Turning AI promise into real-world value

The numbers from the US findings stopped me in my tracks. Eighty-four percent of US healthcare professionals are optimistic that AI can improve patient outcomes. That’s a clear signal from the people actually delivering care that something meaningful is happening. 

And they’re right to feel it. Nearly half of US clinicians (49%) report time savings of at least 132 hours annually, more than three full working weeks handed back to them every year. More than a third (36%) say AI has increased their capacity to see more patients, with a median increase of five additional patients per week. Almost 3 in 10 (27%) say AI helped them identify or prevent a potential medical error at least three times in just the past three months. 

These aren’t pilot program statistics. These are real clinicians, in real health systems, reporting real impact. And 72% of US healthcare professionals now believe the benefits of AI already outweigh the risks.

What strikes me most, though, is where that reclaimed time is going. Among clinicians saving time with AI, 61% say they’re using it to stay current with research and clinical developments, 60% to be more focused in their work and 48% to have more thorough interactions with their patients. Instead of making care more transactional, AI is creating space for the deeply human moments that no algorithm will ever replace.

That last point matters to me enormously. In strategy and growth, we talk endlessly about the customer experience. In healthcare, that experience can be the difference between someone feeling seen and someone feeling like a case number. The fact that 84% of clinicians say AI will never replace the relationships they build with patients – and 73% believe human interaction skills will become more important as AI advances – is wisdom about what technology is actually for. 

Here’s where I’ll push back on the narrative that’s become a little too comfortable in boardrooms and conference keynotes: the constraint isn’t the AI. It’s us. Seventy-two percent of US healthcare professionals turn to their own personal AI tools when their workplace options don’t meet their needs. Read that again. Nearly three-quarters of clinicians are essentially working around the systems their organizations have provided. That’s an organizational readiness problem that carries real risks – to patient safety, data governance and the kind of clinical accountability that trust in healthcare depends on. 

The training picture is equally sobering. Seventy-seven percent of US healthcare professionals say AI training at their organization is unavailable, limited or inconsistent. Clinicians want to understand how to check the accuracy of AI recommendations (63%), how to navigate these tools technically (53%) and – critically – how to understand their legal liability (52%). We’re asking people to work with tools they don’t fully understand, in environments that haven’t yet defined what “good” looks like. That makes responsible scaling a challenge. 

And then there’s the governance gap. With 76% of clinicians concerned about AI tools making errors and 69% saying that clear monitoring processes are not yet in place. We built the car. We just haven’t finished building the road.

None of this is reason for pessimism. But it is a reason for urgency. Because the clinicians who are using AI well – in integrated, supported, well-governed environments – are seeing outsized gains. Fifty-eight percent report improved workflow efficiency. Fifty-four percent see faster diagnostic decision-making. Half report fewer hours on routine or administrative tasks. The opportunity cost of not solving the readiness problem is something we can now measure. 

One of the most striking reframes in this year’s Future Health Index is the shift from thinking about AI as a tool to thinking about it as a teammate. Clinicians don’t see AI replacing their expertise; rather, they see it functioning alongside them as a second set of eyes, a cognitive layer that surfaces relevant information faster, a system that flags risk before it becomes harm. 

This is the hybrid care team that the data is pointing toward. It includes AI handling data processing and administrative work (61% of clinicians see this happening), supporting aspects of clinical reasoning and decision-making (61%) and, increasingly, helping patients show up to appointments better prepared. Sixty-seven percent of US clinicians already report patients arriving with AI-generated health information – and the same percentage see these more informed patients as an integral part of the future care team. This is a shift in the patient-clinicianrelationship that, navigated well, could meaningfully improve outcomes. 

This one is personal for me. Philips has been doing significant work in rural healthcare – building sustainable, evidence-based programs that help bring the kind of care that major urban medical centers take for granted to communities that have historically gone without. It’s one of the things I’m most proud of.

So, when 73% of US clinicians say they believe AI will improve services in underserved and rural areas, I read it as a mandate more so than just an optimistic footnote. AI has a genuine role to play in narrowing the quality gaps between different healthcare settings – 66% of US clinicians say it can help. It can bring specialist expertise to communities that don’t have specialists. It can flag deterioration in patients who don’t have easy access to follow-up care. It can help rural health systems do more with the resources they have.

But only if we deliberately build it that way. Access to the benefits of AI can’t be determined by zip code. If we’re serious about “better care for more people,” then “more people” must include the people who have been the hardest to reach. 

From isolated pilots to integrated AI ecosystems Here’s the hard truth that I keep coming back to: isolated AI pilots won’t get us where we need to go.  

The clinicians saving time, seeing more patients, catching more errors – they’re working in environments where AI is connected. Connected to workflows, to data, to care teams, to governance structures that protect patients while enabling innovation. Bottlenecks happen when a fragmented infrastructure keeps those tools from adding up to something bigger than the sum of their parts. 

The path forward requires action on multiple fronts: 

This requires collaboration across health systems, academia, technology partners, policymakers and regulators. No single organization can do it alone. But every organization can start. 

I’ve spent my career building things that drive impact in healthcare – from marketing strategies to infrastructures to ecosystems and partnerships that make something genuinely new possible. 

What the Future Health Index 2026 tells me is that we’re at an inflection point. AI is no longer a future promise in healthcare – it’s here, it’s working and it’s delivering measurable value for clinicians and patients alike. The question is whether we’re willing to do the serious, sustained work of responsibly scaling it. The cost of getting this right is significant. The cost of not getting it right is immeasurable – in lives, in trust, in the credibility of everyone who said this technology could change healthcare for the better. 

Healthcare leaders, technology partners and industry stakeholders: move beyond the isolated pilots. Invest in integrated, governed, scalable AI ecosystems that connect workflows, data and care teams. Not because it’s a good technology story. Because the people on both sides of every clinical interaction deserve nothing less.