Worki Enables Healthcare System Partners to Remove Hundreds of Millions in Overhead with AI Workforce Unifying Infrastructure

In the face of significant market headwinds, many health systems need to cut 20% or more from administrative overhead, but have difficulty on how to and where to start given AI and shifting demand:

• Non-clinical roles represent 34% of health system overhead, and most organizations lack the visibility, phased pathways, or AI infrastructure to convert efficiency into actual cost removal. Worki closes that gap with a phased pathway based on proprietary data from 30+ years of operational leadership and consulting expertise applied to administrative tasks.
  
  
• Worki launches as healthcare's first AI workforce operational infrastructure, unifying fragmented HR and workforce systems into a single data layer for visibility and action; no rip-and-replace required. Worki starts small with flat files and no API requirements and evolves with you from there.
  
  
• Worki's AI agents amplify existing workforce and HR roles: one workforce partner scales from 90 employees to 1,500+, another supported by credentialing and recruiting agents sees a 60-65% administrative burden reduction, and a Human Conductor governs every agent output through structured approval gates. Partners project millions in first-year overhead savings.

SAN FRANCISCO, March 24, 2026 /PRNewswire/ -- Non-clinical roles represent 34% of total health system overhead, but most organizations lack the operational visibility needed to manage that workforce effectively. While many health systems aim to reduce administrative costs by 20% or more, they often lack visibility into which roles can be amplified, how to redeploy those workers, or how to turn efficiency gains into real cost savings.

Worki, a healthcare workforce infrastructure company, emerged from stealth with a group of leading health systems implementing to close what it calls the AI workforce operational gap. The company connects an organization's existing systems into a single infrastructure layer without requiring a rip-and-replace approach.

On that foundation, Worki's four components operate as a continuous improvement system. AI-powered agents monitor workforce operations, benchmark performance, recommend changes, and automate efficient workflows, while insights are fed back into the system to drive ongoing optimization. Over time, this creates compounding gains in cost reduction and productivity. Workforce and HR leaders remain at the center of decision-making while AI agents execute administrative workflows that amplify their ability to manage complex workforce operations.

Worki's architecture includes four core layers designed to unify workforce systems and automate workforce operations:

• The Where to Start Problem: Pathways: Creates a dynamic map of where every functional role sits today against where the organization needs to be with AI, referencing Stanford WORKBank/SALT task-level AI research, a proprietary workforce skills and tasks database from veteran healthcare operators across 100+ engagements, and de-identified data shared by coalition member health systems. This generates a sequenced plan for each administrative role amplified by AI, giving leaders a concrete, phase-by-phase path to 20 percent or greater overhead reduction with projected hard-dollar cost and productivity impact at every stage.
  
  
• The Fragmented Data Problem: Unify: Connects fragmented HR and workforce systems, other internal data sources, and external resources into a unified workforce data layer, giving leaders visibility across their workforce systems and the agents context. Worki keeps existing systems in place while creating a connective infrastructure that provides visibility across workforce platforms such as Oracle, Workday, ServiceNow, UKG, credentialing, scheduling, and learning systems.
  
  
• The How to Do It Problem: Amplifiers: Deploys governed AI agents leveraging the unified data around the functional HR and workforce roles to amplify their effectiveness and reduce administrative costs. Research agents surface optimization opportunities, and orchestration agents coordinate findings across workflows and systems to enable a continuous improvement cycle. There is always a Human Conductor, the right person trained to govern agent output through structured approval gates, retaining human authority over every decision.
  
  
• The How to Scale Problem: Infrasharing: Enables coalition-based AI workforce infrastructure where health systems share de-identified data, standardized agents, human conductors, and development costs, improving the system with each new member. Together, they create a continuous cycle that learns, evolves, and compounds savings with each new member, lowering operating costs that no single health system could achieve alone.

Worki was founded by a leadership team that has spent decades working in healthcare operations and workforce technology and set out to solve the workforce challenges they repeatedly saw health systems struggle with throughout their careers. The founding leadership team includes:

• Craig Allan Ahrens, MHA, MBA - CEO - 20 years in healthcare workforce operations. Built the first healthcare workforce marketplaces and saw that staffing platforms address the symptoms and not the structural problem. Founded Worki thesis to be the missing AI connective infrastructure layer.
  
  
• Harvey Hongwei Li, PhD - CTO - PhD in AI from UC Berkeley, tech lead of AI and machine learning at Uber and Airbnb. Recognized healthcare workforce challenges as the same pattern: massive, fragmented data, ready AI capability, and no connective infrastructure. He built that layer.
  
  
• Michael Biggs - Chief Commercial Officer - 30+ years in healthcare finance across Arthur Andersen, Navigant, and FTI Consulting with $3B+ in documented improvement. Three decades of watching health systems spend heavily on workforce management without the infrastructure to optimize it convinced him Worki was overdue.

"The average health system runs 10 or more workforce platforms, a growing number of AI point solutions, and still relies on paper and spreadsheet tracking to fill the gaps between them," said Ahrens. "Worki is defining a new category as one that unifies fragmented workforce systems, surfaces the friction points and waste hiding across them, charts a path forward for workforce and HR overhead roles being reshaped by AI or shifting demand, deploys intelligent agents to execute real operational workflows around HR and workforce functions, and delivers the connective, scalable AI workforce infrastructure layer that healthcare has never had."

The company is implementing in three health systems, including a large multi-state Midwestern health system and a Southeastern health system. Partners project millions in first-year administrative savings, with the system's compounding architecture designed to increase impact over time. Health systems can start small with one role or one department and can choose where they have gaps through one or more of Worki's four infrastructure layers and expand as efficiencies are realized. While Worki's initial focus is healthcare, the company plans to extend the infrastructure to other complex industries.

For more information about Worki, visit www.worki.ai.

About Worki
Worki is building the connecting AI infrastructure layer for dramatically lowering costs and improving productivity in healthcare workforce and HR operations. The company's AI-native infrastructure sits between the systems health systems already use, including Workday, UKG, Oracle, ServiceNow, AI point solutions, ATS, LMS, and others, unifying workforce and HR data into the connective tissue that provides a context layer powering AI-driven decisions and the development of AI agents around functional roles. Four capabilities organize the platform: Pathways (mapping how AI reshapes healthcare administrative tasks), Unify (creating a single modular data identity across siloed systems), Amplifiers (translating intelligence into operational action via agents that amplify traditional HR roles), and Infrasharing (scaling workforce intelligence and AI agent infrastructure across organizations).

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SOURCE Worki