Orchestrating, Not Just Digitizing: How Sponsors Reduce Cost, Improve Quality, and Enable Open Innovation

Clinical operations teams sit at a crossroads. On one side, the industry has experienced an unprecedented expansion in digital capabilities such as eCOA, eConsent, IRT/RTSM, CTMS, EDC, safety platforms, eTMF, and AI-driven tools.

On the other hand, operational realities have remained stagnant with study start-up delays, protocol amendments, and the expectation to manage an increasingly complex portfolio of logins still prevail as pain points. The global eClinical solutions market is projected to almost double from approximately $11.6 billion in 2024 to more than $22 billion by 2029, driven by the promise of efficiency, cost savings, and higher-quality data.¹

Yet despite this growth, many organizations still experience digitized fragmentation: more systems, more portals, and more dashboards but no coherence. This article explores how adopting a “workflow” mindset anchored in measurable sponsor outcomes meets this challenge, ultimately yielding the lower costs, higher quality, and an architecture ready for open innovation sponsors expect from technology adoption.

The Balancing Act: Electronization Is Not Enough

Think of “electronization” as the process of simply bringing manual tasks online. The industry’s adoption of electronization technologies has brought clinical research into the digital age for certain.

But this approach still misses the mark when it comes to solving underlying process issues. In contrast, digitalization goes the distance by addressing the root causes of operational inefficiency.

Truly “digitizing” means connecting people, processes, and technology in an ecosystem that enables sponsors, CROs, and sites to collaborate seamlessly. It also means easy leverage of data utilization and analytics, with embedded clinical decision support.

A successful clinical trial workflow orchestration will not be “yet another platform.” It should be adopted as a coordinating layer that makes the protocol itself the organizing principle, rather than an org chart or vendor list.

This approach is very different from simply adding one more tool into an already crowded landscape. Clinical operations and digital strategy leaders should treat workflow orchestration as a capability to build, not merely a product category to buy.

Pragmatically, that entails:

• Mapping the current situation honestly
  • Catalogue where work and data move between sponsors, CROs, and sites. Identify the top five “friction points” and quantify their impact on timelines, cost, and quality.
• Defining orchestration objectives
  • Identify what matters most right now. For example, reducing start-up timelines, improving amendment agility, enhancing site experience, or creating a foundation for AI. The answer will guide which workflows to tackle first.
• Setting interoperability and governance requirements upfront
  • Make open standards, data provenance, and role-based control non-negotiable in RFPs and internal build decisions.
• Co-designing with sites
  • Invite site representatives into planning. The site experience of fragmentation is invaluable in stress-testing orchestration plans before they are implemented at scale.
• Measuring and iterating
  • Establish a small, meaningful set of KPIs such as time from protocol amendment approval to full implementation, number of portals per site, or rate of avoidable data rework and then track them systematically.

Design Principles for Effective Workflow Orchestration

1. Make the protocol the operating system

Historically, technology has been implemented per function: EDC for data managers, IRT for supply teams, CTMS for project managers, and so on.

Orchestration flips this approach. The protocol is treated as a living model to minimize amendment rework and protect data quality across systems:

• Every visit, assessment, and operational activity is represented in a structured way.
• Systems are configured to subscribe to that model, rather than each interpreting it independently.
• When the protocol evolves, workflows and systems update in a controlled, traceable manner.

This “protocol-as-operating-system” approach is essential to reduce amendment pain and enable more adaptive designs.

2. Build interoperability and governance in from day one

Modern regulators assume, not hope, that sponsors can demonstrate control over digital workflows. FDA’s DCT guidance and EMA’s AI reflection paper both implicitly point toward the need for system-level governance across the lifecycle of medicines, from data capture to decision-making.²

For orchestration, this implies:

• Open APIs and standards-based integrations
• Configurable, role-based workflows
• Embedded audit trails and data provenance

Without these elements, orchestration becomes a marketing term rather than a quality enabler.

3. Start with sites and participants, not the tech stack

Successful orchestration projects start with site and participant journeys:

• How does a potential participant first encounter the study?
• What tools does the site already use to manage its workload?
• Where do handoffs between roles consistently fail?

Only after that lived experience is understood should teams decide e where an orchestration layer is needed, and which workflows to standardize versus allow to vary.

Early Lessons from the Field

Across sponsors of different sizes and geographies, a few patterns are already emerging.

• Start small, but design for scale
  • Pilots that focus on a limited set of workflows (such as screening and randomization across a handful of sites) can demonstrate value quickly, providing the underlying model supports expansion to safety, data management, and post-marketing phases.
• Don’t confuse dashboards with orchestration
  • Visualization is valuable, but a dashboard that reports delays without the ability to re-route work or change task sequencing is not orchestration. It is an instrument panel without controls.
• Treat change management as a first-class deliverable
  • Invest in training, co-design workshops, and clear communication about what will tangibly become easier in day-to-day work.
• AI might augment judgement, but it does not replace it
  • Many sponsors are using AI to prioritize workloads, flag atypical patterns, or suggest optimal visit windows. However, final decisions about participant safety and data integrity must remain with qualified professionals, supported by transparent, auditable models.³

The Regulatory and Market Context: Why Orchestration Matters Now

Three converging forces make workflow orchestration a strategic necessity rather than a “nice-to-have.”

1. Decentralized and hybrid trials

In 2024, FDA issued final guidance on Conducting Clinical Trials with Decentralized Elements, explicitly recognizing the role of remote assessments, digital tools, and alternative care locations.² Subsequent analyses highlight that sponsors must still demonstrate robust oversight, data integrity, and participant safety, regardless of where or how data are captured.⁴

This makes orchestration more than a buzzword. As assessments take place in participants’ homes, community centers, and traditional sites, and as data arrives via multiple channels, coordination must clarify who performs each task, when activities occur, where the data is routed and how deviations and risks are surfaced.

2. AI and data provenance expectations

Regulators have become much more direct about what they expect when organizations bring AI into the medicinal product lifecycle.

The European Medicines Agency’s reflection paper on AI on makes the message clear: if AI is involved at any point, sponsors must be able to show exactly where their data came from, how it was handled, and how decisions were made throughout development and after approval.

To meet these expectations, sponsors must be able to demonstrate the following at all times:

• Clean, well-structured operational data suitable for downstream analysis and regulatory review.
• Transparent, auditable workflows that clearly document how decisions are made, escalated, or overridden.
• Robust data provenance, enabling reviewers to trace inputs, transformations, and outputs, and not just final results.

These requirements highlight why workflow orchestration is increasingly strategic. Orchestration converts fragmented, system-specific events into structured, analyzable signals, allowing sponsors to maintain clarity and control, even as new digital or AI-driven tools enter the ecosystem.

Most crucially, this orchestration layer does not add AI into the mix. Instead, it ensures that when AI is used, the resulting data can be safely, consistently, and confidently processed.

3. Sites investing in their own digital future

Recent survey work published via Applied Clinical Trials and Tufts CSDD shows that more than 75% of sites now have experience with digital and remote trial solutions, and more than one-third have invested in their own technologies to expand research capacity.⁵

Notably, 93% of sites consider remote monitoring tools essential to adherence and data capture, and data quality is the top reported benefit of digital tools. Sites, therefore, are not passive recipients of sponsor technology decisions; they are active investors.

Orchestration respects those investments by connecting sponsor and CRO systems with site-native workflows, rather than forcing sites to work around new tools.

Conclusion

The next phase of digital transformation in clinical research will not be defined by the number of platforms deployed, but by how well those platforms work together in service of participants, sites, and science. Workflow orchestration enables a transition from fragmented, tool-centric operations to a coherent, protocol-driven, and site-aware ecosystem.

It is neither a silver bullet nor purely a technology challenge. Rather, it’s an opportunity for sponsors, CROs, and sites to redesign how they collaborate, supported by systems that are as integrated as the science they aim to deliver.

If implemented effectively, orchestration will create clinical trial experiences that are easier to run, easier to join, and more capable of delivering timely, trustworthy evidence to accelerate patient care.

About the Author

Kees Van Ooik is Vice President of eClinical Solutions at Almac Clinical Technologies. He was the Co-Founder and CEO of Your Research (now part of the Almac Group) and has over a decade of experience in clinical research technology. An entrepreneur in integrated trial platforms, Kees is known for designing participant-centric, site-empowering digital ecosystems that modernise how clinical trials operate end-to-end.

References

1. MarketsandMarkets. eClinical Solutions Industry worth $22.09 billion by 2029, with a CAGR of 13.7%. 2024. (MarketsandMarkets)
2. US Food and Drug Administration (FDA). Conducting Clinical Trials with Decentralized Elements – Guidance for Industry, Investigators, and Other Stakeholders. Final guidance, 2024. (U.S. Food and Drug Administration)
3. European Medicines Agency (EMA). Reflection paper on the use of Artificial Intelligence (AI) in the medicinal product lifecycle. 2024. (European Medicines Agency (EMA))
4. Tufts Center for the Study of Drug Development (CSDD) / Applied Clinical Trials. Investigative Site Investment, Preparedness, and Experience with Digital Solutions. 2025. (appliedclinicaltrialsonline.com)
5. McGuireWoods LLP. Recent FDA Guidance Signals Future Growth for Decentralized Clinical Trials. 2024. (McGuireWoods)