A copilot, not an autopilot.

The narrative around artificial intelligence in healthcare often centers on a question that misses the point: "Will AI replace healthcare workers?" Recent research from leading organizations including the OECD, World Economic Forum, and McKinsey reveals a more nuanced and promising reality: AI's greatest potential lies not in replacement, but in augmentation.

The evidence for augmentation

The OECD's comprehensive November 2024 report on artificial intelligence and the health workforce examined fears about AI displacing healthcare workers. Their finding? Concerns about AI displacing the health workforce are not evident in current data.¹

The report points to pathology as an illuminating case study. Despite predictions of AI replacing pathologists, current shortages persist. Instead, AI has aided in productivity and error reduction without displacing human experts.¹ The integration of AI into pathology has demonstrated the potential to improve diagnostic accuracy, reduce errors, enhance productivity, and ultimately benefit patients — all while maintaining the essential role of trained pathologists.

Displacement vs. productivity

The OECD report introduces an important framework for understanding AI's impact on the healthcare workforce. There are two competing effects:²

Crucially, the productivity effect may partly address workforce shortages, though it won't cover the entire spectrum of healthcare labor challenges.² With a projected shortage of 9.9 million physicians, nurses, and midwives globally by 2030,³ augmentation isn't just desirable — it's necessary.

The World Economic Forum's vision

In November 2024, the World Economic Forum released research finding that generative artificial intelligence has the potential to drive significant improvements in workforce productivity at the level of tasks, organizations, and economies.⁴

However, the report emphasizes a critical condition: Delivering those gains depends on the deployment of GenAI to augment jobs — to partially perform tasks in such a way that technology effectively supports or enhances human capabilities through human-machine collaboration.⁴

This isn't about replacing entire jobs, but about intelligently dividing tasks between humans and AI based on each party's strengths.

Real-world success stories

Digital scribes transform documentation

Digital scribe technology exemplifies successful augmentation. These AI systems integrate speech recognition with Natural Language Processing to synthesize provider-patient interactions, summarize them in the EHR, populate diagnostic fields, and create billing codes.⁵

Early studies show digital scribes improving documentation efficiency by almost 2.7-fold.⁵ Physicians don't lose their jobs — they regain their time. They spend less time on documentation and more time with patients, delivering the kind of care that drew them to medicine in the first place.

Nursing productivity surge

In nursing, AI-enabled tools have reportedly increased productivity by 30–50%.⁶ This doesn't mean nurses are being replaced — it means they can care for more patients more effectively, with AI handling routine monitoring, documentation, and alerting tasks while nurses focus on patient assessment, education, and compassionate care.

Healthcare system transformation

McKinsey's research shows that AI can increase productivity and the efficiency of care delivery, allowing healthcare systems to provide more and better care to more people.⁷ This helps improve the experience of healthcare practitioners, enabling them to spend more time in direct patient care and reducing burnout.⁷

Why augmentation works better

Healthcare is fundamentally human

Healthcare delivery requires judgment, empathy, ethical reasoning, and the ability to navigate complex social situations. These uniquely human capabilities cannot be replicated by AI. What AI can do is handle the routine, repetitive, and time-consuming tasks that distract from these human-centered activities.

The complexity problem

Healthcare decisions often involve incomplete information, ambiguous symptoms, and patients with multiple comorbidities. AI excels at pattern recognition and data processing, but human clinicians excel at navigating uncertainty and applying contextual understanding. The combination is more powerful than either alone.

Trust and accountability

Patients seek care from trusted human professionals. While AI can support decision-making, accountability ultimately rests with human clinicians. Augmentation preserves this crucial trust relationship while enhancing the quality and efficiency of care.

The training and adaptation challenge

Healthcare professionals represent massive investments in education and training. Rather than replacing this expertise, augmentation multiplies its effectiveness. A physician supported by AI can deliver more, better care than either could alone.

Designing for augmentation

Successful AI augmentation in healthcare requires intentional design:

The Sarthi approach: AI as operating layer

Sarthi takes a different position than the dominant copilot framing. We treat AI as the operating layer beneath the practice — the system the practice runs on for intake, charting, coding, prior auth, and follow-up — while clinical judgment and the patient relationship stay where they belong, with the clinician. In practice, that means an operating layer that:

• Handles the mechanics: Takes care of routine tasks so clinicians can focus on strategy and patient care.
• Provides guidance: Offers insights and recommendations based on data analysis.
• Enhances capabilities: Multiplies human effectiveness without diminishing human agency.
• Maintains proper roles: Recognizes that ultimate responsibility and judgment remain with human professionals.

Looking forward

The evidence is clear: AI's role in healthcare is not to replace the workforce but to augment it. With a projected shortage of nearly 10 million healthcare workers globally by 2030, and current burnout rates affecting nearly half of all physicians, augmentation isn't just philosophically preferable — it's practically essential.

The HIMSS report on the impact of AI on the healthcare workforce emphasizes the need to "balance opportunities and challenges."⁸ This balance comes through thoughtful augmentation: deploying AI to enhance human capabilities, reduce administrative burden, and enable healthcare workers to practice at the top of their licenses.

As we move forward, the question shouldn't be "Will AI replace healthcare workers?" but rather "How can we design AI systems that make healthcare workers more effective, more satisfied, and better able to deliver the compassionate, high-quality care that patients deserve?"

The answer is to build AI as operating layer, not as substitute — systems that handle the mechanics of running a practice so the clinician's hours go to the work only a clinician can do.