AI in healthcare finance: Practicality over hype, strategy over speculation
- Start with ERP-embedded AI for quick wins. Leading healthcare finance teams activate native AI features in platforms like Sage Intacct, Microsoft Dynamics 365 Business Central, and Oracle NetSuite to streamline operations and leverage existing data, security and audit controls.
- Pilot programs deliver measurable results. Launching focused 90-day pilots tied to KPIs—such as close time, manual entry reduction, and payer mix insights—helps organizations to prove value and build momentum for broader AI adoption.
- Governance is essential for compliance and trust. Successful AI initiatives incorporate lightweight governance aligned with HIPAA, NIST AI RMF and the EU AI Act, embedding human-in-the-loop review, bias monitoring and audit-ready controls into financial workflows.
Artificial intelligence is no longer a distant promise — it’s a present reality, reshaping how healthcare organizations manage finance, compliance, and operational efficiency. Yet, the challenge isn’t whether to adopt AI, but how to do so with discipline, clarity, and measurable impact. Healthcare leaders must resist the allure of moonshot solutions and instead focus on pragmatic, incremental wins that align with their organization’s current systems and strategic trajectory.
Meeting organizations where they are
Healthcare finance operates under relentless pressure, characterized by thin margins, complex payer mixes and intense regulatory scrutiny. AI’s value emerges most powerfully where data is structured and workflows are repeatable. Revenue cycle automation, financial close acceleration, and predictive analytics are not just buzzwords; they’re proven levers for cash flow improvement and cost containment.
The key is to deploy AI where it can deliver immediate results, freeing staff for higher-value work and enabling finance teams to become strategic partners in organizational growth.
This pragmatic focus is especially important given the historical barriers healthcare finance has faced in adopting advanced analytics and automation. Fragmented data silos across EHRs, ERPs, and revenue cycle systems, years of customization debt layered onto core platforms, and legitimate audit and compliance risk have made many finance leaders understandably cautious.
Rather than signaling resistance to innovation, this caution reflects the reality that financial accuracy, control, and trust must come before experimentation. AI strategies that acknowledge these constraints — and work within them — are far more likely to succeed.
Start with an AI inventory
Before embarking on any AI initiative, healthcare finance leaders should conduct a comprehensive AI inventory. This means:
- Assessing your current state: Catalog all existing systems, platforms, and tools in use across finance, revenue cycle, and operations. Identify which solutions already have embedded AI features, such as automation, anomaly detection, or predictive analytics, and which are still manual or rules-based.
- Understanding what’s available today: Review your ERP, EHR and analytics platforms for native AI capabilities that may be underutilized. Many organizations already own licenses for features like Copilot, AP automation or other available AI agents but haven’t activated them.
- Mapping the roadmap: Engage with your technology vendors to understand what AI enhancements are planned for the next 12 to 24 months. Most leading ERP providers, including Sage Intacct, Microsoft Dynamics 365 Business Central and Oracle NetSuite, publish detailed AI roadmaps. Knowing what’s coming allows you to pilot new features early and align your internal strategy with vendor innovation cycles.
This inventory process ensures you maximize the value of your current investments, avoid redundant purchases and set a realistic pace for AI adoption that matches your organization’s readiness and risk tolerance.
Governance: The foundation for sustainable AI
As AI becomes embedded in ERP platforms like Sage Intacct, Microsoft Dynamics 365 Business Central, and Oracle NetSuite, governance must keep pace. HIPAA, NIST AI RMF and emerging standards such as the EU AI Act demand robust oversight. Human-in-the-loop review, bias monitoring, and continuous performance surveillance are no longer optional. Healthcare organizations must build governance frameworks that safeguard patient data and help ensure AI-driven decisions remain transparent and equitable.
Importantly, governance should not be viewed as a hindrance to innovation, but rather as an accelerator. Clear guardrails enable faster adoption by giving finance leaders, auditors, and compliance teams confidence that AI-driven outputs are explainable, auditable, and aligned with regulatory expectations.
When governance is lightweight, well-defined, and embedded into existing financial controls, organizations reduce the risk of shadow AI, shorten approval cycles, and empower teams to scale AI responsibly rather than stall it indefinitely.
ERP roadmaps: Aligning quick wins with long-term strategy
The leading ERP vendors are rapidly evolving their AI capabilities:
- Sage Intacct: Embedded AI features like Sage Copilot, which includes close workspace and automation, subledger reconciliation assistant, AI-driven AP processing and the AI-powered Import Service, as well as GL Outlier Detection, are streamlining close processes and anomaly detection. The roadmap points toward deeper automation and reconciliation tools, making it essential for finance teams to activate these features now and plan for expanded capabilities in 2026.
- Business Central: Copilot-driven automation, bank reconciliation assist, and Power platform integrations are transforming everyday finance operations. Healthcare organizations should leverage these tools for immediate efficiency gains while preparing for agentic Copilot features, which will further enhance order intake and supply chain management.
- NetSuite: AI-native workflows, Bill Capture, and Analytics Warehouse are accelerating AP automation and predictive insights. The trajectory is clear: autonomous close and compliance AI assistants will soon be standard, and organizations should pilot these features to stay ahead.
ERP-embedded AI offers a structural advantage over bolt-on tools. It operates on a shared data model, inherits existing security roles and controls, and aligns naturally with audit and compliance workflows. Unlike standalone AI applications that require data movement, custom integrations, and parallel governance processes, embedded ERP intelligence reduces complexity, lowers risk, and accelerates time-to-value.
For healthcare finance teams, this distinction matters. AI that lives inside systems of record is inherently more scalable, defensible, and sustainable than AI layered on top of them.
The playbook: From pilot to scale
Thoughtful adoption starts with targeted pilots — 90-day sprints focused on measurable KPIs like payer mix analysis, close cycle time, and manual entry reduction. Success requires clean, standardized data and a willingness to iterate. As pilots deliver results, organizations can expand AI use, modernize data structures, and publish a 24-month roadmap that aligns technology investments with strategic goals.
Ownership is a critical success factor in these pilots. In healthcare finance, the pilot should be finance-led with clear executive sponsorship from the CFO or VP of Finance while being jointly executed with IT, compliance, and operational stakeholders. Finance owns business outcomes and success metrics; IT ensures data integrity, security, and integration; and compliance provides early input to avoid downstream audit or regulatory friction. When ownership is diffused or delegated entirely to IT, pilots often stall or drift away from financially meaningful outcomes.
Equally important is knowing what not to pilot first. Early AI initiatives in healthcare finance should avoid common missteps, including:
- Starting with unstructured or clinical data that sits outside core financial systems.
- Over-customizing AI workflows before baseline processes are standardized.
- Selecting pilots that lack a clear owner, measurable KPIs, or decision-rights framework.
- Introducing standalone AI tools that bypass ERP controls and governance.
- Attempting enterprise-wide transformation before proving value in a contained use case.
Disciplined pilots that stay close to systems of record, focus on repeatable workflows and deliver measurable monetary impact create the credibility required to scale AI across the organization.
Vision: AI as a native asset in healthcare finance
The future of healthcare finance is not about chasing the latest AI trends. It’s about embedding intelligence into the systems you already trust. By activating embedded ERP features, building lightweight governance, and tracking vendor roadmaps, healthcare organizations can deliver results today and position themselves for the next wave of innovation. The leaders who succeed will be those who combine pragmatism with vision, leveraging AI to drive both operational excellence and strategic growth.
How Wipfli can help
Wipfli’s team guides healthcare finance leaders through every step of AI adoption — from assessing your current systems and identifying embedded AI features to building robust governance frameworks and piloting solutions that deliver measurable impact. With deep knowledge of ERP platforms, Wipfli helps you activate native AI capabilities, streamline financial operations, and help ensure compliance with evolving regulations. Learn more about our services.
