From orchestrated AI to scalable value creation – why governance is key
The life sciences sector has moved beyond the initial phase of AI euphoria. Many pharmaceutical and medtech companies now use AI assistants and AI agents for clearly defined tasks, such as documentation, planning or logistics. In a subsequent step, these individual solutions were orchestrated: several AI components work together to manage complex processes along the value chain – from production and quality assurance to distribution.
However, as the technology matures, a key problem is becoming apparent: scalability. A significant proportion of AI initiatives remain stuck in pilot phases and never reach stable, routine operation. The cause rarely lies in the technology itself, but primarily in the lack of clear governance structures. Without binding guidelines, there is a lack of trust, decision-making capacity and regulatory certainty.
The scaling trap in regulated environments
In highly regulated sectors such as pharmaceuticals and medtech in particular, a lack of governance acts as a stumbling block. Typical patterns include:
- Data silos and a lack of integration, which prevent end-to-end optimisation.
- Compliance uncertainty, particularly in the case of AI-supported decisions, which must be traceable for audit purposes.
- Unclear business benefits, because objectives, KPIs and responsibilities are not defined.
Without governance, AI pilot projects degenerate into never-ending experiments. Scaling up therefore requires not more technology, but clear structures that make innovation manageable.
Governance as an operating system for scaling AI
AI governance is the regulatory framework that strategically aligns AI initiatives, manages risks and ensures compliance. It acts as an operating system for scaled AI:
- Strategic anchoring: AI projects need support at board level and must make a measurable contribution to business success (e.g. efficiency, resilience, risk reduction).
- Clear roles and responsibilities: From development through to operations and monitoring, responsibilities must be clearly defined between IT, business units and Quality & Compliance.
- Risk management and control: Potential impacts on product quality and patient safety are assessed in advance; protective measures are integrated from the outset. A key element here is the ‘human-in-the-loop’ principle: when it comes to quality-critical decisions, humans remain the final authority.
In this way, governance does not act as a hindrance, but rather as an enabler for reliable, auditable and value-adding AI processes.
Regulatory Reality: GxP, GDP and Data Integrity
In the fields of pharmaceuticals & medical technology, GxP standards apply without exception to AI as well. In logistics in particular, the focus is on GDP requirements: transport, storage, the cold chain, track & trace and security must be managed and documented at all times.
A key cornerstone is data integrity in accordance with ALCOA+. Data – including training data, model results and the decision-making logic of AI systems – must be traceable, verifiable, accurate and permanently available. This is supplemented by audit trails and electronic records in accordance with 21 CFR Part 11 or comparable regulations.
Practical example: If an AI system suggests a route change to avoid temperature deviations, it must later be possible to trace which data this recommendation was based on and who approved it. Governance ensures that such decisions are made transparently and in compliance with regulations.
From CSV to CSA: Validation as an enabler
A key driver for faster scaling is the transition from traditional Computer System Validation (CSV) to Computer Software Assurance (CSA). Whilst CSV was often associated with extensive documentation regardless of risk, CSA takes a risk-based approach: documentation and testing are carried out where there is a genuine risk to patients or product quality.
For AI applications, this means:
- Shorter validation cycles for non-critical changes.
- A focus on essential functions, such as temperature control in the cold chain.
- Greater agility, as models can be adjusted more frequently without compromising compliance.
CSA is not deregulation, but a more precise form of quality assurance that enables innovation whilst safeguarding safety.
Shared governance across the supply chain
AI governance does not stop at company boundaries. In the pharmaceutical and medtech supply chain, manufacturers and logistics service providers must take joint responsibility. Shared governance encompasses:
- Common data and process standards to ensure end-to-end transparency.
- Coordinated quality agreements that define who is responsible for AI models, their operation and validation.
- End-to-end auditability, ensuring that AI decisions remain traceable across organisational boundaries.
Only when all partners act in unison can orchestrated AI realise its full potential.
Executive Summary
AI orchestration has become established in many organisations. However, sustainable value creation only arises when AI is scaled, controlled and made compliant with regulatory requirements. The decisive factor here is governance.
For management, this means:
- Establishing AI governance as a top priority.
- Consistently implement ‘compliance by design’.
- Manage the transition from pilot to routine operation in a structured manner and align it with measurable benefits.
Companies that consistently combine governance with AI expertise build trust among auditors, partners and employees – and turn AI initiatives into scalable, robust and value-adding solutions.
Grieshaber Logistics Group AG advocates the sensible use of AI-supported systems and is open to the concept of shared governance.
Yours
Grieshaber Logistics Group AG