Part 2: Fit for Purpose: Aligning AI Strategy to Your Company
Building AI strategies that reflect function, regulatory risk, and organizational maturity
This article is the second in a series on AI strategy in life sciences. It follows "The Strategic Imperative: Why Life Sciences Organizations Need a Deliberate AI Strategy," which outlined why AI cannot be adopted ad hoc in regulated environments. This article focuses on tailoring your strategy based on what kind of company you are — and what kind of AI you use.
Introduction: The Myth of the Single AI Strategy
If you’re looking to "develop an AI strategy," you’re already ahead of many. But here’s the reality: most life sciences organizations don’t need one AI strategy — they need several.
AI strategy isn’t monolithic. It should be layered, risk-aware, and tailored to the organization’s role, maturity, and regulatory exposure. And just as critically, it should flex based on the type of AI being used and where it's being used.
This article breaks down methods you can use to construct a fit-for-purpose AI strategy: one that works for your organization, your stakeholders, and your regulators.
And it's important to note: an AI strategy isn't an execution plan. Much of it may live as a roadmap — with clear intent, structure, and prioritization — but without immediate implementation. The value lies in knowing where you're going, even if you're not building everything at once. It sets the direction and helps align everyday decisions — from product design or vendor selection to infrastructure and staffing — even when AI isn’t the explicit focus.
🔁 If you haven’t read Part 1 — "The Strategic Imperative: Why Life Sciences Organizations Need a Deliberate AI Strategy" — it’s foundational. Among other things, it defines the seven core elements of a pragmatic AI strategy for GxP organizations.
One Company, Multiple Strategies
Your AI strategy must account for organizational complexity. A single company may require domain-specific (what the AI does) and function-specific (where the AI is used) sub-strategies — all aligned under a unified governance framework.
Without a cohesive governance framework, your strategy risks becoming siloed, inconsistent, or noncompliant — especially as AI use scales across teams, functions, and regions. AI is far from static — it is evolving continuously through model updates, retraining, and new applications — that framework is essential to ensure developments across the organization are coordinated, auditable, and aligned to business and regulatory priorities.
Examples:
A clinical operations group using predictive analytics for site selection will need statistical validation and drift monitoring.
A pharmacovigilance team using Natural Language Processing (NLP) to extract coded terms from unstructured reports will require traceability and high linguistic accuracy.
A legal or communications team piloting generative AI tools needs content review guardrails but likely no formal validation.
These aren’t edge cases. They’re business as usual. Each use case demands its own approach to oversight, validation, and operational control.
Smaller organizations may struggle to resource multiple sub-strategies. In those cases, it’s better to start with high-priority domains and expand incrementally. Consider lightweight validation protocols for low-risk applications or working with external advisors to scale strategically.
It’s also critical that these sub-strategies align with your broader IT and digital transformation plans — including shared data models, governance standards, and architectural consistency.
The takeaway: your strategy should include multiple functional and domain-specific AI sub-strategies, each right-sized to the risk and purpose of the system.
Know Your Role: Organization Type and Strategic Emphasis
The nature of your company shapes how you approach AI.
GxP Tech Vendors / SaaS Providers
Embed validation, explainability, and change control into product design.
Provide client-facing documentation, inspection readiness, and lifecycle risk tiering.
Document model version history, clearly state intended use, and offer validation-ready materials — such as test scripts or templates — that clients can adapt to their QA systems.
Biotech / Pharma
Ensure internal strategy that evaluates vendor claims, validates high-impact systems, and governs cross-functional use.
Harmonize R&D-driven innovation with QA/Compliance-driven oversight.
CROs and Service Providers
Align with sponsor expectations and study-specific SOPs.
Provide AI implementations that are modular, auditable, and transparent.
Many organizations play more than one role — and your AI strategy must reflect that dual identity.
Need help translating these roles into a governance model? Contact us for a working session.
Aligning with Global Regulatory Expectations
This approach aligns with key principles from the FDA’s draft Good Machine Learning Practice (GMLP) guidance, including transparency, traceability, cross-functional oversight, and lifecycle-based risk control.
Your AI strategy must be geographically aware — because what’s acceptable in one region may be non-compliant in another.
FDA: Increasing emphasis on transparency, traceability, and continuous oversight (as of May 2025). See FDA’s Good Machine Learning Practice (GMLP) draft guidance, which emphasizes explainability and model reusability.
EMA: AI systems may soon fall under classification and post-market monitoring requirements under the EU AI Act. High-risk AI applications will need conformity assessments and live performance documentation post-deployment.
Global: Data sovereignty, documentation standards, and validation expectations vary — strategy must account for these shifts.
📌 This builds directly on the “Regulatory Engagement”, the 7th element from Part 1. If you missed it, it describes how proactive documentation strategies support inspection readiness.
Strategic Use Case Planning
Not every use case warrants the same strategy depth. Use a structured method to prioritize and right-size your AI initiatives.
Use these dimensions:
AI Type: Predictive, NLP, Generative, Agentic
Function: R&D, Clinical, Commercial, Regulatory, QA
Regulatory Exposure: Direct impact on patient safety? Submission-critical?
Organizational Maturity: Do you have the oversight structures needed?
A simple prioritization grid helps you:
Focus on lower-risk, high-value use cases first
Avoid overspending validation time on exploratory tools
Flag which sub-strategies require build-out before implementation
Keep in mind that implementation isn’t just about risk — it’s about change management. Functional leaders may resist varying oversight requirements. Successful implementation often depends on early involvement from cross-functional stakeholders, change champions, and clear communication around why a layered strategy protects both speed and compliance.
You’ll also need metrics. Consider tracking: number of AI-supported decisions under oversight, number of retraining events due to drift, time from pilot to validated use, and rate of audit findings tied to AI systems.
We’re developing a tool to help clients map use cases by risk, value, and readiness. Reach out to beta test.
Operationalizing Your Strategy
Even the most well-designed AI strategy will fail without proper implementation. Two critical elements deserve special attention:
📚 Training: Everyone Needs to Understand the Strategy
People across the organization have different levels of familiarity with AI — and that's a problem if they're expected to work with it, validate it, or make decisions based on its outputs.
The company's AI strategy — both the overarching approach and the sub-strategies by function or system — must be clearly communicated and understood. That means training needs to be structured, repeatable, and relevant to each role. Not just a one-time orientation.
This isn't an academic exercise. If teams don't understand how AI fits into the company's risk posture, validation approach, or oversight model, they can't work in alignment with it. And that creates compliance risk and operational inconsistency.
📄 Policies and SOPs Must Reflect the Strategy — and Vice Versa
Controlled documents are where process and strategy meet. If the AI strategy says one thing and the SOPs say another, the system breaks down.
Your AI strategy must be reflected in controlled documents — and updates to those documents should trigger a review of the strategy itself.
When a process changes or a new AI tool is introduced, the related SOPs, policies, and training materials need to be updated.
When updates to documents are happening frequently or inconsistently, it may be a sign the strategy needs to evolve.
This isn't just documentation hygiene. It's how you ensure consistency, accountability, and audit readiness as your use of AI grows.
Conclusion: Alignment is Your Differentiator
As we’ve described, your AI strategy isn’t monolithic, it also isn’t purely about immediate execution. Your roadmap may include capabilities you won’t implement for quarters or years — but defining them now sets direction, governance expectations, and resource alignment that position you to act when the time is right. In the meantime, that roadmap influences architecture, hiring, procurement, and compliance — long before specific AI tools are deployed.
You don’t need to be first to adopt AI — but you do need to be deliberate. A fit-for-purpose AI strategy recognizes:
That your company likely needs multiple sub-strategies
That your role (vendor, user, regulator-facing entity) matters
That different flavors of AI carry different validation burdens
That geographic regulatory environments shape what “compliance” really means
The various layers and categories of AI strategy we’ve described here are not independent, they don’t exist in isolation. They must reinforce each other, share common governance elements, and align with broader digital initiatives across the organization. Otherwise, you’re just adding complexity without control.
Coming next: “Governance Models for AI in Regulated Teams” — how to implement policy, ownership, and oversight structures that scale with your AI maturity.
Ready to take stock of your current AI use cases? Contact Driftpin Consulting for a strategic readiness session or AI validation review.
This article is Part 2 in our AI in Life Sciences Strategy series.
Part 1: The Strategic Imperative: Why Life Sciences Organizations Need a Deliberate AI Strategy
Part 3 (Coming Soon): Governance Models for AI in Regulated Teams