The Healthcare AI Adoption Index

The Healthcare AI Adoption Index — Bessemer Venture Partners, AWS and Bain & Company

We partnered with AWS and Bain & Company to learn from 400+ healthcare buyers on what’s driving AI experimentation, from proof-of-concept to production, and how this will impact startups and innovation partners.

— the page’s own summary

TL;DR

A venture-capital report on a survey of 408 health-care executives in three segments: payers (insurers), pharma and providers (health systems). The survey is Bain’s; the framing is aimed at startup founders and buyers.

1. About 30% of completed proofs of concept reach production. “Only ~30% of completed proofs of concept make it into production” (chart 5). Payers 31%, pharma 24%, providers 35%; large providers 46%.
2. Most use cases are still early. “Nearly half (45%) of the use cases” across 59 jobs-to-be-done are “still in the ideation or POC phase.” In chart 3, only 0–9% of use cases per category are in implementation or production (provider care 9%, revenue cycle 7%).
3. Ambient scribes are the exception. Among providers, “30% have system-wide deployments, another 22% are in implementation, and another 40% are actively piloting” ambient clinical documentation.
4. Ambition and budget are not the constraint. 95% say GenAI will be transformative; 60% see GenAI budgets growing faster than IT budgets; most say budget is not a barrier to scaling. The named barriers are security, in-house expertise, integration cost and AI-ready data.
5. Startups supply under 15% of projects. 64% of buyers are open to co-developing with startups, but 55% “would only consider working with startups with proven track records.”

Key data

Measure	Figure	Where
Completed POCs that reach production	~30% (payer 31%, pharma 24%, provider 35%)	chart 5
… large providers	46%	chart 5
Use cases still in ideation or POC	45%	“The state of AI adoption today”
Use cases in implementation/production, by category	0–9% (care 9%, RCM 7%)	chart 3
Providers with system-wide ambient-scribe deployment / implementing / piloting	30% / 22% / 40%	same section
”Meaningful ROI within the first year” of GenAI implementation	54%	same section
Have a clear AI strategy / an AI governance committee	about half / 57%	same section
GenAI budgets growing faster than IT budgets	60% (payer 65%, pharma 57%, provider 56%)	“Experimentation but not yet production”
Barriers to scaling: security (payer / provider / pharma)	61% / 50% / 52%	same section
… lack of in-house AI expertise	41% / 48% / 52%	same section
… costly integrations	51% / 43% / 49%	same section
… preparing AI-ready data	39% / 41% / 47%	same section
Projects developed with or procured from startups	under 15%	“The development strategy”
Open to co-developing with startups	64%	“How startups can win”
Expect positive ROI in under 12 months	60%	same

Method and definitions

• Sample. “More than 400 leaders across Payer, Pharma, and Provider segments”; the chart footnotes give N = 408. The page does not give fieldwork dates, countries, job titles, how respondents were recruited or how many per segment.
• Unit. Organisations’ projects and use cases, reported by executives. The 30% is a share of completed proofs of concept, not of all projects.
• Stages. Not started, ideation, proof of concept, pilot, implementation/production. The AI Dx Index scores 59 jobs-to-be-done on an Opportunity Score (pain and manual effort) and an Adoption Score (weighted stage average, “Not Yet Started is 0-20% adoption and Implementation/Full Rollout is 80-100%”).
• Sponsors. Bessemer Venture Partners (healthcare investor), Amazon Web Services (cloud provider, chosen “for its unique position in the health AI ecosystem”) and Bain & Company (consultancy, survey owner).

Scope and reliability

• Vendor- and investor-sponsored. All three partners sell to or invest in the buyers surveyed. The report closes with advice for startups and points to the AWS Partner Network and AWS Marketplace.
• Conflict worth naming. Steve Kraus’s bio lists a board seat at Qventus, a health-AI vendor whose CIO report is in this batch.
• Thin method disclosure. No fieldwork window, no country, no response rate. The full deck and appendix sit on SlideShare, which was not accessible.
• Mixed segments. Two thirds of the segments are not care providers; health-system figures are the provider subset only.
• Self-reported ROI. The 54% “meaningful ROI within the first year” has no definition of “meaningful”.

Dynamic-capabilities reading

• digital-seizing/balancing-digital-portfolios: the report is about the step from proof of concept to production, and how many experiments a portfolio carries to scale (~30%).
• digital-sensing/digital-scouting: a cross-segment scan of where health-care buyers are on the adoption curve, use case by use case.
• digital-transforming/navigating-innovation-ecosystems: it maps who builds health-care AI (internal teams, AI labs, cloud providers, incumbents, startups) and argues for co-development with startups.

How it connects

• enterprise-ai-adoption — a pilot-to-production rate for one sector, with the breadth of experimentation set against few use cases in production.
• ai-maturity-measurement-comparison — a stage-based adoption measure per use case, from a vendor survey.
• BBC AI Decoded — Peter Grant cites a vendor index that puts roughly 30% of pilots in production across industries; this survey gives a similar share for health-care proofs of concept.
• Dinakaran (Luminai) — a startup selling automation into hospital systems through trusted referrals; this survey finds 55% of buyers only consider startups with a track record.
• In this batch: Poon et al. (JAMIA 2025) measures ambient documentation and other use cases across 43 US health systems with a different stage scale. 2026-04-09-qventus-cio-report-2026 is a health-AI vendor’s survey of US health-system CIOs on pilots and payoff.
• ai-in-health-care: the concept page that sets the health-care sources in this wiki side by side.

What was actually ingested

The full article text as served on 7 November 2026, and transcriptions of three of the sixteen chart images (charts 1, 3 and 5). The SlideShare deck and appendix returned a bot challenge, and Bain’s own page returned HTTP 403; neither was read. The recorded presentation was not transcribed.

Linked entities and concepts

• Bain & Company — survey owner and partner.
• Amazon Web Services — partner.
• enterprise-ai-adoption — concept. ai-maturity-measurement-comparison — thread.

Dangling (single-source mention, deferred): Bessemer Venture Partners (publisher), Sofia Guerra (Bessemer partner, co-author), Steve Kraus (Bessemer partner, co-author; Qventus board member), Qventus (in Kraus’s board list).