Industry
Life Sciences
Patient-centered Care Models Taking Center Stage in Life Sciences
Highlights
- Pharma companies are infusing generative AI into regulated, business-critical work. This increases the threat to the organisation’s intellectual property, with the risk of know-how leaving the company.
- There are other risks, such as escalating costs with increased usage, model changes requiring validation, and lock-in risk in the case of a single provider.
- The blog discusses which architecture protects company-knowhow.
- Based on the task’s risk, workload, and AI maturity, organisations can choose between a provider-hosted LLM with RAG, or a private Specific Purpose Model (SPM) or a hybrid with routing.
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What are the five risks leaders must consider before scaling AI?
While AI as a pilot may work well, scaling it calls for multiple considerations. Organisations need to address several risks and governance considerations before deploying AI at scale. These include:
- Exposure of know-how: Hosted AI makes it possible for prompts, documents, and outputs to leak outside the company. Contracts, retention, encryption, and hosting location decide the real risk.
- Cost at scale: Retrieval Augmented Generation (RAG) adds text to every request. Compare total cost per approved task, not token price alone.
- Speed: Retrieval and guardrails slow responses.
- Quality and validation: General models may apply company rules inconsistently. Every model change must be tested and documented.
- AI maturity: Organisations must choose a design that the teams are confident of running safely today. If it needs more data, skills or governance than a company already possesses, the risk factor increases.
- Why now: AI is moving from pilots into regulated, business-critical work such as safety case intake. Data exposure, cost, and model changes increase at this scale. Regulators now expect proof for each AI model being credible for its intended use, with every change tested and documented. The choices that organisations make will shape control, cost, and lock-in as AI scales.
How to choose the right AI architecture based on risk factors?
Pharma AI use cases are not always similar. Some, like safety case intake, handle high-volume sensitive data. Others need broad answers from fast-changing knowledge. The approach is to match each use case to the pattern that fits it best:
- Hosted LLM + RAG: Best for broad, exploratory questions and rapidly evolving knowledge. With its ease of deployment, it suits the early AI maturity stage.
- Private SPM (with optional private RAG): Best for sensitive, stable, high-volume work. The model is trained with rules and formats. Changing facts are retrieved.
- Hybrid with routing: Best for a mix of routine and complex tasks. This model ensures that sensitive requests stay in-house. Based on complexity, quality, and cost, others are sent to the LLM. Uncertain requests use a safe fallback.
How to get there, step by step:
- Classify: Assign scores to use cases on sensitivity, volume, repeatability and regulatory impact. Assess AI maturity honestly.
- Control: Set non-negotiable rules for data location, retention, encryption, access and exit.
- Baseline: Measure today’s accuracy, cycle time, cost and review effort.
- Golden set: Build expert-agreed test cases before fine-tuning.
- Prove, then scale: Run a controlled proof of value. Scale one validated use case at a given time.
What do organisations gain?
With good data, accurate routing and strong governance, this risk-based approach to choosing AI architecture helps pharma leaders gain control, steadier costs, and proven results.
- Protect know-how: Sensitive data and company rules stay in a controlled environment.
- Control cost: Steady, high-volume work can cost less per task on dedicated capacity.
- Improve task fit: Specialised models apply company terms, formats, and rules more consistently.
- Stay audit-ready: Routing decisions, model versions and changes are logged and governed.
- Reduce scale-up risk: Measured results, and not assumptions, drive decisions.
- How to prove benefits: Each option must be put to a fair test. In the current as-is process, the RAG-led design, and the hybrid design should be applied to the same real cases simultaneously. Then evaluate accuracy, speed, cost of a good outcome, security of sensitive data, issues to be fixed, and auditreadiness. Only the design that clears the agreed bar should be chosen.
What are the advantages of a risk-based architecture approach?
The risk-based approach considers the specific task and the organisation’s readiness.
- Risk-based, not tool-led: No pattern is considered arbitrarily. Each option is scored against ten clear decision factors.
- A maturity path: Architecture grows over time. As capability builds, SPMs are added to RAG.
- Honest evidence: Published findings, internal results and assumptions are kept separate.
- Clear ownership: Pharma owns what makes it different: expert-labelled data, golden sets and acceptance criteria. Standard parts can be sourced, with clear exit options.
- Grounded in practice: Built on credible best practices in regulated operations.
Rent or own?
Ultimately, choosing the right AI architecture is not a one-time decision or a simple choice between renting and owning. Each pattern has valid uses and different risks. A hybrid design may offer more control and steadier cost for sensitive, repetitive work. However, this is possible only with accurate routing, good data and strong governance. A well-controlled hosted design still suits a broad range of tasks.
Bottom line: It is important to prove that a model or a specific architecture works before scaling. Scaling must be step by step. Organisations must revisit the choice as models, prices, rules, and AI maturity change.