Healthcare AI In The Real World: What Sets It Apart

By Nadiia Wyttenbach and Paul Hemings

Healthcare AI has matured. Today we see companies generating $10–300m in

revenues, with multi-year contracts scaling to $40–60m, near-zero churn, and

profitability within 12–18 months. Unlike consumer AI, errors in healthcare carry life-

and-death consequences, so specialist, clinically grounded providers win where

generalists and Big Tech consistently struggle. Based on tenders and commercial

track records across Europe, the US, and MENA, our conclusion is clear: for

investors, Healthcare AI provides resilient revenues, defensible positions, and long-

term growth visibility.

Not That Kind of AI: Two Relevant Models

When most people say “AI,” 80% of the time they mean tools like ChatGPT writing

emails. That is not what we are talking about.

At the base sit foundational models, like cloud – a land of giants. But they are

generalists and still need fine-tuning for each industry. That segment, in our opinion

backed by our market diligence, faces classic big-market dynamics: falling prices,

commoditisation, simplification (as we saw with DeepSeek). We don’t invest there.

Where We Do Invest: Healthcare AI

Here the story is different. Some assume Big Tech will dominate. In practice, they

have consistently struggled to build successful organic healthcare IT or AI products.

Many of the leaders in this space are specialist firms, now billion-dollar businesses.

(Even AlphaFold came via acquisition through DeepMind.)

The same pattern is playing out in Healthcare AI. And this market is far from

nascent. Companies are already winning $40–60m tenders for enterprise-wide,

multi-site deployments. Ask surgeons, radiologists, or bioprocessing engineers which

AI they use: they name specialist Healthcare AI leaders — not Big Tech, not raw

foundational models.

Eight Reasons Healthcare AI Is Defensible Infrastructure

1. National security & trust → High barriers to entry

Healthcare operates at a different level of criticality. In certain bioprocessing sites,

security is military-grade because failure risks millions of lives. Winning contracts

requires the highest compliance, airtight interoperability, and single-point-of-entry

systems.

2. Near-zero churn → Revenue predictability

Leaders report near-zero churn over 6–7 years of commercial history, especially in

Physical+AI models. Once a hospital chain or pharma supplier builds infrastructure

with a health-AI partner, it becomes deeply embedded. Even pure-AI care-delivery

models show near-zero churn (vs. top-quartile SaaS churn of ~10%).

3. Very large enterprise contracts → Scale and visibility

We now see contracts of $10m, $40m, even $60m, often won by European players.

Pan-European healthcare chains are signing multi-country agreements. These early

large-scale deployments build a track record of enterprise-level execution.

4. National programmes → Step-change in scale

Entire national-level health-AI programmes are emerging, particularly in the Middle

East. These represent a new stage of scaling, where solutions move from single

institutions to system-wide adoption.

5. Long durations → High switching costs

Average contracts run 5–10+ years. Once a leader is embedded, they co-develop

solutions with clients over a decade, making them extremely difficult to unseat.

6. Profitability → Rare in the AI landscape

Unlike most AI verticals, many Healthcare AI firms are already close to EBITDA or

free-cashflow profitability. Others will be within 12–18 months. Growth plus

profitability makes them stand out in an AI landscape dominated by loss-making

ventures.

7. No Big Tech dominance → Specialist defensibility

Despite white papers, clinicians do not cite Big Tech as their suppliers. Big Tech

provides cloud and foundational models, but when it comes to industry-specific AI,

specialists dominate. Big Tech’s few entries into health AI/IT have been almost

exclusively inorganic.

8. Inorganic entry by strategics → Barriers to replication

Replicating today’s leaders is difficult. New entrants would face years of commercial

track record to overcome, alongside the need to build interoperability layers.

Industrial conglomerates and strategics recognise this: they do not build from

scratch, they acquire.

Bottom Line

Healthcare AI is not consumer AI. Both the clinical and business reality is that

generalist models consistently fall short. Specialists win because trust,

interoperability, contract structures, and clinical expertise are non-negotiable. The

evidence is already visible: near-zero churn, national-scale deployments, long-term

contracts, and clear paths to profitability.

This is a defensible market and one of the most resilient and attractive corners of AI

today, in our view.