This paradox reveals a critical gap: data alone does not equal insight. To reverse this trend, pharma must focus not just on generating data, but on producing it in a structured, reproducible, and decision-ready format. Lab automation plays a central role here – not just as an efficiency tool, but as a foundation for producing high-quality data that can fuel better, faster scientific decisions. It doesn’t stop at implementing new instruments – it’s about integrating those instruments into a complex ecosystem where data is FAIR (Findable, Accessible, Interoperable, Reusable) at the point of production and ready to support decision making, whether as standalone insights or as part of training datasets for AI/ML models.

However, most digital transformation or lab automation efforts in pharma labs fall short. They stall in pilot purgatory, spiral into over-customised complexity, or simply get bypassed by frustrated scientists who default to Excel and USB sticks.

This isn’t because the ambition is flawed. It’s because the context is misunderstood.

Unlike other functions, R&D labs operate under unique pressures: they are highly specialised, built on decades of instrument-level and workflow-level customisation, and often less uniformly regulated than manufacturing environments. This relative lack of standardisation can turn them into "Wild West" ecosystems of bespoke tools and processes. 

Digitalisation in this context isn’t a plug-and-play affair. It’s a deliberate act of system design - technical, organisational, and cultural. Schneider G., 2018 states:

Implementation raises considerable conceptual, technical, and organizational challenges that require careful consideration of current capabilities versus hype.

This article is designed to help leaders in pharma R&D navigate and work through that complexity. It covers the five pillars that underpin resilient, future-ready transformation strategies. We’ll break this down into four key chapters:

• The core challenge: Understanding complexity in the lab context
• The foundation: Why data and connectivity come first
• The catalyst: AI-readiness as a transformation driver
• The differentiator: Why people - not platforms - make or break success

Together, these challenges form a structural reality: lab complexity isn’t a temporary hurdle. It’s the context in which all digital transformation must operate. Ignoring it leads to expensive failures. Designing for it leads to sustainable change.

Why point-to-point integration isn’t enough

Many digital lab efforts begin with connecting individual instruments to specific tools – a data lake, a LIMS, an analytics dashboard. But without a broader orchestration layer, these point-to-point setups become fragile and unscalable.

Roch et al. (2018) demonstrate how orchestrated systems manage multiple workflows simultaneously, enabling experimentation, data routing, and decision-making without human intervention. This orchestration is essential to build scalable, modular lab ecosystems.

Validation matters when platforms differ

Even when systems are technically connected, comparability isn’t guaranteed. Yang et al. (2021) show that different automation platforms can yield divergent results, especially in complex sample types. That means connectivity alone doesn’t solve interoperability – standardised validation and performance alignment are critical to maintain scientific integrity.

This is where FAIR principles (Findable, Accessible, Interoperable, Reusable) become essential: ensuring that data is structured, contextualised, and ready for reuse across systems and workflows. Initiatives such as the GO FAIR network offer valuable frameworks to support this effort.

Standardisation enables reuse

When data formats and metadata are harmonised across platforms, researchers can reuse, analyse, and share results more effectively. Kritikos et al. (2022) underscore that standardisation is a prerequisite for lab efficiency and adaptability. Without it, automation adds fragmentation instead of reducing it.

What connectivity unlocks

Once data infrastructure is in place, everything else accelerates:

• Scientists spend less time on manual exports and formatting
• AI models gain structured, reproducible input
• QA teams access full audit trails with minimal overhead
• System evolution becomes faster, safer, and cheaper

Data flow may not be glamorous, but it is the condition for every downstream success in lab automation.
 

As stated by Schneider, 2018: 

Implementation raises considerable conceptual, technical, and organizational challenges that require careful consideration of current capabilities versus hype.

That tension is precisely what makes AI valuable. It surfaces the foundational work labs must do – around data standards, infrastructure, and governance – to unlock broader digital transformation.

AI-readiness is not an endpoint. It's a measure of maturity.

Rather than treating AI as a final goal, leading pharma organisations now use AI-readiness as a transformation metric. This shifts the question from “How do we deploy AI?” to:

• Can we access and harmonise data across instruments and teams?
• Are our data structures machine-readable, and semantically consistent?
• Can we trace how data was generated, modified, and interpreted?

These aren’t technical niceties – they’re preconditions for trust in AI systems, especially when used in regulated environments. As Yang et al. (2021) showed, different automated platforms can yield diverging analytical results, underscoring the need for validated and transparent data pipelines.

When foundations are in place, AI can transform discovery

With the right data infrastructure, AI becomes more than an efficiency booster – it becomes a collaborator. In a now-iconic study, Burger et al. (2020) built a mobile robotic chemist capable of autonomously conducting experiments, learning from outcomes, and optimising protocols. What once took weeks of human trial-and-error was completed in days, with minimal intervention.

Similarly, Segler et al. (2018) demonstrated that deep learning systems can plan synthetic pathways with skill rivaling expert chemists – particularly when paired with structured chemical knowledge and high-quality training data.

These aren’t isolated cases. They represent a new class of tools that augment the scientist’s intuition with statistically grounded, reproducible insight.

A system-wide shift: From automation to orchestration

To truly integrate AI, labs need infrastructure that supports continuous learning, feedback, and reuse. These platforms move beyond automation – toward systems that adapt, evolve, and learn over time. Roch et al. (2018) describe orchestrated experimentation platforms that not only manage devices and data but also incorporate AI-driven optimisation loops. These platforms move beyond automation – toward systems that adapt, evolve, and learn over time.

This shift is reminiscent of the 'lab-in-the-loop' concept being pioneered by Genentech, where AI systems work in tandem with automated labs to iteratively refine experiments and accelerate discovery. Genentech is actively deploying lab-in-the-loop strategies to speed up drug discovery, where AI systems and automated labs work iteratively to test and refine hypotheses. As highlighted by Buntz (2024), the Aviv Regev lab, in particular, has highlighted how such tightly coupled systems can significantly reduce iteration cycles and improve discovery outcomes.

Co-creating with scientists: The human factor

AI in pharma labs doesn’t replace scientific judgment – it enhances it. But adoption depends on usability and trust. Systems must be:

• Transparent in their recommendations
• Easy to interpret and override
• Designed to support, not obscure, decision-making

Granda et al. (2018) illustrated this by developing a robot-chemist that used machine learning to discover new reactivity patterns. Crucially, the system didn’t replace the chemist – it extended their reach into previously unexplored experimental space.

For labs to adopt AI successfully, they must equip scientists not just with tools, but with ownership. That means involving them in model development, interface design, and iteration – building confidence alongside capability.

The human side of digital transformation

Pharma labs are collaborative environments. Scientists, quality managers, IT teams, and data scientists each have different goals, tools, and regulatory constraints. Aligning these functions is not optional – it’s foundational.

As Cherkaoui & Schrenzel (2022) emphasise, total lab automation isn’t just about replacing manual steps. It reshapes workflows, impacts training, and forces teams to revisit diagnostic strategies and decision-making habits. If that cultural adaptation doesn’t happen, the technology gets ignored.

Co-creation beats top-down rollout

User adoption improves dramatically when lab staff are included early in system design. That means:

• Shadowing real workflows before drawing up requirements
• Rapid prototyping with scientist input
• Involving QA and IT in interface design to align on usability vs. compliance

These practices are echoed in Kritikos et al. (2022), who stress that aligning stakeholder incentives – not just deploying tools – is the key to sustainable transformation.

Metrics that actually matter

Traditional success indicators – like uptime, validation status, or deployment timelines – often miss what really matters: are people using the system?

Stronger signals of success might include:

• Reduction in manual data entry or duplicate workflows
• Increase in metadata completeness and traceability
• Researcher satisfaction and confidence scores
• Speed of onboarding for new tools or workflows
• Increased reproducibility
• Speed and cost of data production
• Speed of data intake from point of production to AI/ML models

When transformation efforts measure adoption, not just functionality, they can adjust and improve in real time.

Culture enables performance

When digital transformation is coupled with human-centred change, results follow. Brown & Badrick (2023) highlight that automation systems drive significant improvements in laboratory performance – including quality control, safety, and sample handling – but only when workflows are rethought and staff are trained and engaged. Technology alone doesn't deliver ROI. People do.

Pharma labs aren’t broken – they’re complex. That complexity isn’t a flaw. It’s the context that gives these environments their power, flexibility, and regulatory resilience. But it also means that transformation can’t be treated like a software deployment. It must be treated like system design – one that accounts for legacy realities, evolving regulation, and human variability.

The case studies and research referenced throughout this piece show a clear pattern: digital transformation doesn’t fail due to a lack of vision. It fails when labs try to shortcut the foundational work – data harmonisation, modular infrastructure, user engagement – in favour of fast fixes or top-down tech pushes.

But when labs invest in the right foundations, the payoff compounds:

• Connected data unlocks reproducibility and analytics
• Modular systems reduce vendor lock-in and validation burden
• AI shifts from slideware to everyday augmentation
• Scientists gain time and trust in their tools

Automation success depends on integrated quality control systems, not just connectivity.

– states Brown & Badrick, 2023. Transformation is not a roadmap to be completed. It’s a capability to be grown. And the most successful labs won’t be those with the most sophisticated technology – but those that know how to adapt deliberately, iteratively, and together.

Zühlke supports pharma organisations in building these capabilities – through engineering, co-creation, and regulatory insight. Because in complex systems, it’s not control that creates success – it’s momentum.