\u2022 Healthcare organizations are scaling AI faster than many can consistently measure<\/strong> its financial impact.\u00a0 Healthcare organizations have moved quickly from experimenting with artificial intelligence to deploying it across clinical, financial, and operational workflows. As those investments grow, executives increasingly want to know what they are getting in return. For many organizations, answering that question remains difficult.<\/p>\n A recent Becker\u2019s Payer Issues article<\/a> highlighted a growing gap between healthcare AI adoption and the ability to demonstrate its financial value. Deloitte found<\/a> that 44 percent of surveyed healthcare organizations qualified as AI \u201cscalers,\u201d meaning more than one third of their generative AI initiatives launched during the previous two years had reached scaled deployment across multiple functions.<\/p>\n However, only 18 percent of those organizations reported mature capabilities for consistently measuring AI\u2019s impact on revenue growth or cost savings.<\/p>\n The findings raise an important question for healthcare organizations moving deeper into AI. For example, how can you prove the value of AI if the data needed to measure that value is not ready?<\/strong><\/p>\n For the past several years, much of the healthcare AI conversation has centered on adoption. Organizations have launched pilots, evaluated use cases, and explored how AI could improve everything from clinical documentation and patient engagement to revenue cycle operations and analytics.<\/p>\n The conversation is changing as more applications move into production. CFOs and other healthcare leaders increasingly need to understand whether AI investments are reducing costs, increasing capacity, improving quality, creating better patient experiences, or generating other measurable results.<\/p>\n There are already examples of significant returns. Becker\u2019s reports that CommonSpirit generated more than $100 million<\/a> in annual value from AI and robotic process automation in 2025, while Boston Children\u2019s Hospital reported more than $7 million<\/a> in redeployed labor savings after reclaiming approximately 60,000 hours through AI enabled workflows. Mount Sinai Health System expects approximately $50 million<\/a> in bottom line impact from its AI portfolio in 2026.<\/p>\n Those examples demonstrate what is possible. They also reinforce the importance of having a consistent way to establish baselines, track performance, and determine what AI is actually contributing.<\/p>\n Organizations evaluating AI ROI naturally focus on the performance of the technology. But the calculation begins much earlier with the information feeding the application and the data used to measure its results.<\/p>\n If clinical and claims data is fragmented across multiple systems, uses inconsistent terminology, contains duplicate records, or requires significant manual preparation, those problems become part of the true cost of deploying AI.<\/p>\n They can also make results more difficult to evaluate. Consider an AI application designed to identify patients who would benefit from earlier intervention. Measuring its value may require organizations to compare changes in care gaps, utilization, clinical outcomes, or costs. Those comparisons depend on having consistent and trustworthy information before and after implementation.<\/p>\n As we discussed in our earlier IMAT Solutions blog on how to make health data AI ready,<\/a> effective AI requires data that can be integrated across sources, normalized, governed, and made available in a form that applications can reliably use.<\/p>\n That same foundation is essential for measuring what AI produces.<\/p>\n The ROI equation should also account for the work required to prepare healthcare data for AI.<\/p>\n When data is fragmented or inconsistent, teams may spend significant time reconciling records, mapping terminology, addressing missing information, and creating separate data pipelines for individual applications.<\/p>\n That work may not appear on an AI vendor invoice, but it still consumes resources and can make applications more expensive to scale.<\/p>\n There is also a risk that organizations automate existing data problems. As we explored in AI Shouldn\u2019t Automate Healthcare\u2019s Data Problems<\/a>, AI can magnify fragmented data, redundant processes, and inconsistent information when organizations apply new technology without first examining the data and workflows underneath it.<\/p>\n Building a trusted data foundation can reduce the need to repeatedly prepare information for each new AI application while making it easier to evaluate results consistently across the enterprise.<\/p>\n Financial returns are important<\/a>, particularly as healthcare organizations face growing pressure around margins and technology spending. However, healthcare AI can generate value in other ways.<\/p>\n Becker\u2019s notes that Mount Sinai evaluates AI projects across financial impact, patient experience, operational efficiency, quality, and safety. Other healthcare leaders are similarly taking a broader view of ROI that includes workforce productivity and capacity.<\/p>\n This broader definition is particularly relevant for clinical applications, where the most important outcome may be closing more care gaps, identifying risk earlier, reducing administrative burden, or improving the quality of care.<\/p>\n For example, our previous IMAT discussion of AI ready data and population health KPIs<\/a> explored how stronger data foundations can help organizations measure performance against population health goals. The same principle applies when evaluating AI. Organizations need reliable data to determine whether technology is actually moving the metrics that matter.<\/p>\n As healthcare organizations expand AI across multiple departments and use cases, preparing data separately for every application becomes increasingly difficult to sustain. A reusable enterprise data foundation provides a different approach.<\/p>\n IMAT Intelligence<\/a> aggregates, normalizes, validates, and operationalizes clinical and claims data so healthcare organizations can create a trusted source of information that supports multiple priorities. The same foundation can be used for AI, analytics, quality reporting, population health, and value-based care.<\/p>\n This can also make AI ROI easier to understand. With more consistent information across applications, organizations are better positioned to establish baselines, compare results, and evaluate whether individual AI investments are delivering measurable improvements.<\/p>\n As AI investment increases, organizations should understand the condition of the data supporting those investments.<\/p>\n The IMAT Health Data Quality Assessment<\/a> provides a measurable baseline by evaluating five critical areas of data integration, normalization, completeness, accuracy, and AI readiness.<\/p>\n The assessment helps organizations identify data gaps and risk areas, prioritize opportunities for improvement, and develop a practical roadmap for strengthening their healthcare data foundation before expanding AI investments.<\/p>\n That can give leaders a clearer understanding of what needs to improve upstream so they can more confidently measure what AI delivers downstream. Healthcare organizations are entering a new phase of AI adoption where simply deploying the technology will no longer be enough. Leaders will increasingly be expected to demonstrate what those investments are producing and decide which applications deserve to scale.<\/p>\n AI ready data can help make those answers clearer.<\/strong><\/p>\n Contact IMAT Solutions<\/a> to learn how the Health Data Quality Assessment<\/a> and IMAT Intelligence<\/a> can help your organization build a trusted data foundation for AI and measure the value those investments deliver.<\/em><\/strong><\/p>\n <\/p>\n Additional Insights\u00a0<\/strong><\/small><\/p>\n \u2022 How to Make Health Data AI Ready: A Smarter Path for Healthcare Systems<\/a> Top Takeaways \u2022 Healthcare organizations are scaling AI faster than many can consistently measure its financial impact.\u00a0\u2022 Deloitte research cited by Becker\u2019s found that 44 percent of surveyed healthcare organizations were AI \u201cscalers,\u201d but only 18 percent of those organizations had mature capabilities for consistently attributing AI investments to revenue growth or cost savings.\u00a0\u2022 Measuring AI ROI requires reliable baselines, consistent performance data, and clear metrics for determining whether AI is actually improving outcomes.\u00a0\u2022 Fragmented,…<\/p>\n","protected":false},"author":12,"featured_media":89928,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":[],"categories":[202],"tags":[311,306,254,369,572,576,322,274,597,287,376,330,245,406,302],"acf":[],"_links":{"self":[{"href":"https:\/\/imatsolutions.com\/index.php\/wp-json\/wp\/v2\/posts\/89926"}],"collection":[{"href":"https:\/\/imatsolutions.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/imatsolutions.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/imatsolutions.com\/index.php\/wp-json\/wp\/v2\/users\/12"}],"replies":[{"embeddable":true,"href":"https:\/\/imatsolutions.com\/index.php\/wp-json\/wp\/v2\/comments?post=89926"}],"version-history":[{"count":1,"href":"https:\/\/imatsolutions.com\/index.php\/wp-json\/wp\/v2\/posts\/89926\/revisions"}],"predecessor-version":[{"id":89927,"href":"https:\/\/imatsolutions.com\/index.php\/wp-json\/wp\/v2\/posts\/89926\/revisions\/89927"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/imatsolutions.com\/index.php\/wp-json\/wp\/v2\/media\/89928"}],"wp:attachment":[{"href":"https:\/\/imatsolutions.com\/index.php\/wp-json\/wp\/v2\/media?parent=89926"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/imatsolutions.com\/index.php\/wp-json\/wp\/v2\/categories?post=89926"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/imatsolutions.com\/index.php\/wp-json\/wp\/v2\/tags?post=89926"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
\u2022 Deloitte research cited by Becker\u2019s found that 44 percent of surveyed healthcare organizations were AI \u201cscalers,\u201d but only 18 percent of those organizations had mature capabilities for consistently attributing AI investments to revenue growth or cost savings.<\/strong>\u00a0
\u2022 Measuring AI ROI requires reliable baselines, consistent performance data, and clear metrics<\/strong> for determining whether AI is actually improving outcomes.\u00a0
\u2022 Fragmented, incomplete, or inconsistent healthcare data can increase the cost of AI initiatives <\/strong>while making their results more difficult to measure.\u00a0
\u2022 AI ready data gives healthcare organizations a trusted foundation<\/strong> for measuring performance and scaling successful applications.\u00a0<\/small><\/small><\/p>\nHealthcare AI Has Entered the ROI Phase<\/h3>\n
Measuring AI ROI Starts with the Data<\/h3>\n
Poor Data Can Create Hidden AI Costs<\/h3>\n
Healthcare AI ROI Goes Beyond Dollars<\/h3>\n
Build an AI Ready Data Foundation That Can Scale<\/h3>\n
Measure AI Readiness Before Measuring AI Returns<\/h3>\n
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\n\u2022 Why AI Ready Data is the Key to Hitting Population Health KPIs<\/a>
\n\u2022 Interoperability in 2026: Progress, Gaps, and What It Means for Closing Care Gaps<\/a>
\n\u2022Health IT Answers: Why Data Intelligence Is the Missing Link in Healthcare Modernization<\/a><\/small><\/p>\n","protected":false},"excerpt":{"rendered":"