Healthcare AI spend is zeroing in on ROI and margin lift
Healthcare leaders are prioritizing AI that proves itself on the balance sheet. A new report from KLAS Research and Bain & Company finds providers and payers are concentrating investment where AI can improve profit margins and show clear, near-term returns.
Early outcomes are promising. In areas where AI is live, fewer than 5% of executives say results failed to meet expectations. Precise dollar-for-dollar returns are still forming, but the direction is clear: fund what pays for itself fast.
Where the dollars are going
Among providers, the four most common AI use cases are all close to cash flow: ambient notetaking, clinical documentation improvement, coding, and prior authorization. These are repetitive, rules-based workflows with measurable outcomes and short time-to-value.
That focus aligns with margin pressure and staffing gaps. The goal: compress administrative burden, reduce denials, speed throughput, and free up clinical time.
Providers: RCM leads the roadmap
Seventy percent of providers have an AI strategy in place or in development, up from 60% last year. Nearly half rank revenue cycle management among their top three IT investment priorities.
Documentation AI is the most common deployment. About 1 in 5 providers report full implementation, and roughly 2 in 5 are piloting notetaking assistants. The bet is straightforward: cleaner notes, better codes, fewer denials, quicker payment.
Payers: utilization and coordination under pressure
Nearly 60% of payer leaders list care coordination and utilization management as a top IT priority. Plans are targeting smoother care management workflows, analytics to close gaps, and automation in prior authorization.
So far, many insurers have started with AI in call centers and member follow-up and engagement. The next wave is moving upstream to reduce avoidable utilization while improving member experience.
Why now: the margin math
Providers are dealing with persistent workforce shortages and the prospect of more uninsured patients as Medicaid rolls shrink and enhanced ACA assistance winds down. Insurers are managing higher utilization, particularly in Medicare Advantage, alongside policy turbulence in Medicaid and ACA products.
"Executives want quickly scalable solutions that address key business challenges and pay for themselves with tangible results and short time-to-value windows," said Aaron Feinberg, partner at Bain & Company. "This is all about the bottom line now."
What this means for healthcare leaders
- Pick use cases tied to cash flow: ambient scribing, CDI, coding, denials management, prior auth, call center containment.
- Set hard ROI targets: aim for 3-6 months to value with clear KPIs and owner accountability.
- Pilot fast, measure, scale: track denial rate, days in A/R, coder productivity, clinician documentation time per visit, prior auth turnaround, and call center AHT/First Contact Resolution.
- Design for workflow: tight EHR integration, minimal clicks, clinician sign-off, and feedback loops to improve model suggestions.
- Governance and compliance: PHI safeguards, bias testing, audit trails, and clear escalation paths with legal, privacy, and security.
- Vendor diligence: proven results in your EHR, transparent performance metrics, referenceable clients, and TCO modeling that includes implementation, training, and change management.
Early results, with guardrails
Adoption is moving from exploration to focused implementation: 70% of providers and 80% of payers now have an AI strategy in place or underway. Leaders are satisfied so far, but CFOs want tighter attribution of financial impact before scaling everything.
Keep risk controls tight. Require explainability where it affects care, document decision logic for audits, and monitor outcomes by population to prevent drift or inequity.
Benchmarks and next steps
For deeper benchmarks and case studies, see KLAS Research and Bain & Company's healthcare AI insights:
If you're upskilling teams to execute AI pilots and measure ROI, explore targeted programs by job function: Complete AI Training - Courses by Job.
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