Highlights from our XTalks fireside chat with Craig McIlloney, Stephen Rayda, and Jay Leonard. Watch the full conversation on demand.
Functional service provider models have supported clinical trials for years. What has changed is that technology now plays an integral role in enabling more efficient data collection, analysis, and process automation within these models. In our recent XTalks session, “Improve Trial Readiness with Tech-Enabled FSP Delivery Models,” Craig McIlloney, SVP of Worldwide Flex, sat down with Stephen Rayda, our Chief Digital and Information Officer, and Jay Leonard, VP of Enterprise Technology Transformation and Architecture, to talk through what that shift looks like on real programs. Here we provide some highlights.
A live poll showed most attendees were early in the tech-enabled FSP journey. The largest group was exploring use cases, while another sizable group was still deciding where to begin.
Why Is the Timing Different Now?
Trials keep growing more complex, and adding more people to keep pace has reached a practical limit. The opportunity now is to let technology support those people so teams can do more without simply expanding headcount.
Stephen described trial data as two kinds. Structured endpoint data, which machine learning has long handled well, and a large volume of document-based information that has been much harder to use. Over the past two years, generative AI has made that second category workable. Combining machine learning with generative AI lets teams act on information across the entire trial, end-to-end.
Jay noted that the plumbing has caught up, too. A couple of years ago, exchanging data meant batch files landing in a secure folder. Today, the first question is about APIs and direct connections, supported by shared data standards. Once connections and standards are in place, governance becomes the manageable part. You can even see the change in how sponsors write RFPs. Where they once asked about headcount, locations, and SOPs, they now ask about the data fabric, where data lives, and how AI output is validated.
What Does Tech-Enabled FSP Delivery Look Like in Practice?
The panel was candid that a polished dashboard is easy to build. What matters is the data underneath it and the confidence you can place in it.
Stephen described the move from outdated status decks to live dashboards. When a sponsor and its partner look at the same real-time data, the familiar exchange about why a number looks off, only to hear it was corrected yesterday, disappears. Access is immediate and shared, quality improves, and the path from last patient last visit to database lock becomes more predictable.
Jay framed the technology as three layers:
- The integration layer connects systems securely through APIs
- The model layer is where AI handles much of the repeatable work
- The third layer, governance and audit, records what the model was asked and what it produced, so the decision behind every output is traceable
Jay’s point was that governance belongs in the design from the start, built in and not added at the end. A theme ran through the session: AI suggests, and a person decides. Because that human decision point does not change, SOPs hold. Instead, what changes is the time spent on repeatable work, which frees experienced people to provide the judgment expertise they bring.
Visibility & Shared Ownership
Jay contrasted the old three-tier reporting model, where the operational, program, and steering layers each waited on the level below to produce a deck, with one in which all three see the same data at the same time through different lenses. The shift is from a backward look at month-old information to a forward view that helps teams head off issues early.
Stephen tied that to trust. When issues surface fast and can be acted on quickly, a partner can share what it is seeing openly, along with what it is doing about it, in real time. Craig called that the mark of a digitally enabled CRO, and said it turns the sponsor relationship into shared ownership.
Transparency turns a status update into shared ownership.
Access for Lean Teams
An audience member asked how smaller biotechs without internal AI capability can reach these benefits. Craig’s answer was direct when he said it comes down to the right partnerships and a willingness to learn quickly, more than to the size of a budget. An FSP relationship lets a lean sponsor draw on the investment the partner is already making, so the team gets the real-time benefit without building everything itself.
That value is clear in transition work. When a mid-size biotech needed to move two Phase III studies to a new monitoring partner at speed, Worldwide Flex completed candidate screening and onboarding within two weeks and had clinical research associates in the field by the start of week three. A strong partnership keeps a transition like that steady and predictable.
Where Is This Heading?
Looking 12 to 24 months out, the panel expects AI tooling to continue maturing, with study-specific solutions consolidating onto shared platforms that still reflect differences across therapeutic areas. Jay closed on a practical note. The teams that pull ahead will be the ones with the governance and integration discipline to put AI inside regulated workflows without breaking them, and that owes more to discipline than to budget.
Want to explore what tech-enabled FSP delivery could look like for your program? Watch the full fireside chat on demand for more and speak with a Worldwide Flex expert today.
