With readiness reframed, where should healthcare leaders start?
Our 2026-27 Readiness Report finds that more healthcare leaders are deploying AI in production environments, with over half (57%) now seeing returns on their investments. That marks a leap forward for an industry increasingly moving AI into workflows to improve care, reduce clinical burdens and strengthen operational resilience.
The challenge now is not just adoption; it’s scale. Across sectors, healthcare organizations are the least likely to report moving beyond pilots and the most likely to say they’ve had an AI investment fail. Half say they struggle to keep up with technological change.
These findings will come as little surprise to those engaged in a sector dependent on trust, safety and human judgment. Few industries have greater stakes and a lower margin for error; the bar for moving from pilot to production should be significantly higher when patient safety is on the line.
Industry leaders also face a confluence of structural constraints that make each decision more difficult: regulations continue to evolve; operating margins are stretched thin; and workforce shortages limit both staffing capacity and organizations’ ability to absorb change. With so little slack, moving too fast can cause otherwise avoidable harm.
Yet moving too slowly is a threat of its own. The greater risk now is standing still, particularly when the healthcare industry reported steep drops in key readiness measures and the smallest year-over-year increase in AI ROI across sectors.
The Readiness Report puts it well: As AI moves deeper into the business, the bar for readiness moves with it. Healthcare leaders who introduce AI into environments that are not able to support it will continue to fall behind. In the year ahead, capturing value will require building out strong foundations that enable leaders to keep moving forward with confidence.
have deployed AI beyond pilots and in production environments (vs. 84% all-industry average)
report positive ROI on their AI investments (+3 pts since 2025)
A strong foundation starts with data
Data readiness is essential for AI, and it’s often where healthcare leaders feel least prepared. Forty-three percent of healthcare leaders say their data isn’t ready for AI — a measure that correlates with higher rates of failed AI investments across all industries.
Hindsight has sharpened leaders’ sense of just how much AI adoption depends on data. When asked what they would change about how their AI strategy unfolded, healthcare organizations most often said they would have invested more in data quality, infrastructure and technology readiness.
Data too often remains fragmented. One patient’s information may be dispersed among primary-care providers, specialists, laboratories and clinics, with none holding the full picture. As interoperability advances, organizations need to put the patient at the center of data modernization. They must turn fragmented records into trusted, contextual and usable data that can support AI at the point of care. With a strong data foundation in place, organizations will be poised to use AI to surface relevant information in the critical moments that determine how care unfolds.
Make trust part of the design
In healthcare, governance is an accelerant of AI value rather than a source of friction. This year’s report found that healthcare leaders who have taken more AI governance actions are more likely to report positive ROI.
But achieving that payoff means prioritizing governance early. Every AI initiative should be governed by design, with decisions that are transparent, traceable and auditable from the start.
To begin, leaders must define clear operating lanes that determine when AI systems may proceed independently, when they must request human permission, and when they cannot act without full human involvement. Administrative scheduling, for instance, may be highly automated, while high-risk clinical decisions continue to require explicit human oversight.
These lanes must also be consistently enforced: rules may be automatically programmed so that AI systems act only as intended and every action remains explainable and auditable. Over time, that consistency earns the trust of clinicians and patients and gives organizations the confidence to continue expanding AI into new workflows.
Work must evolve with AI
While 76% of healthcare organizations say they’ve deployed AI in production environments and 66% say they are using AI agents to automate workflows that previously required handoffs, fewer are advancing organizational transformation at the same pace.
Just 19% of healthcare leaders have fully redesigned roles around AI — the lowest level of key industries — and only 24% have implemented change management for AI across the organization. Neglecting these measures is a mistake when they are linked to ROI: healthcare organizations that have taken both actions are more likely to report positive returns.
How organizations approach AI is another source of untapped value. Today, most are adding it on to existing processes. But healthcare leaders have a greater opportunity to transform each workflow itself, considering what work can be automated, where AI can augment human judgment and when people must remain firmly in control. That redesign should begin at the pilot stage, not after AI has already scaled.
Scale one workflow at a time
The path forward for healthcare organizations may be deliberate, but it does not need to be slow.
With the right foundation in place, organizations can begin to scale AI. Rather than try to solve every problem at once, leaders should start with one high-impact workflow where patients, clinicians and the organization experience friction. The strongest pilots solve a meaningful patient or workforce problem, have the data needed to support them, include clear guardrails for human control and produce measurable outcomes.
Leaders then need to redesign care considering how people, AI and other technologies can work together to improve outcomes. This work involves mapping delays, streamlining handoffs and determining where people must remain in control. Skipping this step risks building a solution in search of a problem, and healthcare already has plenty of real ones to solve.
Among the most pressing is how little time clinicians have to spare. More intelligent tools can cut the hours they currently spend searching for the right information, moving between systems and completing documentation. The greatest value comes from what this gives back to clinicians: more time to think, apply their judgment and focus on the person in front of them. For patients, this shift promises a more human experience in the moments that matter most.
The real measure of AI readiness in the year ahead will be how confidently organizations can put AI to work. Start with one strategic workflow, prove the outcome and build from there.
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