AI in healthcare has moved past the hype – now the real work begins

Here’s the uncomfortable truth about adoption: getting people interested in AI was never the real challenge. Clinician demand is so strong that 72% now reach for their own personal AI tools when the options their organization provides fall short. People aren’t waiting for permission. When a tool gives time back, word travels fast (and it has). So, interest spread. Across departments, across roles, sometimes faster than anyone planned for. And that’s genuinely good news. But widespread enthusiasm isn’t the same as enterprise-wide value.  

The report directly names the real constraint: workflow integration. AI is showing up everywhere – in personal tools, hospital-built applications and clinical systems – but a dozen teams each finding their own workaround is more patchwork than strategy. And patchwork, in a healthcare system, has a way of creating new gaps even as it closes old ones.     Fragmented tools, inconsistent data, decisions made in silos. We’ve seen this movie before with every wave of new technology, and the ending depends entirely on how disciplined we are early on.  

I don’t want to undersell the wins, because they’re already measurable. The report calls them “AI dividends,” and they’re showing up where it counts. Nearly half of clinicians (49%) report time savings of at least 132 hours a year on average – more than three full working weeks handed back. Workflow efficiency is improving for 58%, and 54% say their diagnostic decision-making is faster. Confidence is climbing, too: 58% report greater confidence in their decisions. That extra time is changing how clinicians work, not just how much. They’re using it to think through cases in more detail, improve precision and have more thorough conversations with patients. Capacity is expanding as well – 36% say AI has helped them see more patients, with a median increase of five additional patients per week. And in higher-stakes territory, 27% say AI has helped them catch or prevent a potential medical error at least three times in the past three months. A second set of eyes, in other words.

But individual gains only become organizational value when they connect to something bigger. We have a challenge in that when we see AI solve a particular task, we tend to focus on what it just did for us versus the bigger picture. This slows down adoption at scale, because organizations focus on the point-in-time impact of AI and lose sight of how that task fits into much more important healthcare requirements, like physician-patient relationships, clinician satisfaction and higher-quality patient outcomes. And honestly, that’s the part I find most interesting – because it’s less about the algorithm and more about the how it fits into caring for communities of patients.  

When we keep our focus on our greater mission, caring for communities of patients over their lifetime, the need to strategize, standardize, scale and adopt is stays in view. This shifts the conversation from siloed, incremental improvements to integrated, interoperable workflows.

Data exchange and consolidation are AI’s heart and vasculature system, and the AI models are its logical brain (interoperability). AI’s eyes, ears, nose, mouth, hands and feet are how it fits into a doctor’s daily workflow and clinical decisions (integration). No interoperability = no intelligence. The report describes AI’s best use as a “cognitive layer” – pulling fragmented patient information together so clinicians can see the full picture and act on it. That only works when the underlying systems can actually share accurate, complete and defined data.

The genesis of Philips ECG AI Marketplace was never to consolidate a bunch of Philips applications into place. It was to use our network of integrated, standardized, consolidated data to feed any AI algorithm and to seamlessly integrate AI’s answers into the physician’s current workflow – then stitch workflows together to help healthcare systems innovate care pathways. Our vision was to enable populations of patients to be consistently cared for by providing every physician real AI-enabled solutions exactly where they need to make their clinical decision. AI is a detail orientated laborer for the physician that never gets in the way.  

There’s a tendency when speaking about AI and governance to only focus on the actual algorithm. It’s absolutely necessary to deep dive on patient safety, privacy, equity and trust. In my “body” example, it’s the nervous system of healthcare AI. The report found that 69% of clinicians say there are no clear processes yet for monitoring AI-enabled tools. That’s a gap worth closing, not to slow scaling down but to make it safe. AI, like any clinical solution, needs strategic planning followed by ongoing monitoring, auditing and traceability to perform uniformly across different patients and settings.

I would like to encourage healthcare systems to also strategically manage/govern the success of the algorithm to achieve its intended strategic initiatives. Consider the bigger picture: do we want an algorithm like Anumana Low Ejection Fraction [1] to find a patient with cardiovascular disease earlier or do we want to put a scalable platform in place for the early detection of cardiac disease? This helps us face the fact that tomorrow there will be two, three, four… 20 algorithms all with unique abilities that compound together to really move the needle on cardiac disease in our communities. The governance of solutions requires evaluation of today’s and tomorrow’s workflows and care pathways. We need to train physicians on these workflows and the impact of new results to provide consistent care for a patient, no matter where they are. This won’t be perfect, and governing continuous improvement is the only way to successfully empower our most valuable humans. Our absolute reliance on physicians and other caregivers doesn’t move: 93% of clinicians say it’s essential to keep a human in the loop as AI advances. AI can generate an answer; the clinician is still accountable for the decision.  

Here’s a dynamic the 2026 report surfaces that we can’t ignore: patients are now part of this story in a way they weren’t before. Two-thirds of clinicians (67%) say patients are showing up to appointments with AI-generated health information. That can be a good thing – better-prepared patients, better questions – but it cuts both ways. About 65% of clinicians say they’ve had to correct AI-generated misinformation, and 33% have seen patients lose trust in their care after learning AI was involved.

That’s why transparency matters so much. The vast majority of patients – 88% – say they should be told when AI is used in their care. The people best positioned to build that trust are the clinicians themselves, and they can only do it if our systems are clear about where and how AI is working behind the scenes. Trust is built one conversation at a time, and our infrastructure has to support those conversations rather than complicate them.  

So here’s my thesis, stated as simply as I can manage it: the AI conversation in healthcare needs leadership. We’ve proven the value. The dividends are real and measurable – time returned, capacity expanded, errors caught, decisions sharpened. The next chapter isn’t about bigger promises or flashier demos. It’s about disciplined implementation – integration that connects, governance that protects, interoperability that flows, training that prepares people and workflow design that treats the whole enterprise as one system rather than a hundred disconnected experiments.

The report frames the same shift its own way: moving from AI in practice to AI at scale will take integrated ecosystems, a workforce with the confidence and skills to use these tools and care models designed around a new hybrid team where human judgment stays central. That’s the work in front of us. It’s less thrilling than a robot hand reaching toward a human hand. I know. But it’s the work that turns potential into outcomes.

The organizations that win the next few years won’t be the ones that adopted AI first. They’ll be the ones that scaled it well – deliberately, responsibly and with the patient at the center of every connected pathway. The hype got us in the door. Discipline is what gets us somewhere worth going. We know how to improve healthcare. Now, let’s start the work of transformation.

Read the full Future Health Index 2026 for the complete findings, and check out more thought leadership articles on these topics.