Healthcare and life sciences AI report: statistics from 119 leaders

Adoption is concentrated in operational use cases that deliver measurable value within compliance boundaries. According to the research, the highest-adoption applications across 119 healthcare and life sciences organizations are customer and market research (52%), security and performance monitoring (50%), product planning and prioritization (46%), idea and design creation (46%), testing and QA (45%), and coding production features (45%). Prototyping sits at just 22%, showing that regulated industries prioritize lower-risk, measurable applications over experimental ones.

63% of healthcare and life sciences organizations achieve measurable ROI within six months of deployment, faster than other regulated sectors like financial services and manufacturing. The report details ROI timelines by use case category, sector, and AI maturity level. For a strategic view of how to plan AI initiatives that deliver this kind of return, our guide to scaling AI in life sciences covers the operational disciplines behind faster time-to-value.

The research surveys 119 product leaders across healthcare and life sciences to understand where AI is actually being deployed in regulated environments. Adoption is led by customer and market research (52%) and security and performance monitoring (50%), followed by product planning, idea creation, testing and QA, and production coding (all between 42% and 46%). The data shows that AI investment is concentrated in measurable, lower-risk applications where impact is easier to quantify and compliance easier to manage. The full report details where each use case sits on the maturity curve in regulated environments.

Governance is the number one blocker according to the research. 79% of healthcare and life sciences organizations slowed an AI deployment last year because regulatory or ethical considerations weren't resolved upfront. Execution capacity is the second-largest barrier, cited by 83% of leaders. The report quantifies how often each barrier appears and segments responses by sector, giving leadership teams the data they need for their own roadmap conversations.

Most organizations have governance practices, but they're rarely complete. The research shows 51% have a centralized data governance policy, 51% use continuous monitoring dashboards, 49% maintain a formal model-risk framework, and 46% have a dedicated ethics committee. Only 7% have none of these in place. The challenge is not the absence of standards but fragmented ownership across compliance, risk, and engineering teams. The full report introduces a five-question shift-left framework used by top performers to embed governance from day one.

Healthcare and life sciences companies face nearly identical AI challenges: patient and trial data governance, FDA and HIPAA compliance, GxP requirements, and board pressure for ROI. The patterns in the research are remarkably consistent across the 119 organizations surveyed: 79% faced governance delays, 63% achieved ROI within six months, and 98% are modernizing infrastructure. The report presents combined statistics that reflect how aligned both sectors are when it comes to deploying AI in regulated environments, making the benchmarks directly applicable whether you're operating in a hospital, pharma, biotech, or medical device company.

98% of healthcare and life sciences organizations are modernizing legacy infrastructure to support AI workloads, and 53% have active cloud migration plans underway. The report details which HCLS-specific cloud services are reshaping infrastructure decisions and shows how organizations maintain validated, compliant environments through migration.

The data shows top performers are reskilling existing developers (55%) at twice the rate they're reducing headcount (27%). Other common patterns include shifting roles from manual QA to test automation (42%), consolidating roles into AI-augmented generalists (42%), and hiring more data scientists or ML engineers (39%). 96% of healthcare and life sciences product leaders consider external partners important to AI product development, with the highest demand in security and compliance (32%), AI and machine learning model development (27%), and data infrastructure (27%). For a deeper look at the operational and team structures that sustain AI in regulated environments, see our playbook on scaling AI from pilot to enterprise.

The report is built for C-suite executives, CTOs, product leaders, and technology decision-makers in hospital systems, health technology companies, pharmaceutical R&D, biotech operations, and medical device manufacturers. If you're responsible for proving AI ROI, managing compliance risk, or scaling AI deployments in FDA, HIPAA, or GxP-regulated environments, the AI statistics inside will support your strategic decisions and board conversations.

Modus Create partnered with Ascend2 Research to develop a custom online questionnaire surveying 119 product development decision-makers in healthcare and life sciences. The research was fielded in August 2025 and represents organizations across hospital systems, pharmaceutical companies, biotech firms, and medical device manufacturers operating under FDA, HIPAA, and GxP regulatory frameworks.