At Knowledge26 by ServiceNow, something we’d long believed was confirmed: The best AI tools and models don’t automatically translate to outcomes.
Healthcare and life sciences organizations have spent years digitizing systems and collecting data, from EHRs to portals to dashboards, with the expectation that better data would lead to better, AI-driven decision-making and value. Yet many organizations are still working to see those results, because collecting data is only part of the equation.
What slows progress is a gap in workflow design, i.e., how information moves between systems, between teams, and between the moment something is recorded and the moment someone can act on it, and the operational integration that holds it all together. In many cases, data lives in one place, decisions happen somewhere else, and people are left bridging the distance through manual work.
AI has a meaningful role to play in healthcare enterprises, but outcomes depend just as much on the workflows surrounding it. At Randstad Digital, we see ServiceNow as the layer that makes realizing AI-driven value for our clients, connecting the data to the people who need it, when they need it.
the 63% problem: automating what gets in the way of provider care.
One of the most sobering statistics shared at the event was that nurses spend only 37% of nursing time on direct patient care. The rest disappears into administrative work: chasing information, filing reports, coordinating between systems that were never designed to talk to each other. It’s a significant amount of skilled time going into their work that, in many cases, could be handled differently.
Knowledge26 pointed out two innovations that can reduce manual tasks and put the focus back on patient care:
- Voice AI agents: Coming Q2 ‘26, AI agents will handle IT, Biomed and Facilities requests via voice. Practically speaking, a nurse can report a broken piece of equipment without leaving the room, without touching a computer and, most importantly, without diverting attention away from the patient.
- Epic triage support: We saw how AI-assisted workflows reduced EHR routing delays from 4 hours to under 15 minutes, clearing backlogs of 2,000+ tickets in a single day.
For providers, these innovations clear the administrative layers so clinicians can focus on care.
precision logistics for payors & life sciences.
Expand the frame beyond providers, and a separate set of workflow problems comes into focus. Home-based dialysis. Decentralized clinical trials. Equipment moving across facilities, patients and state lines. The care continuum has expanded dramatically over the past decade, and most tracking systems were built for a world where equipment stayed in one building.
Fresenius Medical Care showed what closing that gap looks like. They went from fragmented records, where different teams held different pieces of the picture and nobody had the whole thing, to a centralized view integrating mobile barcode scanning and real-time location data. Projected outcome: 15% reduction in units per patient, with compliance improvements on top of that.
For life sciences and payor organizations, this plays out in two directions:
- End-to-end visibility: Managing the asset lifecycle from the factory to the patient’s home.
- Governance at scale: Using centralized dashboards to manage AI strategy and risk, preventing Shadow AI and ensuring ROI across siloed departments.
Centralized oversight is ultimately about knowing what’s actually running and whether it’s working as intended.
three takeaways for healthcare and life sciences leaders.
- Intake before everything else. The quality of your AI outputs is a direct function of the quality of your data inputs. High-quality data through structured, governed intake isn’t a prerequisite handled once. It’s ongoing and determines whether automation is reliable or just fast.
- Governance isn’t a phase two problem. In healthcare, human oversight is a clinical and regulatory reality. Organizations that design for it from the start are in a different position than those who bolt it on after deployment.
- Interoperability is the foundation, not a feature. FHIR and HL7 integration underpin every AI initiative in HLS worth taking seriously. This is the infrastructure layer. Without it, everything above it costs more and delivers less.
the randstad digital perspective.
As a ServiceNow partner, Randstad Digital works with healthcare and life sciences organizations on the foundations that determine whether AI actually performs well. Our Skills, Platform, Automation, ROI, and Cost (SPARC) framework exists to connect implementation to outcomes because the technology only delivers value when it’s operating in the context of real work, not alongside it.
Explore how we help HLS organizations close the workflow gap and maximize their ServiceNow investment.