Webinar: Unlock the Power of Quick AI Wins

1. Operational Quick Wins: Call Navigation and Scheduling

1.1. Contact Center Efficiency Gains

Organizations are experiencing rapid wins with conversational AI applied to phone traffic navigation. Once example from the conversation was about a health system with a 200-person call center that could only handle 30 percent of total traffic, leaving the remaining 70 percent in a long hold queue. Frustrated healthcare consumers waiting for information or for transfers to appropriate departments where they are often forced to wait in another queue.

AI agents really improve the phone experience, often demonstrating 80 percent engagement rates within the first week of deployment. As one panelist noted, AI implementations don’t always fix all problems, but they help reveal exactly where operational inefficiencies exist.

Conversational AI technology addresses decades-old infrastructure problems. Despite 50 years of push-button menus, many healthcare systems still operate “press one for this, press two for that” systems. This results in patients being transferred repeatedly because most health systems don’t have one-number access. Many organizations lack the ability to efficiently route their call volume, creating massive inefficiency.

1.2. Scheduling Department Productivity

The same scheduling department with the same number of agents scheduled 20 percent more appointments after AI implementation automatically routed calls that belonged elsewhere. This demonstrates how removing non-scheduling calls from scheduling staff queues enables dramatic productivity improvements without adding headcount.

2. Implementation Strategies for Rapid Deployment

2.1. Six-Week Implementation Model

University of South Florida Health implemented a call center AI solution in approximately six weeks. The urgency stemmed from immediate operational need—the organization required help immediately and recognized that extended timelines would diminish value.

This rapid deployment model works for low-hanging fruit where discrete, automated tasks can be captured quickly in existing systems. The approach requires organizational willingness to move decisively when business need is clear and technology risk is low.

2.2. Starting Small with Proven Use Cases

Successful implementations begin with initiatives everyone can agree to—such as appointment rescheduling with the same provider, where all necessary information already exists. Organizations can easily navigate calls or locate appropriate appointment slots for these straightforward scenarios.

The entry point focuses on common-sense applications that don’t require extensive backend standardization. For example, if a patient simply wants to reschedule an existing appointment with the same provider, the AI can easily find that slot or navigate the call to the right place. This builds confidence and demonstrates value before tackling more complex use cases.

The strategy acknowledges that perfection is not required for initial deployment. Healthcare systems work toward common-sense standardization over time. Success requires blending AI capabilities with realistic expectations and outlining a journey where health systems and technology partners arrive at the same destination together.

Organizations finding success start with automation and intelligence applications for operational functions. The operational applications often deliver faster ROI with lower risk profiles.

3. Governance Models That Accelerate Adoption

3.1. The Governance Spectrum

Health systems demonstrate a wide range of approaches to AI governance. Some treat it as standard IT technology requiring no special consideration—they have governed IT for decades and will apply the same processes. Others have hired specific executives, formed dedicated committees, and created specialized contract language.

One health system has operated a specific AI governance structure for two years with multidisciplinary stakeholders evaluating business cases alongside security risks and compliance considerations. This approach applies the same rigorous vetting used for other technologies while acknowledging AI’s unique characteristics.

Academic health centers leverage their learning and experimentation foundations to enable rapid innovation. One health system operates approximately 300 different AI solutions across clinical areas by empowering individual physicians and practice areas to make their own decisions within established governance frameworks. This decentralized model acknowledges that if centralized evaluation takes two years, technology may become irrelevant by go-live.

3.2. Balancing Oversight with Speed

The key governance challenge is implementing sufficient safeguards while enabling rapid innovation. Organizations are learning that small-scale deployments with limited consequence carry less risk than system-wide implementations affecting entire clinical records. This risk stratification allows faster movement on lower-risk applications like call navigation.

Critical governance elements include:

• Establishing technology governance as foundation—many organizations lack strong governance even for traditional technology

• Implementing data governance, as AI requires solid underlying data to generate value

• Engaging operational leadership early, as staff will be impacted by technologies that may eventually change workforce needs

• Creating communication strategies that address operational leader concerns about workforce reduction

Organizations must also address legal considerations and risk tolerance variations. Some ask vendors questions so granular that the only honest answer is acknowledgment that perfection cannot be guaranteed. For example, concerns about bias in speech recognition (difficulty understanding thick accents) must be contextualized — humans may struggle equally with the same accents.

4. Measuring ROI and Operational Impact

For automated tasks discretely captured in systems, measurement is straightforward. Organizations can document time savings per transaction and multiply by transaction volume. When AI implementations demonstrate 80 percent caller engagement within the first week, and scheduling departments with unchanged staffing schedule 20 percent more appointments, ROI becomes immediately visible.

However, organizations must maintain realistic expectations about measurement. Rather than attributing every efficiency gain to a single initiative, health systems should measure whether the pace of improvement is increasing. Multiple changes occur simultaneously, making isolated attribution difficult. The question becomes: Are we improving faster with these tools than before?

Organizations must stay focused on ultimate outcomes:

• Patient experience measures: Access, satisfaction, and journey quality

• Bottom line financial performance: Operational cost reduction and revenue optimization

Commitment must focus on perceivable, meaningful changes to end metrics.

Organizations should recognize that certain costs cannot be extracted regardless of efficiency gains. The approach should focus on optimizing processes rather than assuming automation eliminates all labor costs. It does not and will not. 

5. Key Takeaways for Healthcare Leaders

Every Organization Requires a Unique Recipe: While fundamental elements needed to move the needle on AI remain consistent across health systems, the amounts of each ingredient differ substantially. Leaders must customize approaches based on organizational culture, maturity level, risk tolerance, and operational priorities rather than copying peer strategies wholesale.

Keep Moving Forward Across All Initiative Sizes: Every initiative is not equal—think in T-shirt sizes of small, medium, and large. Organizations that focus only on big-picture, transformational applications leave substantial direct benefits on the table. The doers implementing initiatives across the board will see the most benefit over the next five years.

Mission and Empathy Must Drive Adoption: Organizations must put their mission first. AI adoption must be empathy-driven and improve compassionate, equitable care. Leaders should adopt technologies with clear purpose rather than implementing them because they represent shiny objects. Relentless focus on the people served must guide every decision.

Data Foundation Determines Direction: Leaders should focus on digitizing, organizing, and maintaining data quality. Strong data will indicate the direction organizations must take, as each health system is unique with distinct cultural and organizational characteristics.

Optimism Tempered with Realism: Leaders should remain incredibly optimistic about AI’s potential while maintaining clear-eyed realism about implementation. Most importantly, health systems must recognize they are in this together—settings that enable peer learning and collaboration prove incredibly valuable.