The way we talk about AI in healthcare has shifted. A few years ago, most health systems were running small pilots—trying a voice tool here, testing a risk model there. Now many are pushing toward enterprisewide rollouts and asking what kind of clinical impact and measurable ROI they can prove at scale. Progress isn’t uniform—some organizations sprint ahead while others watch from the sidelines—but the signal is clear: AI isn’t a side project anymore; it’s becoming part of the core infrastructure.
That doesn’t mean the path is smooth. It’s a fragmented landscape, featuring a diverse range of startups, pilot programs and varying levels of organizational maturity. In some areas, particularly ambient listening and revenue cycle management (RCM), we’ve seen an explosion of vendors and a wave of early adoption. But we’ve also seen caution—health systems waiting to see which tools will last and which will quietly disappear. The volume of solutions makes it hard to know where to invest, which is why many organizations now partner with trusted vendors to validate use cases before scaling.
What matters most, however, is a change in mindset. The key question isn’t “What can AI do?” but “What can AI enable—for providers, for patients and the relationship between them?” The opportunity isn’t just to go faster—it’s to give providers more time with patients, to spot trouble before it starts and to clear away the paperwork that gets in the way of genuine care. The real promise of AI, then, is to hand time back to care itself. So, whether it lightens documentation, flags rising risk or untangles billing, every advance moves us closer to medicine that feels unmistakably human.
The Return of Presence: Ambient Listening and Empathy
One of the most evident signs of that humanfirst shift appears right in the exam room. For years, the screen has quietly become the third party—absorbing the provider’s attention, directing the pace of interaction and pulling focus away from the patient. Ambient listening technology is beginning to reverse that dynamic.
These tools operate in the background, capturing the natural conversation between the provider and patient and automatically drafting the clinical note. The provider still reviews and finalizes everything, but the burden of typing and clicking is gone. That shift—removing the keyboard and mouse from the center of the visit—allows providers to return to what they are trained to do: make eye contact, listen and build trust. Empathy and presence are not luxuries in healthcare. They’re essential to care plan adoption, adherence and better outcomes.
Part of the reason ambient listening is gaining traction so quickly is that it is relatively low-risk. It’s not making decisions. It’s not intervening in clinical reasoning. And because providers have the opportunity to review and edit the output, they retain complete control over the medical record. It’s a supportive tool—not an autonomous agent. That perception, paired with the very real reduction in documentation burden, is helping ambient listening move from pilot to enterprise in a growing number of health systems.
Unburdening the Provider: Automation That Makes Space for HigherValue Work
The problem isn’t just what happens during the visit. Outside the exam room, providers face inboxes full of messages, constant priorauthorization requests and documentation tasks that stretch into nights and weekends. AI isn’t just about highprofile breakthroughs. Some of the most meaningful impact comes from automating the background tasks that weigh down care teams—what I often call the “work about work.” That’s the inbox messages, the forms, the authorizations—the stuff that piles up after hours.
When we use AI to automate routine tasks, we’re not just saving time. We’re giving providers the headspace to do what they do best: think through complex cases, collaborate with their teams and truly connect with patients. That space matters. It’s what keeps care human. Every click removed is one less demand on an already stretched workforce.
Taken together, ambient listening and click reduction aren’t just technical upgrades. They’re qualityoflife improvements—ones that reconnect care teams to the reasons they entered medicine in the first place.
Anticipating Needs: Proactive, Predictive, and Personal
The same philosophy—human-led, AI-assisted—applies upstream as well. Predictive analytics are starting to provide healthcare with something it has long needed: the ability to act before things worsen. Instead of waiting for patients to show up in crisis, we can use data to spot risks early and reach out in time to make a real difference.
The conversation shifts from, “Here is a diagnosis and a prescription,” to, “Our data suggests we have an opportunity to improve your health together. Let’s build a plan that works for you.” That kind of partnership helps patients feel seen, and it gives providers a better chance at driving longterm positive outcomes. It creates shared ownership. And in valuebased care models, that kind of partnership is key. It fosters trust, promotes adherence and yields better outcomes over time.
It also opens the door to personalization. When AI can analyze not only medical history but also lifestyle and, when appropriate, genomic data, it enables tailored treatment paths that make patients feel seen and heard. When we put AIpowered tools in the hands of generalists, we level the playing field—patients everywhere get the same highquality insight, and care feels personal rather than onesizefitsall.
Financial and Operational Gains: Building a Foundation for Scale
While the clinical story is compelling, it’s worth noting that some of the clearest early returns are coming from operations—particularly in RCM. AI systems review clinical documentation to suggest ICD and CPT codes, reduce under-coding, expedite prior authorizations and predict and prevent claim denials. RCM may not be the most visible part of the patient journey, but it’s an essential area where AI is proving its worth and building the financial case for broader adoption.
Behind the scenes, AI is stitching together data that used to live in separate silos. Records stream in from state HIEs, national networks and outside specialists, and it’s tough to see the whole story. An AI summary can pull those threads into a concise brief, flag what doesn’t align and surface details that would otherwise remain buried, giving providers a clearer picture and a faster path to the next best step.
Guardrails for the Future: Ethics, Accountability, and Equity
As AI becomes more embedded in healthcare, its success will depend on trust—and that means getting the ethics right. There are real concerns about alert fatigue, biased training data and overreliance on black box systems. To avoid those kinds of issues, AI must be transparent, accountable and equitable. Providers should be aware when it’s in use and still be the ones making the final call. These systems need robust training on data that reflects the patients we serve. Patients, too, should know that AI is there to support care, not to replace human connection.
We’re starting to see movement on the policy side, too—with new guidance expected around data privacy, model transparency and how AI gets evaluated as a clinical tool. Organizations that bake these principles into their AI strategy now will be wellpositioned as oversight increases.
Looking Ahead: Clearing the Way for the Art of Medicine
So, what do the next five years look like? Ideally, AI won’t be something we notice. Instead, it will be a change that we feel through less time spent on keyboards and more time spent on human connection. We’ll see earlier interventions, more personalized care and a system that adapts to each patient, not the other way around. We’ll see providers supported, not stretched. And we’ll see technology fade into the background, allowing the art of medicine to return to the forefront.
This vision isn’t for sterile, algorithmdriven care; it’s for care that feels more human—because AI has made space for empathy, wisdom and presence to lead the way.
