Successfully unlocking the transformative power of AI in biopharma and biotech demands a strategic, holistic approach focused on building robust foundational capabilities. Many organizations, despite experimenting with AI, struggle to scale initiatives and achieve consistent, enterprise-wide value, often due to shortcomings in these foundational areas.6
What are these foundational areas? We explore some of the key ones below:
Before Deploying Any Model : Defining Use Cases and Assessing Readiness
Immaturity or significant weakness in any single dimension can create critical bottlenecks and ultimately derail an organization’s entire effort to establish a robust and value-generating AI capability.
A clear, business-driven AI strategy is paramount. McKinsey research indicates that around 75% of life science organizations lack a comprehensive vision or strategic roadmap for GenAI linked to business priorities, often proceeding with decentralized, use-case-by-use-case experimentation.6
This fragmented approach rarely leads to scalable success. Instead, organizations need a purposeful, top-down commitment to align AI initiatives with core business objectives and well-defined use cases.1 Most firms lack the internal knowledge for the AI ecosystem and empirical testing required to assess this. So what do they do instead? The same McKinsey report found that, given the sheer human and economic cost of end-to-end AI ecosystem development, the majority resort to external experts.
Before embarking on large-scale implementation, a thorough assessment of organizational AI readiness is crucial. This involves evaluating maturity across several key dimensions 31:
Technical Infrastructure: Assessing current systems, cloud capabilities, computing power (including high-performance computing for complex models), and system interoperability.20 As evidenced in biopharma R&D, robust and modern infrastructure is foundational for developing, deploying, and scaling demanding AI applications and ensuring seamless data flow necessary for advanced analytics.
Data Maturity: Evaluating data availability, quality, integration across silos, and governance practices. Organizations must ensure their data is fit for purpose, as biased or poor-quality data will invariably lead to flawed or unreliable AI outcomes.34
Human Skills: Understanding existing AI/data science talent, identifying skill gaps, and upskilling the broader workforce in AI literacy and application.6
Processes & Governance: Reviewing existing workflows and establishing frameworks for responsible AI deployment and regulatory compliance. A clear AI strategy and roadmap are essential to guide these efforts and ensure alignment with overarching business goals.37
Culture: Gauging the organization’s adaptability and willingness to embrace AI-driven change. This starts with strong leadership commitment and C-suite engagement to champion the AI vision and manage the transformation effectively.20
The Interconnectedness of Readiness: Why Holistic Maturity is Non-negotiable
It is critical to understand that the five dimensions form a highly-interdependent ecosystem. Immaturity or significant weakness in any single dimension can create critical bottlenecks and ultimately derail an organization’s entire effort to establish a robust and value-generating AI capability. State-of-the-art infrastructure is ineffective without mature data and skilled personnel. Talented teams are hamstrung by poor data or inadequate processes. Even the best models built by top talent will fail if the underlying data is flawed or governance is weak. Similarly, a resistant culture or lack of strategic direction from leadership can neutralize technological advantages. Successful AI transformation, therefore, demands a balanced and holistic approach, ensuring all foundational dimensions mature
in concert.
Frameworks like the EY.ai Maturity Model can provide a structured approach to this assessment.31
Current data suggests a significant readiness gap, with one report indicating36
The journey to AI maturity is often hampered more by organizational and foundational gaps than by the technology itself. Fragmented strategies, insufficient talent planning, weak data governance, underinvestment in change management, and a lack of clear operating models are frequently cited as the primary barriers preventing biopharma companies from moving beyond isolated pilot projects to achieving scalable, enterprise-wide AI value.6 Addressing these foundational pillars proactively is therefore essential for unlocking AI’s full potential.
Essential Pillars to Build the AI Ecosystem
Building a successful AI capability rests on several interconnected pillars
Data Strategy and Governance
- High-quality, well-governed data is the bedrock of effective AI.
- This requires establishing robust processes for data collection, cleaning, integration, harmonization, and ongoing governance.34
- Poor data quality inevitably leads to flawed AI outputs and unreliable insights.41
- Adhering to FAIR data principles (Findable, Accessible, Interoperable, Reusable) is crucial for maximizing data utility.15
- Centralized governance models for data and risk are common.1
Model Selection, Validation, and Monitoring
- Choosing the right AI model depends on the specific use case, data characteristics, and performance requirements (e.g., accuracy, interpretability, speed).43
- In the highly regulated biopharma environment, rigorous, independent validation of AI models before deployment is non-negotiable.44 This involves assessing conceptual soundness, data inputs, assumptions, and performance against predefined metrics.43 Furthermore, continuous monitoring after deployment is essential to detect performance degradation, data drift (where the input data changes over time, affecting model accuracy), or emerging biases.44
Technology Infrastructure and Platforms
- Scalable and flexible technical infrastructure is necessary to support demanding AI workloads. This includes adequate cloud resources, computing power, and data storage.20
- Ensuring interoperability between new AI platforms and existing systems (including legacy systems in large organizations) is a significant challenge too.33
- Adopting a platform-driven approach from the outset promotes sustainability, reusability, and consistency across different AI initiatives.6
Talent Acquisition and Upskilling
- The shortage of skilled AI professionals, particularly those with domain expertise in life sciences, is a major bottleneck.6
- Organizations need strategies to attract, develop, and retain talent in roles like AI engineering, data science, and prompt engineering.6
- Equally important is upskilling the existing workforce to foster AI literacy and enable effective collaboration between humans and AI systems.37
- A significant gap often exists between employees’ readiness and desire for AI training and the level of support provided by organizations.30 Bridging this gap is key for adoption.
Operating Models and Responsible AI Governance
- Defining an appropriate operating model – whether centralized, decentralized, or a hybrid model utilizing a Center of Excellence (CoE) – is crucial for managing AI initiatives effectively.6
- Crucially, establishing and embedding a robust Responsible AI framework is imperative. This framework must address ethical considerations, fairness, transparency, explainability, accountability, privacy, and security.37
- Proactive bias mitigation strategies, including using diverse datasets, employing algorithmic fairness techniques, and conducting bias audits, might be ideal but are extremely complicated to establish.48
- Risk management must be integrated throughout the entire AI lifecycle, from conception to retirement.6
Specific use case
These 4 pillars defend against failure to embed the principles of fairness, transparency, validation, and robust governance from the outset; without these, the entire AI initiative will likely succumb to regulatory non-compliance, eroding trust among stakeholders, and ultimately hinder adoption, particularly for applications deemed high-risk.44
