The Real Reason We Haven't Fully Automated Cell Therapy Yet

By Arnaud Deladeriere, Ph.D., Cell&Gene Consulting Inc.

Automation in cell and gene therapy manufacturing is often framed as a question of technological readiness. Are the platforms mature enough? Are tools sufficiently robust? Is the industry ready—and prepared—to automate? The sixth Cell & Gene Foundry challenged that framing. The constraint is primarily structural.

Automation is slowed less by a lack of tools (Phacilitate's Advanced Therapies Week 2026 showcased dozens of them!) than by how the industry defines acceptable variability, how much standardization it is willing to tolerate, and how regulatory expectations shape risk perception. The core issue is not whether automation works, it is whether unmanaged variability remains culturally and structurally acceptable.

Across development and manufacturing, automation is frequently approached as a customization exercise. Processes are often treated as inherently unique. Deviations from established manual workflows are then viewed as risks rather than design opportunities. Automated systems are therefore expected to conform to existing practices instead of prompting a reassessment of whether those practices are appropriate for scale.

This mindset is reinforced by regulatory framing. Manual processes, despite their operator dependence and variability, are often accepted as the default. Automation, by contrast, is treated as a change that requires additional justification. The burden of proof falls on those attempting to reduce variability rather than on those maintaining it.

The Foundry participants repeatedly returned to a central observation: for a given modality, most processes share a common operational core. Differences tend to appear at the margins rather than in the fundamental unit operations. Yet the industry continues to prioritize customization (fragmented workflows, bespoke consumables, non-standard quality approaches), often increasing complexity without demonstrably improving outcomes.

Automation succeeds when variability is deliberately defined and controlled. It struggles when uniqueness is treated as a default virtue.

Modular Versus All-in-One Automation

Early automation strategies in cell therapy favored fully integrated, end-to-end systems. The appeal was clear: one platform, one workflow, and minimal interfaces. Operational experience has shifted that thinking.

This shift is closely tied to how the industry manages customization. Fully integrated systems can introduce tight coupling to a single vendor architecture. Standardization occurs within the platform, but process ownership and flexibility may become constrained.

Modular approaches standardize at the level of individual unit operations rather than entire workflows. Selection, activation, expansion, and harvest can each be optimized using fit-for-purpose tools. This enables deliberate standardization where operational impact is highest while preserving flexibility where biological variability genuinely exists.

However, modularity redistributes risk rather than eliminating it. Relying on a single integrated platform creates single-vendor dependency. Conversely, assembling multi-device modular workflows can increase vendor management complexity, supply chain coordination issues, and integration burden. The question is not whether modularity is safer, but rather under what conditions does it reduce systemic risk?

Hybrid models are emerging as a pragmatic middle ground. High-impact unit operations are automated, while robotics and semi-automation address material handling and workflow integration. Customization is constrained to areas that deliver clear value rather than embedded throughout the entire process. This is not a rejection of integration; It is a recalibration of where standardization delivers the greatest leverage.

Automation, Comparability, And Process Ownership

Automation is often pursued with the expectation of minimal process change, particularly in later clinical stages. In practice, highly integrated platforms challenge that assumption.

When a process becomes embedded within a specific automated system, comparability becomes more complex. Scale-down models may be limited or unavailable. External replication may be constrained. Process understanding can shift from being biology-driven to platform-dependent. This is not inherently negative, but requires acknowledgement. Automation does not remove the need for process understanding; in fact, it intensifies it.

The Foundry participants emphasized that automation performs best when applied to well-characterized processes. Attempting to automate poorly understood workflows often exposes variability that manual intervention previously masked.  It surfaces variability that was always present but insufficiently measured.

Ownership therefore shifts; the critical question becomes “Which elements of the process must remain invariant, and which can tolerate controlled variability?” Automation without clarity on that distinction increases complexity. Automation with clarity reduces it.

Digitalization As The Primary Lever For Scale

While hardware automation receives the most attention, digitalization emerged within The Foundry as the most immediate and under-leveraged driver of efficiency.

Manual documentation remains one of the largest sources of operational burden in cell therapy manufacturing. Analyses cited during the roundtable showed that documentation can consume nearly twice as much labor as manufacturing itself – even in facilities equipped with automated systems.

Digital batch records and structured QMS architecture address this imbalance. They reduce narrative documentation, guide execution in real time, and standardize decision pathways. Variability becomes measurable rather than anecdotal.

Importantly, digitalization scales differently than hardware. It reduces ongoing operational costs rather than increasing capital expenditure. It can be deployed across programs and sites without proportional increases in staffing. For smaller organizations, this may have a greater impact on sustainability than incrementation gains in yield or throughput.

Digitalization enforces clarity, as processes must be defined precisely enough to be executed digitally. In doing so, unnecessary customization becomes visible and therefore harder to justify.

Pats And The Management Of Variability

Process analytical technologies (PATs) connect automation to control.

In-process measurements such as glucose, pH, and metabolite levels frequently reveal deviations long before final product attributes are affected. These signals enable intervention. In manual workflows, such feedback is delayed or absent.

Without analytics, automation has the potential to worsen an already poorly understood process. With analytics, it defines acceptable process boundaries based on data rather than assumption. PATs also strengthen deviation investigations. Objective data replaces retrospective operator interpretation, enabling more defensible root cause analyses and targeted corrective actions.

Many failures attributed to biological variability are often operational in origin. Without measurement, operational variability is misclassified as biological inevitability, and customization expands in response to this misjudgement. Analytics allow for a more pragmatic response to that variability.

QC Automation And The Limits Of The Market

Despite progress in manufacturing analytics, QC automation remains uneven, particularly for complex assays such as multi-color flow cytometry. The limitation is not purely technological; many enabling technologies exist. The constraint is economic.

Vendors face high development costs for a fragmented and relatively low-volume market. When each customer requests customized assay configurations, standardization becomes commercially unattractive, and investment slows.

This reinforces a recurring theme: excessive customization weakens vendor ecosystems and slows collective progress. Off-the-shelf solutions that address most use cases – even if imperfect – may provide greater long-term scalability than bespoke assays that are difficult to support. If we take the multi-color flow cytometry example again, automation of such a workflow should focus on standard panels first - like a TBNK or a T cell identity panel - to answer the needs of the many, before automating larger panels that will inevitably include bespoke strategies or reagents.

Standardization does not mean compromising quality; it means defining where differentiation truly matters and where harmonization strengthens the entire ecosystem.

Regulatory Framing And The Burden Of Proof

One of the most forward-looking themes raised within The Foundry was the need to reconsider how automation is evaluated from a regulatory perspective.

Current frameworks often treat automation as a source of risk that must be justified, while manual processes are implicitly accepted despite their variability. The discussion questioned whether this framing remains appropriate.

Well-designed automated processes frequently reduce variability, enhance traceability, and improve control through integrated analytics. Shifting the burden of proof from justifying automation to justifying unmanaged manual variability would better align regulatory evaluation with manufacturing reality.

Such a shift would not mandate automation – low-throughput programs or early-stage development may justify manual approaches – but it would remove structural disincentives that favor customization over robustness. Regulation shapes incentives, incentives shape design choices, and design choices determine scalability.

Conclusion

The sixth Cell & Gene Foundry conveyed a clear message: automation in cell and gene therapy in not primarily a technology problem, but a discipline and incentive alignment problem.

Automation succeeds when variability is deliberately defined, measured, and controlled. It struggles when uniqueness is assumed, rather than justified. Modular and hybrid strategies have emerged because they enable selective standardization without rigidity. Digital QMS infrastructures deliver immediate operational returns. PATs transform uncertainty into manageable variation.

Progress will depend less on acquiring more sophisticated equipment and more on aligning expectations around reproducibility, data transparency and systemic robustness. Automation is not about replacing people; it is about enabling consistent, defensible decisions in an industry that can no longer afford unmanaged variability.

Five Key Takeaways

About The Author:

Arnaud Deladeriere, Ph.D., is principal consultant at Cell&Gene Consulting Inc. Previously, he was head of MSAT and Manufacturing at Triumvira Immunologics, and before that, manufacturing manager at C3i. He received his Ph.D. in biochemistry from the University of Cambridge.