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Article

The Key Challenges of Automation and Digitalization Initiatives

in the pharmaceutical industry in 2026

From a single digital project to scalable routine operations

What is missing in practice to ensure that digital and automated solutions in pharmaceutical processes not only work in pilot projects but can also be operated reliably and in compliance with regulations on a day-to-day basis?

Pharmaceutical companies are under significant pressure to transform. Worldwide, exclusivity for drugs with annual sales of approximately $350 billion will expire by 2030^[1]; IQVIA expects a global loss-of-exclusivity effect of $192 billion by 2028^[2]. At the same time, regulatory requirements and operational complexity are increasing. Value streams must therefore be managed more quickly, transparently, and cost-effectively.

The real challenge usually does not lie in a lack of ideas or individual use cases. Many initiatives fail because they are unable to translate local progress into a regulated, centrally managed operational framework. A pilot project can demonstrate benefits yet still fail to scale if data remains fragmented, integration pathways are lacking, and new solutions must contend with legacy system landscapes as well as differing priorities across business units, IT, and quality assurance.

What matters most, therefore, is not so much the number of individual use cases as how they are designed and implemented. This requires a robust data foundation, interoperable integration, clear governance, and a sustainable operating model to be considered together. This is particularly true in regulated pharmaceutical processes, because release capability, data integrity, validation, and auditability are integral components of digital operational capability^[3],[4],[7].

The report therefore addresses three key points: the central challenges of digital transformation in the pharmaceutical industry, the typical scaling gap between pilot projects and full-scale operations, and the prerequisites for a robust transition to full-scale operations. This perspective is particularly relevant where operational management, quality, and shop-floor realities converge.

1. Key challenges in process automation in the pharmaceutical industry

Several structural challenges are simultaneously impacting pharmaceutical value streams: the loss of exclusivity, pressure on prices and reimbursement rates, increasingly complex portfolios, and rising demands for quality and traceability are significantly increasing the pressure to act^[1],[2]. Research, laboratory operations, production, and quality assurance must therefore not only operate more quickly, but above all, more consistently, transparently, and with greater controllability.

Operational weaknesses often manifest in the same areas: time is lost along the value stream, control diminishes, and quality is not consistently ensured. In many organizations, the prerequisites for stable routine operations are still lacking: a robust data foundation, clear integration paths, traceable handoffs, and an operational framework that does not depend on individuals or local workarounds.

This problem manifests itself very clearly in day-to-day operations: data is scattered across devices, individual systems, spreadsheets, and manual documentation processes. Legacy interfaces, data silos, and ad-hoc local workflows slow down decision-making and undermine control. What initially appears to be an IT issue quickly becomes a matter of productivity, quality, and risk.

Flowchart with blue and gray boxes showing different process and roles in the pharma industry — Fig.1: Fragmented data flows, disconnected processes, and legacy system landscapes limit transparency, control, and scalability across the pharma value stream.

These patterns are also familiar from the digitalization of industrial plants: existing systems, proprietary interfaces, and separate production and IT systems often prevent a seamless flow of data. Standards such as ISA-95, OPC UA, and NAMUR Open Architecture (NOA) are therefore particularly relevant from a strategic perspective because they provide guidance for interoperable integration and data models^{[7],[8],[9],[10]}.

This is precisely where the specific difference between pharmaceutical processes and many other industries becomes apparent: available data and technical connectivity alone are not enough. What is also crucial are validatable systems, traceable decisions, clear roles and approvals, and a reliable data lineage. The European GMP Annex 11 for computer-based systems, the FDA regulation 21 CFR Part 11 on electronic records and electronic signatures, and the MHRA guidance on GxP data integrity make it clear that governance, validation, and documented risk assessment must be integral components of the digital operating model^{[3],[11],[12]}.

As AI continues to evolve, this requirement becomes even more apparent. The joint guiding principles for AI in drug development published by the EMA and FDA in 2026 confirm that a clear intended use, robust data and model foundations, appropriate risk management, and GxP-compliant governance are also critical in this context^[4]. This makes it even clearer: The central issue is not additional individual solutions, but rather robust data, integration, and governance foundations upon which scaling becomes possible in the first place.

Infographic with three colored rectangles, the middle one containing text and the outer ones containing icons, connected by arrows. — Fig.2: The scaling gap emerges where successful pilots meet insufficient data, integration, and governance foundations.

2. The scaling gap between pilot operations and full-scale operations

Many digital initiatives begin with a clear use case and a technically feasible solution. However, whether they generate lasting value is usually only determined once they move into cross-departmental or cross-site implementation.

Industry studies paint a consistent picture: digital transformation and AI are high on the agenda, but successfully scaled initiatives remain the exception^[5]. Often, it is not the vision itself that fails, but the ability to integrate systems, teams, processes, and data in a way that generates repeatable value from individual initiatives^[6].

The most common mistake is to confuse the success of a use case with the maturity of the subsequent operational model. Locally optimized solutions may seem convincing in the short term, even though there is no robust data logic, clear architectural path, or governance for routine operations in the background. This is precisely where the scaling gap emerges: The use case works, but the system behind it is not sufficiently scalable for rollout, compliance, and repeatable operations^[6],[7].

This gap is particularly evident in the pharmaceutical and laboratory sectors. Fragmented infrastructures, organizational inertia, and uncertainty regarding standards and interoperability are hindering implementation. Added to this are conflicting priorities at the interfaces: In R&D and science, applicability and speed are paramount; in IT, stable and integrable system landscapes are key; and in quality functions, traceability, control, and compliance are essential. This is precisely where friction, delays, and conflicts over prioritization arise.

Another common issue is a structural imbalance: use cases are often launched before the necessary foundations have been laid. A pilot project demonstrates results even though the underlying data model has not yet been standardized. Automation saves time even though interfaces are still too customized. Analytical value becomes apparent even though data sources, role models, and operational frameworks for routine operations are not yet sufficiently prepared.

3. Implementation outlook: The path to operational readiness

Against this backdrop, another question comes to the fore: What foundations must be in place first to ensure that digital potential is not limited to local impact but can be scaled up into routine operations? The challenge lies less in identifying additional individual solutions and more in integrating multiple solutions onto a common data, integration, and operational foundation.

This requires a combination of process understanding, integration capabilities, and clear management throughout the entire value stream. The goal is not to launch as many individual applications as possible, but to create the conditions that make it feasible to run multiple applications simultaneously. The scope extends from research and development (R&D) through quality control (QC) and technology transfer to production and the supply chain, because it is precisely at these interfaces that rework, delays, and compliance risks arise.

As we move toward routine operations, four key drivers stand out clearly. These can be derived from recurring ZEISS projects and implementations by ZEISS Digital Innovation (ZDI) in regulated industrial, laboratory, and medical technology environments. At the same time, they combine general integration patterns with the requirements of regulated operating environments.

Device integration into existing environments (brownfield): In regulated environments, established laboratory and production landscapes are the norm. It is therefore strategically crucial to ensure that devices a systems are made interoperable in a phased manner without creating new siloed solutions. Standards such as OPC UA, NOA, and, in the laboratory environment, SiLA 2 provide guidance in this regard^{[9],[10],[13]}.
Seamless data flow throughout the process: Control improves when operational events, quality context, and decision-making information are reliably linked. Only then do digitized individual steps come together to form a process that can be controlled and evaluated across departmental boundaries.
System integration for quality control (QC), review, and approval: In pharmaceutical manufacturing and quality control, integration is more than just a matter of efficiency. Processes in quality control, review, approval, and nonconformance handling must be based on consistent, reliable information. As a result, access rights, signatures, auditability, and documented role models become integral components of operational capability^[3].
Scalable architecture and operating model: Local progress only becomes a standard when the architecture and operations are designed for reusability. Recurring ZEISS projects and implementations show that a Manufacturing Execution System (MES) remains central to compliant execution, but is often insufficient for cross-departmental transparency and data-driven optimization. Therefore, an integrated data and platform logic that complements existing core systems is required.
Whether digital initiatives have an impact beyond the pilot phase depends primarily on whether the key prerequisites are addressed early on and maintained through clear oversight.

For regulated pharmaceutical and laboratory environments, this means above all that systems and data must be interoperable, information must be structured and validated, and automation must be built on a solid foundation. Only then can scaling be viable.

In practice, these topics often overlap. Therefore, what matters most is not so much a rigid sequence of steps as the consistent development of connectivity, data logic, validation, and operational readiness. This is particularly important in the pharmaceutical sector because quality control (QC), review, approval, technology transfer, and deviation handling can only proceed in a robust and compliant manner if data provenance, roles, approvals, and system boundaries are clearly traceable^[3].

Leadership and oversight must be established early on. Management and program leads need clear decision-making and escalation processes to ensure that conflicts of interest between business units, IT, and quality do not surface only during the rollout.

Key performance indicators (KPIs) serve not only for reporting purposes but also as criteria for approving scaling. It makes sense to focus on a few measurable metrics that highlight operational impact, such as cycle time, data quality, rework, approval time, or susceptibility to failure.

Pilot projects should therefore not only demonstrate technical feasibility, but also operational relevance and a sustainable operating model. This includes clearly defined business objectives, a robust validation and handover process, and the organization’s ability to adopt new ways of working on a permanent basis.
At the enterprise level, scalability is not determined by individual projects, but rather by the interplay of standards, reference architecture, and procurement.

Standards provide a common language and structural principles. ISA-95 is particularly relevant as a conceptual and structural model for the interface between enterprise and production systems. OPC UA and, in the laboratory environment, SiLA 2 are especially important for open communication and interoperable information models^[7],[8],[13].

When these standards are translated into a reference architecture, system roles, data flows, and responsibilities can be defined more consistently across the entire organization. This becomes concrete in core operational areas: A Manufacturing Execution System (MES) remains central to the compliant execution and documentation of production processes. Supplementary data or platform layers create transparency across system boundaries and enable faster analyses. A reference architecture thus describes not a single control system, but the interaction of the core systems within the ecosystem.

Through procurement, these architectural principles are translated into concrete requirements. If interoperability, open interfaces, security requirements, and connectivity are established early on in tenders, supplier evaluations, and architectural specifications, the subsequent integration effort is significantly reduced. If these criteria are missing, costs shift from product selection to projects and operations. This is precisely why manufacturers, through their product architectures, have a greater impact on the ecosystem’s actual scalability than is often apparent on paper. Standardization then becomes more than just a goal; it is concretely implemented in procurement and operations.

Conclusion

The strategic challenge lies in structuring digital advancements in such a way that local benefits translate into robust operational capability. This is precisely where many organizations run into difficulties: fragmented data flows, pilot solutions designed for local use, legacy system landscapes, and regulatory requirements limit scalability, manageability, and cost-effectiveness.

This leads to three priorities:

Prioritize value streams with the greatest impact on earnings and risk over individual initiatives.
Establish the necessary data and integration foundations before rolling out additional applications on a large scale.
Incorporate existing environments (brownfield), standards, core systems, and regulatory compliance into the architecture, procurement, and implementation from the very beginning.

A sensible next step is to identify a value stream where data and documentation gaps are particularly evident and where tangible operational results can be achieved. The starting point and target metrics should be clearly defined, and a pilot should only be approved if the architectural path, validation, and operating model are considered from the outset. An incremental approach is often the more realistic path. However, it is crucial that individual steps do not simply stand side by side in an additive manner, but are held together by a long-term integration, architecture, and governance framework. In this way, an initiative with local benefits evolves into a program with real scalability potential.

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David Heinz Business Development Manager

Leonie Müller Key Account Manager

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¹
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²
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³
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⁴
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⁵
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¹⁰
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¹¹
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¹²
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¹³
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