Artificial intelligence is rapidly changing the face of the healthcare industry. From reducing administrative burden on practitioners and accelerating drug discovery to analyzing clinical data and supporting more effective decision-making, new applications of AI are emerging at a pace that would have seemed unlikely only a few years ago.
But the speed of adoption raises an important question: Are healthcare organizations building the processes and oversight needed to keep up?
AI systems rely on enormous volumes of complex and often sensitive healthcare data. Without strong governance, organizations may struggle to understand where that data originated, whether it can be used for a particular purpose, how accurately it represents the population being studied, or why an AI system produced a specific result. These gaps can lead to bias, reduce model performance, and even create compliance and security risks.
Closing the gaps will require more than just updating existing compliance policies, however. As AI continues to revolutionize the healthcare industry, organizations will similarly need to evolve their traditional data governance into a modern framework that enables innovation while maintaining accountability and transparency.
What is data governance?
Data governance is the framework an organization uses to manage data responsibly. It outlines the policies, processes, and roles needed to ensure data is reliable, secure, compliant, and useful. Within the healthcare industry, a provider’s data governance program might establish rules for protecting patient information, controlling access to medical claims, documenting where data came from and how it may be used, and more.
At its core, data governance helps organizations answer several fundamental questions: Where did this data come from? What does it represent? Can it be trusted? Who is permitted to use it? And is it suitable for the decision or application at hand?
These questions remain essential; however, the spread of AI is also introducing a range of new considerations around ownership, privacy, quality, and representation. After all, an AI model is only as effective as the quality and completeness of the data used to train it. Without proper governance principles in place, the model may use biased or incomplete data sets, which can damage trust and lead to issues around compliance.
Take, for example, a healthcare organization training a predictive model to identify high-risk patients. If the training data skews toward certain demographic groups, the model may underperform for others. The issue traces back to insufficient governance. There were no processes in place to ensure the data sets appropriately represented all captured demographics.
AI adoption is sweeping across healthcare
As you know, AI is swiftly moving from emerging technology to a major investment opportunity. According to Stanford University’s 2026 AI Index Report, global corporate investment in AI more than doubled in 2025.
And, perhaps surprising to everyone, healthcare is becoming one of the most active areas for investment.
Historically, healthcare has always lagged behind other industries when it comes to adopting emerging technologies. That notion is a thing of the past now. Today, healthcare is becoming America’s AI powerhouse. Menlo Ventures revealed that in just two years, healthcare went from 3% adoption of domain-specific AI tools to 22%. By contrast, it is the broader U.S. economy that is slow on the draw. Only 9% of companies across other sectors of the economy have implemented AI solutions.
The technology is also becoming part of physicians’ everyday work. In 2026, 81% of physicians surveyed by the American Medical Association reported using AI professionally, more than double the 38% recorded in 2023.
NVIDIA’s State of AI in Healthcare report breaks down the top areas of AI adoption across the healthcare landscape.
Fig 1. Top areas of AI adoption across major segments of the healthcare industry. Data sourced from NVIDIA’s 2026 State of AI Healthcare and Life Sciences survey.
From the survey, generative AI tools and large language models were the most widely used AI workloads. Data analytics and data science ranked second, followed by predictive analytics. A significant number of respondents were also using agentic AI, a newer class of systems designed to perform multistep tasks and act independently.
Is AI adoption moving faster than organizations can handle?
The evidence suggests that it is, but the answer is more nuanced than a simple yes or no.
Healthcare leaders are well aware that using AI requires clearly defined policies around oversight and accountability. Many have already created internal governance structures to help ensure the technology is used responsibly. However, those efforts are not advancing consistently across the industry.
A 2026 MGMA Stat poll illustrates the gap among medical groups. Forty-two percent of medical group leaders said their organizations already have a formal policy governing AI use in place or are working on establishing one. Meanwhile, 56% of respondents said their organizations had neither, while another 2% were unsure.
The results suggest that while progress is being made, effective governance requires organizations to know which AI tools are in use, what data they can access, who approved them, what risks they introduce, and how their performance will be monitored.
Without those processes, your organization might lack visibility into how AI is being used internally, which could lead to “shadow AI,” or tools used by employees without formal review or approval. Staff may enter sensitive or proprietary information into publicly available platforms simply because the tools are convenient, and no approved alternative exists. This behavior increases the potential for privacy violations and data breaches, which can open your organization up to a litany of other (costly) problems.
Data quality and completeness can also jeopardize the effectiveness of AI tools, whether the data is in-house or from a third-party vendor. When no process is in place for an organization to trace an output back to its underlying data or explain how a conclusion was reached, they leave themselves vulnerable to unchecked errors and reinforcing biases.
Four components of an effective AI and data governance framework
Fortunately, you don’t need to build an AI and data governance program entirely from scratch. You can find useful guidance from existing frameworks, such as the National Institute of Standards and Technology’s AI Risk Management Framework, the European Union’s AI Act, and ISO/IEC 42001.
Of course, no single framework will address every healthcare organization’s unique business challenges, data environment, or regulatory obligations. As you look to develop or strengthen an AI and data framework for your own organization, you should consider the following four areas:
Establishing guiding ethical and governance principles
An effective framework should begin with a clearly defined set of principles for the safe, responsible, and appropriate use of AI. These policies establish the organization’s expectations and provide a common foundation for evaluating new tools and use cases.
Depending on your organization, your guiding principles may address patient safety, data privacy, transparency, managing bias, accountability, or authorized data use.
For example, a commitment to transparency might require teams to document the data sources, intended users, known limitations, and approval history of every AI system. A commitment to human oversight might define which outputs must be independently reviewed before they can influence a clinical, operational, or commercial decision.
Developing a consistent assessment method
Healthcare organizations also need a standardized method for collecting information about AI tools and determining whether they meet defined standards.
The assessment should begin with the proposed use case. Leaders need to understand what the system is designed to accomplish, who will use it, what decisions it may influence, and what could happen if it fails. An administrative tool used to summarize internal meeting notes, for example, may require a different level of scrutiny than an AI system supporting diagnosis, treatment, coverage, or patient outreach. A consistent assessment method can help organizations identify those disparities before the system is widely deployed.
Addressing governance throughout the AI lifecycle
Governance should extend across the full implementation lifecycle, rather than being limited to a one-time approval.
Start by clearly defining the problem your organization has and then determine whether AI is the right solution. If the answer is yes, identify who the intended users of the solution are, what benefits the tool offers, and the data it requires.
When your organization is ready to procure the AI tool, be sure to evaluate whether it is supported by accurate, diverse, and appropriately licensed data. When working with a third-party vendor, you should also review their contractual terms governing data use, incident response, and security controls. Our buyer’s guide for evaluating data and analytics vendors can offer additional tips and best practices.
Ensuring oversight
Finally, organizations need a formal mechanism for overseeing AI strategy, use, and risk. This may take the form of a governance group, review board, or committee with the authority to establish standards, evaluate high-risk applications, resolve disagreements, and intervene when a system creates unacceptable risks. We recommend staffing the group with experts and representatives across departments for the most effective oversight.
A strong foundation is key to AI innovation
Effective data governance programs give healthcare organizations a clearer, more consistent way to adopt AI tools and use them to their greatest potential.
But that work begins with having great data at your fingertips. Organizations need to understand where their data came from, what it represents, how it may be used, and whether it is accurate, complete, and appropriate for the decisions they need to make. Without that foundation, even the most sophisticated AI tools may produce unreliable insights or introduce unnecessary risk.
Definitive Healthcare gives organizations the data and analytics they need to build a clearer view of the healthcare market and make more informed decisions. We bring together years of AI innovation and billions of healthcare signals to help you answer questions related to growth, strategy, investment, and much more. Book a demo today to see how we can support your organization.
