AI is an exponential technology. It enables things once thought impossible, for better and for worse, and it gets there fast. For leaders, the challenge isn’t whether to use AI, but how to handle it responsibly. Used with intention, it can provide real leverage such as increased referral completion rates, appointment availability, and staff efficiency. Used without it, it can just as easily burn the house down. The difference is whether wisdom and discernment keep pace with capability. Slow is smooth, smooth is fast has never been more relevant.
Across boardrooms and C-suites, one expectation is consistent: results. Leaders care deeply about mission, culture, and improvement efforts, but when the conversation turns to decisions, results lead. That isn’t cynicism; it’s clarity. A health system that isn’t financially sustainable cannot serve its mission long-term, and sustainability doesn’t excuse compromising people or values. The how matters and the how must deliver outcomes.
That tension is where AI now lives
Reimbursement pressure is increasing. Costs continue to rise. Consumer expectations are accelerating. Many leaders feel behind but hesitate to admit it. In that environment, activity becomes seductive, pilots, experiments, tools, dashboards, anything that feels like progress. But activity is not the same as traction. And you can’t manage what you can’t see.
The systems getting value from AI aren’t doing more, they’re choosing better. Instead of chasing ten initiatives, they align around the few levers that actually move the enterprise. That requires discernment: knowing where to start, what to measure, and why it matters.
In access, this shift is especially clear. Health systems that treat access with the same discipline long applied to revenue cycle are seeing different results. By connecting fragmented access functions into a single, manageable system, leaders move from reacting to acting, using AI to illuminate the decisions that matter most and scaling what works.
When AI is applied with focus, its value becomes clear in three places:
- Enabling dynamic networks in real-time.
- Leapfrogging the patient experience to meet rising expectations.
- Reducing administrative burden so humans can focus on coordination, care, and judgment.
AI powering dynamic networks
Network management often rises quickly as a high-impact lever because it affects both top- and bottom-line performance. Historically, networks were managed through static, annual processes, lists built over months, tiered by priority, and largely unchanged until the next cycle. It was nearly impossible to understand network health in real time.
Today, leaders are using AI to manage networks dynamically. Performance, follow-through rates, and availability are visible in real time. AI can adjust network recommendations to optimize outcomes, favoring providers with higher completion rates or earlier availability, and can influence search and scheduling behavior automatically.
Participation has always been a constraint. Many systems have experienced stalled affiliate network initiatives due to lack of buy-in. Where AI is lowering the implementation burden for community providers, making participation nearly hands-off, leaders are seeing far stronger engagement and results.
AI is also changing how capacity is managed. Systems are using AI to optimize provider capacity at the individual level and then aggregate those insights to manage network-wide availability. As capacity shifts, patients can be re-routed dynamically, including proactively offering earlier appointments. Some systems are even using these insights to inform physician recruitment plans or optimize staffing across facilities to maintain access targets that support revenue goals.
Patient experience needs more than a facelift
AI is also reshaping patient expectations. The tools patients use every day are training them to expect clarity, speed, and responsiveness. The old assumption — they’ll come anyway — is proving less reliable.
One-way leaders are responding is by using AI to actively monitor patient behavior during digital interactions. For example, if a patient enters a provider search and scheduling flow and exceeds a known abandonment threshold, AI can intervene, offering conversational assistance, clarifying confusion, or seamlessly transitioning the patient to voice support.
Another application is concierge-style engagement within referral workflows. When referrals are canceled, rescheduled, or result in no-shows, AI can identify likely barriers and engage patients proactively offering alternative times, transportation assistance, virtual visits or help understanding their care plan. These are often logistical issues, not intent issues, and addressing them improves both access and outcomes.
Administrative work is still low-hanging fruit
Reducing administrative burden remains one of the most practical uses of AI in access. While it’s sometimes harder to tie directly to revenue, the operational impact is real.
Systems are using AI to ingest and manage referrals from fax, mail, and digital platforms, sorting, completing missing data such as NPI number, service line need, insurance information, and routing referrals into the correct workflows. Referrals stall easily. Every day a referral sits idle increases the risk of leakage, lost revenue, and degraded quality scores. AI’s ability to accelerate this work helps teams meet productivity targets and reduces waste.
AI is also improving front-end navigation by adapting to how patients want to interact, offering multiple paths instead of rigid questionnaires. By incorporating service-line rules dynamically, AI helps ensure patients reach the right provider the first time, reducing dissatisfaction for both patients and clinicians.
Focus is the advantage
AI has a lot to offer. The systems seeing traction are those aligning AI use with clear goals, elevating access to a cross-functional discipline, and using intelligence to guide dynamic decision-making. Activity is easy. Results require focus.
3-step action plan for leaders to align on focus:
- Pick the few outcomes that matter and say “no” to the rest. Define 2–3 measurable access outcomes (e.g., referral completion, time-to-appointment, leakage reduction, staff minutes saved) and explicitly de-prioritize anything that doesn’t move those needles.
- Elevate “Access” to a cross-functional operating cadence (not an IT project). Stand up an Access leadership huddle/operating group (Access + Ambulatory Operations + Service Lines + Digital + Contact Center + RCM + Compliance + IT) with decision rights and a weekly dashboard. The aim is to treat access with the same discipline leaders already apply elsewhere and make it a shared enterprise system.
- Instrument the system, then let AI join as a “team member”. Put in place the minimum data/telemetry to see bottlenecks and variability (referral aging, fallout reasons, abandonment points, capacity/availability, completion rates), then deploy AI where it can drive dynamic actions (routing, prioritization, proactive outreach, schedule optimization).
The most successful leaders aren’t chasing every idea. They’re using AI to illuminate the few levers that matter most — and acting with intent. In a market that constantly pulls attention in every direction, focus has become one of the most important leadership disciplines and AI will either amplify that focus or burn the house down if left unguided.
Photo: Vithun Khamsong, Getty Images
Jake McCarley is the CEO and co-founder of Alluvium, the access and capacity performance platform for healthcare — built to drive revenue, and market share for health systems by giving them enterprise-wide visibility and control of access, capacity, and provider network performance in a disparate data world. A serial entrepreneur at heart, Jake is most energized by building and backing projects that help local communities flourish. He lives in McKinney, TX with his wife and three kids.
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