Case study
AI Strategic Plan for Home Healthcare Provider
A detailed description of the case study can be found below
s
s
g
u
z
d
d
g
s
r
.svg)
A growing multi-location home healthcare services organization engaged New Clarity to perform an AI audit and evaluate how AI could reduce operational friction, improve visibility, and support long-term scale. The company had built a successful business, but many critical workflows across intake, scheduling, payroll, billing, compliance, and reporting were still managed through disconnected systems, spreadsheets, manual follow-up, and duplicate data entry.
New Clarity conducted a comprehensive AI audit and strategic planning engagement to identify the largest operational constraints, quantify the business impact of those constraints, and define a practical roadmap for transformation. The result was a detailed strategic plan centered on a unified AI workflow platform designed to reduce manual work, improve data consistency, and create a single source of truth across the organization.
A review of the organization’s operations revealed the same pattern across the business. Core workflows were spread across multiple disconnected systems, which created duplicate data entry, inconsistent reporting, and excessive administrative effort. Intake staff had to enter the same client information into multiple places. Supervisors relied on manual coordination and personal knowledge to match staff to clients and manage schedule changes. Finance and compliance teams spent significant time exporting data, reconciling discrepancies, and manually preparing payroll, billing, and audit-related work.
Leadership also lacked a reliable real-time view of the business. Different systems produced conflicting numbers for the same metrics, so teams had to manually compare reports before they could trust the data. This made it difficult to act quickly, limited visibility into performance, and increased the operational burden of growth. The business was functioning, but it was doing so through a highly reactive operating model that became harder to sustain as complexity increased.
These issues were not isolated to one department. They spanned four major operating areas: acquisition and onboarding, service delivery and scheduling, finance and compliance, and communication and reporting. The audit showed that smaller point solutions would not be enough. The organization needed a coordinated AI strategy tied to an underlying operational redesign.
New Clarity produced a comprehensive AI strategy focused on building an integrated operational platform rather than layering isolated automations on top of fragmented workflows. The strategy centered on creating a unified system that could serve as the single source of truth for core data, process flows, reporting, and automation while still connecting to required third-party systems.
The strategy begins with AI-powered intake workflows that read incoming documents, extract structured information, and eliminate duplicate entry across systems. It also introduces centralized tracking for approvals, status changes, expirations, and renewals so staff can manage these workflows from one place instead of manually checking multiple systems. This reduces administrative time, improves accuracy, and prevents downstream disruptions.
Scheduling was one of the most operationally expensive areas in the business. The strategy introduces an intelligent matching layer that ranks the best available staff options based on multiple constraints, along with a unified scheduling interface, live utilization visibility, and automated handling of open shifts and visit exceptions. This reduces coordination time, improves service coverage, and helps prevent missed revenue tied to underutilization or overservice.
The plan also addresses labor-intensive financial workflows. It outlines a unified payroll pipeline, automated pay-code calculations, structured validation, automated claims submission, invoice generation, payment reconciliation, and workflows for handling payer changes and rebilling. These improvements reduce manual effort, shorten cycle times, and protect revenue that is otherwise lost to delays, errors, and rework.
Compliance work was another major burden. The strategy includes automated documentation workflows, digital quality assurance forms, centralized compliance dashboards, and audit readiness reporting. These systems reduce time spent assembling records manually and improve visibility into missing items before they become urgent issues.
To support all other initiatives, the strategy includes a centralized data layer and real-time dashboards for operational and executive reporting. It also introduces a structured communication and task layer so work can be assigned, acknowledged, and tracked more consistently. This reduces the “which number is right?” problem and gives leadership direct visibility into performance across the organization.
The roadmap is structured deliberately to build foundational systems first, then operational automation, and finally more advanced intelligence and optimization capabilities. The sequencing is based on dependency, expected business impact, and the speed at which value can be unlocked. The presentation outlines a phased rollout over approximately 18 months.
This phase focuses on the systems required for all future automation. It includes the centralized data foundation, secure access controls, intelligent document intake, elimination of duplicate data entry, system synchronization, and early visibility into approvals and expirations. These changes create immediate operational relief while establishing the structure needed for later automation.
Once the foundation is in place, the roadmap adds intelligent scheduling, matching, utilization controls, open-shift automation, visit exception handling, payroll automation, and early workflow improvements in recruiting and onboarding. These initiatives significantly reduce day-to-day coordination work while increasing service consistency and financial efficiency.
With operational systems stabilized, the roadmap introduces deeper billing automation, invoice generation, payment reconciliation, expense automation, compliance reporting, audit dashboards, structured task execution, and automated reporting infrastructure. This phase extends the operational gains into finance, compliance, and leadership visibility.
The final phase expands the platform with more advanced training, retention, quality assurance, and role-based operational intelligence capabilities. By this point, the business has the data quality and workflow consistency required to support more proactive and strategic AI use cases.
The strategy identified a conservative projected annual financial return of approximately $242,000 through a combination of cost savings, revenue uplift, and operational risk reduction. The materials break this down into roughly $101,000 in direct cost savings, $91,000 in revenue uplift, and nearly $50,000 in other operational and control-related benefits.
The expected gains come from reducing manual work in intake, scheduling, payroll, billing, reporting, and compliance, while also improving service coverage, increasing utilization visibility, reducing missed revenue opportunities, and improving leadership decision-making. The plan also highlights important benefits not fully captured in the financial model, including the ability to grow without increasing administrative overhead at the same rate, improved compliance posture, better staff experience, and a stronger long-term foundation for future AI initiatives.
.png)
Queen Anne Pillow and Moso Pillow are sister companies that provide specialized pillows and other unique bedding products. Both companies were growing quickly, but operations, inventory planning, and retailer outreach relied heavily on manual work that absorbed a large portion of the team’s week.
The AI audit and strategic plan from New Clarity have been extremely valuable for MOSO Pillow and Queen Anne Pillow. It gave us a transparent view of how much time we were losing to manual tasks and how AI could reduce that workload while protecting our Amazon sales and improving our retailer pipeline. The roadmap made it easy to decide what to do first, what to postpone, and how each project fits into the long term vision for the business.
.png)
New Clarity design an AI-powered agent used to monitor real-time news feeds, identify market events likely to trigger stock price movements, estimate the probability of such movements over the next 30 days, and continuously refine its predictive accuracy through feedback-driven machine learning.
The AI stock analysis engine built by New Clarity has given us invaluable insights into our trading strategies and has even improved our offering to clients.
.png)
A global consulting firm partnered with New Clarity to analyze how 400 HVAC companies communicate value to their customers.The goal: uncover patterns in messaging that reveal which aspects of value most influence purchasing decisions, without manually reviewing thousands of web pages.
"The client loved the results of New Clarity's AI analysis, and the 500 CEO in the room was blown away by the insights."
New Clarity designed and implemented a custom Model Context Protocol (MCP) server in Python to streamline one of the financial workflows for a fractional CFO. The solution connected directly to Claude desktop as the LLM client, enabling the AI to intelligently transfer financial data between spreadsheets. This project reduced hours of manual work by automating the matching and population of data fields across models.
New Clarity built a Claude enhancement that automates financial modeling from P&L statements, saving me countless hours of manual data entry.
New Clarity created a custom AI sales agent tailored to guide potential clients through unfamiliar therapies, address their concerns, and convert website interest into real consultations.
Prior to working with New Clarity, our sales conversion rate was low, and we were losing time spent on calls that ultimately did not lead to revenue. Our new AI sales agent has led to an exponential increase in sales-qualified leads, while actually reducing our overall time spent on sales calls.
.png)
