Finance leaders prioritize responding to payer behavior because denials and growing payer requirements add to providers’ administrative burden and costs.

This assumption is incomplete. Payer behavior contributes to health systems’ financial challenges, but it is not the only driver of downward pressure on margins. Rising labor, supply, and drug costs; care model disruptions; and revenue erosion are creating structural margin pressures. Additionally, this framing reinforces an adversarial dynamic that may prevent providers and payers from finding ways to rebuild trust and move toward a more efficient system.

Leaders must position revenue cycle efficiency within a broader margin management strategy. Leaders who focus too much on reacting to payer behavior may miss more impactful opportunities to strengthen overall financial performance. To manage margins, health systems should enhance operational efficiency to address access constraints and patient flow bottlenecks, improve workforce productivity, and optimize the supply chain. How much leaders prioritize revenue cycle optimization against other strategies will vary by organization, market, and balance sheet position.

The media and vendors are hyping up AI-enabled clinical revenue cycle solutions, specifically ambient listening and autonomous coding.

This assumption is overly optimistic. AI has the potential to prevent avoidable revenue loss by improving documentation and coding accuracy. However, few organizations have implemented AI at scale or achieved substantial revenue lift from these investments.

Tools that automate transactional front-end and back-end processes, such as eligibility checks and claims submission, have the deepest evidence base and most reliable returns. Providers should target investments at imminent revenue gaps, instead of diverting resources to new tools with unclear financial ROI. For instance, for providers with a large Medicaid population, investing in augmenting capabilities in the front-end of the revenue cycle mitigates coverage churn and improves collection rates from self-pay patients.

Providers and payers often use “AI” and “automation” interchangeably, which leads both stakeholder groups to overestimate AI adoption in the market.

This assumption is false. Believing in this assumption can lead organizations to prematurely invest in tools to “catch up” to where they imagine their peers already are. What most providers call “AI” is automation at scale. Most deployments today still rely heavily on deterministic rules, workflow automation, and point solutions with limited learning or adaptability. The distinction between automation and AI matters because each has its own implications for how much leverage providers have against payers. Automation enhances efficiency but maintains the status quo with payers, whereas AI learns from and predicts payer behavior so providers can proactively prevent denials or rework. Additionally, providers may fall victim to opportunistic vendors that mislabel their automation solutions as AI.

Design AI strategies grounded in your own operational readiness, priorities, and risk tolerance, not on unproven ROI or media hype.

Tools that automate processes and enable staff to make faster, more accurate decisions lower the cost to collect. This allows organizations to work through growing volumes of denials with the same or less headcount.

This assumption is misleading. Providers are primarily using technology to augment, not replace, human capability and decision-making. Providers are already leveraging automation to bridge existing and projected staffing and expertise gaps among clinical coding and denials management teams. However, most tools still require a human backstop to review and act as the final decision-maker.

Amid high turnover rates and projected retirements,¹ leaders must prepare for a future where their revenue cycle workforce has fewer (and less experienced) human workers and more technology. This shift will require managers to develop new skills to coordinate, oversee, and troubleshoot high-tech workflows. Leaders will need to invest in manager training and development to help managers to adapt to a changing workforce.

An EHR is one of the largest investments that systems make. Understandably, leaders want to maximize their platform’s capabilities before turning to third-party vendors, which poses additional challenges around interoperability and vendor sprawl.

This is not a proposition without trade-offs. Waiting for the EHR platform to make changes may be lower risk, but organizations may forgo financial gains at a time when they can’t afford to wait. Platform-led strategies offer standardization and ease of implementation. However, they stifle progress where EHR tools are not yet fully developed. For instance, Epic often lags in developing revenue cycle solutions for functions like denials management, payment integrity, or AI-driven prediction.

Leaders must anchor investment decisions around a defined problem and choose solutions that integrate smoothly with their EHRs, regardless of whether they are native or external. Organizations won’t be able to fully avoid point solutions or interoperability hurdles. Therefore, leaders need to be clear-eyed about what they’re giving up by ceding the development road map to their EHR vendor. Organizations should keep their options open through shorter contracts, clearer exit paths, and pilot programs. This approach raises the bar for vendors and shortens the window of time for them to prove their value. For vendors, this dynamic elevates Epic integration to a strategic imperative.

These five assumptions underpin many of the investment decisions finance leaders are making.

While the pressure and urgency to act is high, slow down and make decisions based on real, proven impact, rather than making rushed decisions based on vendor or media hype, as the impact of emerging disruptive technologies is still largely unproven.