Insights from the “The AI Compounding Effect in Closed-Loop Quality and Manufacturing” Webinar
Most pharma organisations are still in the early stages of AI adoption, with 60% actively exploring but not yet implementing AI in quality or manufacturing.
Key challenges include poor data quality (38%), lack of system integration, and uncertainty around readiness.
Before AI can deliver value, organisations must first establish integrated systems, reliable data, and a strong digital foundation.
1. Most Companies Are Still Exploring AI (60%)
● Exploring / evaluating – 60%
This highlights a key trend.
AI in pharma is not yet mainstream at the operational level.
Most organisations are still in the evaluation phase—assessing use cases, risks, and regulatory implications before full adoption.
2. What Needs to Be in Place Before AI?
Before deploying AI, respondents pointed to several key prerequisites—most notably an AI-educated workforce (35%).
AI readiness is not just about technology.
It requires alignment across systems, data, and people.
Without this foundation, AI initiatives often struggle to move beyond pilot stages and deliver real operational value.
3. The Biggest Barriers to AI Adoption
Poor data quality / availability – 38%
In regulated environments such as pharmaceutical manufacturing, where data integrity is essential, poor-quality data can limit both compliance and AI effectiveness.
4.Why Organisations Want AI
Despite the challenges, the motivation to adopt AI is clear:
● Enhancing decision-making with data-driven insights – 35%
Organisations are looking for practical, measurable outcomes—improving performance, reducing errors, and enabling faster, more informed decisions.
The Reality: The AI Readiness Gap
When combining these insights, a clear gap emerges:
- High interest in AI
- Limited readiness to implement
This gap is driven by:
- Fragmented systems
- Inconsistent or incomplete data
- Limited digital maturity
Without addressing these areas, AI initiatives are likely to remain in the exploration phase.
From Exploration to Execution
If your organisation is currently exploring AI—you are not alone.
In fact, this is where the majority of the industry stands today.
The key is not to rush into AI implementation, but to build the right conditions for success:
→ Establish a strong digital foundation
→ Connect your systems
→ Ensure your data is reliable and compliant
This is where solutions such as:
play a critical role.
Platforms like MasterControl, supported by Factorytalk’s implementation expertise, help pharma organisations:
- Digitise and standardise processes
- Strengthen data integrity and compliance
- Create a connected data environment ready for AI
If you missed the session, you can still watch it anytime.
Learn how pharma organisations are approaching AI in quality and manufacturing—and what it takes to move from exploration to real implementation.
