Agentic AI in Healthcare: From Pilots to the Operating Model of Care
Updated: 4 days ago
Executive summary. Agentic AI in healthcare is moving from demonstrations to deployment, but not where most observers expected. The first durable returns are administrative: ambient clinical documentation, prior authorization, eligibility and claims work that consumes clinician and staff hours without improving care. Strategic Revenue Insights estimates the global agentic AI in healthcare market at USD 920.00 million in 2025, rising to USD 23.05 billion by 2035 at a 38.00 percent compound annual growth rate. This article sets out five lessons from the health systems and vendors moving first, with takeaways for strategy, governance, technology, talent and revenue.
The numbers describe a market that is small today and compounding quickly, and the detail behind them explains why. Strategic Revenue Insights' Agentic AI in Healthcare Market research finds that software platforms are the largest component segment, cloud is the largest deployment model, hospitals and health systems are the largest end users, and clinical documentation and ambient scribing is the fastest-growing application. North America is the largest regional market and Asia Pacific the fastest growing. In other words, the growth is concentrated in the parts of healthcare where the work is repetitive, the data already exists, and the business case can be measured in hours saved and claims paid.
That concentration is the story. For a decade, healthcare AI was framed around diagnosis: models that read scans, flag sepsis, or predict deterioration. Those use cases remain important, but they are slow to scale because they touch clinical liability, device regulation and physician trust at the same time. Agentic systems, which can take a goal, plan a sequence of steps, use tools and complete a task with limited supervision, have found their first home in the back office and at the edge of the clinical encounter. That is where the operating model of care is now being rewired.
Why the Agentic AI in Healthcare Market Is Entering a New Phase
Three structural forces are converging.
The administrative burden has become a workforce problem. Physicians in the United States report spending roughly 13 hours a week on prior authorization alone, according to the American Medical Association's physician survey, and documentation after hours is a recognised driver of burnout. Health systems cannot hire their way out of this; the labour is scarce and expensive. Agents that draft notes, assemble authorization packets, verify eligibility and chase claims attack the cost base directly.
The money has started to move. Rock Health's tracking indicates that spending on AI for prior authorization rose from about USD 10 million in 2024 to roughly USD 100 million in 2025, a tenfold increase from a small base. Deloitte's 2026 healthcare outlook reports that more than 80 percent of healthcare executives expect agentic and generative AI to deliver significant back-office value this year. Spending is following the use cases with the clearest payback.
The regulatory environment has become legible, if uneven. In January 2026 the US Food and Drug Administration revised its guidance on clinical decision support and general wellness products, narrowing active oversight of lower-risk software while keeping diagnostic and treatment tools under full device regulation. The Centers for Medicare and Medicaid Services' prior authorization rule now holds payers to 72 hours for urgent decisions and seven days for standard requests, with public reporting of turnaround times and approval rates from 2026. In Europe, the 2026 Digital Omnibus package defers the AI Act's high-risk obligations for AI embedded in medical devices toward 2028, while transparency duties still begin in August 2026. The rules are not settled, but they are now specific enough to design around.
These forces explain the shape of SRI's forecast: a 25-fold expansion over ten years, led by software platforms delivered through the cloud to hospitals and health systems.
Five Lessons From the Health Systems Moving First
The organisations converting agentic AI from pilot to production share a recognisable pattern. We have distilled it into five lessons, each followed by a takeaway for a specific leadership function.
Lesson 1: The First Return Is Administrative, Not Clinical
The most mature agentic deployments in healthcare sit in patient access, documentation, prior authorization and claims. Ambient scribing is the clearest example: an agent listens to the encounter, drafts the note, proposes codes and routes the result for clinician sign-off. SRI's research identifies clinical documentation and ambient scribing as the fastest-growing application in the market, and the vendor landscape reflects it, with Microsoft's Nuance, Abridge, Ambience Healthcare and Suki competing for the same clinician minutes, and Epic and Oracle Health embedding similar capability into the electronic health record itself.
Prior authorization follows. Agents assemble the clinical evidence, submit the request through payer portals, track its status and escalate exceptions. Vendors report high rates of straight-through processing, though most of these figures are self-reported and independent evaluation is still thin. What is not in doubt is the direction of spending and the regulatory pressure on payers to decide faster and explain denials.
The commercial logic is simple. Administrative agents produce measurable savings within a budget year, do not require device clearance, and can be switched off without clinical risk. That makes them the natural entry point for a chief financial officer who needs evidence before funding anything closer to the bedside.
"The health systems that are furthest ahead did not start with diagnosis. They started with the work nobody wanted to do, measured it to the hour, and used the savings to fund the next step."
Takeaway on strategy. Sequence the agentic portfolio by proximity to clinical liability. Fund documentation, prior authorization, eligibility and claims first, measure returns in clinician hours and days-to-payment, and use those results to build the governance and trust needed for clinical decision support later. A roadmap that starts with diagnosis will stall in committee.
Lesson 2: Governance Is the Bottleneck, Not the Model
The constraint on scale is not the capability of the models. It is the ability of health systems to inventory, audit and sign off on them. Black Book Research surveyed 182 hospital leaders between October and November 2025 and found that only 29 percent had implemented and enforced policies covering AI model inventory, lineage and sign-offs, and only 22 percent were highly confident they could produce a complete AI audit trail within 30 days if a regulator or payer asked. The median share of 2026 IT and quality budgets allocated to AI governance and safety was 4.2 percent.
Medical groups are earlier still. A January 2026 poll by the Medical Group Management Association found that 42 percent of medical group leaders either have an AI governance policy in place (20 percent) or are developing one (22 percent), which means the majority do not. The Coalition for Health AI released governance playbooks in May 2026 precisely because so many organisations were buying agents faster than they could govern them.
For vendors, this is the hidden sales cycle. An agent that cannot produce a lineage record, an audit log and a human sign-off path will not pass a hospital's risk committee, regardless of its accuracy. For health systems, governance is the capability that converts a pilot budget into an operating budget.
Takeaway on data and governance. Treat the AI model inventory, audit trail and sign-off workflow as infrastructure, funded before the next agent is bought. Require every agentic vendor to deliver lineage, logging and a human review path as part of the product, not as a professional services add-on. Governance maturity, not model choice, will decide which health systems scale first.
Lesson 3: Regulation Is Diverging, Not Disappearing
Three regulatory movements are shaping the market at once, and they point in different directions.
In the United States, the FDA's January 2026 revisions to its clinical decision support and general wellness guidance reduce active oversight of lower-risk software, including tools that offer a single clinically appropriate recommendation when the clinician can review the underlying logic and sources. High-risk products that diagnose or treat disease remain fully regulated as devices. The guidance is largely silent on consumer-facing chatbots and on autonomous agents, which suggests further action is coming.
In Europe, the direction is deferral rather than relaxation. The 2026 Digital Omnibus pushes the AI Act's high-risk obligations for AI embedded in medical devices toward August 2028, with standalone high-risk systems following a slightly earlier timeline, while transparency requirements for chatbots and AI-generated content still apply from August 2026. Compliance work has not gone away; it has been rescheduled.
On the payer side, CMS has moved from guidance to enforcement. Medicare Advantage, Medicaid and marketplace plans must decide urgent prior authorization requests within 72 hours and standard requests within seven days, publish their metrics from 2026, and give a specific reason for every denial. That creates demand for agents on both sides of the transaction: providers want faster submissions, and payers need defensible, auditable decisions.
"Regulation is not slowing agentic AI in healthcare. It is telling vendors exactly where the human has to sit in the loop, and the companies that design for that will move faster than the ones that argue with it."
Takeaway on technology and operating models. Design agents for the regulatory position of each task. Administrative agents can run with light-touch review; anything that influences diagnosis or treatment needs a clinician decision point, a visible rationale and a record of what the agent saw. Build one architecture that can expose or suppress autonomy by jurisdiction and by risk class, because the US and EU timelines will not converge soon.
Lesson 4: The Platforms Own Distribution
The competitive map in agentic healthcare AI is defined by who owns the workflow. Epic and Oracle Health own the electronic health record; Microsoft, Google and Amazon Web Services own the cloud and the foundation models; Salesforce owns the engagement layer. Each is embedding agents into surfaces that clinicians and administrators already use, which means a specialist vendor's product can be adopted or displaced depending on how the platform owner chooses to compete.
Specialists are responding in two ways. Some, such as Abridge and Ambience in documentation, Qventus in operations, Infinitus in payer-facing voice work and Notable in patient access, are building deep, outcome-measured products and integrating tightly with the EHR. Others are positioning as orchestration layers that coordinate multiple agents across vendors. Waystar's acquisition of the assets of Olive AI illustrated the risk on the other side: a well-funded standalone that could not reach durable distribution.
SRI's research shows software platforms as the largest component segment and hospitals and health systems as the largest buyers, which is consistent with a market that will consolidate around platforms with existing enterprise relationships. The specialist opportunity is real, but it depends on measurable outcomes and on integration depth that is costly for the platform owner to replicate.
Takeaway on talent, adoption and change management. Adoption is a clinician and staff decision, not an IT decision. Measure success by how many encounters, authorizations and claims actually run through the agent, and by whether clinicians keep using it after the novelty fades. Appoint clinical and operational owners for each agent, invest in training that explains what the agent does and does not decide, and treat clinician feedback as a product input. Vendors should staff accounts with people who understand revenue cycle and clinical workflow, not only machine learning.
Lesson 5: Evidence Will Decide the Winners
The agentic AI market in healthcare is rich in demonstrations and poor in published evidence. A 2026 scoping review of agentic AI in healthcare identified only seven eligible studies, and just one of them involved patients. Most reported outcomes come from vendors, often without baselines or independent review. Research on AI-generated prior authorization letters has found strong clinical content paired with weak administrative scaffolding, a reminder that an agent must satisfy the payer's process as well as the clinician's judgement.
This evidence gap will not persist. CMS reporting, hospital audit requirements and the EU's transparency obligations will generate data on how agents perform, and buyers will use it. The vendors that publish outcome studies with baselines, that submit to independent evaluation, and that can show where their agents fail will win the enterprise agreements that follow the pilots.
The capabilities that build that trust are specific:
Measured outcomes with baselines: clinician hours, days to payment, denial rates and note accuracy, before and after.
Auditable behaviour: a record of what the agent saw, what it did and who approved it.
Integration depth with the EHR, practice management and payer systems where the work happens.
A clear human review model for each class of task.
Companies evaluating these competitive dynamics in greater detail can use SRI's Agentic AI in Healthcare Market analysis to assess component, application, end-user and regional segments and the positioning of the leading platform and specialist vendors.
Where the Opportunity Is Concentrating
Value is moving toward the workflows where an agent can complete a task end to end with a defined human checkpoint.
By component, software platforms lead, with professional and managed services growing as health systems look for help with integration, governance and change management rather than with model building.
By application, clinical documentation and ambient scribing is growing fastest, with revenue cycle and prior authorization, patient engagement and scheduling, and care coordination close behind. Clinical decision support and drug discovery workflows are larger prizes with longer regulatory paths.
By deployment, cloud dominates, which favours vendors that can meet hospital security, data residency and audit requirements inside the major cloud platforms.
By end user, hospitals and health systems are the largest buyers today, while payers represent a fast-emerging second market as prior authorization transparency rules bite. Pharmaceutical and biotech companies and ambulatory practices follow.
By geography, North America leads on adoption and reimbursement pressure, while Asia Pacific is the fastest-growing region as governments in China, India and Japan invest in digital health infrastructure. Europe's market is shaped by GDPR and the AI Act timeline, which rewards vendors that can document compliance.
At the Horizon: What to Watch Over the Next Three Years
Published outcome evidence. The moment peer-reviewed or independently audited results for documentation and prior authorization agents become available at scale, procurement will shift from pilots to enterprise agreements, and the gap between leaders and followers will widen.
Payer adoption. CMS reporting makes payer performance visible. Plans that adopt agents to meet the 72-hour and seven-day limits while explaining denials will set the standard, and their vendors will gain a reference market.
Regulatory clarity on autonomy. The FDA's current guidance does not address autonomous agents directly. A specific position, in either direction, will reprice the clinical end of the market. The EU's deferred timeline means that European obligations will arrive just as US positions settle.
"The next phase of competition will not be won by the agent that does the most. It will be won by the agent whose outcomes a hospital can prove to a regulator, a payer and a physician at the same time."
The Strategic Revenue Opportunity
For technology providers, the task is to identify which applications are converting from pilot to procurement, which buyers have budget authority, and which regulatory positions favour their architecture. For health systems, the priority is to fund governance ahead of the next agent and to measure every deployment in hours, days and dollars. For payers, the opportunity is to turn a compliance obligation into a service advantage. For investors, the signal is evidence: the vendors that publish results will own the enterprise contracts.
Market intelligence turns these questions into decisions. Segment-level sizing shows where spend is concentrating, competitive benchmarking shows where platform owners are exposed and where specialists can defend a position, and regional analysis shows which markets are opening first. This is the path from market signals to strategic insight to revenue impact.
Takeaway on revenue. Treat agentic AI in healthcare as four revenue pools with different buyers, cycles and evidence requirements: provider documentation and clinical workflow, provider revenue cycle, payer operations, and life sciences workflows. Each needs its own outcome data and its own regulatory posture. Enter where the buyer already has a budget line, a reporting obligation and a metric to defend.
The Next Leg: Preparing for the Journey Ahead
The defining shift in agentic AI in healthcare is that the technology has found a place in the operating model before it has found a place in the clinic. That is not a disappointment; it is the route by which healthcare has always adopted new tools, from electronic records to telehealth. The administrative returns fund the governance, and the governance earns the trust that clinical use will require.
The next three years will decide which platforms and specialists hold the workflows that matter. Decision-makers should sequence investments by regulatory distance, build governance as infrastructure, design for diverging rules, win clinician adoption deliberately, and insist on evidence from every vendor. Companies evaluating growth opportunities in this market can use Strategic Revenue Insights' Agentic AI in Healthcare Market report to assess market size, competitive dynamics, regional opportunities and strategic growth priorities.
Related analysis: Continuous Glucose Monitoring: How OTC Sensors Widen a USD 12.5 Billion Market; Radioligand Therapy: What Pluvicto's USD 2 Billion Year Means for Nuclear Medicine; Ambulatory Infusion Centers: How the Site-of-Care Shift Is Rebuilding Specialty Drug Delivery.
Frequently Asked Questions
How big is the agentic AI in healthcare market?
Strategic Revenue Insights estimates the global agentic AI in healthcare market at USD 920.00 million in 2025, rising to USD 23.05 billion by 2035 at a 38.00 percent compound annual growth rate.
Where is agentic AI delivering returns in healthcare first?
The first durable returns are administrative: ambient clinical documentation, prior authorization, eligibility checks and claims work that consume clinician and staff hours without improving care. Clinical documentation and ambient scribing is the fastest growing application.
Which segments lead the agentic AI in healthcare market?
Software platforms are the largest component segment, cloud is the largest deployment model and hospitals and health systems are the largest end users. North America is the largest region and Asia Pacific the fastest growing.
Why does governance matter for agentic AI in healthcare?
Agents act on patient and payment data, so health systems scaling them need clear governance over accuracy, accountability and oversight. Early movers pair deployment with governance so that hours saved and claims paid can be measured and trusted.
About the authors: This article was prepared by the Strategic Revenue Insights research team, which combines market intelligence, competitive analysis and strategic advisory for growth-stage, mid-market and enterprise organisations across the healthcare and life sciences sector. Sector analyst: Kamrul Hasan, Research Manager. Our market estimates combine primary research (interviews, surveys and expert panels) with secondary data (industry reports, databases and public disclosures); read more about how Strategic Revenue Insights works. To discuss this market with our analysts, contact our team.
Comments