Key Points
- AI adoption in healthcare has outpaced AI maturity, with most health systems still stuck running isolated AI pilots instead of scaled, enterprise-wide programs.
- Fragmented patient data across EHRs, revenue cycle, and engagement platforms is the single biggest barrier preventing AI from improving patient experience at scale.
- A human-in-the-loop governance model, paired with a specialized partner, is what separates organizations seeing measurable patient experience gains from those stuck experimenting.
Healthcare organizations are not short on AI ambition. Adoption has surged, with 22% of healthcare organizations now running domain-specific AI tools and almost two-thirds of healthcare leaders naming patient experience as the area where AI will matter most. AI investments topped $46 billion in 2025, representing 46% of all healthcare spending that year.
The spending is real. The impact, for most organizations, is not. Of the 78% of health systems that have adopted AI, just over half feel prepared to implement it, citing data quality issues, governance gaps, and unclear strategic ownership. Pilots stall. Systems fail to scale. Patient experience gains remain incremental rather than transformative.
The gap is not technology. It is maturity.
Why Most AI Pilots Never Scale
According to a recent AI Maturity Roadmap, organizations move through five levels in their AI journey: Awareness, Active, Operational, Systemic, and Transformational. Most healthcare organizations are stuck early. Just 9% have reached mature AI adoption. The reason is rarely the algorithm. It is what surrounds it.
In our recently published playbook “Operationalizing AI in Patient Experience”, we’ve identified five key reasons why AI maturity stalls. Patient data lives in fragments, spread across EHRs, revenue cycle systems, and engagement platforms that rarely communicate with one another. Contact center agents often work without visibility into the clinical context. Clinicians often lack insight into a patient’s prior scheduling or billing interactions. The result is an AI system trained on a partial picture, which means partial, low-context outputs and a patient who has to repeat the same information at every touchpoint.
“AI is rapidly emerging as a transformative capability, and healthcare leaders are eager to begin operationalizing its value. However, many AI-driven customer experience initiatives fall short because organizations prioritize the technology before clearly defining the underlying processes and business problems they are solving for.”
– Jimmy Guest, Vice President of Information Technology, Sutherland Global
What Maturity Actually Looks Like
When AI is deployed inside a mature operating model, the nature of patient engagement changes. Predictive analytics flag patients at risk of disengagement before they fall through the cracks. Automated outreach delivers reminders and education tailored to the individual. Omnichannel engagement keeps interactions consistent whether a patient calls, chats, or logs into a portal. Higher-maturity organizations also free up clinician time, reducing the administrative load that drives burnout while improving transparency for patients managing their own care.
None of this happens without governance. Ethical guidelines, compliance frameworks, and information security policies need to be in place before AI runs at scale, not after. A human-in-the-loop approach, validating outputs before they reach a patient and capturing feedback after each interaction, enables organizations to move faster with automation without losing oversight. Done well, this kind of governance does not slow innovation. It is what makes scaling possible at all. According to McKinsey, AI solutions could save healthcare payers up to $300 billion in administrative costs per $10 billion in payer revenue, while increasing revenue by up to $1.24 billion.
The Partnership Advantage
The global AI healthcare market is projected to surpass $500 billion by 2033, up from $36.6 billion in 2025, and tools that improve patient communication and engagement are expected to drive much of that growth. Organizations that wait to build internal maturity from scratch risk falling further behind those who partner their way to scale faster.
Sutherland brings proven expertise in AI-driven patient experience, spanning digital engagement, automation, and contact center optimization built specifically for healthcare. Working with an experienced partner gives organizations ready frameworks, risk mitigation, and measurable outcomes from day one, freeing internal teams to focus on care delivery while AI maturity scales in the background, reliably and compliantly.
The organizations seeing real movement in patient experience scores are not the ones with the most AI pilots. They are the ones who treated maturity, not technology, as the goal from day one.
