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Pharmaceutical outsourcing is entering a new phase where efficiency and precision matter as much as capacity. Timelines for drug development and scale-up are under unprecedented pressure, and contract manufacturers are expected to deliver not just volume but adaptability. The conversation is shifting to AI-readiness and how far companies have progressed in their digital maturity. As a joint study by McKinsey and MIT observes, leaders who apply AI systematically “pull ahead” by embedding it across operations rather than piloting in isolation.

For CDMOs, this makes AI in pharma manufacturing a factor shaping competitiveness and sponsor choice.

Digital Twins

Contract Pharma highlighted earlier this year how digital adoption is shaping CDMO competitiveness. Among the most powerful tools is the digital twin, a virtual replica of a manufacturing process that allows teams to simulate scale-up before steel is cut.

Grand View Research estimates the digital twin market in pharma will grow at more than 20% CAGR through 2030, reflecting demand for risk-free testing of process changes. For CDMOs, this can mean compressing technology transfer timelines from months to weeks, a critical edge as clients seek accelerated market entry.

Siemens’ acquisition of Dotmatics, a Boston-based Life Sciences R&D software provider,  underscores the momentum. By combining data management and process simulation, CDMOs can validate recipes, identify bottlenecks, and anticipate regulatory scrutiny in a digital environment before exposing live assets.

Firouz Asgarzadeh, President of OSD Pharmaceutical Solutions, presented at Contract Pharma’s Conference on AI in Pharma earlier this year. Asgarzadeh showed in his work on wet granulation, neurofuzzy logic and gene expression programming outperform traditional scale-up rules, enabling accurate modeling across equipment from 25L to 600L. This illustrates how digital twins can reduce the uncertainty of technology transfer by simulating complex processes before assets are committed.

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Predictive mAIntenance and LAI Development

Pharma plants run on narrow tolerances. An unexpected equipment failure can wipe out weeks of production and millions in value. AI-enabled predictive maintenance is becoming a safeguard. Algorithms trained on vibration, temperature, and flow data predict failures before they happen, shifting maintenance from reactive to proactive.

The approach has roots in broader industry, but in pharma its impact is magnified by validation requirements and batch sensitivity. As one Deloitte report noted, predictive analytics in maintenance can increase overall equipment effectiveness (OEE) by 10–20%, gains that translate directly into reduced downtime and higher yields.

Asgarzadeh’s research on robotic labs and AI in formulation development demonstrates how automation can cut development times, too. In some areas by up to 50%. While focused on early-stage R&D, the lesson translates to manufacturing: predictive tools are not just about avoiding downtime, but about embedding speed and adaptability into the system itself.

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Continuous Manufacturing

For CDMOs, the biggest step-change may be the move from batch to continuous production. It promises shorter timelines, tighter process control, and greater efficiency. Improving smart technologies is a primary indicator and enabler for continuous manufacturing. According to a Deloitte study, 49% of respondents reported operational benefits as the primary value sought with smart manufacturing. For clients, this means faster time-to-market; a business priority cited again and again in 2025.

Continuous production often requires significant CAPEX, but CDMOs that invest strategically can spread those costs across multiple clients, making them more attractive partners.

“Continuous manufacturing provides a more reliable, lower-cost supply of medicines, and we want to see broader adoption across the industry,” said Janet Woodcock, Former Acting Commissioner of the U.S. FDA.

Asgarzadeh’s research backs this up, too. In his studies of AI-enabled granulation, Asgarzadeh concludes that these models “exceed traditional dimensional rules, proving reliable with unseen and dissimilar data.”

These comments underscore how regulators see continuous production as a pathway to resilience and how they are being put into practice with AI in pharma. For CDMOs, the focus is less on proving the technology and more on how rapidly digital infrastructure can sustain it at scale.

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The Digital Maturity Gap

For CDMOs, the commercial risk is in adopting AI with uneven data infrastructure. The digital maturity gap has been an issue since before 2016, according to a McKinsey report. That gap is where sponsors will increasingly differentiate, by choosing partners who can validate their digital competence as rigorously as their GMP credentials.

High-level digital maturity involves more than scattered pilots. It begins with embedding AI into everyday operations so that models inform routine scheduling, quality checks, and maintenance decisions rather than sitting in a test environment. The second step is linking predictive tools to regulatory-grade datasets, ensuring that insights can withstand audit scrutiny as well as accelerate throughput. Lastly, digital maturity requires a workforce capable of acting on AI outputs, like technicians, engineers, and quality teams who can interpret and implement model recommendations in real time. Without this integration of people, process, and data, even the most advanced algorithms remain sidelined.

As the McKinsey/MIT study emphasized, “The leaders are not the ones experimenting on the edges, but those who have reorganized workflows around AI as a core capability.”

Asgarzadeh also cautions that regulatory compliance, limited datasets, and workforce readiness remain barriers to implementation. Without addressing these fundamentals, even the most advanced models can remain sidelined — reinforcing why sponsors assess digital maturity as closely as GMP compliance.

Some firms like Veeva are making strides towards strengthening their data infrastructure.

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What This Means for Pharma Partnerships

Sponsor expectations are expanding. While capacity and geography remain important, digital maturity is emerging as a decisive factor. CDMOs that apply AI as working tools (shortening transfers, preventing downtime, and sustaining continuous output) stand to be chosen as partners in strategy, not just execution. 

For CDMOs, this means presenting AI capabilities not as future pilots but as live differentiators: digital twins that accelerate scale-up, predictive maintenance that reduces downtime, and AI-enabled continuous manufacturing that assures quality while improving efficiency. CDMOs that master this shift will move from filling orders to shaping outcomes.