Key Survey Findings
AI is already in production.
Organizations are focused on operational outcomes.
Adoption matters as much as the technology.
Governance must extend beyond committees.
ROI requires clear ownership and measurement.
Strategic partnerships matter more than point solutions.
Change Happens. EXPERTISE WINS.™
Let Connection experts power up your team with modern infrastructure, multicloud, and cybersecurity solutions that support the future of healthcare.Talk to an Expert
800.998.0067
How are you consuming AI today?
AI in Healthcare Is Already Here
Most participants reported active AI deployments across clinical, operational, and administrative environments. Organizations are leveraging a mix of EMR-native capabilities, Microsoft Copilot, third-party applications, and cloud-based AI platforms.
The Use Cases Driving Investment
Healthcare organizations are prioritizing AI investments that remove friction from existing workflows—not futuristic use cases. Clinical documentation, revenue cycle optimization, prior authorization, document intelligence, and patient access ranked among the highest-value opportunities.
Clinical Priorities |
Operational Priorities |
|---|---|
| Clinical documentation | Denials management |
| Revenue cycle optimization | Prior authorization support |
| Prior authorization | Claims automation |
| Patient access | IT service desk automation |
| Document intelligence | Staff productivity |
Which AI use cases are most important to your organization over the next 12 months?
Which non-clinical AI use cases would be most valuable to your organization?
Ambient Documentation Is a People Problem
Ambient documentation highlights a common AI challenge: value alone does not drive adoption. Some organizations saw strong clinician demand, while others watched pilots stall because providers did not see enough reason to change their workflow.“We had a pilot of some ambient learning in our clinic areas, and it just died. The clinicians, for some reason, were just not interested. And I think that’s sad, because it’s a really great tool.”Director of IT Service Delivery
Academic Medical Center
Leaders pointed to practical adoption drivers: physician champions, proof of time saved, peer support, and attention-to-workflow pain points like pre-charting and note review.
Electronic Health Records Are the Hub for AI in Healthcare
The EHR is becoming the hub for healthcare AI because the data, workflows, and integration paths already live there—but the strategy is not platform versus vendor. Healthcare leaders are standardizing where core platforms like Microsoft, Epic, Oracle Health, ServiceNow, and Google Cloud are strong, while reserving experimentation for specialty AI tools that solve problems the platform does not.How much of your AI activity is connected to the EMR today?
Where do you see the biggest need for AI to pull intelligence from existing applications and data sources?
Governance, Risk, and ROI
Governance emerged as one of the biggest barriers to scaling AI safely, with healthcare leaders rating their confidence at 3.8 out of 5 on average. While committees and checklists are important, leaders emphasized the need for governance that operates in the flow of work—with clear accountability, risk-based controls, and technology that can enforce policy at scale.The strongest models separate decision-making from execution: committees define what is safe, allowed, and funded, while operational teams translate those decisions into working policy and platforms enforce controls. Cybersecurity exposure and PHI / privacy concerns ranked as the top risks, followed by hallucinations, bias, and regulatory compliance. Proving ROI also requires stronger operating discipline, including clearer use-case prioritization, baseline metrics, post-pilot adoption planning, and business ownership.
Which AI risks concern you most?
What would make AI ROI easier to prove?
Beyond the Data: The Real Challenge Is Turning AI into Value
While governance, security, and compliance remained important considerations, healthcare leaders consistently raised broader concerns around scaling AI beyond initial use cases. Organizations expressed enthusiasm for ambient clinical documentation but greater uncertainty around expanding AI into clinical and operational workflows, managing new AI-related costs, and selecting vendors that can provide long-term stability and support.The takeaway was clear: healthcare AI is no longer a future initiative—it’s already in use across the enterprise. Success will depend less on adopting more tools and more on choosing the right workflows, establishing governance that enables progress, driving user adoption, and continuously measuring outcomes. The organizations that do this well will be best positioned to turn AI investments into meaningful results.
Choosing the Right Partner
One of the sharpest lessons from healthcare leaders was that a successful pilot can still become a liability if the vendor cannot last. A tool may prove its value in a controlled deployment, but if the company changes direction, leaves healthcare, or cannot support long-term adoption, the organization is left to rebuild from scratch. Leaders emphasized the need to understand not only what a vendor can do, but what business they are in, how committed they are to healthcare, and what accountability they are willing to put behind their claims.“You’re going to save me 30 percent. Great. Are you going to guarantee the 30 percent? It’s interesting how that conversation then changes.”VP and CTO
Large Health System
When evaluating AI solutions, which partner model do you prefer?
Which companies or platforms are most influential in your healthcare AI roadmap?
Capability is often the easiest thing to evaluate. The harder questions are how the model was trained, whether it uses the organization’s data, how that data is protected, what the contract actually guarantees, and whether efficiency claims hold up once the tool is live. In healthcare, a new AI solution is rarely just a tool. It requires integration, workflow redesign, validation, training, and clinical trust before it can scale. The strongest partners are the ones that understand that reality and remain accountable after the pilot ends.
Turning AI Strategy into Outcomes
Every healthcare organization is at a different stage of its AI journey, but the challenges are increasingly familiar: identifying the right use cases, preparing data, establishing governance, measuring outcomes, and scaling success across the enterprise.The CNXN Helix™ Center for Applied AI and Robotics helps organizations navigate this journey and move from AI exploration to measurable business value. As our dedicated AI practice, CNXN Helix combines expertise across AI strategy, data, infrastructure, automation, governance, security, and industry-specific solutions to help customers deploy AI responsibly, accelerate adoption, and achieve meaningful outcomes at scale. Whether you’re launching your first AI initiative or expanding mature AI programs, the CNXN Helix team is here to help.
16