Health systems often view AI as the next step in digital transformation. But as organizations rush to evaluate chatbots, predictive outreach, AI-powered search, and ambient clinical documentation, an important question remains: what actually makes a health system digitally mature?
Modea’s 2026 Digital Maturity Research examined 27 U.S. health systems and surveyed more than 1,300 healthcare consumers to better understand the capabilities, organizational structures, and priorities shaping digital maturity today. As part of this research, our digital maturity assessment evaluated how effectively health systems align governance, technology, data infrastructure, teams, and digital capabilities to support organizational goals and patient needs. Rather than measuring individual technologies in isolation, the assessment examines how these elements work together to create scalable, connected digital experiences.
The findings suggest that AI may not be the starting point of digital transformation at all. Instead, AI success is often the outcome of years of investment in platforms, data, governance, and digital operations.
The most digitally mature health systems consistently demonstrate larger digital budgets, larger dedicated digital teams, product-led governance models, and more advanced digital capabilities. They are also more likely to have implemented AI at scale. Rather than viewing AI as a shortcut to digital maturity, our research suggests it is often the result of the investments and operating models mature organizations have been building for years.
As health systems continue investing in AI-powered search, intelligent navigation, and predictive technologies, the question may not be which AI tools to adopt. The more important question is whether the organizational and technical capabilities required to support those tools are already in place.
Digital Maturity Starts with Operating Models, Not Technology
When organizations discuss digital maturity, the conversation often centers on technology. New platforms, emerging AI tools, and consumer-facing capabilities tend to attract the most attention. Yet the research suggests that organizational structure may be just as important as technology investment.
Health systems identified as digital leaders share several common characteristics. They typically maintain annual digital budgets exceeding $5 million, dedicate more than 50 full-time employees to digital experiences, and operate within product-led or committee-based governance structures that support cross-functional decision-making.
These findings point to a broader truth about digital maturity: transformation is difficult when digital initiatives operate in silos.
The most mature organizations increasingly treat digital as a business capability rather than a collection of projects. Marketing, IT, operations, analytics, and clinical stakeholders work together within shared governance models and align around long-term objectives. Digital investments are funded strategically, measured consistently, and supported by teams designed to sustain continuous improvement.
This approach mirrors how successful product organizations operate. Instead of launching disconnected initiatives, they focus on building repeatable systems for prioritization, execution, measurement, and optimization.
As health systems continue pursuing AI initiatives, personalization strategies, and connected patient experiences, governance and alignment are increasingly important. The organizations most prepared for future innovation are those that have already established clear ownership, cross-functional collaboration, and long-term digital roadmaps.
Why Health Systems Are Investing in Infrastructure Before Innovation
The industry’s current investment priorities reinforce this idea.
More than 75% of surveyed health systems reported either actively planning a website or digital experience platform replatforming initiative or having recently completed one. The most commonly cited reasons include performance and scalability concerns, security requirements, content governance limitations, and the need to better support personalization and AI enablement.
At first glance, replatforming may not appear directly connected to AI. However, many of the capabilities organizations hope to unlock through AI depend on having the right infrastructure in place.
Personalized content recommendations require structured content and accessible data. AI-powered search depends on content quality and governance. Predictive outreach requires integrated CRM and analytics capabilities. Intelligent navigation depends on well-organized information architecture and connected systems.
The research highlights a broader trend across healthcare: organizations are increasingly focused on strengthening the underlying systems that support digital experiences. Priority initiatives over the next 12 to 18 months include platform modernization, data management, CRM integration, accessibility improvements, EHR transformation, and AI integration.
In many ways, these investments represent the less visible side of digital maturity. They may not generate the same attention as a new AI chatbot, but they create the conditions that allow advanced capabilities to succeed.
Patients Still Define Digital Maturity Differently
While health systems invest heavily in technology modernization, consumers continue to evaluate digital experiences through a much simpler lens.
Can they find the information they need?
Can they schedule care online?
Can they understand costs before receiving care?
Can they access content in a language they understand?
Can they complete tasks without frustration?
The research revealed significant gaps between consumer expectations and current health system capabilities. The most striking example involves multilingual experiences. More than 82% of consumers identified home language support as important, yet only 22% of health systems currently meet industry benchmarks for multilingual capabilities. This represents the largest capability gap identified in the study.
Additional gaps exist across online scheduling, cost transparency, accessibility, and other core digital experiences. The research also found that 59% of consumers reported at least one issue navigating or accessing a health system website or application.
These findings serve as an important reminder that digital maturity is not solely measured by technology adoption. Patients rarely judge organizations based on platform architecture, governance frameworks, or AI capabilities. Instead, they evaluate whether digital experiences make accessing care easier, faster, and more intuitive.
For health systems pursuing digital transformation, balancing long-term infrastructure investments with immediate consumer needs will remain a critical challenge.
AI Rewards Organizations That Have Already Built the Right Capabilities
Survey findings suggest that AI maturity is closely tied to organizational maturity.
Large health systems lead the industry in AI governance, AI-powered search and discovery capabilities, and enterprise-wide AI implementation. Every large health system surveyed reported implementing at least one system-wide AI capability.
Importantly, these organizations are not succeeding because they adopted AI first. They are succeeding because they have already invested in the capabilities required to operationalize AI effectively.
AI requires governance. It requires quality data. It requires modern platforms, integrated systems, and cross-functional ownership. Without those capabilities, even the most promising AI initiatives can struggle to deliver measurable value.
The consumer findings reinforce this point. Trust in AI varies significantly across demographic groups. Millennials and Gen X consumers demonstrate the highest levels of trust and adoption, while older consumers remain more cautious. Interestingly, Boomers are substantially more likely to support AI use by healthcare providers than they are to trust AI making decisions independently. Consumer trust and adoption also increase alongside household income levels.
As health systems expand AI-powered experiences, success will depend on more than technical implementation. Organizations will need governance structures that support responsible use, data strategies that improve accuracy and relevance, and digital experiences that accommodate varying levels of consumer trust and comfort.
Digital Maturity Beyond the AI Hype
The conversation around digital transformation often treats AI as the destination. Modea’s research suggests a different perspective.
AI may be one of the most visible signs of innovation, but it is rarely the starting point.
The health systems best positioned to benefit from AI are often those that have spent years investing in modern platforms, strong governance models, dedicated digital teams, data infrastructure, and consumer-centered digital experiences. They have built the capabilities required to scale innovation effectively.
As AI adoption accelerates across healthcare, those capabilities may become the clearest indicator of digital maturity. Organizations that continue investing in operating models, platform strategy, data infrastructure, and consumer experience will likely be better positioned not only to implement AI, but to generate meaningful value from it for patients, providers, and the business.
For health systems evaluating their own digital maturity, the next step may not be identifying a new technology. It may be understanding whether the governance structures, digital capabilities, data infrastructure, and operating models needed to support future innovation are already in place. At Modea, we help healthcare organizations assess their current state, identify opportunities for growth, and build practical roadmaps for digital transformation.