Artificial intelligence (AI) is becoming an integral part of the CDMO operating model. Organizations are deploying AI to accelerate batch record preparation, summarize deviations, draft regulatory narratives, and standardize documentation workflows. These applications reduce administrative burden, improve consistency, and support margin discipline in highly regulated environments.

Focusing solely on documentation efficiency may overlook AI’s broader impact, however. The key question is not how quickly AI can generate documentation, but whether it will influence capital allocation, risk modeling, and long-term strategy for CDMOs. Over time, AI adoption may separate organizations that use it for process efficiency from those that integrate it into strategic decision-making.

Path 1: Automation at the Execution Layer

For many CDMOs, AI adoption begins at the execution layer, including:

• Drafting and revising SOPs
• Accelerating batch record documentation
• Summarizing deviations and CAPAs
• Supporting regulatory submission narratives
• Standardizing quality documentation

The benefits are clear: shorter cycle times, less administrative burden, and improved audit consistency. In regulated environments, this is a practical and low-risk starting point.

As these tools become more common, execution-layer automation will likely become a standard capability. In a few years, documentation automation may be as routine as electronic batch records are today. Importantly, this layer does not materially alter structural drivers of CDMO performance, including:

• Scale-up variability
• Asset utilization
• Capital deployment decisions
• Modality demand shifts
• Client concentration risk

While documentation efficiency improves operations, long-term performance depends on capital allocation and portfolio dynamics. As AI-enabled documentation becomes standard, productivity gains at this level may narrow rather than widen competitive differences.

Structural Volatility and Capital Intensity

The CDMO sector operates in inherently cyclical markets. Periods of strong demand, such as pandemic-driven biologics expansion, have historically led to significant capital investment. Subsequent shifts in funding, clinical attrition, and modality preference often create utilization pressure.

Only a fraction of early-stage programs reach commercialization. At the same time, emerging modalities, mRNA, cell and gene therapies, and antibody-drug conjugates, can rapidly redirect demand across platforms.

For capital-intensive assets, especially large-scale biologics facilities, utilization variability is a key factor in return on investment. In this context, modeling uncertainty before deploying capital is essential for economic success.

Path 2: AI as a Risk Compression Tool

Some CDMOs are extending AI beyond documentation into predictive and simulation domains, including:

• Yield variability modeling
• Raw material sensitivity analysis
• Scale-up scenario simulation
• Cross-program deviation pattern recognition
• Inspection trend analytics

These applications allow for earlier identification of operational risks.

For example, in biologics scale-up, predictive modeling may identify parameter sensitivities during development rather than at late-stage validation, reducing costly disruptions. Similarly, cross-program analytics can reveal recurring technology transfer bottlenecks across sites, issues that may not be visible within individual programs.

At this level, AI helps reduce operational risk by lowering variability, stabilizing performance, and improving reliability.

However, it still primarily improves decision-making processes rather than determining which decisions are carried out.

Path 3: AI Embedded in Strategic Decision Processes

A more advanced level of integration embeds AI into capital allocation and portfolio strategy.  AI-informed modeling can support:

• Portfolio mix decisions across modalities
• Capacity allocation across sites
• Probability-weighted demand forecasting
• Client concentration risk analysis
• Timing and scale of capital investment

In the past, expansion decisions have relied on backlog visibility, market outlook, and executive judgment. AI introduces probabilistic modeling into these discussions.

Consider a biologics capacity expansion.  A traditional approach may rely heavily on current backlog and projected growth. An AI-informed approach may incorporate additional variables, such as attrition-adjusted clinical probabilities, sponsor funding risk, and yield variability across similar programs. In some scenarios, this may indicate that expected utilization is materially lower than headline demand suggests.

Similarly, in emerging modalities such as cell therapy, AI-based demand modeling may show that peak capacity requirements are driven by a limited number of late-stage programs. This can support modular or phased expansion strategies rather than full-scale facility buildout.

In this context, AI shifts from an efficiency tool to a core decision-support system.

CDMOs that limit AI to documentation may improve efficiency while still making structurally suboptimal capital decisions. Those embedding AI into capital planning may improve asset utilization, reduce overcapacity risk, and strengthen long-term returns.

This represents more than a technological upgrade; it is an evolution in governance.

Barriers to Deeper Integration

The primary challenges are organizational, not technical Execution-layer AI fits within existing processes. Strategic AI, however, requires new approaches to data integration, governance, and incentive design.

Regulatory Considerations

AI-informed models must meet data integrity, validation, and audit expectations. In practice, this does not require fully “black box” systems to be used in regulated decision contexts. Many organizations are instead adopting hybrid approaches, combining interpretable models, constrained datasets, and documented assumptions, to ensure outputs can be explained, challenged, and audited when necessary.

Cultural Norms

In many CDMOs, capital allocation remains one of the least analytically formalized processes, despite being the primary driver of long-term returns. For example, expansion decisions may be justified by backlog visibility alone, without systematically adjusting for clinical attrition or sponsor funding risk. Moving toward probability-based planning requires a shift from deterministic forecasts to quantified uncertainty.

Data Integration

Strategic modeling depends on integrating commercial, technical, quality, and financial data, often siloed across functions. For instance, a robust capacity model may require linking pipeline probabilities (commercial), yield variability (technical), deviation trends (quality), and return thresholds (finance), which are rarely housed in a unified system.

Governance Clarity

When AI informs capital decisions, ownership must be explicit. If a model supports delaying a major expansion, organizations must define who is responsible for validating assumptions, interpreting outputs, and ultimately making the decision. Without this clarity, models risk becoming advisory rather than actionable.

Incentive Alignment

AI-driven insights may support restraint, such as delaying or resizing expansion. However, if commercial or operational incentives are tied primarily to growth or capacity expansion, these insights may be overridden. Aligning incentives with long-term capital efficiency is essential for strategic AI to influence outcomes.

These factors help explain why strategic AI integration may progress more slowly than execution-layer automation.

Over time, AI adoption may differentiate CDMOs not by whether they use AI, but by how deeply it is integrated.

• Execution-layer users may achieve efficiency and consistency.
• Predictive users may shift variability from a reactive problem to a proactively managed variable, improving consistency and delivery reliability.
• Strategically integrated organizations may enhance capital discipline and long-term asset performance.

The key distinction may ultimately be whether AI is applied to operations or to decisions that shape the balance sheet.

The Leadership Question

For CDMO executives, the critical question isn’t whether AI has been implemented, but at what level it informs decision-making—whether at the execution layer, in managing operational risk, or in shaping capital and portfolio strategy.

Automation at the documentation layer is increasingly expected. Strategic integration requires broader organizational alignment and stronger leadership commitment.

AI will not eliminate uncertainty in CDMO markets. However, it can make uncertainty more visible, more quantifiable, and more actionable. Organizations that incorporate it into decision-making processes may be better positioned to navigate cyclical demand and capital intensity.

As AI capabilities mature, the degree to which they are embedded in decision architecture, rather than limited to workflows, may determine which CDMOs achieve durable returns, and which remain exposed to cyclical overcapacity.  

Note: The views expressed in this article are the author’s own and do not necessarily reflect those of Thermo Fisher Scientific.

Gregory Kline, PhD, JD, is Senior Director, IP Counsel for Thermo Fisher Scientific’s Pharma Services Group, where he leads intellectual property strategy for a multibillion-dollar biologics manufacturing and development organization. With more than 17 years of experience in biotechnology and pharmaceutical innovation, he advises senior leadership on intellectual property, portfolio strategy, and risk management in regulated global manufacturing operations.