In our previous article, we looked at a major blind spot: predictive models that perform well in training but break down in clinical reality.
This follow-up explores the flip side of the same issue—not just what’s going wrong with AI performance, but how health systems are thinking about standardization, compliance, and the leadership required to make AI work in clinical settings.
By Brian Bocchino, Partner, Ventures Practice – Health and Life Sciences
3 takeaways
- AI adoption is running into real-world barriers. Clinical buyers are moving beyond pilot projects—and asking how AI tools actually work in complex, regulated healthcare settings.
- Compliance and standardization are still evolving. Without clear frameworks, health systems are building their own—often looking for AI tools that come pre-validated for fairness, explainability, and clinical fit.
- Leadership is the difference-maker. Organizations need AI executives who can bridge technology, regulation, and frontline operations—not just build models, but integrate them safely and successfully.
In our previous article, we looked at a major blind spot: predictive models that perform well in training but break down in clinical reality.
This follow-up explores the flip side of the same issue—not just what’s going wrong with AI performance, but how health systems are thinking about standardization, compliance, and the leadership required to make AI work in clinical settings.
Because increasingly, the conversation isn’t just about the tech. It’s about trust, usability, governance—and whether leadership teams are prepared to scale AI responsibly.
Standardization is on everyone’s radar—but it’s still fragmented
There’s a growing consensus that AI in healthcare needs consistent standards. But the landscape is still patchy. Right now, we’re seeing:
- Institution-led governance at places like Mayo Clinic and Stanford, where internal AI councils are setting policies
- Industry efforts like the Coalition for Health AI pushing for common evaluation frameworks
- Vendor variability, where model validation and clinical relevance depend heavily on who built the tool
For health systems evaluating AI solutions, that variability is a problem. Many clinical buyers are now looking for tools that come pre-aligned with emerging best practices—think fairness audits, explainability features, FDA guidance, and ISO certifications.
In other words: The bar is rising, even if the playbook isn’t fully written yet.
Compliance is no longer just a data privacy issue
AI compliance in healthcare has expanded far beyond HIPAA. Clinical buyers are asking tougher questions, like:
- What happens if an AI-generated recommendation leads to harm?
- Can we explain the model’s logic to patients or regulators?
- Does it perform equally well across different patient groups?
Between FDA oversight of Software as a Medical Device (SaMD), the upcoming EU AI Act, and various U.S. state-level bills, AI governance in healthcare is becoming a strategic function—one that requires close collaboration between legal, clinical, data, and executive teams.
The strongest AI leaders are the ones who treat compliance as a core competency—not just something to deal with post-deployment.
Integration is less about software—and more about change management
For many health systems, the hard part isn’t building or buying AI. It’s integrating it. They’re asking:
- Will this interrupt or improve workflows?
- Will clinicians trust the recommendations?
- How does it affect EHR use, alert fatigue, or clinical decision-making?
Here’s the truth: Clinical AI integration isn’t a plug-and-play experience. It’s a long-term transformation. Successful adoption depends on cross-functional alignment, ongoing monitoring, and thoughtful rollout plans.
AI vendors that provide training, change management support, and clinician co-design are starting to stand out. But what really moves the needle? Leaders who understand how to make those things happen internally.
What this means for hiring and AI leadership in healthcare
The pressure is growing on healthcare organizations to hire AI executives who can operate at the intersection of innovation and accountability. These leaders aren’t just technologists—they’re orchestrators.
Here’s what many systems are prioritizing:
- Regulatory fluency
Leaders who understand SaMD, the EU AI Act, and upcoming U.S. policies—and can build governance into the AI lifecycle. - Operational empathy
The ability to embed AI into clinical environments without disrupting frontline care or overloading teams. - Cross-functional leadership
These execs build bridges between clinicians, engineers, legal teams, and product owners. They don’t just speak every language—they help others understand each other. - Standards-minded thinking
Many health systems are still defining their AI policies. The right leaders can help shape internal standards—and contribute to industry-wide progress.
Where AI success stories will come from next
If there’s one thread connecting all of this—from predictive model failures to governance strategy—it’s this:
The systems that succeed with AI won’t just be the ones with the best tools. They’ll be the ones with the best leadership teams—executives who know how to scale AI responsibly, build trust across departments, and translate innovation into measurable improvements in care.
Brian Bocchino is Partner, Ventures Practice – Health and Life Sciences at Riviera Partners. Connect on LinkedIn.
