The AI Divide: What Separates Practitioners Getting Results From Those Who Aren't

[00:00:09] DAMIEN:

Hello and welcome, everyone, to the Health Practice Buzz. Today's topic: the AI divide — what separates practitioners getting results from those who aren't.

Now, for those who are familiar with our webinars, my name is Damien. I'm one of the co-founders here at Zanda, and also a registered psychologist. I can also say I'm the bestselling author of The Nine Secrets of Successful Health Practices.

CHLOE:

Great book. Everyone should get it. I also have one.

[00:00:42] DAMIEN:

Totally agree. Wonderful. Perfect. Today I'd like to introduce Chloe to everyone. This is her first time co-hosting a webinar with me, so thank you very much for joining me today, Chloe. I'll let you introduce yourself and give a brief background about where you come from.

CHLOE:

Sure. Thanks for letting me be a part of this. This is my first one. I'm super excited, also a little nervous. I'm the Brand and Content Lead here at Zanda. I haven't been here that long, but I'm already loving it. I always thought I wanted to work in healthcare — I thought I was going to be a doctor. I was a biology major and did a lot of heavy science in school, and I started as a research assistant fresh out of college before I realized I had more of an affinity for strategic creative content roles, taking things that are very dense, like a lot of medical jargon, and turning them into something more applicable to everyone. I wrote the State of AI Adoption in Allied Health report, which we'll talk about in this survey, and I'm so excited.

[00:01:57] DAMIEN:

Wonderful. Thanks for that, Chloe, and it's awesome to have you on for the first time. No need to be nervous — this will be great fun. So what we're covering today: we're going to look at the AI adoption survey, what we actually did and some of the key findings, which Chloe is going to take us through. We're also going to cover some tips for using AI, and talk a little about features that are coming next for Zanda in the AI space. So, without further ado, let's jump into the survey. Chloe, I'm going to hand over to you to talk about what the survey is all about and set up the context for the rest of what we're talking about.

[00:02:41] CHLOE:

Perfect. Yeah. So earlier this summer, Zanda surveyed 682 allied health practitioners. We got responses from practitioners in Australia, the US, the UK, Canada, and eight other countries, so we really tapped into our global market. We also had respondents from psychology, physiotherapy — or physical therapy, as we say in the US — occupational therapy, speech pathology, massage therapy. There were so many varied industries that we really captured a wide breadth of information from all of these different groups.

We asked practitioners questions about their practice and how it's set up. We asked them about their burnout risks. We asked them how they use AI day to day, or if they use it at all, how it has affected their work, what impact they're seeing, and a lot of questions around how people are thinking and feeling about technology, and AI specifically, in healthcare, and where they think the profession is heading. So we had a lot of great data, and we put all of that into a report, which is the State of AI Adoption in Allied Health report. It's available to download, and I think it's going out as a link with this webinar, so you should be able to get it.

But today's session, we're really pulling out the findings that we thought were most worth discussing — the ones that were the most interesting to us. We know AI has been a really hot topic and we have a lot of resources on it already. I think we've done other webinars on AI. But what makes this one so special and so interesting is that so many of the respondents in this survey are likely on this call, attending this webinar, or are people like you. So it's really an accurate pulse on what's happening with AI in healthcare right now, in your industry and amongst your peers.

[00:04:45] DAMIEN:

Wonderful, thank you. Because we have an international audience — when you said we did this in summer, you're talking about the US summer. So for those of you in the opposite hemisphere, this is not like last year for us. This is a very recent survey.

CHLOE:

This is recent, yeah, exactly.

DAMIEN:

So, all right, let's jump in. Finding number one — over to you.

[00:05:09] CHLOE:

Yeah, perfect. So we're going to go over six findings. The report itself is packed — it's over 15 pages long, I think, if you want to really dive into more of it. This first finding was one of the most interesting to me, because it really solidifies what we've been seeing: AI has infiltrated every part of our lives, and we are navigating an unprecedented cultural phenomenon as a result. So we aptly named this finding "AI is already in the room."

What we found is that 70% of practitioners have had a client consult AI about their condition. Of course, that's 70% of people who would even admit to it, so it's potentially even higher than that. It means people are seeking healthcare information from AI sources and then coming to their provider almost as a secondary measure. Interestingly, 33% of practitioners have had a client challenge their clinical judgment based on something AI has told them. I think we're seeing more of that — it's become a more prevalent issue people are experiencing.

But what it really showed us is that the question for practitioners is no longer whether AI is part of the clinical picture. It's here. Now we need to look at how we respond to it. How do we react when clients come in with their own information? How do we react when we want to use AI tools in a session and clients are resistant? I think there's a lot to explore there, and we explore a lot of it in the report. It's going to be a further conversation.

[00:06:56] DAMIEN:

Absolutely. We used to joke about Dr. Google in the past, but this is now Dr. Google on steroids, isn't it? Because now it's very targeted to the question that person is asking.

One of the flip sides of this — you could say it's positive or negative, it depends — but one of the positives is raising awareness about a health condition or prompting people to get treatment. The fact that they're consulting AI could actually lead to people seeking treatment, which is a good thing. Things they might otherwise have ignored, because they weren't sure whether it was relevant or significant enough — AI will prompt people. If it thinks something's wrong, it will say, "Hey, you need to get a professional involved."

I had that myself. Some weird thing happened with my knee, which was blowing up, and normally I'd ignore it — I'd say, okay, it's painful, but I'm sure I'll be fine. I sent a few photos to AI because I thought, hmm, maybe not. And AI said, "No, you need to get to an emergency department immediately," because it could be certain types of infections. So I did, in the middle of the night, because of the way it was progressing and what AI was telling me. When I got to the hospital, I told them I was one of these 70%. I said, "Look, I wouldn't have come in, but it told me to." And they said, "No, it's spot on," and explained the same thing that I had explained, and why it was important.

So you can look at it positively and negatively, but I think it's great in the sense that it's not just about being challenged — it can actually bring awareness and prompt people to get help. It really has become a thinking partner as well.

[00:09:08] CHLOE:

Yeah. On both sides of it.

[00:09:10] DAMIEN:

Exactly. Let's jump into the next finding. So who is and isn't using AI? What do we find?

[00:09:19] CHLOE:

Okay, we found some interesting stuff. The second finding we want to talk about is who is, and also who isn't, using AI. What we found is that adoption is a lot broader than you might expect. You can see in our pie chart — I'll also tell you what it is — 28.6% of practitioners use AI daily. Almost the exact same number, about 28%, do not use it at all. About 16.4% are weekly users and 15.1% are occasional users. Of course, this is from our survey data of almost 700 people. There were also a handful of people who were interested in using AI but weren't using it yet, and that group was very interesting to us.

We also looked at it across age and country — we analyzed it from a lot of different angles, to be honest — and we didn't see much of a difference across the board. Pretty much every age group except 60-plus was within a pretty narrow band, with approximately 30% of them daily AI users. And it was about a 60–40 split by country, with 60% being AI users in some way and 40% not, or falling into that "interested but not yet" category. Like I said, that category was really interesting to us and has become a real focus of what we looked at in the survey and the report: about 12% were interested in AI but not yet using it.

We didn't see a difference in a lot of aspects, but we did see a difference in how confident users feel about AI and how experimental they are with technology in general. We had people rate themselves on their relationship with technology — active experimenters, hesitant experimenters, "only when I need to" experimenters. What we found is that 60% of this group in particular feel behind on technology, 35% said their workload is already unsustainable — which was a big factor we'll get into more — and 36% feel overwhelmed by the pace of change in technology. So these are people who probably want to adopt AI but have these hesitations, which we're going to get into more.

We also looked at why people aren't adopting AI, since that's still about a 40% split. And Damien, I think you also said that if we had looked at this even a year prior, those numbers would absolutely be flipped. AI really blew up around 2022, and we're only now in 2026. Every single year, I feel like the number of people engaging with AI, and our relationship to it, is growing exponentially.

[00:12:14] DAMIEN:

Yeah, absolutely. And that rate of change even fed into some of our product decisions. When we first started building our AI features, it was interesting, because the sentiment among practitioners wasn't super strong. We thought this was going to become really important, even though the hesitation and the adoption-rate concerns were much higher then. Sometimes the way people view technology moves very quickly, and sometimes there are external factors. We saw this in COVID: a lot of people who might have been considered hesitant adopters of technology were suddenly forced online, forced to do more telehealth and all the rest of it. Those hesitant adopters were forced into it, but then became really enthusiastic adopters. With most things we're a bit fearful of or not sure about, once we dive in and do it, we tend to feel better about it — "Oh, actually, that's pretty easy."

It's a similar pattern, and we've observed it just talking to people. You can talk to the same person a few months apart and see that their view has shifted and they're using AI much more. They say, "Oh my God, it's making such a difference. I can't live without it now."

[00:13:56] CHLOE:

Yeah, it's one of those things that's become so prevalent. If you're not a user, you're really falling behind, because it's everywhere. It's permeated every part of our lives — even healthcare, and things where maybe we wouldn't have thought it would.

DAMIEN:

We have some of our own internal data coming up about that.

CHLOE:

Yeah. About that.

[00:14:23] DAMIEN:

Absolutely. Okay, let's look at finding number three: documentation. So what's happening here?

[00:14:30] CHLOE:

Well, no surprise to anyone, really: the documentation crisis has been driving adoption. One of the most interesting findings was that about 47% say the administrative burden — documentation, note-taking and paperwork — has absolutely grown over the past three years. That's not the trend you want to see when you got into healthcare thinking you'd spend all your time seeing patients and helping people one-on-one, and instead you're spending more time than you'd like on note-taking and other documentation, and on government regulations and whatnot. So what we're seeing is that this documentation crisis is really pushing people to become more comfortable with technology and with AI, even if maybe they'd prefer not to be.

About 18.6% of practitioners are spending nine or more hours a week on documentation alone, and unsurprisingly, burnout risk climbs massively with the hours spent on documentation and notes. Daily AI users — people who are active experimenters, very pro-technology, pro-finding efficiency — are seeing massive results, and they're also generally carrying far fewer burnout risk factors than non-users.

We also looked, of course, at our own data, since we have over 40,000 users in Zanda. We've been actively producing AI tools, leveraging AI, and encouraging Zanda users to take advantage of them. In case you didn't know — hopefully now you will — we have an AI note-taking tool, and usage of it grew 88.2% over the past year. That's a huge increase in people leveraging this tool to get time back. We also have a note-editing tool, also AI-based, and that one grew 92.4%. So we've always been at the forefront. Damien, you've always said we're just scratching the surface of what AI can do. You've always been an advocate of it, and you've really pushed Zanda to be at the forefront of adopting new technology — anything that's going to make practitioners more efficient and give them more time back. I think you can really see that in our data, even just within Zanda, but also within the global context of our survey.

[00:17:09] DAMIEN:

Yeah, absolutely. I think part of that is that you can think about AI products in two different ways. You've got the consumer-facing kind, like ChatGPT, that sits out there and that everyone would be familiar with. But when it comes to AI within a healthcare context, it's not appropriate to grab patient information and stick it into any of the models that are out there publicly, because of the sensitivity of the data and so forth.

So in building our AI tools and encouraging health practitioners to really leverage AI, we've had to architect them and go through a lot of compliance, security and safety work, and set things up so that while we're leveraging AI within Zanda, it's occurring in an environment that we control. If we think about ambient transcribing and writing clinical notes, for example, that's leveraging the models, but they're hosted separately from the company that created them, in our own environment that we're in control of. So data going back to those companies is not used to train models. It adds an additional layer that health practitioners would otherwise have to think about, and that's why we've been so forward in building AI in: we can give people a really safe way of using AI, and confidence that their data isn't going off to the big AI companies, where they could learn from it or harvest it or do anything we wouldn't want.

Hopefully our numbers also show that people try it and actually like it, because they keep going back to it. I've lost count of the number of improvements and enhancements we've made over the last few years since we launched it, because we constantly get feedback about what's working well and what's not, and we keep updating to get better and better outcomes. Like I say, the data is showing it: people jump on, start using it, and then it just becomes part of their workflow. So that nicely segues into what practitioners are using AI for.

[00:19:41] CHLOE:

Yeah, so this is our fourth finding. People have been very interested in the answer to this question, which has been surprising. And I think we structured the last part of this webinar to talk about all the ways you can use AI, because, like I said, we're really just scratching the surface of it. When we surveyed practitioners four months ago, this is what we were seeing: about 76% of practitioners use AI for clinical note drafting — no surprise, that's a pretty big use case. About 72% use it for letters and reports, 38% for marketing content and 36% for treatment planning. We had a long list of things you could use it for, which people just selected from.

Clinical note drafting and letters and reports tend to be very high on that list. We think most practitioners are not fully leveraging what AI can do, because only 24% have used it for admin automation and only about 7% for financial forecasting. So we think there's a gap between what AI is actually being used for and where it could add more value. I think so much of it comes down to learning how to trust the technology and learning how to use it optimally. And I think Zanda does a really good job — we've put security at the forefront of everything, which I think makes it a little easier for healthcare practitioners to use it for more purposes in their business.

So stay tuned, because closer to the end we're going to talk about more ways you can use AI so you can actually become a daily user and really leverage it in your business. But this is what we found. People have been asking about the answer to this question, and from almost 700 practitioners, this is how they're using AI.

[00:21:44] DAMIEN:

Exactly. And the question was framed to us not just about using AI within their practice management system, but overall, how they're utilizing it. So it was fascinating to see what people were actually doing, rather than just theorizing. Excellent. Okay. So there's a difference here in terms of which practitioners are getting results and which aren't. What do we find?

[00:22:12] CHLOE:

Yeah, so this is definitely one of our biggest findings. We really wanted to see which practitioners were getting the biggest results, and why. Unsurprisingly, daily users are seeing the biggest payoff — what's maybe surprising is how big. About 71% of this group have cut their documentation time by 26% or more, which, compared to even occasional users, is more than double. So the more engaged you are with using AI, the more ways you use it, the more you explore how it can help with your efficiency and automate things, the more massive the results — in terms of lowering documentation time, which as we talked about is one of the biggest pain points for a lot of practitioners, but also one of the biggest burnout risks. So this is a really important opportunity in the industry.

As I mentioned before, we also talked about mindset, and how people would rank themselves in terms of their comfort with technology. We asked people to rank themselves — are you an active user, an occasional user, an "only if I have to" user? The people who described themselves as active experimenters saw a 66% reduction in documentation time, while practitioners who only used AI when absolutely required really bottomed out at around 10%. So even if you're a dabbler in AI tools, you're not going to see the same level of results as someone who's a more active experimenter, a daily user.

Of all the AI users we surveyed, about 12 or 13% said they saw no measurable time savings at all, which we'd assume is because you're not really giving the technology a chance to work as intended and find the flow. Our report read that practitioners who try it a few times and then say "it's not working for me" aren't going to see a change in their underlying workflow, and therefore aren't going to see as big a result.

[00:24:56] DAMIEN:

And then that can become a negative cycle too, right? Okay, well, I didn't really get any kind of results out of it. And that was even true before we were thinking about AI in practice management. I remember when ChatGPT was, I think, the first big one that had widespread adoption — this is outside of healthcare — I'd be doing school pickup or something, talking to other parents and friends, and asking, "Are you using this for your job?" Someone who wasn't in healthcare, but might be writing lots of reports, or proposals for funding and all that sort of stuff. And it was interesting — they might have tried it once, and they'd say, "I tried it once and I just thought it was garbage — ChatGPT or whatever it was." And I'd say, "Yeah, yeah, but it's about the prompting, right? What did you prompt? You can't just say..." So there were people who would dabble, not really get a result, and be turned off, and I'd be that enthusiastic: "Go ahead — what did you do? Tell me your prompts. Okay, try this, try that."

Then I'd see them at a subsequent school pickup and they'd say, "Oh my God, this is now..." I'd get text messages from people saying, "Oh, I understand what you mean now. Oh my God." If we're easily discouraged by not getting that immediate success, that can be a thing. So what I'd say about this data is: for those who might have been like that — not just in healthcare but in other areas too — just persist a bit. Because if you get over that hump, people say, "Oh, now I get it."

CHLOE:

Absolutely. Sometimes there's a startup cost to setting something up, like an automation.

DAMIEN:

Oh yeah, yeah. That can create some resistance — you think, "Oh, I have to figure out how to use this to be able to do this thing."

[00:27:04] CHLOE:

But really, once you set it up, the idea is that it runs automatically, and then it becomes very effective.

[00:27:14] DAMIEN:

And it's completely understandable too, right? That barrier — like we were showing on the previous slide, people are really busy, and they don't feel like they've got the time to focus on it. They feel like they're just keeping their head above water. So it perfectly makes sense when we look at it through that human prism. Okay, finding number six: tell us about the trust gap.

[00:27:40] CHLOE:

Perfect. This is our last finding that we're going to talk about today: the trust gap. We asked people, do you believe AI improves efficiency when it comes to business, when it comes to your workflow? 61% of practitioners do believe that AI improves efficiency. We also asked them whether they think AI improves patient outcomes, which is another use of AI in healthcare, and only about 34% believe it does.

This trust gap was very interesting, because what we're seeing is that people are far more trusting of AI when it comes to business decisions — marketing, administrative — but bringing AI into healthcare decisions is a huge point of contention and resistance. That's not a bad thing; it's just acknowledging that there's a gap between what practitioners believe and trust AI to do and what they're still uncertain about. One thing that was quite interesting is that daily users actually break this pattern: their belief in AI's outcome benefits is above the overall average across the board. So what this might suggest is that your confidence grows with use, and maybe your trust in the product grows with use too. The more you know about it and how it works, the more you might be inclined to bring it into more sensitive parts of healthcare, of business decisions — any way you might improve patient outcomes.

Of course, when we asked people this, I imagine most of them were thinking, "Does AI improve patient outcomes?" as in, am I using AI to consult about health or decisions or conditions? But we've also seen that there are other ways AI can improve patient outcomes without being that consulting partner. Right? You've talked about this many times before — how AI can be a tool that allows practitioners to be better practitioners.

[00:30:06] DAMIEN:

Yeah, yeah. And this is where I think the way people think about this question in the survey is spot on, because if you look at it through the prism of "Am I asking AI to make clinical decisions?" — no. And that's not what it's set up to do, or what we're advocating for.

But if we think about it in a different way — does it allow me to have more information at hand, for instance? Does it help me get an overview of the client or patient? Does it help bring more relevant information to the forefront of the practitioner's mind? All of those things can flow on to better patient outcomes, because we're all human and we can only hold so much information in our heads, whereas AI can surface things that might be relevant for the practitioner to then use however their clinical judgment says.

But the other big lift, where I think patient outcomes can be improved by AI without it being directly involved, is the mental space of the practitioner, and taking a lot of the administrative load off. Even in very practical terms: sitting in a consult and knowing that ambient note-taking is occurring, so the transcript is being taken automatically by AI and the notes are going to be drafted, means you can be present in that consult. That allows for more eye contact, more capacity to pick up on body language, and more mental space. You're not trying to think, "Either I need to remember this to write it down afterwards, or I need to be in the session, writing or typing." So there are different pathways, and with the way people thought about that question, I think the answer makes perfect sense.

CHLOE:

Yeah, it was really interesting that people were obviously thinking about it a certain way, but I think what we're seeing is that there are so many ways to think about it, actually.

DAMIEN:

Exactly. That was our last finding, so there's plenty more. Oh, we have another one. It's not really a finding — it's more of an interpretation, really, around what the fears are, and one of the core things that surfaced here. This really came out of the freehand responses. We gave people the chance to give us more qualitative responses as well, and we analyzed those to understand what the barriers might be.

The time and effort element makes perfect sense, and you see it with any new feature or tool adoption. Even as Zanda, as the technology provider, we try to make everything as easy as possible, but learning something new or adopting something can be a barrier.

The second one, which we talked about before, is completely understandable, and we encourage people to think about it really strongly: security and privacy. As practitioners, we have legal, ethical and moral responsibilities around the way we handle data. The information people entrust to us is super sensitive, and practitioners carry that and take it very seriously, as we should. So when new technology comes along, there's that question: what is the security around this? What's the privacy? Is this going to be a big tech thing that uses data inappropriately? That's obviously where we come in the middle of that and say, okay, we need to do it in a way that attends to all of those things — legal, ethical and moral.

And then there's the human connection fear that surfaced: a concern around the relationships health professionals have, which are based on human connection. It's more than collecting data and interpreting data; it really is about that connection. And we know — not from our own research, but from other research findings — that there's a very strong correlation, and likely causation, between the therapeutic alliance, the connection someone has with their practitioner, and the outcomes they get.

[00:35:16] CHLOE:

Yeah, independent of their treatment modality, right?

[00:35:21] DAMIEN:

So the quality of that relationship really is a big determinant of the treatment outcomes people have. So naturally, people fear that AI will somehow impede, overtake or replace that kind of connection, and of course that's something people are understandably worried about. I would say we can almost flip this one on its head, as we were talking about before: in a way, it's easier to keep that human connection when AI is leveraged in the right way. But it's an understandable concern, and it's certainly something that surfaced.

[00:36:06] CHLOE:

One thing that was actually interesting in this survey is that in a lot of the freehand responses, practitioners were almost defensively saying that AI will not replace them. I think it's almost a subconscious fear — maybe something that seems like a big topic: "Well, it'll replace you." A lot of people in allied health can acknowledge that AI will never fully replace you, for all the reasons you just said. And we've always believed that it will not replace you, but it could make you a better practitioner. It could make running your practice easier. There's definitely a time and place for it. And I think it's about finding the balance between not being resistant and also not getting sucked into the belief that it's going to replace you.

[00:37:00] DAMIEN:

Yeah, it's unlikely to happen. Exactly. I think about AI-enhanced practitioners and AI-enhanced practices as the goal we're aiming for: it allows practitioners to be the best version of themselves as practitioners, and also for their overall life balance. We looked at burnout in this as well, and we know burnout is a real issue in the health industry. It affects how people feel while they're working, but it's also strongly linked to intention to leave the profession, which is not good for the community — if people burn out and leave, that reduces the available services. So I think that, used really well, AI can actually alleviate those things rather than being something we need to worry about. People will always want human connection, and no matter how good AI may get, it's still AI. It's not human. Like I was saying before, I think it can actually lead to people seeking treatment that they otherwise wouldn't, which is the flip side of that coin.

So, just shifting the pace and the focus a little: how to use AI inside practices, and what we're seeing in terms of adoption of some of the AI features. We've got a rolling roadmap of AI features that we have released and are releasing. We call it Bizzy AI inside Zanda. There's the scribe for ambient note-taking, which is able to capture the transcript in a way that's safe and secure and then turn that into notes, using whatever template the person prefers, and also in their style, so it matches their way of writing notes. Then we've got Refine, which is for editing notes. And then there's AI assisting with writing reports and communications, things like emails, all the rest of it.

AI can also support other components, and the note-taking part, as we saw in the survey, is where people are utilizing it a lot. But inside the practice, it can support treatment planning. It can be used as a thinking partner — you can actually interact with AI to brainstorm ideas around treatment planning, which doesn't replace clinical judgment, but particularly if you're a solo practitioner and don't have someone in your clinic to bounce off, it can be really helpful. Then there are other things it can do, like automating follow-ups and building things out. In Zanda we have an operations manual, and in a section there you can prompt it and say, "Hey, write me a policy for whatever it is that you want." And then it can actually help. It can have hundreds of templates, actually. So you can pull any template and have it do that. There are hundreds of them.

[00:40:37] CHLOE:

If you're on this call and you don't have these things turned on or you haven't explored them, definitely do. There's also tons of automations you can set up to make things easier.

DAMIEN:

Absolutely. And when we released the manual, we pre-loaded it the old-school way — we literally had professional health documentation writers draft templates for everyone. So they're in there for you to use, and you can customize them. But you can also just use AI if you want something that we don't have as a pre-existing template: you can go in there and do it.

The other thing that was really interesting, and this changes week to week, is the percentage of practices within Zanda — nearly 44% — that now have Bizzy AI features enabled. There are different settings people can choose for how much AI they want within Zanda, and we're seeing that just grow.

[00:41:52] CHLOE:

Fantastic to see. And it ties back to what we were talking about before — the adoption rate, people's attitudes, comfort, everything changes and it's moving really fast.

[00:42:01] DAMIEN:

The next thing we wanted to talk about, because I think it's a missed opportunity at the moment, is using AI outside of the treatment room, or outside of the immediate running of the practice or the clinical components of it. As we were talking about before, you can use AI as a thinking partner. I personally do this: I talk to AI all the time and bounce ideas off it. I use voice mode constantly, right in Claude. I've got so many conversations like that, and it's awesome. I'll often do it driving in the car: I've got an idea, whether it's a business idea or a product idea, and I might be worried I'm going to forget it. In the past I might have used Siri or something — "Hey, make a note for me" — which feels very old school now. Now I'll just activate voice mode and have a conversation. I use a few different AI products, and I use them for bouncing ideas.

So, brainstorming. You can use it for comparative research or marketplace research. You can set it a task — for example, if you're looking at a price analysis for practices in your area, you can get it to go out, research and find that out for you.

[00:43:43] CHLOE:

And then there are other things that can be really annoying tasks, like writing job descriptions and job postings. Personally, I'm confessing here. But stuff like that — I also think it can help with compensating for your weaknesses. I was so bad at math, I don't know how I made it through school. I was great at every subject except math, and it's so important for my job, and for practices too, to look at the numbers, look at the data, make inferences and see how things are trending. AI has been such a useful tool, taking the mental load of that and handling it without me having to be good at it. I can just put data in, see what the trends are, and have it tell me exactly what the insights are. That has been one of my favorite uses. So I would definitely encourage you to use it like that, where it's compensating a little bit.

[00:44:53] DAMIEN:

Exactly. And it's the same, I find — I can think more clearly talking at things. Let's use the example of a job description or a posting. I'll have an idea of the vibe I want it to have, or the feeling I want it to have, or if we're producing content — it's sometimes hard to get there. If I sit there and try to type it out, that's very inefficient. But if I talk to it and say, "Okay, this is what I'm after," then it can actually start helping refine it and get my voice into it. It's much more efficient than sitting there and slogging it out on the keyboard.

The other thing to look at, outside of that immediate clinical work, is the way we communicate. In the practice context, that could be creating things like social media posts, or turning content that's clinically technical, or harder to explain, into something accessible. Often, by interacting with AI, you can get that content and then have AI produce versions of it that are appropriate for particular audiences. You can say, "I want this to be appropriate for this age group," and it's very good at thinking about using the right language for that age group, or demographic, or people with particular cognitive difficulties and so forth. So we can use it to produce educational material, or whatever else it might be, that takes your expertise and amplifies the way in which you can communicate it.

The same goes for marketing materials, an area where, when we're talking to practice owners, something we hear very commonly is, "I'm a practitioner — I'm not really a business person, I'm not a marketing person." Practitioners have lots of amazing ideas, but often struggle with the confidence of how to put that idea into something on social or on their website. AI can really leverage that in a fantastic way.

[00:47:35] CHLOE:

Yeah, I think AI has made the barrier to entry for creating things a lot lower. You can have videos created, you can have great pieces of content created, and you don't need a designer or the skill set or to hire branding to achieve great results, which I think is also...

[00:47:57] DAMIEN:

Exactly. And the final thing I'd say on this point is that there's a big difference in how you use it. You can create volume, but you can also create a lot of slop, right? And that doesn't do anyone any good. We have seen a lot of that. The way to get around it is to make it about your knowledge and your perspective, and this is why I really advocate for talking to it when you're doing this sort of work — giving it some really key points about your perspective. Rather than giving it a prompt like "Make me a flyer on the treatment of depression" — you're going to get slop. It will probably be technically okay, but it's not going to be you. If you want something for your company, talk to the AI about what you want it to be like. What's your perspective on it? What's your take? What are the key things that are unique to you that you want to communicate? When you prompt it that way, you'll get something that is far more you, and unique, and different from the slop a lot of people produce.

CHLOE:

Absolutely. And we also have a lot of resources on how to use AI and create things without it sounding like AI, because I think that's another concern. Most people don't know how to name it, but it's like the uncanny valley: a resistance to anything that seems like it was created with AI, or seems not human or robotic. We have a natural resistance to that. But you can absolutely create amazing things without having that. So go check out those resources — they're on our social pages and in our blogs.

[00:50:02] DAMIEN:

Yeah. Totally. And then the second thing, from one of those findings, about going from occasional use to confident user: it's really like anything else. Build it into your workflow. Just pick one task that you maybe do manually now and see how you can use AI to do it. You don't need to do it in a big-bang way — "I'm now going to convert and transfer everything." Just start, and think of it like any other skill. As we've talked about today, the way you prompt makes a big difference. If you get underwhelming results, keep trying and learning. You can even ask the AI: if it produces slop, you tell it, "I don't want that" — I'm very direct, I'm very firm about it — and then you can ask it, "How do I prompt better? Why did you produce that? Tell me how to prompt better." So think of using AI like any other skill. It takes practice, so learn from it.

And then the other thing is going broader. I know we're a practice management system and we come from there, but think about the non-clinical sides of things you can use it for. We think about the whole ecosystem of running the business and people's lives, so think about other things you can use it for, and practice using AI in that way. And like any other skill, you go from occasional — "I'm not so sure" — to "Hey, I feel good about this."

So, just quickly before we wrap things up, a quick heads-up about what's newly out or coming soon. If you're on the beta program — which you can join by logging into Zanda; you'll see in the menu that you can apply to join betas — some things are coming out. Bizzy AI Insights will bring AI-powered analysis and reporting directly into Zanda. It will allow a better ability to look at all of the client or patient history, bring that data together and surface relevant insights. You can prompt it and ask questions, or have some things prepared to help you going into a session — because it's so easy as a practitioner to have mentioned something two sessions ago and, unless you read back through, forget to ask the follow-up questions. All of that is coming out, so look out for Bizzy AI Insights as we roll it out. It's going to be awesome. Just teasing that.

The other thing: the full State of AI Adoption report that we've been talking about, which you can see on the screen now, we'll be sending to everyone who has signed up for this webinar. And a couple of things to wrap up, just reminders. We've got the ideas board, so if there are things you'd like — AI or not — go in there. You can put in your ideas, see other ideas people have put forward, and vote on them: if you think, "Oh, that's a good idea," add your vote. We use that feedback very, very heavily to create our roadmap. It's core — building the stuff people want is what's made Zanda grow over the last 16-plus years. It's a really great way to do it, so jump in. We've also got Zanda Academy if you want to learn more: you can get certificates for completing different courses, you can get your admin team to go through it, and you can optimize the way you're using Zanda. And of course we've already plugged the book, but you can grab yourself a copy on Amazon. If we can help with anything, or you'd like more information, you can contact us through all the usual channels, or even jump in and book a live call, and our team can help you with anything Zanda-related or answer any questions you have. All right, well, thank you very much. Anything you want to add, Chloe, before we wrap up?

[00:54:44] CHLOE:

That's it. Yeah, this was a labor of love, this project. We have more versions of this coming out, and I hope that if we do more surveys, we get even more participants, because it really helps to have such good insight into what your peers are doing and how the industry is looking. We're so lucky because we can see it on a global scale, being Zanda. It was a really fun project to work on. I hope everyone saw something useful or interesting, and we're always open to feedback as well.

[00:55:23] DAMIEN:

Absolutely. Well, thank you, Chloe. Well done on your first webinar — I feel like it won't be the last. Thank you for putting the report together, and thanks, everyone, for watching. We'll see you next time.