# The State of AI Adoption in Allied Health

What 682 allied health practitioners are actually doing, thinking, and worried about with AI. A 2026 benchmark study across Australia, the UK and the US.

_Published 2026-09-15 by Zanda._

## Contents

- Executive summary
- Methodology
- Introduction
- 1. Losing a day a week to paperwork
- 2. The heaviest users are in their fifties
- 3. Daily users get three times the results
- 4. AI can have its admin, not its patients
- 5. Clients who consulted AI first
- Concluding thoughts

## Executive summary

AI has moved from novelty to baseline in allied health. More than 60% of
practitioners are now using it in some form, and Zanda's own platform data shows
that use deepening rather than leveling off.

This report draws on responses from 682 allied health practitioners across
Australia, the UK, the US, and beyond to document what that shift actually looks
like on the ground: who is using AI, what they are using it for, what results
they are seeing, and what questions the profession has not yet answered.

Five findings define the current moment:

### 1. A rising paperwork burden is pushing allied health to AI

Nearly one in five practitioners (18.6%) spend nine or more hours a week on
documentation alone, and 47% say that load has grown over three years. AI
offered the first credible relief they had seen.

### 2. Allied health's heaviest AI users are in their fifties

Daily use peaks among 50-59 year olds at 32.7%, ahead of under-30s at 30%.
Adoption here is driven by need, not novelty, and the most overloaded
practitioners are reaching for AI first.

### 3. Practitioners who use AI daily get far more from it

Among daily users, 71% cut their documentation time by a quarter or more,
against just 23% of occasional users. The returns scale with commitment, and
the biggest gains go to those who build AI into their everyday workflow.

### 4. Practitioners trust AI with their time, not yet with their care

61% believe AI improves their efficiency, but only 34% believe it improves
patient outcomes, with 43% undecided. The profession has settled the
productivity question and is discerning about the clinical one, wanting AI in
the admin layer rather than the care relationship.

### 5. Most clients now arrive already informed by AI, and some are pushing back

70% of practitioners have had clients turn up having first consulted AI about
their condition, and 33% had a client challenge their clinical judgment based
on what an AI tool told them. The clinician is no longer the first or only
source of health information in the room.

## Methodology

This survey was distributed to Zanda's practitioner user base, and completed by
682 allied health practitioners between March and May 2026. This report also
draws on platform data from Zanda itself. Survey findings and platform data are
presented separately throughout: survey findings reflect the 682 respondents
specifically, while platform data reflects Zanda's broader user base of 44,000
practitioners.

Given sample sizes vary by country and subgroup, country-level and age-level
findings should be read as directional rather than precise, particularly for
markets with smaller samples.

### Definition of "allied health"

This report uses "allied health" broadly, covering the range of specialties
represented in our respondent base. Alongside psychology, counseling,
physiotherapy, occupational therapy, speech pathology, and massage therapy,
respondents also included podiatrists, dietitians, exercise physiologists,
chiropractors, and practitioners across a range of complementary and allied
disciplines. In some markets, including the UK, "allied health professions"
refers to a narrower, formally regulated list of professions. Readers in those
markets should note the broader scope used here.

### Definition of "AI usage"

For the purposes of this survey, "using AI" means using any AI-powered tool,
whether built into practice management software or a standalone product like
ChatGPT or Claude, for tasks such as clinical note drafting, treatment planning,
written communications, marketing, or admin work. It does not include AI
features practitioners may be using without realizing it, such as spell-check or
basic scheduling automation.

## Introduction

_How a paperwork crisis pushed allied health toward AI_

There is a version of the AI-in-healthcare story that positions the technology
as something arriving from the outside, disrupting a stable profession, and
forcing practitioners to respond. The data in this report tells a different
story.

AI did not arrive uninvited in allied health. Practitioners invited it in,
because the alternative, spending the equivalent of a part-time job each week on
documentation, was no longer workable. The technology did not create a problem.
It arrived at a moment when a long-standing problem had become acute enough that
practitioners were willing to try something new.

Understanding that context is the key to understanding everything that follows.
The adoption rates, the usage patterns, the beliefs about outcomes, the emerging
concerns about consent and safety: all of it makes more sense when you start
from the premise that AI entered this sector through the door of desperation
rather than the door of innovation.

This report documents where the profession stands today. It is designed to be
useful for practitioners, for practice owners, and for anyone building the tools
and policies that will shape how AI develops in allied health over the next
decade.

### About this report

The Zanda State of Allied Health 2026 benchmark survey was completed by 682
allied health practitioners across Australia, the UK, the US, New Zealand,
Canada, Ireland, South Africa, and other markets. Respondents represent a
range of specialties including psychology, counseling, physiotherapy,
occupational therapy, speech pathology, massage therapy, and others. The
survey was conducted between March and May 2026.

## Section 1: Allied health is losing nearly a day a week to paperwork

_AI is how practitioners are getting it back_

To understand why AI entered allied health so quickly, you have to understand
what practitioners were doing with their time before it arrived.

**Hours practitioners spend per week on clinical documentation**

| Response | % |
| --- | --- |
| 0–2 hrs | 19.5% |
| 3–5 hrs | 35.5% |
| 6–8 hrs | 23.5% |
| 9–12 hrs | 8.7% |
| 13–16 hrs | 4.8% |
| 17–20 hrs | 2.6% |
| 21+ hrs | 2.5% |

**47%** — of practitioners say documentation requirements have increased over the past three years

The cumulative picture is striking. A significant portion of the allied health
workforce is spending hours each week on work that has nothing to do with the
reason they entered their profession. And it has been getting worse, not better.

Each individual compliance change, each new funding body requirement, each
insurer documentation standard seemed manageable in isolation. The aggregate
effect is that practitioners in 2026 are carrying an administrative burden that
would have been unrecognizable to the profession a decade ago.

> "[I wish I could] get paid for my actual time spent working, not just face-to-face clinical time."
>
> — South African speech pathologist

One Australian psychologist and owner-practitioner said, "I would like a ratio
of about 85% direct client contact and 15% admin, report writing, case notes,
session preparation and communication. Currently it feels the opposite."

The relationship between documentation burden and burnout is not subtle. The
data shows burnout risk reaches nearly 50% among practitioners spending 21 or
more hours per week on notes alone. The documentation crisis and the burnout
crisis are connected at the root.

AI entered allied health through the documentation door, and that is still
overwhelmingly where it lives. Zanda's own usage data traces the same shape from
the platform side. Over the past year, usage of our AI note-taking tool grew
88.2%, and usage of our AI note-editing tool grew 92.4%, both nearly doubling in
twelve months.

**76%** — of AI users use it for clinical note drafting

Three quarters of AI users are using it for clinical note drafting. Nearly as
many for written communications and reports. The secondary uses, particularly
treatment planning and clinical decision support, represent where the technology
is heading rather than where it currently sits.

At Zanda, our approach to AI has been shaped by what this data confirms: that
practitioners do not need more technology for its own sake. They need fewer
roadblocks between them and their clients. Every AI feature we build is tested
against one question: does this give practitioners time back, or does it just
add complexity?

**What practitioners are using AI for**

| Response | % |
| --- | --- |
| Clinical note drafting | 76% |
| Letters and reports | 72% |
| Marketing content | 38% |
| Treatment planning | 36% |
| Admin automation | 24% |
| Financial forecasting | 7% |

### The clinicians who most need AI are the ones too burned out to start

Not everyone who wants to use AI is using it. 12% of practitioners in this
survey describe themselves as interested but not yet adopting. Their profile
represents both the challenge and the opportunity for the sector.

These practitioners are convinced, just not yet converted. Getting this group
from interested to active does not require more evangelism about AI's potential.
It requires accessible integration, a low barrier to entry, and an immediate
practical payoff that justifies the short-term investment of learning time. The
practitioners most likely to benefit from AI are precisely the ones least
resourced to pursue it independently.

**35%** — of the “interested, not yet” group say workload is unsustainable

**60%** — of the “interested, not yet” group feel behind on technology

**36%** — of the “interested, not yet” group feel overwhelmed by tech pace

What is holding the remaining third back more broadly? The concerns that come up
most consistently in the data are legitimate ones.

### Time: the one thing practitioners already don't have

Learning a new tool requires an upfront investment that practitioners who are
already stretched thin find hard to justify, even when the long-term payoff is
clear.

### Security and privacy: the questions the industry hasn't fully answered yet

Practitioners working with sensitive clinical information have real questions
about where their data goes when it passes through an AI system, and those
questions do not yet have universally satisfying answers from the industry.

### The human connection question: what changes when AI enters the room

For a profession built on human connection, there is a deeper hesitation worth
taking seriously: the concern that bringing AI into the therapeutic
relationship changes something fundamental about it, even if only in the room
with the practitioner and not visible to the client.

These are not reasons to avoid AI indefinitely. They are questions the
profession needs better answers to, and reasons why the on-ramp to adoption
matters as much as the technology itself.

## Section 2: Allied health's heaviest users are in their fifties

_More than half of allied health is already using AI. Surprisingly, nearly 40% are not_

The tipping point has passed. AI is no longer something allied health
practitioners are considering. It is something most of them are already doing.
And given how thoroughly AI has embedded itself into the tools practitioners use
every day, the more surprising statistic is how many are not using it.

**AI adoption spectrum**

| Response | % |
| --- | --- |
| Daily | 28.6% |
| Weekly | 16.4% |
| Occasionally | 15.1% |
| Interested, not yet | 12.2% |
| Not at all | 27.7% |

Nearly 40% of practitioners are not using AI. In a sector where the
documentation burden is leading to greater burnout, and where the software
practitioners rely on for scheduling, billing, and note-writing is embedding AI
directly into core workflows, that number stands out.

More than half of non-users already feel behind on technology, and 12.2%
describe themselves as interested but not yet using it, meaning the desire is
there but the follow-through has not happened yet.

This tracks with what we see on our own platform. As of July 2026, 43.9% of
companies using Zanda have BizzyAI features enabled, with 28.8% specifically
using our AI note-taking tool. The company-level number sits below the survey's
individual-level adoption figure, which makes sense: company-wide feature
rollout tends to lag individual practitioner behavior, and the survey captures
use of any AI tool, not only ones built into Zanda specifically.

### Allied health's heaviest AI users are in their fifties

When practitioners and commentators imagine the early AI adopters in any
profession, they typically picture the youngest cohort. The data tells us
differently.

**Daily AI use by age group**

| Response | % |
| --- | --- |
| Under 30 | 30% |
| 30–39 | 27.7% |
| 40–49 | 31% |
| 50–59 | 32.7% |
| 60+ | 21.2% |

> "[I wish I knew how to] decrease confusion around technology."
>
> — Canadian psychologist, 30–39

Daily AI adoption is remarkably consistent across age groups, sitting between
27% and 33% for every cohort except the 60+ group. Surprisingly, the 50-59 age
group edges out under-30s as the heaviest daily AI users.

**50–59** — year old age group are the heaviest daily AI users in our survey group

This may be because mid-career practitioners running established practices tend
to carry the heaviest administrative loads. They have strong financial
motivation to recover time, enough clinical experience to know exactly what they
want AI to do, and enough practice infrastructure to implement it
systematically.

The 60+ drop-off is the more notable shift, and it likely reflects proximity to
retirement rather than any resistance to technology. Across the board, what
varies more meaningfully than age is the context practitioners are working in
and the pressures they are navigating.

### Why AI adoption rates differ across the US, UK, and Australia

Among the three markets with sufficient sample sizes to report with confidence,
there are meaningful differences in adoption that reflect structural differences
in how healthcare is organized and funded.

**AI adoption by country**

| Response | Daily | Weekly/occasionally | Interested, not yet | Not at all |
| --- | --- | --- | --- | --- |
| US | 25.8% | 37.1% | 9.7% | 27.4% |
| UK | 15.6% | 37.7% | 11.1% | 35.6% |
| Australia | 32.2% | 30% | 11.5% | 26.4% |

Australia leads on daily use. On overall adoption, the US and Australia are
close enough to call it even. The UK has the highest proportion of practitioners
saying no outright, suggesting adoption is at an earlier stage there. What
drives the differences across markets is difficult to isolate from this data
alone, though the broader pattern is consistent with what we see elsewhere in
the survey: where administrative pressure is highest, the pull toward AI tends
to be strongest.

> "[I feel] exciting possibilities for the near future. Concern for the distant future."
>
> — US psychologist

## Section 3: Daily AI users get three times the results of occasional users

_Consistency matters more than the tool_

This may be the most important operational finding in this report. It's worth
noting this relationship likely runs in both directions: consistent use probably
drives better results, but it's also true that practitioners who see early wins
are the ones who keep using AI daily, while those who don't tend to fall back to
occasional use.

**71%** — of daily AI users get a 26% or better reduction in documentation time

**Documentation time reduction by AI usage frequency**

| Response | % |
| --- | --- |
| Daily users | 71% |
| Weekly users | 44% |
| Occasional users | 23% |

> "I want documentation to happen as a natural, invisible byproduct of the therapeutic conversation, rather than a manual chore that follows it."
>
> — Australian psychologist & owner-practitioner

Consistency and active experimentation matter more than which tools get used.
But the easier the tool is to understand and the lower the barrier to entry, the
more likely practitioners are to stick with it. The practitioners seeing
transformational results have rebuilt their documentation process around AI
rather than adding it as an occasional extra step.

AI isn't working for everyone. 12.7% of AI users report no measurable reduction
in documentation time at all. The ones seeing nothing have tried it a few times
and moved on without changing their underlying workflow. This finding reframes
the question practitioners should be asking. The question is not "does AI work?"
The evidence suggests it does, substantially, for those who commit to it. The
more useful question is "am I using it in a way that gives it a chance to work?"

The same principle holds when you look at practitioners' general approach to
technology.

**Tech mindset vs. documentation time reduction**

| Response | % |
| --- | --- |
| Active experimenters | 66% |
| Open to new tools | 41% |
| Use when it helps | 21% |
| Only when required | 10% |

**66%** — of active experimenters are getting a 26%+ reduction in documentation time

Active experimenters are getting six times the documentation savings of
reluctant adopters. This indicates that a posture toward adoption is the
deciding variable.

Among practitioners using AI, only a third describe themselves as very or
extremely confident. The largest group sits at moderately confident, and more
than one in five report low or no confidence at all.

**AI confidence among users**

| Response | % |
| --- | --- |
| Extremely confident | 9.8% |
| Very confident | 24.1% |
| Moderately confident | 44.6% |
| Slightly confident | 16.6% |
| Not at all confident | 4.9% |

**34%** — of practitioners using AI describe themselves as very or extremely confident users

**<5%** — of AI users are not confident at all

This matters because confidence shapes how practitioners engage with AI. Those
who feel capable enough to rebuild their workflow around it tend to see
transformational results, while those who feel uncertain often use it
tentatively and see limited returns.

But confidence is not the only driver. The data also shows that urgency matters
just as much. The 50-59 cohort uses AI most intensively despite being less
confident than under-30s, suggesting that when the documentation burden is heavy
enough, practitioners will commit to AI regardless of how fluent they feel.

**AI confidence by age group**

| Response | % |
| --- | --- |
| Under 30 | 50% |
| 30–39 | 31% |
| 40–49 | 40.3% |
| 50–59 | 34.8% |
| 60+ | 23.8% |

Under-30s are the most confident age group, while the 60+ cohort is the least
confident. These practitioners are using AI regularly but without full
confidence in their ability to use it well. That gap between use and confidence
is where the profession could do the most to support practitioners.

> "[I feel] positive [about the future of my industry] if we can make better use of AI in administrative tasks."
>
> — UK psychologist

### AI users are more likely to cut documentation time significantly than to see no change at all

The 20% no-reduction group in the US and UK is probably a mix of two things:
practitioners using AI too occasionally to see returns, and practitioners whose
documentation burden is dominated by compliance formats that AI tools don't
handle well yet.

**51%** — of Australian AI users report a 26% or higher reduction in documentation time

The most plausible explanation connects to structural differences in how
documentation is organized across markets, though the precise drivers are
difficult to isolate from this data alone. What the finding does suggest is that
AI documentation tools perform differently depending on the nature of the
documentation they are being asked to handle. Where notes are primarily
narrative and clinical, the tools tend to perform better. Where documentation is
dominated by structured compliance requirements, insurer forms, and funding body
reporting, the current generation of tools may be hitting a ceiling that better
integration alone cannot solve.

**Documentation time reduction by country**

| Response | No measurable reduction | 26%+ reduction |
| --- | --- | --- |
| US | 20.5% | 46.2% |
| UK | 20.8% | 43.8% |
| Australia | 11.4% | 51.4% |

## Section 4: Allied health has decided AI can have its admin, but not yet its patients

The profession has arrived at a working consensus on AI as a productivity tool.
It has not arrived at consensus on AI as a clinical one.

**61.1%** — of practitioners believe AI improves their efficiency

**34.3%** — of practitioners believe AI improves patient outcomes

61.1% of practitioners believe AI improves their efficiency. Only 34.3% believe
it improves patient outcomes. That 27-point gap is the most revealing finding in
this section. Practitioners are broadly confident that AI saves them time and
makes their work more manageable. They are uncertain whether that translates
into better care for clients. When nearly half the profession answers "neutral"
or "unsure" on a question this important, that response carries its own meaning.
The jury is still out on what AI actually does inside the clinical relationship,
and practitioners are saying so directly.

The picture shifts when you look at daily users specifically. Their confidence
in AI's efficiency benefits is near-universal, and their belief in client
outcomes benefits is significantly higher than the overall average. Experience
with AI generates both more confidence and more openness. The practitioners who
have committed to integration are more optimistic about what AI can do across
the board.

**What practitioners believe about AI**

| Response | Disagree | Neutral | Agree |
| --- | --- | --- | --- |
| AI improves efficiency | 9.4% | 29.5% | 61.1% |
| AI improves patient outcomes | 21.4% | 44.4% | 34.3% |

### The youngest clinicians have already decided AI is good for patients (but the rest of the profession hasn't)

The gap between generations on the patient outcomes question is one of the
sharpest age-based divisions in the entire dataset.

**Belief that AI improves patient outcomes by age group**

| Response | Disagree | Agree |
| --- | --- | --- |
| Under 30 | 0% | 60% |
| 30–39 | 26% | 26% |
| 40–49 | 23% | 35% |
| 50–59 | 19% | 35% |
| 60+ | 19% | 30% |

Under-30s have effectively resolved this question for themselves. It's important
to note that this is among the smaller age cohorts in the survey, so read the
sharpness of this split as a signal worth watching rather than a settled
generational verdict.

> "The future of my profession isn't about seeing more people. It's about seeing people better."
>
> — Australian psychologist & owner-practitioner

Every other age group is still working through it. The 30-39 cohort is the most
evenly split, suggesting that the practitioners currently in the thick of
building their careers and their practices are uncertain about what AI means for
the therapeutic relationship. What the data suggests clearly is that the
profession's future relationship with AI in the clinical context will be shaped
significantly by the practitioners now entering the field. They are not waiting
for the evidence to come in. They are proceeding on the basis that AI is a net
positive for their clients, and the rest of the profession is watching to see if
they are right.

There is also a clinical concern worth surfacing that sits underneath the
numbers. The concern that appeared most frequently and most independently across
the free-text responses was this: AI-generated clinical notes describe what was
said in a session but not the clinical reasoning behind what the practitioner
chose to do.

> "AI documents conversations, not interventions."
>
> — Australian psychologist & practice owner

This distinction matters enormously in practice. Clinical notes are professional
documents that record clinical judgment: the observations a practitioner made,
the hypotheses they considered, the intervention they chose and why, and the
outcomes they were tracking. If AI-generated notes capture the content of a
session without the clinical architecture underlying it, they may create records
that look thorough while missing the information that actually matters for
continuity of care, supervision, and medico-legal protection.

A second concern appeared independently across multiple responses: the
possibility that outsourcing note-writing to AI removes a cognitive process that
matters for clinical development.

> "Getting more bureaucratic and less skilled. AI contributes to skill erosion by removing opportunities to develop and refine skill."
>
> — Australian psychologist & practitioner (non-owner)

These concerns reflect the kind of careful clinical thinking the profession is
known for. What makes a clinical note clinically adequate in the context of
AI-generated documentation is not yet settled, and working through that question
thoughtfully will matter for practitioners, supervisors, and regulators alike.

> "Capacity will likely be improved by AI tools and other technology, but this role cannot be replaced by technology."
>
> — Canadian multidisciplinary clinic owner

## Section 5: 7 in 10 practitioners now see clients who consulted AI before walking in

Clients are already using AI before they walk through their practitioner's door.
70% of practitioners already have clients arriving having consulted AI first,
and that number is expected to grow.

**70.2%** — of practitioners have had clients reference AI-generated health information in consultations in the past 12 months

**Clients referencing AI-generated health information**

| Response | % |
| --- | --- |
| None | 29.8% |
| 1–10% clients | 37% |
| 11–25% clients | 16.9% |
| 26–50% clients | 10.3% |
| >50% clients | 6.2% |

At the far end of the distribution, 6% of practitioners are already operating in
an environment where AI-informed clients are the norm rather than the exception.
That is where the broader profession is heading. The consultation dynamic has
already changed for them. The practitioner is no longer the first source of
health information their client encounters. Managing that shift, knowing when
AI-sourced information is helpful context and when it is misleading noise that
needs to be carefully unpacked, is becoming a core clinical skill.

**6.2%** — of practitioners are operating in an environment where AI-informed clients are the norm vs. exception

The experience is not uniformly negative. Several respondents described clients
arriving better prepared, with clearer language for their symptoms and more
specific questions. AI can be a useful primer. The challenge is that its quality
and accuracy varies significantly, and practitioners rarely have time in a
consultation to assess which they are dealing with before the conversation is
already underway.

**>33%** — of practitioners have had clients challenge their clinical opinion using AI

More than a third of practitioners have already had a client push back on their
clinical judgment, citing something an AI tool told them. There is no single
right way to navigate a client who arrives having been told something different
by an AI tool. Sometimes the AI was wrong and the practitioner needs to explain
why. Sometimes the AI identifies something worth taking seriously. Sometimes the
client's trust in the AI is itself clinically relevant information about how
they relate to authority and expertise.

**Clinical opinion challenged by clients using AI**

| Response | % |
| --- | --- |
| Never | 63.9% |
| Rarely | 23.2% |
| Sometimes | 9.2% |
| Often | 0.9% |
| Very often | 0.1% |

_Numbers do not sum to 100 as 2.6% of respondents did not answer this question._

The profession does not currently have a widely shared framework for any of
these situations, and developing one is becoming more urgent with each passing
year.

> "I think there is a possibility that parts of pragmatic life situations that clients might need help with will be taken over by AI therapy."
>
> — New Zealander psychologist

### The AI your client confides in at 2am has no duty of care, and allied health is sounding the alarm

The open-text responses in this survey raised three interconnected concerns
about AI in the clinical relationship that go beyond the question of whether the
technology is useful. They concern whether the profession has adequate
frameworks to manage it safely.

#### Mandatory reporting

Multiple practitioners raised, independently and without prompting, a scenario
that every mental health and child welfare practitioner needs to be thinking
about. What happens when a client discloses suicidal ideation, a safeguarding
concern, or an acute crisis to an AI chatbot they have been using as a therapy
supplement between sessions?

The AI has no mandatory reporting obligations, no duty of care, and no ability
to contact emergency services. It does not necessarily follow safe messaging
guidelines and it may not reliably screen for risk. For clients who cannot
afford frequent sessions, it may already be functioning as a primary point of
contact for their mental health, available at 2am when a practitioner is not,
and with none of the professional accountability that a practitioner carries.

> "AI does not fully replicate human interaction, nor can it be trusted for properly assessing and dealing with risk, ethical obligations, or mandatory reporting when needed."
>
> — Australian psychologist & owner-practitioner

This is not a hypothetical risk. It is a structural gap that exists right now,
in every market this survey covers.

#### Client consent and data privacy

When a practitioner uses an AI tool to draft clinical notes from a session, is
the client aware that the content of their appointment is being processed by a
third-party system? Do they understand what data is being stored, where, and for
how long? Does the informed consent process most practitioners currently use
adequately cover AI processing of clinical information?

> "I worry about breaches of confidential information and non-required or overly personal information being shared unnecessarily and therefore harming clients, their reputation, or their trust of the profession."
>
> — Australian occupational therapist

AI consent has become one of the more talked-about topics in allied health, but
in practice it does not need to be complicated. At minimum, clients should know
which AI tools are used in their care, what happens to the information those
tools touch, and what their options are if they'd rather opt out.

> "[I am] concerned. Safety aspects of AI have not kept up with new developments. Confidentiality is a real concern."
>
> — Australian psychologist & owner-practitioner

#### Skill erosion

A third concern, raised independently by multiple respondents, is subtler but
potentially just as significant. The cognitive work of clinical documentation,
translating a session into written professional judgment, is itself a form of
clinical practice. Removing that process from the workflow may have consequences
for clinical development, particularly for early-career practitioners, that are
not yet understood or measured.

The good news is that these conversations are already happening, everywhere and
at every level. Practitioners who are engaging with AI, noticing what works,
questioning what does not, and staying curious about what comes next are helping
to define what responsible AI use in healthcare actually looks like.

> "I am concerned about deteriorating government standards enabling less qualified people to perform the roles that have traditionally been the responsibility of my profession."
>
> — Australian occupational therapist

## Concluding thoughts

We surveyed 682 allied health practitioners because we wanted to understand,
rigorously and honestly, what AI is actually doing in their practices. The
results surprised us in some places and confirmed what we have long believed in
others.

The data in this report tells a story that is more optimistic than the headlines
around AI in healthcare might suggest.

Allied health practitioners are using AI to take back the hours that
documentation, administration, and compliance have been quietly consuming for
years. The practitioners using AI most intensively embraced it because they
needed to find a way to keep doing the work they love without losing themselves
to the paperwork surrounding it. What the data shows, underneath the adoption
rates and the usage statistics and the belief gaps, is a profession that is
fundamentally resilient.

At Zanda, we believe AI has the potential to transform how allied health
practices work, by removing administrative burden and giving practitioners more
time and energy for the work that actually matters. That belief shapes
everything about how we build.

BizzyAI, our AI suite built specifically for allied health, lives entirely
within the Zanda platform. Client data never moves between systems, never passes
through a generic AI tool, and is never used to train models. We hold ISO 27001
certification, the world's leading standard for information security management,
and we are independently audited for compliance with HIPAA, GDPR, and regional
data protection standards across every market we serve. When a practitioner
records a session and BizzyAI generates a note, that data is processed securely
in real time with nothing retained afterward.

We built it this way exactly because of the concerns practitioners raise in this
survey about data privacy and consent. Keeping AI functionality inside a
purpose-built healthcare platform, rather than asking practitioners to copy and
paste between tools, is how we address those concerns in practice.

The questions this report raises about consent frameworks, mandatory reporting
gaps, and clinical documentation standards are ones we are actively thinking
about too. We do not have all the answers, but we believe that practice
management software has a responsibility to be part of the conversation, and to
build products that make it easier for practitioners to do the right thing.

Allied health is navigating one of the most significant shifts in how care is
delivered and documented that the profession has seen. The practitioners in this
survey are doing that in real time, often alone and often under significant
pressure. We hope this report is useful to them, and we hope the tools we build
are too.