How a Connecticut Based Integrated Health System Transformed Shared Services Workforce Planning with AI

Discover how Draup enabled a leading Northeast integrated health system to assess automation feasibility, model financial impact, and deliver function-level workforce intelligence across four shared services functions in under two months.

48,000+

Employee Count

7B+

Revenue

Integrated Healthcare

Industry

Connecticut, USA

Headquarters

About the Company

The organization is one of the largest and most comprehensive integrated health systems in the northeastern United States, operating a fully integrated network of hospitals, specialty practices, and shared services functions. With over 48,000 employees, it supports a broad range of clinical and administrative functions spanning Revenue Cycle, Finance, Supply Chain, and HR. As enterprise-wide AI adoption accelerates across the healthcare sector, the organization recognized the need to build a rigorous, data-backed view of how automation and augmentation would reshape its shared services workforce and what that meant for talent strategy, role design, and long-term capability architecture.

The Core Challenges

Building a Structured View of AI Exposure

The organization wanted to develop a consolidated view of which shared services roles and tasks were most exposed to AI-driven change, sharpening its ability to prioritize transformation efforts across the function.

Unifying Job Architecture for Workforce Planning

The organization sought to bring headcount and job architecture data spread across multiple teams and systems into a single, unified taxonomy that could anchor enterprise-wide workforce planning decisions.

Translating AI Insights for Leadership Audiences

Senior HR and business leaders sought intuitive, simulation-driven outputs that would resonate across audiences with different depths of technical familiarity, rather than analyses pitched only to specialists.

Difficulty Isolating Net-New AI Opportunity

The organization wanted to clearly distinguish net-new AI opportunity from incremental gains already achievable through tools in use, such as UiPath and Power BI sharpening the strategic case for investment.

Roles Assessed

Functions Covered

Weeks to 1st Stakeholder Meeting

90%+

Automation Feasibility (Top Roles)

Productivity Uplift (Financial Analyst)

Solution Highlights

The organization partnered with Draup to deploy its Etter workforce intelligence platform across 47 shared services roles, delivering structured AI impact assessments and executive-ready simulation outputs in 8 weeks.

AI Impact Assessment

Each of the 47 roles was scored on automation and augmentation potential across all task workloads, providing a structured, comparable view of AI exposure across Revenue Cycle, Finance, Supply Chain, and HR.

Financial Simulator

Draup modeled the capacity-freed and financial impact of AI adoption at the function level, using the organization's actual headcount data to translate transformation potential into concrete business terms.

Tech Stack Prioritization

Role-specific technology recommendations were curated with estimated hours saved, distinguishing existing tool capabilities from net-new AI additions addressing a core requirement from HR leadership.

Workforce Twin & Role Adjacency

A workforce twin mirrored the org's taxonomy to simulate real-time transformation scenarios. Role adjacency mapping outlined no-layoff redeployment pathways as automation freed capacity

Outcomes

Executive Readiness Achieved

The engagement delivered task-level AI quantification and technology prioritization packaged into executive workshop decks across Revenue Cycle, Finance, Supply Chain, and HR — combining insight slides with live Workforce Twin simulation. Output was ready for senior leadership review within 8 weeks of engagement start.

3X Productivity Uplift Modeled for Financial Analyst

Draup's productivity simulation demonstrated a concrete before-and-after impact for targeted roles: Financial Analyst productivity increased from 8 to 25 reports per week with AI assistance — providing leadership with a tangible proof point for transformation investment.

AI Visibility Established Across Functions

no structured view of AI workforce impact to a function-by-function simulation capability, hosted within a CHRO Dashboard deployed as a long-term job architecture and skills taxonomy repository.

Unified Repository as Single Source of Truth

Draup was positioned as the organization's authoritative platform for job descriptions, skills taxonomy, and career frameworks — with planned integration into Oracle/PeopleSoft HCM to embed AI workforce intelligence into core HR operations.

Powered by unparalleled global labor & market data

26K+

Skills

1B+

Professionals

5700+

Locations

1.6M+

Peer Group Companies

1B+

Job Descriptions

4M+

Career Paths

Data Trust & Methodology at Draup

Draup uses a structured methodology to reduce inaccuracies and prevent misleading information, with a focus on transparency and responsible data practices.

1

Data Transparency and Coverage

Draup sources data from diverse, vetted global inputs including public datasets, labor information, professional ecosystems, and industry research. Coverage varies by region and role, and we supplement limited areas with alternative sources and analyst-led modeling.

2

Profiles Behind the Numbers

All aggregate insights related to buyers, roles, skills, and locations are grounded in structured, anonymized human and organizational profiles rather than pure extrapolation. This ensures context, reliability, and protection of personal identities.

3

Data Hygiene and Integrity

We maintain a clean, accurate data environment by removing duplicates, outdated entities, and invalid records. Automated checks powered by ML models and analyst reviews work together to ensure clarity and accuracy for every entity, such as buyer, role, skill, technology, or company. Learn more

4

AI Governance and Bias Mitigation

Our governance framework combines automated evaluations with Human-in-the-Loop reviews. We use statistical checks, audits, and cross-source comparisons to reduce demographic and structural bias before insights reach the platform.

5

Compensation Guardrails

Compensation insights use blended, directional inputs such as aggregated ranges, benchmarks, modeled distributions, and market signals. We distinguish clearly between modeled and reported data and apply guardrails to prevent over-interpretation.

6

Hourly and Frontline Workforce Coverage

For hourly, frontline, and blue-collar roles where digital visibility is limited, we incorporate government datasets, localized labor information, and specialized partners to build a more complete and balanced view of workforce supply and demand.