Top 7 Must-Haves for AI Readiness in Biopharmaceutical Laboratories

by Oana Lungu, Ph.D. | posted on April 28, 2025

AI is transforming biotech and pharmaceutical research, development, and manufacturing by speeding up discovery, unlocking insights, and automating tedious work. However, having AI tools isn’t enough; AI readiness requires clean, contextualized data, along with a clear understanding of how it can be integrated into business processes to surface real insights and drive meaningful outcomes. AI readiness needs a coherent strategy in order to strengthen your business efficiency, scalability, and ability to stay competitive in a rapidly evolving market.

The path to AI readiness begins with understanding your current state, including how your data is captured and structured, its relationships, how it scales, how it’s shared, and whether it’s sufficiently contextualized for AI tools to interpret and act upon. The more fragmented your systems are, the harder it will be for AI to access clean, contextualized data. 

Here are the seven must-haves to ensure your lab is ready to scale with AI:

1. Know Your Business and Your Systems

To start your AI readiness journey, it is crucial to understand your current workflows and the tools that support them. Start by asking the following questions:

• • What are all of your critical laboratory processes?
  • What instruments and platforms are in use?
  • Are your systems integrated, or working in silos?

▹ Quick check:
How many systems does your laboratory rely on to manage data, workflows, and operations?
A. 1-2 fully integrated systems
B. 3-5 systems, some partially integrated
C. 5+ siloed systems with lots of manual effort

2. Understand Your Throughput and Scale

Your ability to capture data and derive meaningful insights through the power of AI must scale with your business and processes. Track your lab’s throughput across different workflows, teams, and sites. Understand the trends that drive your laboratory’s business: changes in processes and how often they occur, expanding teams, or changes in demand. Can the systems that capture the data driving AI keep up with the changes in your business? The more fragmented your systems are, the harder it will be for AI to access clean, contextualized data.

▹ Self-Assessment:
How confident are you in your systems and their ability to scale across new teams, sites, or use cases?
A. Very confident—it’s built to grow
B. Somewhat—we’re hitting limitations
C. Not confident—it’s already a patchwork

3. Align with Your Team

No strategy works without people: AI readiness must align people and their roles in your organization with the critical data they are responsible for. Map out how key roles in your organization, such as scientists, technicians, and data analysts, interact with data and the systems that structure it. Systems in your organization and the AI-generated insights they enable should support your team—not slow them down.

▹ Team Alignment Check:
How long does it take for a new team member to learn your systems?
A. Days—it’s intuitive
B. Weeks—with training
C. Months—everyone learns it differently

4. Evaluate Your Data Practices

Take a baseline for how your data is captured, shared, and contextualized today. Is it still trapped in paper records, scattered spreadsheets, or siloed systems? If so, AI tools will struggle to piece together the data and deliver value. For AI to be effective, your data must follow FAIR principles—it must be Findable, Accessible, Interoperable, and Reusable, with rich context, so that AI can interpret it accurately and act upon it.

▹ Data Practices Check:
Data handoff issues often reveal the cracks—how often do you lose time to broken pipelines, inconsistent formats, or missing information?
A. Rarely
B. Occasionally
C. All the time

5. Consider Regulatory Requirements

Ensure your strategy is built around the appropriate regulatory requirements for data integrity, security, and reporting. Key considerations include: making sure your systems are built for traceability, audit-readiness, and privacy considerations for sensitive data, as well as understanding the specific compliance frameworks that apply to your business: GxP, 21 CFR Part 11, ISO, etc.

▹ Regulatory Compliance Check:
How does regulatory compliance factor into your current laboratory system(s) for data capture?
A. Critical—we require cloud-agnostic, compliance-ready solutions
B. Important—it’s a factor, but not a deal-breaker
C. Not a major concern—we adapt our processes around the tools

6. Identify The Pain Points

AI is a tool to help get the most out of your laboratory data by automating tasks, overcoming pain points, and unlocking key insights that are difficult for humans to discern. Whether it’s inefficient workflows, tedious tasks, or challenging data analysis, AI should plug into those gaps to help move science forward. But first, you need to know where those gaps are. That means having a clear, structured, and documented view of your lab’s processes, data flows, and blind spots. Many labs are trying to leverage AI but have yet to align its use with their actual process challenges. Understanding where AI tools can relieve pressure is critical to effectively harnessing their power, rather than just adding another layer of complexity.

▹ AI Assessment:
What’s your current approach to enabling AI and ML across your research or operations?
A. We have a solid pipeline in production
B. We’re experimenting, but integration is tough
C. We’re struggling to even get clean, contextual data

7. Prioritize AI-Ready Data

Throughout this process, you’re likely to identify a range of pain points and opportunities for improvement, and prioritization will be key. Focus on what will have the highest impact on your lab’s performance and its ability to realize the full potential of AI. And nothing is more foundational, or more powerful, than making sure your data is AI-ready. Data is the lifeblood of your lab. If it’s fragmented, inconsistent, or lacks context, AI can’t deliver meaningful insights.

That’s where the L7|ESP platform comes in. It helps ensure your data is not only clean and connected but contextualized so that AI can actually understand and act on it. With flexible applications and built-in process orchestration, L7|ESP brings together your systems, data, and workflows, and people into a platform designed to scale with your needs. If you want to make real progress with AI, start by making your data ready for it.

Final Thought

Getting AI-ready is about aligning your systems, people, and data to build a strategy that maximizes the value to your business. Start by understanding your current state, and use these seven areas to guide your roadmap. As you answer the AI-readiness assessment questions provided in each section, consider what the results indicate about your laboratory’s AI readiness.

Results:

▹ Mostly A’s: You’re ahead of the curve.
Still, L7|ESP might help consolidate your gains and future-proof your AI investment. Worth a peek?

▹ Mostly B’s: You’re halfway there—but friction is building.
A unified platform like L7|ESP can reduce complexity and accelerate your roadmap.

▹ Mostly C’s: It’s time.
You’re dealing with fragmentation that’s stalling progress. Let’s talk about how L7|ESP can change that.

I thoroughly enjoyed crafting this piece to share insights from my experience with laboratory digitalization and strategic initiatives such as AI readiness. These are areas I’m passionate about, especially when it comes to helping organizations turn their data and processes into real business value. Please don’t hesitate to reach out if you’d like to discuss further or explore how I can assist you.