‘Speed is no longer just one performance metric among many – it has become central to the entire business model’

To move quickly, many companies are buying skid-based manufacturing units – modular, pre-assembled systems – that arrive pre-qualified. It is an effective way to compress project schedules, but it fragments data ownership.

Instead of one coherent operational system, companies end up with dozens. Engineering data, process data, quality data and production records reside in different environments, owned by different vendors.

It is not uncommon to see 25 or more systems supporting a single product. The industry as a whole has been slow to move beyond isolated use cases to more holistic digital transformation.

This fragmentation exists largely because there are no industry-wide standard data models or exchange structures, with each supplier solving immediate project needs. It is also partly due to the challenges of introducing new digital tools in such a heavily regulated industry.

But here’s the rub: fragmented data makes it harder to analyse performance holistically, identify bottlenecks and improve processes.

It also makes regulatory review more manual, more error-prone and more expensive, precisely when volumes are ramping up.

Much of the public conversation around AI in pharma focuses on discovery. However, in practice, the most impactful applications today sit much closer to manufacturing.

Many pharma companies rely on multiple contract manufacturing organisations (CMOs) and contract development and manufacturing organisations (CDMOs) to meet GLP-1 demand. Each batch generates extensive documentation, often PDFs, that must be reviewed for compliance.

In many organisations, this review is still manual. Teams extract KPIs, temperatures, limits and deviations by hand and re-enter them into spreadsheets. It is slow, labour-intensive and risky.

AI can change this through rule-based, narrowly scoped applications that extract and validate data automatically. Instead of humans transcribing information, AI systems surface exceptions, flag deviations and accelerate review cycles.

The pattern extends to other applications, such as predictive maintenance that reduces unplanned downtime during high-volume GLP-1 campaigns.

The common thread is specificity. The AI that works in pharma is narrow, transparent and designed to augment human judgement rather than replace it.

In Europe, Annex 11 and the forthcoming updates to Annex 22 provide early signals of how regulators are thinking about AI in regulated manufacturing environments.

The emphasis is not on banning AI but on ensuring transparency and accountability, particularly for GxP decisions that affect product quality or patient safety.

Three requirements are emerging as non-negotiable:

Traceability: Every AI-generated output must be traceable to its inputs, training data and model version.

Explainability: Black-box models are unacceptable for GxP decisions.

If an AI recommends a process adjustment or supports batch release, the logic must be interpretable by domain experts and auditable by inspectors.

Human oversight: AI can assist, but it cannot autonomously make decisions that affect product quality or patient safety. A qualified person must review, validate and take accountability for the outcome.

These design constraints separate credible industrial AI from research prototypes. AI that cannot be explained, validated or audited will struggle to move beyond pilot phases. AI designed with regulation in mind will not.

CMOs and CDMOs are indispensable partners in scaling production quickly. But outsourcing does not transfer accountability.

If a company’s name is on the box, it remains legally responsible for what is inside it.
That responsibility extends to data integrity, auditability and regulatory compliance, regardless of where manufacturing takes place.

Without data continuity across internal operations and external partners, companies risk losing control precisely when scrutiny is greatest.

The lesson from GLP-1 is that speed must become sustainable.

The companies that come out on top will not simply be those that get to market fastest. They will be those that deploy capital projects intelligently, maintain data continuity across R&D, manufacturing and supply chains, and use AI to make faster, better-informed decisions.

That means treating data interoperability as a capital project requirement and deploying AI where it eliminates manual reconciliation – batch record review and deviation detection – without introducing new compliance risks.

It also means setting contractual requirements for how CDMOs structure and deliver batch data, pushing for real-time access to critical process parameters rather than PDF exports.

Most critically, it means assigning accountability for end-to-end data architecture. In many pharma organisations, IT owns systems, quality owns compliance and operations owns production, but no one owns the data architecture that allows them to work as one.

That needs to change.

In a GLP-1 world, speed has become a defining advantage. But the real test is whether such momentum can be sustained under scrutiny.

Success will depend on balancing rapid execution with long-term resilience; manufacturers must ensure progress holds up not only through the next quarterly earnings cycle but also under the weight of regulatory review and ongoing market expectations.

GLP-1 to 10 – fun facts

1. They were never originally designed for weight loss
GLP-1 receptor agonists were developed for type 2 diabetes because they stimulate insulin release, suppress glucagon and slow gastric emptying. The dramatic weight loss effect was initially a secondary observation in clinical trials

2. They reshape appetite signalling in the brain
GLP-1s do not simply reduce hunger; they modulate reward pathways in the hypothalamus and mesolimbic system. This means people often report reduced cravings, lower impulsive eating and less emotional eating, not just smaller portions

3. They slow stomach emptying – but only temporarily
Early in treatment, gastric emptying slows significantly, contributing to early satiety. Over time, this effect diminishes as the body adapts, but the central appetite effects remain strong

4. They may influence addiction pathways
Emerging research suggests GLP-1 drugs may reduce addictive behaviours, including alcohol and nicotine use. This is because GLP-1 receptors are present in dopamine-driven reward circuits. Several trials are now exploring this directly

5. They dramatically reduce cardiovascular risk
Beyond weight loss and glucose control, GLP-1s have shown robust reductions in major cardiovascular events. This makes them one of the few metabolic drugs with proven heart protective benefits

6. They are transforming supply chains and manufacturing
Demand for GLP-1s is so intense that it is reshaping global pharmaceutical capacity. Billions are being invested in new facilities, and companies are adopting modular, rapid-build manufacturing to keep up with demand

7. They may alter the future of bariatric surgery
Some surgeons report declining referrals as GLP-1s become more widely used. While surgery remains more effective for extreme obesity, GLP-1s are shifting the treatment landscape and prompting new hybrid care models

8. They are being studied for conditions far beyond diabetes and obesity
Trials are under way for heart failure, fatty liver disease, Alzheimer’s disease, Parkinson’s disease and even polycystic ovary syndrome. Because GLP-1 receptors are found throughout the body, the therapeutic potential is unusually broad

9. They are creating new economic and societal ripple effects
From reduced food consumption to changing airline weight load calculations, GLP-1s are influencing sectors far outside healthcare. Analysts predict long-term shifts in retail, insurance and public health spending

10. They are not all the same – and the next generation will be far more powerful
Dual and triple agonists (GLP-1/GIP, GLP-1/glucagon and beyond) are already showing greater weight loss and metabolic improvements than current drugs. The field is moving quickly towards multi-pathway metabolic therapies.

Thomas McCarthy is Industry Principal, Life Sciences at AVEVA