Walk into almost any hospital today and you’ll see it: clinicians caught between care and clicks. One hand on the keyboard, one hand on the patient. Healthcare practitioners aren’t lacking skill or compassion; they’re just struggling to balance administrative work with actually providing patient care.
Healthcare has become a system of extraordinary people working through extraordinary complexity. Behind every diagnosis or discharge are dozens of administrative systems – often disconnected – and clinicians are the glue holding them together. The toll shows up in burnout rates, in staff shortages, and in the subtle erosion of what drew people to medicine in the first place.
AI won’t fix this alone. But if used wisely, it can give time back to the healthcare professionals who need it most.
The inflection point: Agentic AI for clinical workflows
As 2026 unfolds, healthcare is reaching a turning point. AI is evolving from a tool that suggests to an “agent” that executes — from something that merely assists to something that actively works with people.
This shift is not theoretical. It’s already happening.
- In a recent survey we commissioned, 73% of healthcare and life sciences executives reported positive returns on their generative AI investments within the first year.
- Nearly half (46%) plan to direct 50% or more of their future AI budgets toward agentic systems: the kind that can reason, plan, and take initiative on behalf of users.
The real opportunity for AI in 2026 isn’t in emerging new capabilities, but rather in using intelligence to relieve the invisible weight of administrative work that holds clinicians back from focusing solely on providing patient care.
1. From information to action
Most healthcare organizations have already experimented with large language models which generate summaries, discharge notes, or patient education materials. But the next leap forward will come from AI agents, systems that act rather than wait for a prompt.
Rather than simply summarizing a clinical note, an AI agent serves as a proactive collaborator. It coordinates information across EHRs, labs, and imaging to anticipate the next required action. It can prioritize follow-up steps, automatically flag critical missing data, and recommend guideline-based interventions in real-time.
Over the next few years, these agents will begin supporting multi-step clinical reasoning and workflow orchestration, transforming decision support into dynamic, real-time collaboration.
2. Administrative AI earns its keep
The most transformative applications of AI in healthcare are not flashy. They’re the ones that make invisible work visible.
- For healthcare, use cases in inventory tracking and restocking (22%), medical image recognition (22%), and patient screening and on-demand personal care (22%) are already having a clear, tangible impact in helping ease the administrative burden on healthcare providers and professionals.
At one of New Jersey’s largest healthcare systems, AI agents now generate real-time clinical summaries for more than 7,000 clinicians, cutting documentation time dramatically and reducing burnout. At a leading global healthcare solutions provider, a multi-agent system automates up to 80% of routine tasks like claims and coding, freeing teams to focus on care coordination and patient interaction.
These changes represent real signals that the healthcare system is becoming more sustainable. When an AI agent handles documentation, it means one less mental burden on the clinician. When it automatically completes a prior authorization, it means one less point of frustration for everyone involved.
As agentic systems mature, they’ll extend to patient-facing experiences such as verifying coverage, scheduling appointments, and managing prescriptions. The result will be a smoother patient journey, and a healthcare workforce that is finally allowed to breathe.
3. Trust as the true differentiator
For AI agents to succeed at scale, they must earn the same trust clinicians give to their colleagues. This is a shared, sophisticated challenge. As technology partners, we should view clinicians’ skepticism as a signal that the technology needs design improvements.
A “black box” agent, no matter how powerful, will be met with hesitation. A clinician’s question — “can I verify this?” — is simply the natural expression of responsible medical practice.
Healthcare organizations that excel in the next phase will distinguish themselves not by the number of models they deploy, but by how deeply they embed verifiable trust and partnership into every aspect of their implementation strategy.
Two priorities stand out for this constructive path forward:
- Move from data integrity to verifiable trust. Reliable agents depend on clean, secure, and harmonized data. But for an agent that acts, the standard is higher. This means explainability and traceability are non-negotiable. If an agent summarizes a patient chart, it must provide a clickable, traceable citation for every fact, leading directly back to the original source in the EHR. This removes the burden of doubt.
- Evolve governance into a co-development partnership. Establishing C-suite-led AI principles is the crucial “what” and “why.” The “how” is more difficult. To win clinician adoption, we must embed governance directly into the clinical workflow, creating clinician-led AI review boards not as gatekeepers, but as co-developers. By making clinicians active participants in the design and validation process, we ensure that the AI is built for clinical reality, transforming skepticism into shared ownership
Trust is at the core of the healthcare ecosystem. It’s what will determine whether AI deepens the human connection in medicine or distances us from it.
A more human future
The real promise of AI agents lies not in their intelligence but in their ability to deliver empathy at scale. By taking on repetitive tasks and restoring capacity to the people who deliver care, AI can help rebalance the system toward its original, deeply human purpose.
If healthcare leaders focus not only on innovation but on implementation (building systems that are transparent, interoperable, and guided by verifiable trust), then AI will help rejuvenate clinicians to provide the best care possible.
With this momentum, 2026 can rise as more than the year of the AI agent. It can become the year we turn from clicks to care and direct technology toward deepening the human experience of healthcare.
Photo: Weiquan Lin, Getty Images
Aashima Guptais the global director of healthcare strategy and solutions at Google Cloud. She has spent 26 years growing, differentiating, and improving businesses through technology transformation. She is passionate about collaborating with and empowering companies to elevate the strategic value delivered to their ecosystem – ranging from new models for care, revenue generation, and improved patient experiences.
Aashima leads the Gen AI strategy for Healthcare industry at Google Cloud, by navigating the dynamic intersection of industry needs and new technologies to make healthcare more accessible. Her approach involves bringing together industry leaders, researchers, product managers, data engineers, and tech experts to drive impactful solutions.
This post appears through the MedCity Influencers program. Anyone can publish their perspective on business and innovation in healthcare on MedCity News through MedCity Influencers. Click here to find out how.
