AI in Healthcare: A Provider’s Guide to AI Maturity, From First Steps to Transformation

The more headlines that celebrate healthcare providers launching AI (e.g., Cleveland Clinic, Stanford Health Care, Vanderbilt University Medical Center), the more it feels like AI has achieved deeper industry maturity than it really has. Most providers are still finding their footing with AI, and that’s good.

Moving with urgency is important, but healthcare providers who sprint to AI use cases without establishing an operating model or putting the right foundational architecture in place first open themselves up to risk, failed projects, and wasted resources.

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Instead, providers should take a strategic, intentional approach to AI in healthcare. Specifically, one that aligns AI with ROI by:

• Readying data systems and identifying needed infrastructure.
• Prioritizing use cases across your organization by impact.
• Establishing risk management and governance frameworks.

This guide will help you develop and implement a strategic approach to AI. It outlines the stages of AI maturity in healthcare, the obstacles you’ll likely face, and the essential capabilities you’ll need to put in place. Use it to understand where you are, articulate your goals, and create the AI roadmap your provider system needs.

The Stages of AI Maturity in Healthcare

At WillowTree, we help healthcare providers understand their current capabilities and begin building their AI strategy by applying an AI maturity model.

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This model consists of five stages:

• Stage 1: Awareness — Understanding and researching potential applications for AI.
• Stage 2: Exploration — Initial investments in data infrastructure and proof-of-concepts.
• Stage 3: Adoption — Deploying limited AI projects based on an articulated AI strategy.
• Stage 4: Optimization — Integrating AI into core operations and continuously improving AI models and processes.
• Stage 5: Transformation — Embedding AI organization-wide to develop new business models and revenue streams.

The journey to AI maturity looks different for every healthcare provider. The better you understand the key characteristics and challenges of each stage, the more effective your AI strategy will be at driving organizational alignment, shortening your time to value, and improving quality of life for your patients and efficiency for your physicians.

Stage 1: Awareness

The Awareness stage is about building your knowledge, understanding, and comfort with AI. This is where the bulk of healthcare providers are. Only 15% of providers and 25% of payers had an AI strategy in 2024 according to a study by Bain & Company and KLAS Research. The same study also found that 75% of providers and payers had increased their IT spends, focusing on areas like infrastructure, cybersecurity, and clinical workflows.

These are the kinds of moves smart healthcare providers make in the Awareness stage. Investments like those above lay the groundwork for executing an AI strategy and accelerating through later stages of AI maturity.

Your organization is in the Awareness stage when there is:

• Lack of AI expertise within the organization.
• Uncertainty about the ROI of AI in healthcare.
• Concerns about data privacy, security, and compliance.
• Basic understanding of current data infrastructure.
• Willingness to learn and explore AI possibilities.
• Assessing internal AI leadership or/and selecting an AI consulting partner.

Starting from zero (or close to it) in the Awareness stage can feel overwhelming, but a good way for healthcare executives to get past that is to look at peer providers’ successes with AI. For example, in his book The Sound of the Future: The Coming Age of Voice Technology, Tobias Dengel, President, TELUS Digital Experience details how Boston-based Mass Brigham General quickly developed a chatbot to handle COVID-19 inquiries by taking inspiration from Providence St. Joseph Health system in Seattle.

Steps to take before advancing from Awareness to Exploration:

• Educate leadership on AI basics and potential applications.
• Assess current data infrastructure and identify gaps.
• Explore successful AI implementations in peer organizations.

An AI Use Case Workshop is a fast, high-impact way to create alignment and spark enthusiasm during the Awareness stage. In one day, you’ll identify dozens of AI use cases and prioritize them by feasibility and ROI. Those use cases will then inform your data strategy and governance frameworks, so you can confidently build your AI roadmaps.

Stage 2: Exploration

The key objective of the Exploration stage is to begin prototyping AI initiatives that will positively impact business KPIs (e.g., patient wait times, staffing ratios, claims denial rates). Exploring early proof-of-concepts will help you understand where the bottlenecks are within your technologies, processes, and capabilities.

This stage is also the time to assess your data readiness for AI. Doing so will guide the democratization and centralization of your data, show you where data quality improvements are needed, and inform the governance structures you’ll need for organization-wide adoption of AI.

Your organization needs help in the Exploration stage when:

• Siloed AI initiatives exist without a cohesive strategy.
• Successful AI pilots are difficult to scale.
• Siloed data sources lack infrastructure for integration.
• Resistance to change surfaces due to potential disruption from AI.

Key outcomes:

• Build a cross-functional AI task force.
• Improve data quality and accessibility.
• Identify initial use cases for AI.

The pilot projects you begin in the Exploration stage have the potential to transform attitudes across your organization. For example, a leading healthcare charity foundation partnered with us to streamline its research review process, where manual aggregation and summarization led to inefficiencies and wasted resources. The resulting AI prototype slashed compiling reports from 11 hours to 4.5 hours — a 60% increase in operational efficiency — while enhancing trust and reducing risk. The success secured executive buy-in and led to the development of a production-grade AI assistant for optimizing the foundation’s global health research efforts.

Steps to take before advancing from Exploration to Adoption:

1. Develop a formal AI strategy aligned with organizational goals.
2. Invest in data quality improvements and centralization.
3. Identify high-impact, low-risk areas for initial AI deployment.

Stage 3: Adoption

The goal of the Adoption stage is to evolve AI from a few initial proof-of-concept projects to an organizational capability, making it one of the most exciting stages of AI maturity. That leap is made possible by the AI strategy and roadmap you develop in the first two stages, along with your growing internal AI expertise.

Imagine one of your first use cases is an AI assistant for your physicians. Many of the same features and technologies (e.g., natural language processing, ambient listening, transcription generation, data structuring, automated scheduling) could apply to solutions elsewhere, such as patient communications, resource allocation, and claims processing.

Your organization needs help in the Adoption stage when:

• AI solutions need to integrate with existing workflows.
• Regulatory compliance and ethical AI use must be ensured.
• Managing expectations and measuring ROI become priorities.

Key outcomes:

• Define a robust data governance framework.
• Establish AI ethics and risk mitigation committees.
• Create partnerships with AI vendors and consultants.

Siemens Healthineers offers a strong example of how an initial AI project unlocks wider value for an organization. Siemens partnered with WillowTree to develop a secure, AI-driven application to automate PHI/PII anonymization in text and images, enabling research through structured, risk-scored data. Not only did this proprietary solution enhance data security and ensured compliance, but it also unlocked new monetization opportunities from medical data.

Steps to take to before advancing from Adoption to Optimization:

• Institute AI governance structures and policies.
• Develop and implement an AI ethics framework.
• Invest in change management and staff training programs.

Stage 4: Optimization

The Optimization stage focuses on maximizing the value and impact of existing AI implementations. Since AI is deeply embedded in core operations at this point, healthcare providers’ place their attention on fine-tuning models, improving processes, and measuring outcomes against established benchmarks (e.g., diagnostic accuracy rates, length of stay predictions, resource utilization).

This happens through continuous learning and iteration to find improvements that translate to better patient outcomes and operational efficiency. Tools like advanced analytics identify areas where AI can drive even greater value, at the same time discovering new ways to integrate AI capabilities across previously siloed systems.

As your organizational AI matures, it must continuously optimize through:

• Keeping pace with rapidly evolving AI technologies.
• Balancing automation with human touch in patient care.
• Managing large-scale AI infrastructure.

Key outcomes:

• Establish an advanced AI research and development team.
• Implement comprehensive AI performance monitoring systems.
• From strong partnerships with academic institutions and tech companies.

Probius shows us the kind of efficiencies that can be realized in the Optimization stage. A leader in AI-driven healthcare solutions, Probius faced challenges in deploying, monitoring, and retraining its models, originally developed in an academic setting. Partnering with our MLOps team, Probius modernized its AI models by migrating to a scalable cloud infrastructure optimized for seamless deployment and performance. This transformation enabled Probius to efficiently deploy its QES technology and ML-powered healthcare solutions, improving maintainability while reducing infrastructure costs.

Steps to take before advancing from Optimization to Transformation:

1. Implement advanced AI monitoring and optimization tools.
2. Explore cutting-edge AI applications like federated learning for privacy-preserving collaboration.
3. Develop AI-focused talent acquisition and retention strategies.

Stage 5: Transformation

At the Transformation stage, providers leverage AI as a fundamental driver of organizational strategy and innovation in healthcare delivery. By now, AI has gone from discrete solutions to an integral part of an organization’s DNA, influencing everything from clinical decision-making to business model innovation. Think of organizations like Cleveland Clinic, whose accomplishments with AI led to the formation of the Center for Diagnostics and Artificial Intelligence (CDAI).

Providers at this stage actively push the boundaries of what's possible in AI-driven healthcare, often leading industry-wide innovations (e.g., AI-enabled precision medicine platforms, predictive population health management systems). Their focus shifts from optimizing existing processes to reimagining healthcare delivery entirely, with AI enabling new business models, revolutionary patient care approaches, and unprecedented levels of personalized treatment.

As your organizational AI engine stabilizes, it should drive continuous transformation through:

• Staying ahead of the curve in a rapidly evolving field.
• Managing public perception and trust in AI-driven healthcare.
• Navigating complex ethical scenarios in AI decision-making.

Key outcomes:

• Launch a world-class AI research and innovation center.
• Influence AI healthcare policies and standards in the greater healthcare industry.
• Invest in an AI-driven culture of continuous learning and adaptation.

The Mayo Clinic is one of the best examples of healthcare providers who’ve reached the Transformation stage. Mayo Clinic’s data and AI capabilities have become so robust, clinicians are empowered to develop their own AI solutions, effectively democratizing AI development to the people with the deepest understanding of their practice areas.

Steps that lead healthcare providers to the Transformation stage:

1. Lead industry collaborations for AI standards and best practices.
2. Explore AI applications in emerging fields like regenerative medicine and nanomedicine.
3. Develop AI-driven ecosystems that extend beyond traditional healthcare boundaries.

The Future of AI in Healthcare

AI is already powering healthcare trends with use cases such as ambient listening in clinical environments, generating personalized treatment plans, and analyzing medical images to assist diagnoses. It’s also already transforming the business and operational sides of healthcare by automating scheduling and processing claims.

All of those are use case healthcare providers can pursue immediately as they develop their AI strategies. But they’re also just the beginning. A robust AI strategy will position you to pursue more advanced applications in the future, such as:  

1. Continuous remote patient monitoring (RPM) assisted by AI-powered virtual health assistants.
2. Complex medical research streamlined by AI and quantum computing, like Cleveland Clinic collaborating with IBM to predict surgery response in epilepsy patients.
3. Brain-computer interfaces (BCIs) for advanced prosthetics and neurological treatments.
4. AI-driven precision medicine driven by genomic data analysis, biomarkers, and predictive modeling of disease risk.
5. Drug discovery by using generative AI to analyze large, complex datasets and develop new drug candidates.

Applications like these promise a better experience not just for you and your patients, but everyone connected to your provider network. Take AI’s ability to diagnose disease earlier. Greater efficiencies in preventative care will create better outcomes for patients, all while driving down healthcare costs for providers and payers.

Navigate the Stages of AI Maturity With Help From WillowTree

Every healthcare organization takes a unique journey with AI based on their current technology, attitudes across leadership, and business goals already in play. At WillowTree, our team of Data & AI experts meet you where you are to guide your organization through each stage of AI maturity, from initial awareness to transformative innovation.

Our Data & AI services include:

• AI readiness assessments
• Custom AI strategy development
• AI implementation and integration
• Data infrastructure optimization
• AI ethics and governance consulting

Your AI journey can begin simply, too, with engagements such as our AI Use Case Workshop, where we'll help you identify high-impact AI opportunities specific to your healthcare delivery and organizational goals.

Learn more by exploring our Data & AI consulting services.