AI as a Game-Changing Superpower: How Care Delivery Entities Can Win With AI

The narrative around AI in healthcare often follows one of two paths. On one side, we hear bold predictions of “AI doctors” and “AI nurses” that will eventually replace human clinicians, a vision of sterile, automated care. On the other, a chorus of counter-arguments emphasizes the irreplaceable value of the human touch, the nuance of a personal relationship, and the complex legal and ethical frameworks that govern medical practice.

This dichotomy is a false choice.

The real opportunity lies not in replacing clinicians, but in empowering them. Artificial intelligence is not a new kind of doctor, but a new kind of superpower. For care delivery entities—from large health systems to independent practices—the choice is no longer whether to adopt AI, but how to wield it to win in a rapidly changing healthcare landscape.

Recent Advances in Care Delivery AI

The “AI doctor” meme isn’t baseless. Recent advances in healthcare AI have been breathtaking. Algorithms have become incredibly adept at tasks that were once exclusively the domain of human experts. In radiology, for example, companies like AIdoc and Zebra Medical Vision use AI to analyze CT scans and X-rays in real-time, triaging urgent cases like brain bleeds and strokes to ensure they are reviewed by a human radiologist faster. This isn’t replacement; it is augmentation.

Similarly, AI is making strides in diagnostics and patient monitoring. AI-powered systems can now predict the risk of conditions like sepsis or cardiac arrest hours before a human might notice, by analyzing subtle shifts in a patient’s vital signs and lab results. In pathology, PathAI assists pathologists by analyzing biopsy slides with machine learning, improving the speed and accuracy of cancer diagnosis. On the administrative side, a new wave of tools automates tedious tasks like ambient note-taking, patient scheduling, and insurance verification, freeing up clinical staff to focus on what only they can do.

The example of super-shiny startup OpenEvidence is relevant to our discussion. This company has started to organize clinical knowledge to enable doctors to add more value. Note: As with all First Principles examples, we have no financial or other stake in the companies we mention. The examples serve only to make our points easier to understand.

Clinicians Remain Decidedly Hesitant

Despite these clear benefits, many clinicians remain wary, seeing AI as a technology that will inevitably reduce their income, erode their independence, and threaten the privileged relationship they have with their patients. This defensiveness is understandable, but it misses a critical point: the market forces pushing for AI adoption are too powerful to be stopped.

The Horse Has Already Left the Barn

Venture capitalists and private equity firms are not waiting for clinical consensus. In 2024, administrative AI alone grabbed over 60% of healthcare AI investment, with the overall sector attracting over $10 billion in venture capital. In 2025, investment has only accelerated, with firms pouring money into AI that can offer quick, tangible wins in diagnostics, administration, and care delivery.

This investment is fueled by unrelenting pressure from employers and consumers. US medical cost inflation is projected to reach its highest level in 13 years in 2025, with costs expected to rise by as much as 8%. Faced with this intense financial burden, employers are rethinking lavish benefits. While some still offer benefits like IVF and egg freezing, many are looking for ways to control costs and are more open than ever to new, tech-driven solutions that can provide quality care at a lower price point.

The market is also becoming more consumer-driven. The CHOICE Arrangement, proposed as part of the “One Big Beautiful Bill Act,” aims to codify and expand on the existing Individual Coverage Health Reimbursement Account (ICHRA) model. This proposed legislation, which passed the House and is expected to move in the Senate in late 2025, would give more power to individuals to select their own health plans. As individuals become the primary decision-makers for their health benefits, the market will become more price-sensitive, as people will be spending their own money, on a fixed, pre-tax allowance from their employer.

This perfect storm of financial pressure, tech investment, and consumer empowerment means that AI is not a future possibility, but a present reality. The question is whether your entity will lead this transformation or be forced to react to it.

A Tale of Two Skill Sets: Where Humans Win, and Where AI Wins

To navigate this change successfully, care delivery entities must understand the distinct strengths of AI and human clinicians.

AI is as good as, or better than, most clinicians in specific, data-intensive tasks:

• Image Analysis: Accurately detecting tumors from mammograms, identifying fractures from X-rays, and diagnosing diseases like diabetic retinopathy from retinal scans.
• Predictive Analytics: Finding rare diseases by analyzing vast datasets of clinical information and diagnosing conditions from real-time data streams from wearables.
• Rapid Learning: AI models can be trained on millions of data points in a fraction of the time it would take a human to review a tiny fraction of that data. For instance, an AI system for stroke care can rapidly learn from thousands of patient scans and outcomes, constantly improving its ability to identify and triage urgent cases, far surpassing any single clinician's lifetime of experience.

However, humans remain, for now, uniquely superior in critical, non-technical areas:

• Building Trust: Creating and nurturing a trusted, empathetic relationship with a patient, a cornerstone of effective care.
• Holistic Understanding: Understanding the “whole person”—not just their medical diagnoses, but also non-medical factors like their family situation, financial stress, diet, or sleep patterns, which are vital for a successful care plan.
• Legal Liability: Being the final, accountable authority for a clinical decision.

The current, perverse, micro-task-based fee-for-service (FFS) payment system is already creating problems for clinicians, forcing them into a cycle of endless bureaucracy, prior authorizations, and even AI-driven denials. The system that once made American clinicians some of the most highly paid professionals is now working against them. Smart clinicians are already looking for a path forward.

A Playbook for Winning with AI

Any care delivery entity with at least 50 clinicians can follow this playbook to thrive in the age of AI.

1. Proactively Move Away from FFS

The first step is to recognize that the FFS model is failing. Take control by shifting toward bundled payments—a single price for a knee replacement, for example—or service line subscriptions, such as a monthly, all-inclusive price for managing a patient’s diabetes. While this transition may feel risky, it will pay off handsomely. Outcome-based payments are coming with or without your support; it is far better to be in charge of the change and use AI to your advantage.

2. Build a Digital Workforce for Basic Tasks

Start by creating AI agents on your business’s data to handle the low-hanging fruit. This is not about replacing staff but augmenting them.

• Clinical: Use AI for image analysis to flag potential issues for a radiologist's review. Deploy AI to generate preliminary care plans from clinical notes, which a human can then review and finalize.
• Administrative: Automate ambient note-taking during patient visits to reduce administrative burdens. Use AI for patient scheduling, appointment reminders, and insurance verification, which often consume a huge amount of staff time.

Crucially, retain a human final decision in every single case. The AI is an assistant, not the ultimate authority.

3. Expand the Digital Workforce to More Ambitious Activities

As your organization gains a deeper understanding of AI’s strengths and limitations, you can expand its role to more ambitious activities.

• Clinical: Use AI to assist with diagnosing complex diseases by analyzing symptoms and medical history. Deploy it to enable junior and mid-level clinicians to perform the work of more senior clinicians by providing them with real-time insights and decision support. You can also use it to extend specialist care, where AI can serve as a first-pass screen for common specialist conditions.
• Administrative: Move beyond basic tasks to advanced practice analytics. Use AI to analyze your patient population to identify recommended expansion areas and automate sales and marketing, a function most care delivery entities currently neglect.

Summary

The narrative of “AI doctors” replacing humans is a distraction. The real story is about how care delivery entities can harness artificial intelligence to redefine their business and increase their impact exponentially.

AI is not a threat to be feared, but a superpower to be embraced. It’s a tool that allows you to offload the repetitive, micro-tasks that are driving burnout and losing money, so your most valuable resource (your clinicians) can focus on what only they can do: create trust, understand the whole person, and be a partner in health.

Are you ready to stop defending the past and start creating the future?