1,744 Epic hospitals had already adopted or were implementing ambient AI.

That number caught my attention.

But the second number matters even more:

DAX Copilot, Abridge and ThinkAndor accounted for more than 80% of ambient-AI implementations among the Epic hospitals studied. AJMC

For a HealthTech founder, executive or investor, that changes the question.

It is no longer:

Is ambient AI going to become important?

It already has.

The more useful question is:

Has generic ambient documentation moved from whitespace to table stakes?

And if it has, where does the next commercially attractive workflow sit?

That is why I built the HealthTech AI Category Whitespace framework.

My basic thesis is:

Do not look for the least crowded AI category. Look for the largest unresolved workflow with a clear buyer and measurable ROI.

Those are very different things.

First, what does 62.6% actually mean?

The underlying study analyzed 6,561 US hospitals.

Researchers estimated that 2,784, or 42.4%, used Epic.

Among those Epic hospitals, 1,744 had implemented or were implementing an ambient-AI application, producing the 62.6% adoption figure. The adoption snapshot came from June 2025 Epic Showroom data. PubMed

That does not mean:

• 62.6% of all US hospitals use ambient AI
• 62.6% of physicians use it
• every hospital has deployed it enterprise-wide
• every clinical specialty is saturated
• every implementation has demonstrated ROI
• every hospital wants to standardize on the same vendor

So I would not conclude:

“Ambient AI is finished.”

I would conclude:

Generic ambient documentation is becoming a mature enterprise buying category.

And category maturity changes startup economics.

The economics of entering a mature category

Early in a market, founders spend money educating buyers.

Later, buyers understand the problem, but they already have vendors.

That changes the cost from:

Category creation

“Why do I need this?”

to:

Displacement

“Why should I replace what I already bought?”

Displacement can be much harder.

You now compete against:

existing contracts

integration work already completed

clinician familiarity

security reviews already passed

procurement relationships

and increasingly:

platform bundling.

A technically better AI model does not automatically overcome those switching costs.

There is another important signal in the adoption data

Ambient adoption was not uniform.

Adjusted adoption was 70.2% among nonprofit hospitals versus 28.8% among for-profit hospitals, and 64.7% among metropolitan hospitals versus 54.3% among nonmetropolitan hospitals.

Hospitals with stronger operating margins and higher staffing-adjusted workload also showed higher adoption. PubMed

That creates a much more nuanced market picture.

There may still be ambient whitespace in:

smaller hospitals

resource-constrained providers

for-profit systems

nonmetropolitan organizations

and:

specialty workflows.

But founders need to be careful.

Low penetration does not automatically mean attractive whitespace.

Sometimes adoption is lower because the buyer has:

less budget,

less integration capability,

less IT capacity,

or a weaker ROI case.

That is why saturation alone is a poor market-selection metric.

My three-layer HealthTech AI opportunity framework

I would evaluate every category through:

CROWDING → WHITESPACE → COMMERCIAL FIT

1. Crowding

The first question is not:

“How many AI companies exist?”

It is:

How much of the buyer's actual workflow is already covered?

I would examine:

Installed vendors

How many target health systems already have a credible solution?

Concentration

Are buyers spread across dozens of weak vendors, or concentrated around a handful of scaled platforms?

Bundling risk

Could Epic, Microsoft or another incumbent include the feature inside a larger contract?

Switching cost

How painful is replacing the existing workflow?

Procurement maturity

Has the category already become part of standard enterprise purchasing?

Ambient documentation increasingly checks several of those boxes.

That does not eliminate the opportunity.

It raises the standard for differentiation.

2. Whitespace

Whitespace is what remains painful after the incumbent product has already been deployed.

This distinction matters.

The best discovery question may no longer be:

“What problem do you have?”

Ask instead:

“What is still broken after buying the current solution?”

That can reveal much more valuable gaps.

For ambient AI, for example:

The note may now be generated.

But what happens next?

Does somebody still manually:

find the correct code?

submit prior authorization?

schedule follow-up?

contact the patient?

close the referral?

update another system?

resolve the denial?

check whether care actually occurred?

That downstream work may contain far more whitespace than transcription itself.

3. Commercial fit

A workflow can be completely unsolved and still make a terrible startup market.

My commercial test is:

PAIN × BUDGET × ROI × TIMING

You need all four.

Pain

Does the organization genuinely care?

Budget

Who can authorize spending?

ROI

Can the buyer measure what improves?

Timing

Is there a regulatory, labor, financial or operational reason to solve it now?

That is where adjacent healthcare-AI categories become interesting.

Where I would pressure-test HealthTech AI whitespace in 2026

I would investigate at least six areas.

Not because these categories are objectively “better.”

Because each has a different commercialization structure.

1. Ambient documentation

Market signal

Maturing / crowded

The study's 62.6% adoption figure among Epic hospitals and concentration around DAX Copilot, Abridge and ThinkAndor are strong signals of category maturity. AJMC

Other visible companies in the category include:

Suki

Nabla

Ambience Healthcare

DeepScribe

and additional specialty and enterprise platforms.

Buyer pain

Still very high.

Documentation burden has not disappeared simply because adoption has increased.

Commercial challenge

Displacement.

Another product promising:

“Better AI notes”

may struggle to create enough economic differentiation.

Where I would still enter

Specialty-specific documentation

downstream coding

clinical reasoning tied to workflow

orders / referrals / follow-up

proprietary clinical context

underserved provider segments

or:

distribution unavailable to existing vendors.

Founder question

What can my product do that becomes more valuable after the note has been generated?

2. Specialty workflow AI

This is where I think ambient AI becomes more interesting again.

Generic ambient AI asks:

“What happened in the encounter?”

Specialty workflow AI asks:

“What has to happen before, during and after this type of encounter?”

For oncology that might include:

longitudinal history

treatment context

staging

specific documentation structures

coding

orders

follow-up

and:

care-plan coordination.

For cardiology, surgery, emergency medicine or behavioral health, the workflow looks completely different.

Commercial advantage

Specialty depth can create:

higher switching cost

better proprietary data

more specific ROI

and:

greater defensibility.

Buyer

Service-line leader
CMIO
specialty operations
clinical informatics

ROI

Clinician time
throughput
coding accuracy
workflow completion
capacity

3. Coding and revenue-cycle AI

This is one of the areas where the commercialization logic becomes particularly interesting.

A 2026 Guidehouse/HFMA report says 59% of surveyed respondents had not yet implemented AI or automation in revenue-cycle operations. The same research found payer-related challenges remained a major concern for provider executives, including denials, prior authorization delays and documentation demands. Guidehouse

Compare that maturity signal with 62.6% ambient adoption among Epic hospitals.

The two markets are not directly comparable populations, but the contrast is worth investigating.

Companies in the broader coding / RCM market include:

CodaMetrix

Nym

AKASA

Candid Health

Why I like the buyer economics

The economic owner is often clearer.

Instead of saying:

“AI improves clinician experience”

you can potentially say:

$X additional revenue captured

Y% fewer manual coding touches

Z days faster billing

lower denial cost

or:

fewer FTE hours per claim.

Typical buyer

CFO
VP Revenue Cycle
coding leadership
finance operations

My commercial lens

High ROI visibility can compensate for substantial competition.

That is a key principle.

Crowded does not automatically equal unattractive.

4. Prior authorization AI

Prior authorization has a strong timing catalyst.

CMS says certain regulated health plans must implement standardized Prior Authorization APIs beginning January 1, 2027, together with other interoperability APIs. CMS frames the changes around reducing non-digital workflows, accelerating access and improving transparency. Centers for Medicare & Medicaid Services

That gives founders something valuable:

a deadline-driven buying environment.

Potential workflow:

Request
→ eligibility
→ documentation
→ criteria matching
→ submission
→ status
→ denial
→ appeal
→ approval

Companies such as Cohere Health operate in this broader space.

Buyer

Payer utilization management
provider access
specialty operations
revenue cycle

ROI

Administrative labor
turnaround time
denials
care delays
manual touches

Constraint

Integration across payer and provider workflows can be significant.

So this can offer stronger whitespace while also creating higher implementation burden.

5. Clinical follow-up and care-gap automation

Patient communication itself is not new.

The more interesting opportunity is:

communication that completes the workflow.

Examples:

Abnormal result
→ patient contacted
→ appointment booked
→ EHR updated

Missed visit
→ patient reached
→ appointment rescheduled

Referral
→ patient contacted
→ slot found
→ referral completed

Care gap
→ patient identified
→ outreach
→ appointment booked
→ care completed

Luma Health reported in its 2026 product release that its agentic outreach workflows resulted in 28% of targeted care gaps being scheduled and 45% of missed visits being rescheduled in the examples it described. These are company-reported outcomes, so I would treat them as illustrative rather than market benchmarks. Luma Health

Companies in this broader area include:

Luma Health

Artera

and the capabilities formerly associated with Memora Health.

One important update: Memora Health is no longer a standalone company. Commure acquired Memora in December 2024, integrating its care-navigation capabilities into Commure's broader platform. Commure

Buyer

Patient access
ambulatory operations
population health
service-line operations

ROI

Appointments recovered
referrals completed
care gaps closed
staff calls avoided
retention improved

That is a much stronger commercial proposition than:

“We send intelligent patient messages.”

6. Workflow orchestration and agentic AI

This may eventually be one of the biggest opportunities.

But it may also be one of the hardest.

Healthcare has accumulated:

EHRs

AI scribes

patient-engagement systems

RCM platforms

clinical decision support

analytics

and:

specialty applications.

The remaining problem is often:

Who completes the work across all of them?

Imagine:

Clinical encounter
→ documentation
→ coding
→ order
→ authorization
→ patient notification
→ scheduling
→ follow-up
→ outcome recorded.

If AI can safely orchestrate that chain, its value becomes much larger than generating one artifact.

But so does the burden.

Integration burden

High.

Governance burden

High.

Reliability requirement

High.

Potential workflow ownership

Also high.

My advice would therefore be:

Do not sell “agentic healthcare AI.”

Sell:

one closed-loop workflow with a measurable outcome.

Then expand.

The 2026 HealthTech AI whitespace map

Here is how I would conceptually frame the categories.

This is not a formal market ranking.

The weighting should change depending on:

buyer

company stage

distribution

product architecture

regulatory exposure

and:

existing customer relationships.

Why “low competition” can be a dangerous signal

Founders often look for:

Huge problem + few competitors.

That sounds attractive.

But sometimes it means:

Huge problem + nobody has found a scalable buyer.

That is very different.

I distinguish between two kinds of whitespace.

Product whitespace

Nobody has built the solution.

Commercial whitespace

Buyers have an expensive problem, budget exists, alternatives are inadequate, and ROI can be proven.

Only the second one interests me commercially.

My 9-signal HealthTech AI Whitespace Score

The calculator accompanying this article uses nine variables.

Six create opportunity:

Buyer Pain

How urgent and expensive is the problem?

Budget Clarity

Can you name who owns the budget?

ROI Visibility

Can value be quantified?

Workflow Whitespace

What remains unresolved after current tools?

Distribution Advantage

Can you reach buyers efficiently?

Differentiation / Defensibility

What becomes harder to replicate as you scale?

Three represent friction:

Category Saturation

How much incumbent coverage already exists?

Integration Burden

How difficult is implementation?

Evidence / Regulatory Burden

How much validation is required before deployment?

The tool converts those into a directional:

COMMERCIAL WHITESPACE SCORE /100

It is deliberately not a market forecast.

Its purpose is to force better questions.

The founder ROI calculation most teams miss

Here is an illustrative scenario from the calculator.

Assume:

Monthly commercialization burn: $85K
Enterprise sales cycle: 10 months
Pilot / implementation cost: $75K

Capital exposed before rollout:

$85K × 10 + $75K = $925K

Now assume:

First-year ACV: $300K
Qualified win probability: 30%
Gross margin: 70%

Probability-weighted first-year gross profit:

$300K × 30% × 70% = $63K

Now compare:

$925K / $63K = 14.7×

That does not mean the business is unviable.

It means the cost of choosing the wrong category, account or buying pathway can be enormous.

This is where category selection becomes capital allocation

Suppose better buyer intelligence reduces that enterprise sales cycle from:

10 months → 7 months.

At $85K monthly GTM burn, that is:

3 × $85K = $255K

of illustrative capital preserved.

No extra customers are required.

No valuation assumptions.

No inflated ROI claim.

Just three months of commercial time avoided.

This is why I increasingly think about market intelligence as:

RUNWAY PROTECTION

not research.

The second calculator output I care about: buyer concentration

Suppose the founder creates a list of:

25 hospital buyers.

But after scoring:

workflow fit

existing vendors

economic owner

evidence fit

integration

and:

buying timing

only 32% are genuinely high-fit.

That means:

25 × 32% = 8 priority accounts

Now assume only 25% already have warm senior access.

That means:

2 warm accounts

and:

6 high-fit accounts still requiring deliberate relationship development.

That is actionable market intelligence.

A spreadsheet containing “500 US hospitals” is not.

What founders should do before committing another year of runway

I would run five different interviews.

1. User

Would you actually use this?

2. Economic buyer

Would you pay for this?

3. IT / integration

What would stop deployment?

4. Procurement

What would stop purchase?

5. Current user of an incumbent

What remains broken after buying the existing solution?

The fifth conversation may be the most useful.

Because increasingly:

Healthcare AI whitespace lives inside incumbent dissatisfaction.

What executives should ask before adding another AI vendor

Hospital executives face the opposite problem.

There is increasingly no shortage of AI tools.

The challenge becomes portfolio rationalization.

I would ask:

Does this solve a new problem or duplicate an existing platform?

Who owns the measurable outcome?

What workflow disappears after implementation?

What system has to integrate with it?

What happens if Epic or Microsoft adds 80% of this functionality?

Can we measure value within 6 to 12 months?

Does successful deployment eliminate another vendor or add another one?

That last question will matter increasingly.

What investors should diligence

If I were evaluating an AI HealthTech startup in 2026, pilot count would not be enough.

I would want to understand:

Installed-base risk

How many target buyers already have a competing product?

Platform risk

Could Epic, Microsoft or another incumbent bundle the feature?

Buyer clarity

Who signs the contract?

Workflow ownership

How central does the startup become after deployment?

Expansion

Does customer #1 make customer #2 easier?

ROI

Which measurable KPI drives renewal?

Defensibility

What improves as deployment grows?

Sales efficiency

How much capital is exposed before revenue begins?

That tells me much more than:

“The TAM is $20B.”

The company landscape I would watch

The purpose of this list is not to declare winners.

It is to understand how the category boundaries are moving.

Ambient documentation / clinical intelligence

Microsoft Dragon / DAX Copilot
Abridge
ThinkAndor
Suki
Nabla
Ambience Healthcare
DeepScribe

Coding / RCM

CodaMetrix
Nym
AKASA
Candid Health

Prior authorization

Cohere Health

Follow-up / patient access

Luma Health
Artera
Memora Health, now part of Commure. Commure

The important signal is not simply how many companies exist.

It is how quickly adjacent categories are beginning to overlap.

Ambient vendors move downstream.

RCM platforms add automation.

Patient-access companies become workflow agents.

EHR vendors add native AI.

That means category boundaries will become increasingly unreliable.

I would therefore stop defining HealthTech startups by “AI category”

Instead, I would ask:

WHAT WORKFLOW DOES THE COMPANY OWN?

For example:

Bad positioning:

AI documentation startup

Better:

reduces oncology documentation + coding time

Stronger:

owns oncology encounter → documentation → coding → follow-up workflow and reduces X hours / case

The further the company moves toward a measurable completed outcome, the more interesting the commercial proposition becomes.

The new winning formula

My framework is:

PAIN + BUYER + WHITESPACE + WORKFLOW OWNERSHIP + MEASURABLE ROI + DISTRIBUTION

AI alone is not the moat.

Increasingly, everyone has AI.

The moat can be:

workflow

data

specialty expertise

buyer access

embedded distribution

implementation infrastructure

or:

measurable economic outcomes.

Where the free calculator fits

I built the accompanying HealthTech AI Category Whitespace + Buyer ROI Dashboard so founders and investors can change the assumptions instead of accepting my view.

It lets you compare:

Ambient Documentation

Specialty Workflow AI

Coding / RCM AI

Prior Authorization AI

Clinical Follow-up / Care-Gap AI

and:

Workflow Orchestration / Agentic AI

It then calculates:

Commercial Whitespace Score /100

Category Saturation Risk

Capital Exposed Before Rollout

Probability-Weighted Gross Profit

Capital Exposure / Gross-Profit Multiple

High-Fit Buyers from 25 Accounts

Warm Buyer Coverage

Most Urgent Commercial Gap

and:

Recommended Category Posture

The category scores are intentionally editable editorial starting points, not predictions.

The useful output is the conversation they force.

The key missing link after the calculator

A category score still does not tell a founder:

Which hospitals actually have this problem now?

Which already have an incumbent?

Who owns the KPI?

Who controls the budget?

Which organization can move fastest?

Which decision-maker should receive the outreach?

Which account would create the strongest reference?

That is where category intelligence has to become buyer intelligence.

How I can help

This is the gap I built the HealthTech Buyer Pipeline Sprint around.

Instead of providing another generic list of hospital contacts, I map:

25 PRIORITY BUYERS + 15 RELEVANT DECISION-MAKERS

against the specific commercial thesis.

For an AI company, that can mean scoring accounts around:

current workflow

existing vendor exposure

pain intensity

economic owner

integration requirements

evidence burden

ROI potential

procurement timing

and:

reference-account value.

The objective is not:

More leads.

It is:

Fewer wrong buyers. Earlier evidence of where revenue can actually happen.

HealthTech Buyer Pipeline Sprint: 25 Buyers + 15 Decision-Makers

Final takeaway

The headline is not:

AMBIENT AI IS TOO CROWDED.

It is more useful than that.

The 62.6% adoption signal tells us that healthcare AI categories can mature extraordinarily quickly. PubMed

When they do, founders need to move from:

Can AI do this?

to:

Who still has an expensive problem after AI does this?

That may lead to:

specialty workflows

coding

RCM

prior authorization

clinical follow-up

or:

workflow orchestration.

And sometimes it may still lead back to ambient AI.

But only if the startup owns something more defensible than the note itself.

My rule would be:

DON'T BUILD WHERE AI IS LOUD.

BUILD WHERE WORK REMAINS EXPENSIVE.

That is usually where the buyer, the ROI and the commercial whitespace become much easier to see.