without the right foundation, we’ve seen organizations invest heavily in AI only to hit bottlenecks when their tech stack couldn’t scale. A modular, upward-compatible tech stack ensures that your AI capabilities evolve incrementally, adapting to your organization’s needs without requiring costly overhauls. More importantly, it democratizes insights, ensuring that critical data-driven intelligence is not just available to technical teams but empowers decision-makers across commercial, medical, and R&D functions. 

It’s also critical to recognize that progression through the stages is not always linear, or even necessary. In our experience, many companies see the greatest returns at stages two and three, which offer scalability and cost efficiency without overcomplicating the infrastructure. Moving to a fine-tuned LLM or pretraining a model should be reserved for highly specialized use cases, as these steps demand significant investment and may not provide proportional benefits. 

The keys to AI-ready data management 

The success of your generative AI project hinges on the strength of your data management, both structured and unstructured. In our work with life sciences companies, we’ve seen even the most sophisticated AI models fall short when the underlying data foundation isn’t properly established. 

For years, many organizations underinvested in data readiness, prioritizing AI model development over the fundamental groundwork needed for reliable, scalable AI deployment.

Without structured, secure, and connected data, AI systems can introduce bias, produce misleading insights, and even create regulatory compliance risks. To avoid this, organizations must build AI-ready data frameworks that not only support current initiatives but also enable continuous improvement over time. 

To ensure your AI initiatives are effective, compliant, and sustainable, focus on these six key pillars: 

• Data security and privacy. Protecting sensitive patients and clinical data is non-negotiable. Adhering to InfoSec best practices (encryption, access controls, and audit trails) ensures compliance with GDPR, HIPAA, and evolving regulatory frameworks. 
• Targeted data ingestion. AI models underperform when trained on unstructured or irrelevant data. Use domain-specific techniques to segment, preprocess, and contextualize raw data (e.g., breaking unstructured notes into meaningful “chunks” for better comprehension). 
• Bias mitigation and explainability. Unchecked bias in AI models can skew outcomes and introduce ethical risks in decision-making. Establish protocols for bias detection, transparency, and human oversight to ensure fair, explainable outputs. 
• Vector store maintenance. Keeping vector databases refreshed ensures AI models remain relevant and accurate. Regular updates allow for adaptability to evolving data landscapes and enhance the long-term reliability of AI-driven insights. 
• Data connectivity and interoperability. Siloed data limits AI’s effectiveness. Standardizing data formats and enabling interoperability across structured and unstructured sources (EHRs, claims data, genomic databases) ensures AI tools can “see the full picture” rather than working in isolation. 
• Continuous monitoring and improvement. AI data ecosystems must be dynamic. Implementing real-time monitoring, feedback loops, and model retraining protocols ensures AI applications remain adaptive, accurate, and aligned with new data patterns. 

Questions to ask your team 

Every new generative AI project begins with a fair and honest assessment of your current state, and where you would like to get to in terms of capabilities. Bring your leadership team and key stakeholders together and ask yourself the following: 

• What are your current needs? Most life sciences organizations find significant value at the Custom Built LLM level, which balances adaptability and effort, offering a scalable solution without overburdening resources. 
• Where can we start small and progress? Beginning with meta use cases and gradually advancing to relevant workflows that may need fine-tuning or pertaining (if necessary) helps manage resource constraints while achieving meaningful outcomes. 
• Are our generative AI goals aligned with business priorities? Ensuring that use cases directly support strategic objectives allows for measurable and meaningful impact on workflows and outcomes. 
• What is our timeline for progression through the roadmap stages? Clear milestones for transitioning from proof of concept to full-scale deployment help track progress and justify further investments. 
• What internal processes need to evolve as we scale? Identifying bottlenecks or inefficiencies in current workflows ensures generative AI implementations address key pain points effectively. 
• Is our tech stack ready to scale? Evaluating whether your current tech stack can support more advanced generative AI stages, such as Agentic AI, MCP (model context protocol) and A2A (Agent to Agent), RAG or fine-tuning, is essential for long-term success. 
• What is the right balance between simplicity and sophistication for us? Finding the sweet spot between quick wins with pre-trained LLMs and more complex solutions ensures a phased and efficient adoption. 
• Do we have a strategy for incremental tech adoption? Planning for a gradual increase in tech stack complexity minimizes resource strain and optimizes implementation. 
• Is our data infrastructure prepared to support generative AI? Ensuring data stores are well-organized, regularly refreshed, and properly contextualized improves generative AI accuracy and relevance. 
• How strong are our data security practices? Implementing strong security measures like encryption, masking, redaction, partial redaction and role-based access control (RBAC) is critical for compliance and protecting sensitive information. 

By getting your plan, processes and tech right at the beginning of your generative AI journey, you can grow your confidence and prepare yourself for bigger challenges.

Prioritizing the right meta use cases 

While the future of GenAI lies in scalable, system-wide intelligence that goes beyond individual use cases, we’re still in a phase where high impact, low complexity meta use cases are the most practical way to gain traction. That’s why it’s important to prioritize the right ones, those that deliver value today, and lay the groundwork for what’s next. 

GenAI's potential lies in its scalability and ability to address a wide array of use cases. To make the most of its capabilities, you need to prioritize use cases that are impactful, scalable, and aligned with your strategic goals. 

To illustrate the tangible benefits of your generative AI project and get further buy-in from your leadership team, categorize your use cases into three levels of impact: 

• Qualitative feedback. Subjective data, such as “I like the insights,” indicate initial user satisfaction. 
• Quantitative feedback. Measurable outcomes, such as “Turnaround time reduced from five days to one hour,” demonstrate clear efficiency gains. 
• Business impact. Strategic outcomes, such as “Better targets led to incremental sales,” represent the highest level of generative AI value. 

While immediate and shorter-term results may focus on lower-impact levels, organizations can progress through the stages outlined previously and achieve greater strategic value over time. 

Examples of meta use cases 

Once a solid data foundation is in place, organizations can prioritize use cases that are impactful, scalable, and aligned with strategic goals. Meta-use cases—those that encompass a family of related use cases—are ideal starting points.

Examples of common meta-use cases in the life sciences include: 

• Market research insights: Enhance data retrieval and analysis for deeper market understanding. 
• Insight summarization: Tease out insights based on large volumes of data (including unstructured) for comprehensive overviews. 
• Medical notes: Automate the tagging and structuring of medical affairs insights to improve accuracy and reduce human error. 
• Query bots: Enable instant answers to organizational and user queries. 
• Content creation: Automate content generation for scientific, marketing, and operational needs. 

To systematically select appropriate use cases, apply a complexity vs. impact matrix: 

• For impact, assess factors such as long-term value, business urgency, efficiency gains, and new capability enablement. 
• For complexity, evaluate data integrability, resource requirements, familiarity with technology, and workflow alignment. 

How to begin selecting your first meta use case 

• Focus on meta-use cases that align with scalability goals and organizational fit. 
• Start with lower-complexity, high-impact use cases to gain traction and build credibility. 
• Expand into more complex areas as foundational expertise and confidence grow. 

How it looks in action: Medical affairs case study 

By systematically prioritizing and executing impactful use cases, you can improve your chances of success while lowering the downside. 

With a clear plan and prioritized use cases in place, the next step is to drive adoption and measurable impact. 

Driving organizational adoption and impact 

Adoption challenges and the need for change management 

A common myth surrounding technology adoption is that ‘if you build it, they will come.’ In reality, we’ve seen many AI and digital initiatives stall. Not because the technology wasn’t effective, but because teams struggled to integrate it into their workflows. 

Without a clear strategy, even the most promising initiatives can lose momentum. To prevent this, organizations need a structured change management approach that actively builds adoption from day one. 

Six steps to accelerate widespread adoption 

Establish user buy-in  

How it helps: Engage executives and end users early to drive adoption.   Example: A commercial ops team won over skeptical field teams by demonstrating quick wins, like automating call summaries.   

Develop your brand

How it helps: A recognizable identity makes AI adoption seamless.   Example: A company branded its AI insights platform internally, making it feel like an integrated tool rather than a new, unfamiliar system.   

Identify ambassadors

How it helps: Power users drive adoption and train others.   Example: A sales ops team automated market research summaries, saving time and improving insights, encouraging broader team adoption. 

Choose impactful meta use cases

How it helps: Prioritize high-value applications for quick results.   Example: A company used GenAI for market research insights and then scaled it to competitive intelligence and executive summaries.   

Check the blind spots

How it helps: Soft launches refine AI adoption before scaling.   Example: A company tested GenAI with field teams to optimize customer engagement before expanding company-wide.   

Make a splash

How it helps: A strong rollout builds momentum.   Example: Leadership began requesting AI-driven insights weekly, sparking demand and accelerating adoption.   

Where to go from here 

To recap, we recommend three stages to your journey from proof of concept to scalable generative AI integration: 

• Build a strong foundation. 
• Prioritize high-impact, scalable use cases. 
• Maximize adoption with a holistic approach. 

As for next steps, you can get started right now with the following actions. 

• Action #1. Convene a cross-functional team to evaluate current readiness and identify a starting use case. 
• Action #2. Define your first project and establish clear success metrics to validate the concept. 
• Action #3. Conduct a tech stack review to ensure scalability and alignment with future goals. 
• Action #4. Begin building internal momentum by hosting a demo or kick-off event to showcase early potential. 

Bring your generative AI project to life 

Whether you’re launching your first initiative or scaling for enterprise-wide impact, Beghou Consulting has the expertise to guide you.

Explore Beghou Consulting’s AI solutions to learn how we help life sciences organizations turn generative AI into measurable value. To talk our experts, get in touch today.