Are your AI processes risking your profit?

* Transcript created by AI – may contain errors or omissions from original podcast audio

What I like to say about AI is it’s neural. It is now mirroring how the human works, but the difference is it doesn’t have ethics and morals. It doesn’t have integrity, but it does have self-interest. So you can’t trust every AI you come across either. And so people need to realise that they have to be as discerning if maybe not even a bit more discerning in their use of AI than they are in their trust and wanting to transact with people

Welcome to the Intentional Money Show, where we talk business growth, profitability, mindset, and building a wildly successful creative or digital business without sacrificing your life in the process. I’m Clare Wood, business profit coach, speaker, author, and unapologetic believer that you can build wealth and freedom doing work you genuinely love.

Each week on the podcast, we dive into the strategies and honest conversations that will help you scale sustainably, increase your profit, and build a business that actually feels good to run. Let’s grow your profit.

Clare Wood: So a big warm welcome to The Intentional Money Show. Today we have Paige and Kat joining us here. They are from Orca Opti AI, and they are here fresh off the back of a very exciting announcement.

So I think we should share that first and then we can dive into a little bit of what you do. Paige, do you wanna share what is the exciting news with our listeners?

Paige Harkness: So last night, Kat and I are actually coming to you from Sydney. Mm. We are actually a Queensland-based company, but we flew to Sydney last night because we were invited as a finalist for the Australian Financial Review Most Innovative Company Awards.

We went thinking, “Hey, it’s really nice to be nominated.” Had a cocktail, like we’re just celebrating being there.

Kathryn Giudes: Mm.

Paige Harkness: And then we won the award, and then we- Gold.

Kathryn Giudes: Yeah, not just the award. Sorry. We won gold in- … medical services. Um, Most Innovative Company in Medical Services, and I believe there were at least 12 finalists in that category.

Paige Harkness: Yep, some really amazing competitors. Oh, sorry, other… Not,

Kathryn Giudes: not necessarily competitors, but people that operate in the same space.

Paige Harkness: Yeah. And so we were really happy that we took out the award. And then about three wines later-

Kathryn Giudes: They, they were quite generous with the wine, I will- I will- … attest to that. Yeah.

Paige Harkness: We were busy celebrating. Yeah. And then found out that we won the category award.

Kathryn Giudes: For- for, um… Yeah, so it’s, uh, micro organization, under 20 employees, Most Innovative Company in Australia.

Clare Wood: Well, if you’re watching the YouTube version of this, make sure you go and look because the girls have the biggest smiles on their face holding up these beautiful awards.

And that is a perfect segue for me to let you introduce and share a little bit about exactly what Orca Opti AI is, how you help people.

Kathryn Giudes: Well, I’ll probably start. As I went in my acceptance or our acceptance speech last night, I said that Orca Opti is the, tool that I wish I had my entire career.

So it’s a tool that builds in guardrails around information privacy, cybersecurity, quality management, and business continuity. You configure Orca to run your business operations the way you want them run, and Orca might tweak it a bit to make sure that you’re compliant to the regulators or compliant to contracts you have.

Or if you wanna do industry standards like SOC 2 or ISO, um, information management, then it will make sure that your environment is configured to conform to those. And then you just use the product like you would an AI chat, and you say, “Oh, you know, I need to onboard a new employee,” or, “I need to build a board report.”

A, a properly configured Orca Opti environment can build a board report in about three minutes. And so you literally could run that board report an hour or ten minutes before your board meeting, and it would be up-to-date real-time information

Paige Harkness: Oh, go ahead. I was just gonna say from an operations perspective, like being the person on the ground who’s responsible for delivering these board reports, the vision was to make an AI tool that the team actually really wanna use and get a lot of value out of.

And while using it, they’re actually creating evidence for things like audits and board reports. So it’s the AI the tech team can say yes to, and it’s the AI that the team actually really wanna use and get a lot of value out of.

Kathryn Giudes: Yeah.

Paige Harkness: And then as a side effect, the board are happy.

Kathryn Giudes: Yeah. And what’s great about coming out the product from the way we do, I…

My background is software development. I spent most of my career building large scaling software services out of the US for Microsoft. And you… There, there are these principles that you have to take into account when you’re building secure software. And when I came to Australia, I sat on listed and private boards, and it was really frustrating ’cause I’m quite technical, but I also understand the business side and I do have formal education in both.

And it’s difficult to get the right level of technical information, or at least it was, that was my experience. And the… It shouldn’t be that hard. The evidence exists in the business, and if you can fig- figure out a way of capturing it, then you can give it to the board at a level that they can consume, but it’s not the board getting into the operations of the business.

And that, that ultimately was one of the biggest drivers for me, and I knew that the only way it was gonna happen is if you made it work for the operational side, which is what Paige brings to the table. So she makes sure it’s good usability, and I make sure that the boards are happy.

Clare Wood: I love that.

And so you primarily work with medical organizations, or is this a tool that could potentially be used by other industries as well?

Kathryn Giudes: It’s a tool for anyone that is heavily regulated. You don’t have to be he- heavily regulated to use it, but that, that’s what it’s designed for. So medical is a is an area where we found a lot of traction because it’s probably one of the most fragmented from a tools perspective.

They have on average about, i, I, um, don’t quote me and cite this as a source, but I’d say on average around 20 tools based off of the customers that we’ve talked to. And that’s a lot of tools and a lot of logins and a lot of information in different systems, and it’s really hard for the staff to have to try to pull it out of one and put it together and, you know, send that GP referral or g- you know, go and hit this system to get the patient records.

And the idea around ORCA is we do integration. So the… They may still have 20 different tools, so they’ve been able to get rid of quite a few of them, but the reality is that those tools can still be the source of truth, but you only have to work in one place. Mm. So it makes it a lot easier for the staff.

And those gaps, uh- Yeah … go away because ORCA’s providing that, that bridge.

Paige Harkness: Yeah. And especially in healthcare, you’ve got… And one of the examples that Kat mentioned in our application was you’ve got teams who are, with the best of intentions, pasting com patient data into big AI frontier models without even really thinking about- the repercussions of what that means.

Kathryn Giudes: Where… Yeah. So- Where it goes, how it’s stored, who has access to it. Yeah. And I think that over the last three months or so, people are starting to become more aware of it, but I don’t think the average person using AI understands exactly the storage model versus the access model.

And once LLMs became neural, they… it means that there are multiple pa- paths to any one place. So if a large LLM is holding your data, it’s in the corpus. And the analogy I like to use is like taking your quarter of a cup of water and pouring it into a big bucket, and then in 30 days scooping that out.

You can’t guarantee you’re gonna get all the electrons that you poured in. So it means then that… Or, sorry, I should say water, whatever water is. I- … just messing up my analogy. H2O molecules. H2O molecules. Yeah. But you can’t guarantee you’re getting it all out because it, by virtue, there’s multiple paths, and if people are really good at prompting, they can gain access to your data.

It doesn’t mean they will, but if they can, it means that you’ve got a potential leak.

Clare Wood: And can we just the acronym there that I know some of the listeners mightn’t be familiar with. What does LLM actually mean, and why is it important that people understand what implications that has when they are using AI?

Paige Harkness: I can answer that one. Okay. LLM stands for large language model.

Clare Wood: Yep.

Paige Harkness: And those are the things like ChatGPT, Claude, Grok Perplexity. The things that people are typing into and getting answers back and creating agents. I think what a lot of people don’t realise about Orka Opti is that we are an alternative to those.

Kat, I’ll let you explain it in a lit- little more technical detail. Yeah. But…

Kathryn Giudes: So technology has obviously been going on a journey and they say that AI was probably originally created in the 1950s, but it, technology itself builds on it. So we couldn’t have the technology we have today un- unless electricity was harnessed and understood.

And- Our early systems were very procedural. They actually would call them procedural languages. So if this, then that. Give me a piece of data, I have a set of rules that I’ll process it and then I’ll have an outcome. And a large language model doesn’t work like that. They call it a neural network for a reason.

It’s a bunch of information, a bunch of language that has neural pathways. So it starts to act like a human does, and it- that’s why you don’t get the same response from the s- the same prompt and the same conversation each time. Because like a human, it’s like, “Oh, okay, let me take this information in and apply it, and I’m not gonna apply it exactly the same way each time.”

But the way that the, the data is stored and accessed, large language models were created with this neural relationship because that’s how they were able to harness the speed of response and processing. But it also means that it’s assess- it- the speed and the responsiveness means it’s accessible.

So you know, the dark side and the light side are the same coin.

Clare Wood: Yeah. Absolutely. So it’s really interesting ’cause the medical sector, in my perception just as someone who has, you know, been in the healthcare system as a patient or a family member, there’s so much still antiquated processes and systems.

Mm. My sister, who I’m instantly thinking I need to connect you with, she is a practice manager for a really large GP clinic. Yeah. And she told me that she is still printing out files, and they have physical files on pa- And I’m like, “What do you mean? Aren’t the doctors using computers?” And she’s like, “A lot of them don’t like to.”

And so we have all these physical files. And again, having experience going into hospital and they’re writing things on a chart, and I’m like, “Surely you’ve got an iPad now.” but I presume that there’s someone then taking that and putting that into various kinds of practice systems and models and things like that.

So you’ve also got this whole other, um, what’s the right word?

Kathryn Giudes: Admin support.

Clare Wood: Yeah. There’s this whole bit of history as well that’s changing, and then people who perhaps aren’t maybe that tech-savvy who are then using tools without having, you know, had as much experience perhaps.

Paige Harkness: Yeah. And it’s an interesting thought around the extra admin support.

Mm. Because you’d think making more admin work. So there’s this really fine balance of solving the problem and over-engineering the solution. So some of the, um-

Kathryn Giudes: Or under-engineering the solution.

Paige Harkness: Well, yeah.

Or under-engineering. Well, yeah, and that- that’s, this is true. But, so some of the, like, ambient listening, background listening- Mm … tools that they have will then produce three pages of notes where it could’ve been five bullet points, and so then there’s an administration, like an- Yeah … administrative burden where that person has to then go in and refine those notes and figure out what’s important and make sure that they’re correct.

So we… Part of Orca’s solution is to make sure that the governance layer and the checks and things are so streamlined and just kind of happen as part of the work.

Kathryn Giudes: Yeah. And so our renal plio- uh, renal trials customer has quite a few doctors in the, in their practice, and the doctors are very adverse to bringing in new tools.

Regardless of whether they’re digital or otherwise, it’s just another thing to have to remember how to do and how to use. But they’re all using Orca because it is saving massive amounts of time. So a, a trials patient asks a question, they have to verify the appropriate response to that on the, in the context of that trial that the, that patient could be on, and that could take them anywhere from 30 to 45 minutes to answer the question, which meant that a 15-minute check-in became an hour visit because it took a long, it took so long to get the question answered.

Or the question would then have to be sent off to the practice manager to answer the question, and then do the follow-up with the patient after they’d left the clinic. So now the doctors, and while they’re talking to the patients or even the nursing staff, the clinicals or nursing staff can just type the question into Orca, and then they get the answer, and it shows exactly where in the trials document that answer came from.

And if it has to reason it across multiple sources, it includes both sources and why it’s, it’s making the recommendation. So the, the person in the, the practice actually is like, “Oh, okay, I know it’s in, you know, this document on that page, and, um, I feel very confident in the answer.” Where prior to that, they were literally in Adobe using the search functionality to, to keyword search to go find the answer, which is why it took so long, ’cause not all keywords were part of the answer.

Paige Harkness: And part of that process when they see that cited source, they actually have to click into it- And acknowledge it … and, and say that they have clicked into the original source. They’re not just taking the AI’s answer as gospel, because then that becomes part of the audit trail. So it’s, yeah, the, the human in the loop is a really critical- It, it’s important, yeah

part of Orca and the way we operate.

Kathryn Giudes: Yeah.

Paige Harkness: So it aligns with the best practice across the world.

Kathryn Giudes: Yeah.

Clare Wood: I love that. So let… This is a, a perfect segue into the key thing I really wanted to talk about today, which is how we can be using tools, and specifically compliance- Mm … to generate more profit in organizations.

Mm. So we might even take a little bit of a, a macro step back just from Orca as well, but actually from a more holistic perspective. Something, Kat, that you spoke about, you said it’s, it’s not just about the safety, it’s actually about the efficiencies. So let’s talk a little bit more about what that means and how can we keep getting faster- Mm

without getting sloppy with those guardrails.

Kathryn Giudes: Yeah. So, Probably a couple ways I can answer this. I’m gonna try not to go down the technical route, ’cause I would’ve given you a, a computer systems design response

Paige Harkness: Answer as though it’s for me.

Kathryn Giudes: Yeah, that’s fair. Um, so if you think about compliance and its history in our culture, compliance was something that good practitioners did by, you know, almost intuition.

They just knew that this was the best way to, to run their business, and they did it because it was tied to a customer or a financial outcome. And then there were bad practitioners in the marketplace, and this goes across any industry vertical, where, um, those bad practitioners started to have really adverse outcomes and, and was so negatively impacting that the government said, “I’ve gotta get involved, and I’m gonna start putting rules around it.”

And what happens when it when a rule is enforced from the outside is it’s almost bolted on to the business. And so governance, risk, and compliance right now is a, a lot of it is still very much attestation-based. So yes, I promise we’ll do it properly. And I promise we’ll train the staff to do it properly.

But that doesn’t mean you’re doing it properly, and it doesn’t mean you’ve trained your staff to do it properly, and it doesn’t mean that you’ve kept your sta- your staff up to date with changes that might happen in contracts or in regulations or in industry standards. So, if you think about compliance as being the foundation to your business to make it run so that it has less risk in it, so I’m not gonna fall on the ba- on the bad side of the regulators so I won’t have a fine or, you know, negative brand impact.

I’m going to provide higher quality outputs for my employees, which is gonna make them happier and, and what make them want to work here and stay working on, on, you know, whatever, whatever part of the business that they’re in. And I’m going to, to take those tedious tasks and reduce s- some a little bit and some massively, and that not only is gonna make the employees feel like they’re more productive, but it’s also going to make cu- their customers feel like that, that they’ve got better, better res- you know, a better care or a better result.

So it’s a cumulative positive impact into the business. And when it, when your governance, risk, and compliance just becomes part of- the, the way you work and how you do it but it doesn’t create friction, that, that’s what the, our underlying goal of Orca is.

Paige Harkness: Yeah, and it, it really starts to elevate your teams to a higher level of performance.

It st- your, your mistakes go down. Mm. Your incidents become lessons. Like, you can learn from all of the things that are happening inside your organization to make it more efficient when you’ve got that governance layer baked into the way you work-

Kathryn Giudes: Mm …

Paige Harkness: not just something that you have to report on. So-

Kathryn Giudes: And you can be more strategic.

So a lot of businesses that are in the old way of working are I like to refer to it as firefighting versus fire pr- prevention. Mm. So they’re, they’re putting fires out everywhere with… So they’re not, they’re context switching. They don’t have a lot of time to, to really think about, “Well, how do I keep this fire from happening in the future?”

It’s like, “No, I gotta put the fire out so I can go put the next fire out.” Versus if they were able to have time that there weren’t as many fires and they spent more of it in fire prevention, then they’re ac- they’re thinking strategically around their role or around delivery or the, the relationships and it means that it, it, everyone’s happier.

Uh, you know, the firefighting c- you can only do that for so long. It’s exhausting. Yeah.

Paige Harkness: Yeah, and it’s, like after the fire, after the incident- Mm … you’re, the regulator comes in and says, “Well, where’s your evidence that you put this fire out correctly?” With Orca, because the incident has been managed through Orca, all of that evidence just exists.

Mm. So it’s not a three-day scramble for somebody to try and collect as much evidence as possible so that they can justify the way that they handled the incident. It’s like, “Oh yeah, here’s the report.” Mm. “We’ve, actually we already emailed you as part of the workflow that was already baked into Orca so it’s all there.”

Yeah. Yeah. And I can see, I guess, putting on my small business hat, I can see why this happens. Because as you sort of touched on there, Kat, it feels like a lot of times that we’re just putting out fires. And I know that’s one of the things I do with my clients is I’m like, “Let’s step back and actually have a look at this holistically.”

Clare Wood: But in the day-to-day of running it, you’re just literally just trying to keep your head above water sometimes, particularly if you’ve got a rapidly growing business. Yeah. So, um, I love, love, love that you’re talking about this and that you have built this solution there. In the day and age of AI, i- is it possible that people can be…

Like, for example, with, with regulators with compliance, would people be able to cover up their mistakes if they, um, were… I saw that smile there. Well,

Kathryn Giudes: pe- people are creative. Yeah. Yeah. But it depends. It, it you can’t go and change a conversation history, right? Yeah. So we do we do have an, a…

It’s not a, a detailed, but we do have a, an audit trail. We don’t allow bad things to happen, so part of our product is called AI Guardian, and it means that not only does it protect against AI-based cybersecurity incidents like, you know, data leakage and impersonation and you know, take, taking over, access to the bank or, you know, whatever.

It, tho- those are legitimate cyber concerns that we block. But we also block against psychosocial issues in the workplace. So the employer can prove that they have, even with AI, that they have a policy and an enforcement in place that doesn’t allow those types of bullying or self-harm or, you know, I wanna go build some crazy bomb and blow people up type stuff.

It, it tamps that down a lot earlier. And because we have the guardrails in front of, you know, the pro- You type in your, your prompt, and then the guar- it hits a guardrail, and then we do a validation pass Alongside with the response. I mean, the if the response comes back and it’s outside of appropriate, what’s defined for appropriate in that context, then AI Guardian blocks it, and this all happens in milliseconds.

So it, it isn’t really even much of a, of a lag in, in getting the response back. So, you can’t hide that. That’s just happening. Everything that you do digitally has a, has a timestamp. Every time you make a change, it, it has a timestamp. It’s c- for that, that right, and you can’t make that go away either.

The… It’s just in the system and, and people don’t have access to the system logs. The system logs are what they are. So, um, and the audit trail’s based off of those system logs. So the… it, it becomes a lot harder, I think, that where people will still try to, to skirt the issue is to say, “Oh, yeah, I didn’t email that to you.

I, I printed it and put it on your desk,” right? So they’ll step outside of, of that system in order to work around it.

Paige Harkness: I also think that people don’t mean to skirt the system, right? Like, normally it’s human error, like we’ve forgotten- Mm … to do something, or we handled something badly in the moment because we kinda just did what we thought was the right thing to do.

Like, we tried to cover something up because we thought we were gonna get in trouble, whereas if there was a standard operating procedure or a policy that was in place that we had access to in the moment, and we could a- actually see how to do it- We’d follow it, right? Like-

Kathryn Giudes: Well, especially if it’s easier.

Clare Wood: Most people would.

Kathryn Giudes: Yeah.

Paige Harkness: Well, we would if it’s easier. Well, we can’t replace resistance.

Kathryn Giudes: Yes. H- human nature i- is what it is, right? You don’t… Every human you come across, you don’t necessarily trust. And what I like to say about AI is it’s neural. It is now- Mm … mirroring how the human works.

But the difference is it doesn’t have ethics and morals, it doesn’t have integrity, um, but it does have self-interest. So you can’t trust every AI you come across either. Mm. And, and so people need to realise that they have to be as discerning if maybe not even a bit more discerning in their use of, of AI than they are in their, in their trust and wanting to transact with people.

Clare Wood: Do you know something you just said there, Kat, that jumped out to me? Because, you know, I had someone on my podcast recently, and she spoke about voice and how when you meet people and you’re watching their body language- I love that one … and there’s a, a whole bu- a great episode. I’ll link to it in the show notes if you didn’t catch it.

But- Yeah … we’re talking about with humans how there’s sometimes, you know, there’s that ick or there’s that… Or alternatively, there’s that feeling you meet people and you go, “There’s som- I, I just trust you. I just feel really safe with you.” Yeah. I know that what you’re saying is, is in integrity.” With a machine, a lot of that gets lost.

Mm-hmm. And so I know when you’re getting something spat out to you like, absolutely this is the way that it is, you’re not looking at someone’s face, you’re not listening for the little nuances in their voice. It’s black and white on a screen. And it almost feels authoritative because it’s, it’s delivered as a fact.

And- Yeah … you know, even when you say- Very confident … “Is that right?” Very confident, absolutely that’s right. Now, without having the human context or the, the experience, particularly in specific fields, I know for me in, in finance and with numbers- … I can look and say, “Hang on. No, no, no, that’s not right because you haven’t discounted for the time value of money.”

And it goes, “Oh, you’re right.” Yeah. But I think that because it’s a machine, we’re losing that, all those other little layers of nuances. So you’ve hit the nail on the head there that we need to be even more discerning of machines- … than we do of other people because- Yeah … we can’t have all those micro cues that perhaps we would if it was a, a human conversation.

So talking- Yeah … for the, for the average small business owner listening to this podcast, because something I’m getting a sense of is that people don’t- I don’t wanna say don’t get it, but people don’t really understand quite how important it i- compliance is when it comes to AI. Mm. And I’m having lots of conversations with people, particular in the money space and even with things like client data.

People are like, “Oh yeah, I did this.” And I’m like, “That’s got personal client information in that. You don’t know. Do you know where that’s going? Have you put any structures around it? Have you got a policy that you’ve communicated to your customers about that?” So can you, uh, you know, we don’t wanna be, uh, ultra-dramatic here, but I, I do want people to really…

Why should this be important to business owners of all levels?

Kathryn Giudes: Yeah. So I’ll answer that question in the context of AI, but this principle applies to all digital information. It, I suppose physical as well. But, AI is… So we talked about the LLM and that large language model. All of the information in that large language model’s called a corpus.

So it’s a, it’s a corpus of information, and think inside that corpus there’s little, you know, think of a- I, I like to think of it as a beehive. You’ve got the little cells of information, and then you can, you know, the neural pathways are how you get back and forth to, you know, to find d- different information.

But that corpus is stored somewhere in the cloud, and the major AI models, I mean, there are countries that, that have them, but the major A- AI models are either out of the United States or they’re out of China. And I, we don’t see as much of the China models in the news, but the reality is, is they’re, they’re as big and as powerful as the US ones.

And so then when you think about digital da- you know, data being stored in the cloud, you need to think about its residence, where it’s being stored at. And the reason I say that is, so if you live in Australia, Australia has a set of federal and then whatever state you’re in, and then whatever local government you’re in, you know, you’ve got this, this set of rules that you have to adhere to to be proper.

Like, the shock to me was that it’s… On the Sunshine Coast, you have to lock your car. That’s actually a law. And I’m like, “Whoa, okay. Well, I do it anyway, but that’s interesting.” And so, but where I came from in the United States, I didn’t have to lock my car. I did it ’cause it was good practice. And, um, data’s the same thing.

So wherever your data’s stored, it has residency, and that jurisdiction has rules. And so in the United States, any data that’s stored in the United States in, in, in a cloud, um, the government has access to via the CLOUD Act. And any US citizen’s data that’s stored abroad, whether it’s a US-based technology or not, the US government has access to.

So that’s how the US has dis- defined data ownership and jurisdiction. China it’s very simple. Any piece of data that enters the Chinese network space, they own, right? You can still own it, but they own it too, and they can do whatever they want to with it. So any, any information that you have entering China, y- legally they can do whatever they want to with it.

So people say, “Well, you know, China broke into my data,” and I’m like, “Well, if you, if you’re, you know, you sent it through the, the, the Chinese-

Paige Harkness: Through your car.

Kathryn Giudes: Yeah, through your car, through Zoom- Through your fridge … through, through, uh, social media services, then, um, they have access to it. You know, what are they doing with the data?

Then you have to start asking the question around self-intr- or interest. So why does the US have a, a rule like that around data and jurisdiction? And, and the answer is because they’re around… That they have removed so many guardrails around commercial so that the US companies can move very quickly in this space, that they’re reserving the right to access data to, to catch up on it later.

And that is their, their commercial policy trying to meet a, a, a, a country whose policies tend to favor the corporation over the individual. China is about, it’s about understanding how the society works and putting systems in place in order for them to be able to read and then respond to behaviors so that they can m- modify and, and get the right result.

And I don’t, I don’t mean that all negatively. I think there’s some, some really positive things around some of the approaches that they’re taking. I also think that there’s a, you know, a massive data… You, you don’t have privacy, and that’s just the reality of living there. Australia’s, data and jurisdi- ju- jurisdiction is really around the privacy of the individual.

So, the, all of our privacy laws are around protecting, you know, a person’s, um, PII, about protecting the government’s secrets and information, and, you know, putting policies in, in place to enforce that. Right now, Australia doesn’t have a rule that says that any data that’s stored here the government owns, but a lot of countries are moving to that model.

So any data that’s stored inside of a country then becomes the ownership of that coun- country as well, so kind of more like the US. And maybe even a, a hybrid depending on, on who it is. So when people are putting their information into a cloud and that cloud isn’t necessarily storing their data in Australia, their data falls under the, the rules of wherever it’s being stored at or being accessed at.

And right now, does it matter? Probably not, but as it, as the technology becomes more sophisticated and governments get, start to get really s- clear on what the data ownership is and how they have rights to use that data, then that’s when we need to start thinking about, well, gosh, I don’t, maybe I don’t want them to look at my data to help create policy or maybe I don’t want them to look at my data to see if I’ve, you know, done if I’ve said anything negative about, you know, my government, right?

You know, I mean, both are legitimate and there’s probably, an unlimited number of, of scenarios it, and it all comes down to, to who’s creating the laws and why they’re doing it. So it was a really long-winded way of saying that your data, where it sits matters, and it’s gonna matter more and more as the technology itself becomes more sophisticated.

Paige Harkness: And as the political climate becomes more and more unstable.

Clare Wood: Mm-hmm.

Paige Harkness: Not to be

Kathryn Giudes: pessimistic-

Paige Harkness: Yeah … but, you know-

Clare Wood: Absolutely …

Kathryn Giudes: crazy things happening

Paige Harkness: in the world. I, I pro- I, I promised you I wouldn’t talk about the, uh, federal budget.

Kathryn Giudes: Politics.

Clare Wood: Yeah. Well, I’ll leave that off this, uh, off this episode, but- … I totally understand what you are saying, and I, I think that this is something that a lot of people…

You know, what, there’s a comment that really infuriates me that I hear and people are, “I’ve got nothing to hide.”

Kathryn Giudes: Mm-hmm.

Clare Wood: Um, and I’m, and then, you know, I, it k- kind of gets under my skin and I sort of explain to people, “Well, here’s why you should, and here’s what information can look like. Here’s how you can be manipulated and controlled-” Mm.

Yeah … “and things that you think are-” inconsequential pieces of information when grouped together can actually have catastrophic effects on individuals and on countries.

Kathryn Giudes: And- Well, ask Pauline Hanson. Stop.

Clare Wood: She had to throw one in there, didn’t she? She

Kathryn Giudes: did. I, I was, I was much more subtle.

Um, but yeah I completely agree. And if you think about Tesla cars collect all of the information about the user in the car, how they drive, what’s happening on the road, license plates, streets, condition of streets. How- I think probably one of the things that’s the most interesting is the behavior of drivers, right?

So you know when you’re on the road and you’re li- and you’re just driving along and you don’t really think about, and then one day you’re like, “Oh my God, I’ve almost been hit three times. I, you know, I’ve almost been rear-ended. I almost rear-ended someone. You know, this person just stopped for no reason.”

There’s days where you’re just like, “Thee, you know, people are just not driving well.” all of those are getting recorded by these cars, and they’re either going to the US or they’re going to, uh, China. And if you wanna talk about how to, you know, propaganda machine and, and how to influence behaviors at a social level, I would think how people are driving on the roads is a- Mm

is a massive indicator of where that, that community psyche is.

Clare Wood: Well, I will- Yeah … as a Tesla owner, I will say at least externally, the information that they say your data is ne- there’s very, very clear big data policies around what they are and aren’t disclosing. I’m still a bit skeptical. I felt very uncomfortable when Grok got rolled out as a function that you cannot turn off-

Kathryn Giudes: Yeah

Clare Wood: in your Tesla. And, sorry, I know we’re going on a segue here, but- No, that’s okay … um, one day I said to Grok- Totally … I said, um, “Oh, knowing what you know about me from the questions it asked, like what’s something that you would say about me?” And it, it basically said, um, “You don’t…” What did it say? Like, “You don’t really let your guard down.

You don’t really share a lot.” Um, you know, like it was basically like coaxing me to share more- information. Yeah. And I found that very interesting.

Kathryn Giudes: Yeah. It is interesting, but what you said at the beginning of the statement is even more interesting. You used the word disclose.

Clare Wood: Yeah.

Kathryn Giudes: There’s a big difference between storing and using and disclosing.

Clare Wood: Mm.

Kathryn Giudes: Disclosing is taking your information and sending it somewhere.

Clare Wood: Mm.

Kathryn Giudes: But having your information and processing it and making inferences from it, that it isn’t against their data pro- policy. Mm,

Clare Wood: yeah. Yeah, absolutely. I mean, I feel like this is a, a massive conversation that we could end up going down a huge rabbit warren with.

Yeah. But I do wanna close out because, you know, we’ve spoken a lot today about medical. I’m curious to know what other industries you think that ORCA might be relevant to. And if you were objectively looking at, various industries, who else do you think should be considering this and might be a good, um, good user of this tool?

Paige Harkness: I’ll just start on that one, because as the person who kind of oversees most of the marketing- Orka is this incredible tool that is relevant to so many different industries. If you- Mm … if you, if you’re highly regulated or if you just want a tool that your AI can, that your an AI tool that your IT team can say yes to, it’s relevant.

So from a marketing perspective, we had to pick a lane, right? Like, we had to pick a vertical. We had to pick a- an ideal customer persona because marketing to everybody just- Doesn’t work … it doesn’t work. So we’ve chosen healthcare because that’s the, those are the customers- Mm … that have the most immediate need for the technology.

Kathryn Giudes: And they’re willing to adopt new technology if it’s gonna save them time- Mm … and it’s gonna make their practice run better. Yeah. Um, financial services is an area that the product was actually modeled off of, so APRA. I figured if we could model the product off of APRA and CASA, so CASA being the aviation then we can do any regulatory framework because those are, those are the two most complex frameworks.

And they’re not just complex in Australia. The- these types of frameworks in other countries are as complex as well ’cause it, there, there’s so much to the systems that they’re regulating. I, you know, I think I sat on a audit risk committee of a s- of a large Australian super fund for quite a few years, and the I think that the the, the product idea was percolating under the, under the scenes while I was there because I was just like, oh my gosh, there’s so much here.

There’s so many different tools. All of them are off the shelf. None of them are really integrated ’cause they don’t allow that, or it’s too hard, or it’s not safe, or there’s a reason, reason for that. And so, um, I think that, you know, getting into, in, into parts of the, the APRA ecosystem, but the big end of town, they, they’ve got their solutions.

They’ve got their teams. They understand what the answer is. But you’ve got that small-to-medium part that is either their supply chain or, or serving a very you know, specific or boutique segment. I think those are, are great for us. But what we say officially is heavily regulated, so think anyone that falls under the security of critical infrastructure or mining and- Defense

defense, then you’ve pretty much- Mm … got all of the heavily regulated verticals.

Clare Wood: Mm-hmm. Interesting.

Kathryn Giudes: Yeah.

Clare Wood: Okay. Well, I’m really aware of time, so we are unfortunately gonna have to wrap up from here. If people are listening along and thinking, “I need this in my organization,” or, “I know people who need this tool,” how can they get in touch?

Paige Harkness: We actually discussed yesterday that we would really love to give your listeners an offer. Oh. So if anybody wants to reach out to us via our website, um, we’ll give you a link to put in the show notes and a coupon code for your customers so that they can have 30 days free trial and a kind of an onboarding session- Yeah

with us.

Clare Wood: Oh, that’s very generous of you. Well, thank you for sharing that. I will definitely pop the link in the show notes for the episode. Kat and Paige, thank you so much for coming along today. You’ve definitely got me thinking, and I’m sure the listeners will be exactly the same.

Kathryn Giudes: Awesome. Awesome. Thank you for having us.

Yeah. Thank you, Clare. This was awesome. Yeah.

Clare Wood: It’s a pleasure. Thanks for coming along.

Thank you so much for tuning in to today’s episode of the Intentional Money Show. If you’re ready for more profit, come find me on Instagram at clare_wood_coach. Thanks so much for tuning into the podcast, and I’ll chat to you again next week

* Transcript created by AI – may contain errors or omissions from original podcast audio