The Real Cost Impact of AI in Healthcare

Artificial Intelligence (AI) in healthcare was supposed to lower costs through faster diagnoses, smarter triage, and less paperwork. Instead, PwC’s annual medical cost trend survey found that commercial healthcare costs are projected to rise 9% in 2027, with AI-driven revenue optimization among the factors pushing costs higher.

History of AI and Healthcare

AI’s role in healthcare is growing fast, but the biggest cost impact is not coming from clinical tools. It comes from administrative AI, like scribes and coding assistants that draft notes, suggest diagnosis codes, and flag billable complexity. These tools save clinicians time, but they can also lead to higher-cost coding when visits are documented in greater detail, even when care itself has not changed.

How AI Became a Top Cost Driver

PwC found that nearly 70% of health plans rank AI-driven documentation and coding tools among their top cost inflators for next year, and 1 in 5 say AI is the biggest one. The problem is not more care. It is more complex coding that drives higher reimbursement per claim. Key trends behind that shift include:

• Providers are under financial pressure. Hospitals are facing rising costs and cuts to public health programs, which makes accurate reimbursement more important than ever. AI helps them capture it more consistently across every visit.
• The payment system often rewards volume and complexity, not results. Providers are paid based on service volume and coding complexity, not patient outcomes. Because AI helps increase both, it can raise spending even when the care itself has not changed.
• Billing rules weren’t built for AI. Most payment systems were built around human limits. AI scales cheaply and handles work far beyond what traditional billing models were designed for. Until payment policy catches up, that gap will likely keep pushing costs higher without clear gains in patient outcomes.

There are reasons to expect costs to improve over time. As AI matures, it may reduce administrative overhead, ease provider burnout, and shift more value toward earlier intervention and better health. But those gains are more likely to happen slowly across the industry than deliver a quick cost break for any single employer next year.

What Employers Can Do

For plan sponsors, AI-driven coding intensity is becoming a real budget factor alongside hospital labor, prescription drug spending, and behavioral health utilization. A few practical steps can help:

• Push for visibility into your own claims data. Ask your health plan or third-party administrator how coding patterns are changing in your population, not just overall trend numbers.
• Ask about the payment model direction. See whether your carriers and provider networks are shifting toward models that reward outcomes over volume, since that affects how AI-driven documentation is used.
• Evaluate vendor claims on the evidence. Do not assume every AI health solution saves money. Evaluate each tool on its own track record.

Takeaway

AI is reshaping healthcare, but in its most common use today, documentation and billing support, it appears to be raising costs, not lowering them. AI may still help reduce healthcare spending over time through earlier, more efficient, and more personalized care, but that shift will likely take time and changes in how care is paid for. Download the bulletin for more details.