Dr Dani: [00:00:00] Hey Peter. 

Designer Peter: Hi Dani. 

Dr Dani: How are you? 

Designer Peter: I'm fantastic, thanks. How are you? 

Dr Dani: I am good. What are we talking about today? 

Designer Peter: Today, Dani, we are talking about how might we collaborate with AI to accelerate problem solving? Nice. Yeah and I'll go into that in a bit more detail actually. 'cause I think there's more to it than just that simple kind of summing up sentence.

So what we've been talking about, you and I recently is from a organizational community from a country and even as a human species, we've got lots of problems that need solving from the, small and insignificant to let's face it. Huge and almost existential.

Alongside those big problems and small problems, we've got loads of different ways that we could solve them. So we've got lots of methods to help us problem solve. And alongside that, we've got some human strengths and capabilities. So we've got problems, we've got approaches, we've got human evolution has got us this far.

It should be able to get us further. But what we're lacking more and more is [00:01:00] time. And everyone's busy. We're not seeing the progress or outcomes on some of these really important problems. And that's what's led us to this kind of question of, we have this amazing new technology in the form of ai.

So how might we collaborate with it to accelerate problem solving? 

Dr Dani: And this, I know we've been talking about this and I've also been thinking about this from a, as a researcher, from an organizational and problem solving perspective. 'cause there seems to be a lot of talk about AI and everything from ai doom and gloom to AI is gonna be the thing that saves us all.

But not a lot of conversations about like, how do we practically use ai? So to help us with this conversation today, 'cause you and I are not AI experts, although we are fans. 

Designer Peter: Fans and students for sure. Yeah. Fans 

Dr Dani: and students dabbler in ai. So we have a guest with us to help us unpack this conversation.

Dave, do you wanna introduce yourself to our audience? 

Guest Dave Howden: I like that term students of ai. That's quite good. I put [00:02:00] myself in that camp as well. So hello everyone. Dave Howden. I'm the CEO of superhuman. We're AI engineering solutions provider based out of here in wonderful rainy Aucklands today.

A pleasure to be with you guys to help unpack some of this stuff and the crossover between bit of design thinking the natural balance between what humans should do and where should we bring in technology to help us get stuff done. So yeah, looking forward to getting into it.

Awesome. 

Dr Dani: Welcome Dave. 

Designer Peter: Thanks Dave. Welcome. 

Dr Dani: So we usually like to start our conversations with defining some terms and while we've had several conversations on the podcast about ai, we haven't really defined, when we say ai, what do we actually mean? What are we talking about?

Guest Dave Howden: Yeah. Yeah. Oh there is a theme here.

And unfortunately marketing the marketers amongst us. Haven't done an amazing job over the last few big technology revolutions to help us always unpack these big technology shifts. So [00:03:00] ai, like any other technology is multiple parts of multiple disciplines. It's many things, but ultimately from a headline perspective, it's where is the crossover between where technology becomes better at doing something than a human is?

And we've seen that over the past couple of years with the ability for AI to now do human based tasks with a more accurate, more diligent way than a human would naturally. And that started with is it better than a school, age human? Is it better than a college age human? Now we're at that, PhD in your pocket kind of scenario.

So technology blending into the scenario where it can pick up human based tasks. Is that artificial intelligence scenario for reasoning and decision making specifically. And of course, every technology can be considered a force amplifier in a way for humans which isn't considered ai. But we are really speaking about those cognitive tasks where normally human would take a long time or to make [00:04:00] a good or average decision.

We're now seeing the ability to, for AI to sweep in and make better decisions in a fraction of that time. And that goes back to, I think the reason that we're all leaning into this so positively is that with that at our fingertips, how can it advance the challenges we wrestle with as human beings?

And that, that itself is actually even a more fascinating concept for me is that there's always another problem. And once we solve one, we'll need to solve the next one. And I think that's a natural curiosity of human beings and will we solve the problem of never being satisfied. I'm more interested in that one.

'cause we can keep going, right? The more horsepower you give us, the more we use it. 

Designer Peter: I had a feeling that question and definition would get philosophical quite quite early on, David. Yeah. That idea of will, will humans be ever satisfied with, i'm not so sure.

Yeah.

Guest Dave Howden: Yeah, and it's and it's most binary level though. If you look at it from a scientific perspective, the field of AI is around advancing computer science to a point where systems are better [00:05:00] than humans at making decisions. That's the binary edge of it. 

Now you can arguably track this right back to the sixties and seventies when there were certain parts of technology, which were already better than humans are doing things. And in fact, in the nineties, hu the technology was better at playing chess than humans. Humans were. It's just now it's such a scale with such multidisciplinary facts.

So with natural language interface, it's just now more consumable than it's ever been. Because of the amount of compute power we can throw at these things. And that for amplifier across every industry, every demographic, it, it's it's creating this natural and purposeful hype cycle because it's, this has the ability to change everyone and everything.

Dr Dani: And, 

Guest Dave Howden: Yes, there are positives and there's also, we need to manage that on the way through. 'cause change is not always a good thing. But as long as the risk balances the reward, then I feel like it's gonna be a positive thing for human race All. 

Dr Dani: Could we look at some of the, 'cause I [00:06:00] think the other thing that isn't happening very well when we talk about AI is there, it's either there's two camps, there's this AI is this amazing, awesome thing and it's gonna make everything better.

And on the other extreme, it's this doom and gloom and AI's gonna come for your job and humans are gonna end up having nothing to do. And I feel like there's actually lots of room in the middle, but there's not a lot of conversations in the middle. So I'd love to get your thoughts on, how do we talk about it, but what are those things that we need to be mindful of both.

This is why it's good and this is why it's bad. 

Guest Dave Howden: It's like everything, there's a few different ways to skin the cat and a few different ways to apply like that conversational logic. 

I'll do my very best to. Simplify it for the purposes of not making things overly complicated. 'cause actually I'm a simple human being at heart and I look at it in a couple of three distinct ways.

So if you look [00:07:00] at forget technology for a second, and you go up to look at any executive that's in a position of authority within an organization, they tend to get paid to be able to do three things. One is grow in some fashion. So that's either grow revenue or it's growing market share, or it's grow your customer satisfaction, or it's grow, there's an element of growth in an exec's role.

Then there's efficiency with the resources you have at your disposal. How do you efficiently use that to help you grow and do it in a way where you're applying resources that you have to help the mission of an organization. And the third thing is risk. Do all of that in a position where you're not putting yourself, your employees, or the business at risk of.

Appropriate risk, to take the business forward. So if you look at a traditional CEO role, literally what is my job? I've gotta grow the business. I've gotta apply human capital efficiently, and I've gotta do it without being on the front page of the newspaper. That's it. And that's the [00:08:00] commonality that I face when I go and speak to any of the CEO.

With the resources you have at your disposal, how do you grow? Be more efficient and manage risk. Now what tends to happen, and a good 90% of conversations we have with the market is they go, A barrier for growth is access to human capital. I don't have enough people to be able to grow. So when you look at that data point and saying, actually we need more people to be able to grow the business, if a business grows, it has better ability to pay tax dollars.

It's got our ability to employ people, which it can pay staff better, all of these things. And they're screaming out for access to human capital that they can't get. If you now have an option to move some of that human capital into a technology, you can unlock that growth agenda literally overnight.

So it, I don't always look at this as saying, yes, AI may come and do some things that we need, but we are starved of human [00:09:00] capital to help growth of organizations because actually more and more in the age of 2025, what people are really valuing from businesses is that human connection.

But the very small percentage of people's time is spent connecting with customers 'cause they're too busy doing all of the admin, the data processing, the reading legislation and writing policies and stuff, which adds no value to the business. So why am I saying this? If you can unlock the stuff that doesn't add value to the business to stop it growing, and you can move that to technology, you can put your human capital to work to help you grow in a more efficient way, which actually creates more job security.

Creates better value for everyone. And yes, AI will come and take portions of your job. The question I really think is, what skills do you have as a business that allows you to put more percentage of your time at work to meet that new demand where actually AI is doing the stuff that you may get paid to do and you might want to lean into more soft skills, the ability to have a [00:10:00] persuasive conversation, the ability to build empathy with a customer as opposed to just data processing.

So that's a long way of saying, I think there's gonna be a refactoring of what businesses value from their human capital. 

Dr Dani: To 

Guest Dave Howden: help them grow, be more efficient and manage risks. Now that's my glass half full scenario. There is a glass half empty scenario of not every business is in growth mode, that we are in a recessionary time.

There are organizations that are going, look, we need to remove costs from these, from the p and l to be able to to be able to survive and go forward. They're looking to invest in these technologies to help that. And that is a risk. That's not necessarily a risk because of ai, though, that's a risk because we're in a recession and that is caused because of numerous factors, be it tariffs, be it recovery from COVID, be it inflation.

There's numerous dynamics at play. And I do think there is an a want in time for us to, there is a problem, here's the cause for it. [00:11:00] And actually it's very rare that there is a cause and effect scenario here. And when you sprinkle in a bit of AI into that, actually the CEOs are doing the right thing by saying, actually there's technology here that helps me protect the unit of the business, which is the survivability of everybody in the organization.

Its customers. And some people may be may be a victim of that. But this has been no different from any of the technology. Disruption that we've had, be it from telecoms through to social media, through to cloud, through to the internet, through to, now where we're at with ai. So the headline here is, I've not answered your question, but that's my thoughts.

Dr Dani: I think you have answered the question. I wanna go back to something that you said when you're talking about the glass half full scenario of this, which is, I wonder if some of the fear that's coming out of AI coming into the picture is it feels like for so many decades what's been valued are [00:12:00] very technical skills.

So like the hard skills, I don't like the term soft skills, but I know that's how understands it. So for decades and decades, we've been told to invest in. The hard skills to invest in, or if you were, going to university, when I started university, like everybody should go be a programmer and learn coding.

And, but now we're coming to a time where we're seeing actually those hard skills can be done by ai. So now what we really need to do is create humans that have those soft skills in quotations. So I wonder if that, so there is this natural shift happening in now that humans actually add value because they bring these soft skills.

But that's not what we've been training people for the past, I don't know. 

Guest Dave Howden: Yep. 

Dr Dani: 40 years. 

Guest Dave Howden: I really like this. I really like this line of inquiry. There is a side of it, which is frustrating because there's , billions of people re retraining into something that's natural is both challenging, but also [00:13:00] comes with a really interesting lens.

So actually what we're say, what we're seeing. Is that if you can, if there is an opportunity to free people up for what they weren't designed to do, which is we weren't designed to sit behind a computer screen for hours. We weren't designed to do that. We weren't designed to be programmers, right? We were designed to build relationships and grow the population and look after each other.

That's what nature intended for us. It just so happened commerciality came into the mix and saying, actually, you can prosper more if you've got more money and to earn more money. You need to create more value. And that's just got in the way of societal, growth in a way.

But actually what we're seeing is the value is gonna be in those humans that can be more human or what does that mean? It means being really good at building relationships. It means being really good at working in networks, being really good at. Getting in front of people, leveraging your personality, being there to identify problems, solve problems, apply technology, apply experience, all of these things that you don't get at univer.

Sorry, that's not fair. All of these things that you're not taught at [00:14:00] university, you may get it through an experience of being at university, but you are right. It's definitely not rewarding. Those that have sat the, technical architects and, the pieces where they built careers in that doesn't mean there's not a place for them to orchestrate these technologies into place.

And that's good, that's a skill in itself. But look, I'm very quick to have conversations with our customers just to make this kind of on a customer lens perspective for a second and go what is the value proposition of your organization? Because you want, you are, you're engaging us to automate everything.

And actually, we don't believe that you should be automating everything. The value is in the, even the transaction sometimes, you know that, a and I'll, not to make this about different demographics, but you've kinda look at some demographics. They want to speak to a business about their problem.

'cause they want to build a trust relationship and they'll pay a premium for that. So [00:15:00] if you put a high value purchaser into an automated queue where they're gonna speak to an AI agent, where they can't have a negotiation with someone about how bad their day is and the fact that their daughter's going to college and she doesn't know which, which present to buy her or whatever.

What is your value there? Because you're just the same as everybody else. And I'm really liking that thread of actually, what is the value proposition of your business? Where should humans sit? Where is the competitive advantage of leveraging the talent that you have? And what is a universal problem you should be pushing out to, to ai.

I think there is going to be a wash through and it's probably gonna take 15 years for the talent to readjust. 

But who knows where we're gonna be in 15 years time? The acceleration effect has been considerable. I like the scenario though, that humans are gonna be the bottleneck here of the change.

It's not gonna be the technology, it's gonna be how quickly we want to or should embrace these things. 

Dr Dani: But that's also been always the case, right? , With technology implementation, it's really not the technology that, like humans take a lot longer to adapt [00:16:00] and change than technology does.

So that's always been the case. But I think now the technology is just evolving faster and faster. And we're still at the same pace we've been for decades or millennia. 

Guest Dave Howden: Yeah. No, that there is opportunity in that scenario. You look at how long it can take for even a reasonable sized organization, 500 people, $50 million in revenue, it's business, and you walk in, you go, actually, our value proposition here is fundamentally disintermediated by this technology.

We need to pivot. That's a year 18 month change program. You've gotta get everybody on board. You've gotta figure out how you fund it, what happens. You've got a board to deal with. You possibly may be even having investors. You might be approaching being a listed organization. You've gotta deal with all this complexity.

Meanwhile, there is a three person startup that's going, we're gonna accommodate your lunch, who's in market within three weeks? And [00:17:00] you're seeing your current customer base being sold something that is 20% of the cost for, five times the speed of return. 18 months. By the time you've got through your change cycle, you haven't got any customers left.

That is absolutely 100% happening already. You can see it, you can see it happening. And the good news is, we've been here before, we've seen this play out. And I know this is cliche, everyone uses it, but when the internet was first came out, there were those that embraced it and those that didn't, and look what happened.

Yes, there was a resettling, like people didn't value bricks and mortar, but the majority of the market cap of the s and p 500 is in internet based companies. There's, no longer are, brick and mortar, high street stores there, blockbuster gone, the whole taxi industry obliterated just by having the internet available and someone building a smart way to book a ride.

We are gonna see that at very quick light speed with organizations and we're helping a lot of them. So we get to, we get the privilege position to see, traditional consulting [00:18:00] firms being. Spinning some stuff up to disrupt themselves or people that have left these consulting firms, starting businesses to, to disrupt.

It's fascinating to watch from the outside. 

Designer Peter: And from the outside, but also on the inside in terms of leading Edge AI and helping your customers with that technology. I'm really curious about, your what you've learned and where your thinking and knowledge is at the moment.

I'm, what I'm most curious about is what is truly the essence of being human, that even if it could, we ought to protect from ai, if that makes sense. Versus at the end of the scale, what should we absolutely outsource to ai?

And then in the middle, what's, what was that kind of boundary there? 

Guest Dave Howden: We as humans, we get, we do things because we've been either told to do them or it's systemic within a culture or a community driven behavior,

and if you actually lead, strip that back and go out what actually are we good at?

And what will we put here on this planet to do? Yeah. There's a [00:19:00] number of nature that kind of takes over,

so let me play out an example that's not related to ai, but this applies to every little thing in technology. So I can go and break my back on a building site, digging holes with a shovel to build myself shelter.

I'm okay to do that, to build myself some shelter because I need shelter to live. That's all great, right? I, I put myself to work, I explore some calories. I do, I build something that's great. I wasn't built to do that six days a week at an industrial scale to build a hospital or to build for an entire city, right?

I wasn't, that's not what my body was built to do. It was built to face spend for myself, forest, do you know, gather materials and do something that's very human and very primal. So what happens is then is you go, actually we're not designed to do that. Let's get some technology to help us.

So we go and build excavators and hydraulic technology comes along and we realize that we can industrialize this thing and we can do things best of fast faster, stronger with ease of technology. So we weren't designed to dig holes all [00:20:00] day and some technology came along that says, actually don't do that 'cause you weren't designed to do it.

Use a digger instead. And AI is no different. There are things that we are doing today that we do on the daily basis that humans just weren't designed to do, we weren't good at. Reading 200,000 pages of tax legislation to figure out whether we can claim on our Bitcoin, tax advantage or not, right?

Like what human is gonna sit. This is the genuine use case, right? So what the income tax legislation is 2000 pages long just for income tax. What human being can be across all of the detail in 2000 pages of legislation to give good tax advice, no one. Now, that doesn't mean we don't have tax advisors, it just means that they've just read more and have learned it a little bit more, but it's still not appropriate to be across that level of detail as a human being.

We weren't designed to retain that information and recall it in such a way where it's designed with accuracy. It just happens. Computers are [00:21:00] very good at that of reasoning across all the information and giving you the natural relationship between parts and documents and giving you relatively high accuracy context like what's going on in those documents.

Now that exists everywhere from, look at all of the jobs that exist in things like risk and compliance. Understanding anti-money laundering complexities and figuring out what your tax obligations are and working out whether your casino is building its gambling flaws to legislation and figuring out whether your policies conform to WorkSafe code.

There are literally thousands of scenarios where we have human beings trying to figure stuff out and we're just, it's just not appropriate because of how we're built. We can only read so quickly. We can only type so fast. But why I'm telling, why I'm going through this written rule of kind of all these use cases is like digging holes.

We are looking at a whole world of work that \ people get paid to do, which isn't appropriate for [00:22:00] humans to be actually doing if you look at what we're designed to do. And that is what I categorize as where is the appropriate application of ai, which is remove that tedious work that we weren't designed to do, to free that human mind up to do something that is better for their mental health, better value for their organization, free them up to be more human.

That has to be a a good outcome. Yeah. A lot of times people do these jobs 'cause they need the money. That's why people work. They're not doing it for fun. Like again, they don't want to be reading 2000 pages of income tax legislation. They're doing it 'cause they have to make bank to be able to survive.

If you can free them from that and actually, to do different things that are valuable in that space then than great. And it's not always economically viable, isn't it? There's not, everybody's gonna get the luxury of, a free day of not being able to do work. But, maybe that's my purest view of going how do we, to the tagline of superhuman, how do we free smart minds from tedious work?

It's what we, it's what we do, and that is the [00:23:00] application of the technology as we see it. Anything outside of that where we believe we're automating things that are distinctly human, we tend to have a bit of an adverse of a reaction to 

Designer Peter: Yeah. 

Guest Dave Howden: Which is like call center inbound, things like that.

Outbound sales calling it all gives a little bit of a sour taste in my mouth. I'm like, like it's, I genuinely like building relationships with people and doing really good business, solving really hard problems. I don't wanna do that. I don't wanna have a transaction with the technology.

It's not really how I wanna operate. Yeah. It doesn't mean there's not value in it. It's just a case of, we have gone through a technology period of the last 20 years of us all becoming distinctly unhuman about how we interact with each other. That's leading to. Significant levels of loneliness within quite a lot of people, which, leading to teenagers that are just, and not necessarily a bad way 'cause they're building communities in a different way, but how we grew up is not how the generations grow up today.

And we've yet to see the ad, we yet to see what the consequences of [00:24:00] that are, be it positive or negative. So I'm not trying to solve for a problem that we've not necessarily identified yet, but ultimately, being wedded to our phones for the last 20 years has seen some quite significant increases in mental health numbers, suicide numbers going up in teenagers, all kinds of things that seem pretty harmless on the surface of it.

And yeah, we just don't want to go down that road of applying technology where we think it's a human activity. Yeah. Yeah. 

Dr Dani: There's two things there I wanna unpack. So first, you first is going back to your point, when you were talking about the different use cases of ai, you're right, humans were not designed to read 2000 pages of tax legislation.

But I think the thing that we don't talk about though is yeah, we can get AI , to read all of that, to give us some, the summary view, if you will. What I think gets lost in that though is we still need a human that has a basic understanding of tax law to be able to still do the advising.

[00:25:00] 'cause I don't think what we're aiming for is we're gonna replace all attorneys with chat GPT, and now we're all gonna get our legal advice from ai. Sometimes in the conversation that's how it's being talked about, right? All the attorneys jobs are gonna go away 'cause AI's just gonna do that for us.

And I play around with AI a lot and I feed it content that I know really well, like my subject matter expertise. And I know that sometimes when it says things, it's not interpreting it in the way that we do in the real world because there is this like context or nuance that AI just doesn't have. So I think that's where the expertise becomes important be being able to look at what AI spits out and goes, hang on, is that actually applicable in this scenario?

Yeah. So I think that's a bit that we don't talk about enough. 

Guest Dave Howden: So that, that very, that's very good. So we refer to this as consumer [00:26:00] grade intelligence and then industrial strength and. Using consumer grade products or industrial application. That is a buyer beware scenario. Because of the exact reason that you mentioned is that the context has not been engineered into that intelligence to remove the risk, the purview of risk.

And I'll give you an example of that. If you're gonna ask a legal question to a consumer grade AI service, which will be, give you a magical answer, it will, and it's gonna be close to accurate. But if you hadactually, just think about what's happened there. So you are a US citizen in, at this part of the world.

How does it know whether the applicable law is the US state law or the New Zealand law? But it doesn't 'cause you're not told it. So therefore it's gonna give you an assumed answer based on things that it's made an assumption of. And actually it's core premises are you dealing with something from the US or are you dealing with something where you're in New Zealand?

Now when you look at an [00:27:00] industrial application of that, we're industrializing the context into the ai. So everything's pre-con contextualized.

And that's ultimately why solution providers like us exist. It's, we're taking the same capabilities, the same underlying horsepower, but we're providing an industrial context, which reduces failure rate and increases, increases accuracy.

And we will be seeing 100% in your scenario where there is, it becomes the prompter's problem to give the context. And we're actually not very good at giving context because we assume we're talking to someone that knows what we're talking about. And actually we're not, we're talking to a computer, it's not a technology fault, it's a bad application.

If you drive your, if you're driving your ferra, your Ferrari through a field, you will get stuck. It's just the wrong use of the thing at the time of the thing.

But that said, when you do apply an industrial context and you remove that problem from the prompter and you make things very easy to be able to consume, you actually see a power shift occur is yes, there is going to [00:28:00] be low value tasks that you can ask AI for.

There's gonna be services come out there's ones in law already, that you can essentially can do the job of a junior solicitor or a junior lawyer. But what I really like about this is it's going to put a massive value on craft. Like I love this scenario. If you look, if you go back a hundred years, possibly longer, I don't know, my timings are gonna be out.

All shoes were handmade, all of them. There was no machinery. You pay a premium, now you'll pay $2,000 for a pair of handmade leather shoes, which a hundred years ago, everything was a handmade leather shoe. And yet we are gonna see this shift of, or where is the craft. And I think in law is an example.

Four tricky legal cases where you're gonna want to have an excellent presenter in front of a court to be able to influence a jury, to be able to pick up the nuances of personality and read a room. You are absolutely gonna want the human there doing their best thing, which is influencing until all the courts get made AG [00:29:00] agentic, and then the whole thing goes outta the way.

But, you know what I'm saying? There's gonna be that craft. And it's the same, it's the same we see with all of our clients is that we are humans in a room trying to grow a business, be more efficient and manage the risk profile of that. And we're all here because we all get paid and we're trying to create a value, and technology is gonna help us get there, and it's gonna help us be more, more competitive.

But I, to headline that though, I would go look. Yeah. The public headline of a little bit of AI misuse, not through the fault of the user, is gonna lead to some headlines which are gonna slow us all down. That's just gonna happen like it has with pretty much every technology. 

I'm, I can see that Peter's been frantically writing down my analogies. I'm not too sure any of these are very good, but, keep going. No 

Designer Peter: they're good. They're good. I love a good metaphor and analogy. As I said, Dani and listeners know I'm gonna struggle to have one takeaway at the end of this.

Dani, you had something else or will you let me did you have something else you wanted? Oh, so there's a couple of interesting, I'm not sure they're paradoxes, but they're just interesting things for [00:30:00] our brains to consider like this idea of a human strength. David is relationship building.

It's like you say, reading the remain phones thing as well, but coming back to relationship building and I think one of the things you're saying is a human. Need a human skill is to essentially build the relationship with the AI to, understand what it's capable of, what it's not capable of, and then start to consider, what's an appropriate and useful prompt that's gonna give you the information or the result that you're looking for.

I just think that's quite kinda mind expanding. I had a question around, and I think it's maybe not a question or it sounds like what you're saying is old Parkinson's law of the work filling up, the time available. So I think there's a, I guess I'll call it a risk that there's a scenario where we use AI to do the tedious dull jobs.

That that we've been doing. And that frees up our time and instead of using that time to be more human or do more human things, then we will go looking for more [00:31:00] tedious, repetitive jobs.

Guest Dave Howden: No. I think I think no. I think, let me pick up what you're throwing out. Yeah. Because I said, I actually, I had this conversation with Hazel. She's my partner long suffering for the last six years. Yeah. She would consider that she would consider me tedious work. I'm pretty sure.

And I said, this was literally this morning as I was in the bathroom, I said, you know what, like I can get through so much work now. 

Designer Peter: Yeah. 

Guest Dave Howden: But because of the role that I'm in. Yeah. I don't, this isn't unique to me. I, but I'm a, tech CEO founder. I'm driven to be busy to keep going.

Is that what would've taken me a week now? Takes me a couple of hours. I just do more of the two hour things all of the time. I can just service more customers. I can get more proposals out. I can attend more meetings. I can, yeah. I can do more stuff, but the market is so large for our organization that I'm not short of things to come in and fill.

I just, I can just service more of it. Yeah. That is unique in, in a sense, to those roles that carry [00:32:00] that I can only do what I can do and if I can do more, then it probably just keeps getting full. 

Designer Peter: Yeah. 

Guest Dave Howden: But if you look at other roles across organizations, I do think there's a really big leadership moment here for those execs who are in control of the growth agenda and the risk agenda and the efficiency approach within a business to say, how do I stop or manage that process to go work?

Yes. There is a cost out opportunity if I can have the same people doing, if I can have half the people doing the same amount of work, that's a different approach. But how do I put that to work to meet the mission of the business in a way which isn't shortsighted.

And shortsightedness can come from an immediacy of a cost challenge or a pressure point, but actually if you think about things like a finance team, they're normally paid to do accounts payable, accounts receivable, make sure everyone's invoice on time.

Yeah. But there's always projects that they would never, ever get time to do, which are massively valuable to a business. And we're doing a good [00:33:00] handful of projects now that are on revenue leakage. There's just finance are doing their job, invoices are going out, but it's coming in. But what's going on to the invoices is missing.

This thing's just missing off an invoice because there's a downstream process that's missed. How your team apply that. How do you stop a revenue leakage issue for a business? That's 'cause it's business that's been one, it's work that's been delivered. It's just you not billing your customer for it.

Right now, these projects never get done because the humans are there doing what they need to get done. And there's never any more humans to do the work. But if you can free up a whole body's worth of capacity to go and hoover up another $10 million of missed revenue, that feels like a good application of time.

Designer Peter: Yeah. 

Guest Dave Howden: As opposed to a shortsighted view, which is I can get rid of another headcount. 

Designer Peter: Yes. Yeah. Yeah. 

Guest Dave Howden: So there, there is a very strong leadership moment I think, of people saying, let's just pump the brakes a little bit here.

How do I actually create value for my employer and therefore value for myself and get to the stuff that was never got to, which is high value and needs to be done.

And, not just [00:34:00] rip out roles from the payroll, which of course there's always a time and a place for that. Like we, I've been in business long enough to know at some points you have to have a hard conversation and make a call. That's life. It's, that's the merry-go-round of employment.

However, there's absolutely no point in throwing the baby out with the bath water and then need to rehire again. And you lose talent and you demotivate your whole team by making a shortsighted decision for a cost saving exercise. So I think, Peter, to your point do we fill ourselves up being busy with other work?

I think at that point, once you're starting to see capacity being pushed back into the business, it's a leadership moment to go, okay, great. How do we use that to, to drive the growth agenda or to de-risk ourselves as opposed to just worry about efficiency? And that's it's unfortunate that every conversation that you see in the media around AI is about efficiency and productivity.

Yeah. That is literally half the story. The government are talking about going for growth in their agenda, like growth and efficiency don't always correlate. They [00:35:00] just don't always correlate. And, I just you need smart minds to be able to look at opportunities and be able to think deeply and go, okay, who do I need to lobby to get into this position and what do I need to do here?

It's not all about ringing the neck of every employee to get maximum productivity hours out of everybody. But again, maybe I'm too much of a purist when it comes to some of these things. 

Dr Dani: The reason for that is, historically organizations have operated in a very short term thinking approach to things. I agree with you. I think it is a leadership moment because the answer to everything. Over a certain number of decades has been reducing the workforce, right? Oh, times are bad. Let's reduce the workforce. Oh, times are good. Let's see how we can make it better by cutting the workforce.

There is this history of short term wins sacrificing long-term growth. So the shift now, the same way that we talked earlier [00:36:00] about the shift needs to be, we need to start valuing the soft skills. This is another thing where we have to start looking at longer term outcomes because AI is gonna give us the capacity to do that.

Guest Dave Howden: A hundred percent. Yep. One, 100%. There's also a, oh, an oddly it seems to kinda get brushed under the carpet, but if you look at that revenue leakage comment that I made the reason that AI is now. Being applied to some things that would've never got done before is it's it's almost the acceptance that revenue leakage in the business is okay.

As in oh yeah, no we know we're not getting stuff on invoices. Okay, so why is that? Yeah. Okay. We're missing stuff. We're missing billing customers because the process is so tedious that we miss stuff in the data gathering, and therefore we miss stuff on the invoices. Okay, so why are you missing stuff in your billing cycles?

Oh, because we haven't got enough human resources. Okay, so hang on. So if we solve the revenue leakage problem, you think you're le [00:37:00] leading $10 million a year, you can't afford a headcount to solve a $10 million problem. That's not an AI issue. That's just common sense. And we're uncovering these things time and time again, and I'm, this is a genuine issue like this, these numbers are numbers from a conversation that we're having right now.

Obviously, I won't name names in that situation, but. So you're engaging an AI firm to have a conversation about revenue leakage when you can solve the problem with someone that's gonna cost you a full-time equivalent. Yeah. And why we're having this conversation again what? And that, that narrative 100% exists.

So it's, there's this promise of a mythical unicorn pill that you can take, which is called ai that's gonna solve your world's problems. And we will be the first to say, what problem are we actually solving and shining the mirror up to the organization saying, you don't need to be spending a truckload of money with us.

And you can keep someone employed and do this, which is a great outcome because it's immediate. You can get someone on it tomorrow and, and away we go. So I think it's a [00:38:00] definite scenario of what problem I'm actually trying to solve. And who's been accountable for creating that problem in the first place?

'cause the shortsighted decisioning of let's cut people out. The business in times of bad are actually leading to this organization having a 10 million revenue hole because of mis missed billings. 

Dr Dani: This example illustrates, lots of the conversations even, Peter and I are having with organizations where the solution every to every problem now seems to be ai.

And then you sit down and you talk about the problem. This is not an AI problem. This is like a, you need to get your people to, it's another problem. That's 

Guest Dave Howden: right. That's right. And with a design thinking hat on, when we go through a, we don't tag it as design thinking, but the methodology is very akin to that, which is we're defining where the opportunity and the problem is.

Then we're figuring out what the next iteration of that looks like. Be it people, process, or technology. And then we're deciding to prototype in that environment and great, let's test it with a new process. Let's test it with a bit of technology. Let's push that out and let's figure out what then the human impact or the customer [00:39:00] impact is.

And then we redefine it again. We're like did that work? Did it not work? And then the cycle kind of begins. It's not always, oh, it's rarely a technology only play. There's always the people, process and tech to consider as part of that. Yeah. And a lot of the time it's weighted into people and process that it is the tech.

Yeah. Sounds 

Designer Peter: familiar.

Dr Dani: Just in the interest of time I wanna shift this to, so we've been really unpacking what AI is and different ways that organizations are applying it. Some of the good, some of the bad, and how we need to think about it in a very sensible way.

'cause it's not gonna be the solution to everything. And what we've been really curious about and the theme of this episode is around this idea of, if we start thinking about AI as a collaborator, what might that look like?

Guest Dave Howden: I definitely think we're going to have these moments where different demographics and possibly different age groups of [00:40:00] technical literacy, or AI literacy, or whatever you wanna call it, are going to look at that companionship slash augmentation slash collaboration moment and co-creation moment differently.

And an example of a customer that, I won't name them, but a company we're working with right now is a live customer with us. Their sales team are using our technologies to be able to grow, which is how do we find the right lead? What looks like the per the right perfect customer for us, the ideal customer?

How do we find that person and how do we get in front of them, have a conversation with 'em to book an appointment to sell something perfect. A guy that's come in who's 30 years old, who's jumped onto our technology, he's four times more efficient than someone that's been there for 30 years. And he's come in, got to it, understood the tech, and is driving it for his own personal gain and to create a real good name for himself in the organization.

Now, if you look at what that actually means psychologically, not that I'm a psychologist, but I'll unpack it for [00:41:00] myself. There is a tool at the salesperson's fingertips that has give them an, gives them an opportunity to be four times more productive, which means retiring four times their quota, which is how they earn money, yet they won't embrace it because it's not how they used to do it.

But in a world of sales, the quota is the number. You are literally, that's what you're there to execute. And there's something in the way there. Which is I'm showing you a pathway to be able to do your job at 25% less horse, less effort than you've needed before to get the same result, or you do the same effort and you get four times the return, yet you won't click the button.

That is gonna be a fascinating journey as we go through and who actually met, who actually gets competitive advantage as an employee at that point.

To circle back in terms of the notion of what, collaborating with ai, I think as soon as you can start to show a pathway to positivity for the individual 

You're gonna see people lean [00:42:00] into it. And I do think Gen Z millennials are just used to leveraging technology for the purposes of getting their work done, whereas actually I think into the more once you're pushing to the 50, 60 years old, I just think it's just not a natural place for a percentage of that demographic. They grew up in a analog world. And every generation prior to that was either a blended, I was a blend. I was analog until I was like 12, 13. And then, the internet came around when I was 12 years old. And you're like, yeah, shit, let's go. That's great. But my, my neuroplasticity was allowing me to figure that out. And that's not always the case for every, everybody else. That said though, the individuals with the checkbooks, the ones that are writing the business cases and the ones that are writing the big checks to allow this technology to come into a business aren't the Gen Zs, they're the CEOs and the board members who are more akin to the ones that aren't actually collaborating with this technology naturally.

You know that [00:43:00] it's gonna be a fascinating time and I. Do give kudos to those that made AI naturally interactable. What OpenAI did when they launched chat, GPT was essentially democratized how people interact with something that wasn't amazingly smart, but it was still pretty cool.

And to be able to naturally converse with this through voice and typing and have that magic appear on the screen, even though it wasn't wholly useful in a business context, was like, okay, cool. Now the minimum barrier to entry is the ability to speak. That's amazing.

You don't even have to learn how to turn a computer on. You just literally have to write into this thing and and away you go. So I think that natural collaboration's gonna come quicker than we think. But I do think there'll be an element of psychology to it, of do people want to lean into it even though there's the power.

The power is there. 

Dr Dani: When you were talking about that, it brought to mind. Very start of my career, one of my first projects that I led was when banks were moving lending [00:44:00] applications. So home loan applications, they used to all be done in paper and then get in, put into the system.

And they were moving to it all happening digitally. And it shows up on this platform. And then as the underwriter, you process it all online. And when that was being rolled out, what we found is the people that started their career a year before, or six months before this was rolled out, they got it, they picked it up, they barely needed any training.

The people that were doing this for 20, 30 years. We're the ones that needed a lot of training that we got the most resistance from. These are the ones that we're figuring out the work around as well. I'll just print this out, do it on paper, and then put it into the system, which was taking like times longer. Not to mention the waste of paper. So I think there is, to your point about the millennials and the and Gen Z, I think it's naturally easier. I don't know what the workplace was like before the [00:45:00] internet or email, because by the time I showed up that was it.

So I think there is a, what you're used to, and like humans, we're not, we do not do change well generally speaking. So I think the learning curve is higher for those that have done it a different way, because now you're having to learn it, whereas if you're at the start of your career and this stuff is just coming out.

You don't know any better, so that's a shorter curve. I think what we do need to be thinking about to your point is how do we make it like, and part of the reason that people are resisting it, I think, is because there is this fear, like, where is this going? Am I adopting something that's gonna make me irrelevant?

So some of this and one of the reasons that we naturally don't adopt change, even when it's good for us, is because we have this natural fear towards change, which is part of our evolutionary biology and part of what keeps us alive. So it is somewhat a good thing. So the amplified headlines of the doom and gloom of AI is making us [00:46:00] more fearful of collaborating with it.

Guest Dave Howden: I like the fact that you brought up that your first experience with the workplace was essentially a digitally transformed one already. I liked this. Next piece, and I use this in conversation, quite a lot of going what does the workplace look like for talent attraction in 18 months time?

If the role that you are expecting people to come in to facilitate inside your organization as a high percentage of rote tasks, which doesn't exist in other opportunities that individual has got. So therefore you end up in this natural spiral of, you don't get young, hungry talent because they're used to a highly automated, efficient work environment where they can flex their muscles.

That talent doesn't come to you. Talent does come to you who's used to working in a laggard late majority environment.

So therefore, your talent attraction drops. Your ability to innovate drops because the talent's not in the business to drive innovation. And you essentially [00:47:00] end up in a downward spiral.

Designer Peter: Yeah. 

Guest Dave Howden: Which lives in a world of technology mediocrity, because you can't attract the talent to start the change. And I can see how that can happen in large organizations where they're just a revolving door of talent, and the bigger these organizations are, the harder it is to to step out of that cycle.

Yeah. But that creates opportunity for everybody else to to lean and help them out,

Designer Peter: Which then in, in turn, going back to your the scenario where one business is attracting the the more adaptable talent and the other isn't, then it's a two-way spiral, isn't it?

At the same time as one's down Yeah. Spiraling down. The other one's spiraling up yeah, and all because those tasks are there that's pretty interesting, Dave. I've not thought about put at that level of detail. Yeah. Yeah. 

Guest Dave Howden: But I think that's gonna create this natural, and we've seen this throughout the history.

I think the, what is that? I'll make up some numbers, but they'll hopefully be close to what I can recall. I think the average lifespan of a business is around, I think between 15 and 25 years, I think is the average lifespan before they either get acquired or something happens.

I think we're naturally gonna see a compression in [00:48:00] those things. I think new businesses that start are gonna create such a niche so quickly, they're gonna be, become acquirable very fast to the traditional laggards because they're not we can't move quickly enough. We have to buy someone. Yeah.

I think that's naturally gonna happen. And then the alternate reality of that is that those laggard businesses just can't survive because of. The amount of innovation that's going on in that sector. If you think about this in terms of what happened in the.com generation that happened over a 20 year period, really it was a long time to adopt the internet.

We're still arguably not even there yet. And yet, if you turn round in a matter of six months and said your insurance policy that currently costs you $500 a year, I, you can now do that for $50 a year. Because we're a AI only internet only insurer. You could in one year of re of [00:49:00] renewals of an insurance market, you can take out an insurance company 100%.

You can take 'em out 'cause they can't innovate quickly enough. You can't take a bricks and mortar insurance paper based organization and turn it into an AI driven internet only insurer in a year's time. And that year is your only defensible moat because that's when your renewals are gonna happen. We are gonna see that happen time and time again, where actually, the speed at which someone can come in and undercut someone is going to be at price point.

And price point isn't everything. It's just gonna be so rapid and there's not enough gas in the tank or time available for these large organizations to try and be nimble. And the only opportunity is acquisition ultimately to stop the threat. 

Dr Dani: We've gotten a preview of that with fintechs with traditional banks, right?

Yeah, a lot of them got acquired because the traditional banks can't innovate fast enough, so their only way to stay relevant is to acquire, is acquire. [00:50:00] I'm not saying that's working really well, I'm just saying that it gives us a preview of. Of the pattern? 

Guest Dave Howden: We're also we're seeing that directly in the AI field, though.

If you look at apple are making a bid for perplexity, which again, the, turning, they're turning like 6 billion of revenue. It's still a decent number, but nowhere near what they're optimized. 

Scale AI just got a 51% investment from Meta, which is essentially an acquihire.

These the Magnificent seven are doing a very good job of finding those that are going with green shoots of success in AI and going, yep, I'll take you guys. Which essentially stops them being the next Google or the next meta, or the next, the next big cap. Yeah. But, I don't blame these guys.

These founders are walking away with billions of dollars after starting a business three years ago. You know what? They're in business for a re reason. But, there is a significant holding of wealth in those balance sheets of the big mega caps that allows them to just go, yep, cool.

I'll buy that for 10 billion, buy that 400 billion, no problem. And have a lot of influence over, who holds the [00:51:00] power cards going forward.

Dr Dani: Absolutely. 

Guest Dave Howden: That's probably a whole other podcast in itself. Yeah. Yeah. 

Dr Dani: So one of the things that we like to offer on this podcast is also we love unpacking the problem, but it's also providing some tangible ways of particularly around this concept of how do we collaborate better with a ai.

For those that want to use AI in problem solving and think of it more as a collaborator, a partner, what are some ways that people can get started? 

Guest Dave Howden: We say this a lot at Superhuman. We say that multiple things can be true at the same time.

There's not one way to do ai. There's multiple ways to do it. And we would advocate for straightaway, if you are a business, is that justify $20 a month and give everybody access to either Microsoft co-pilot or chat gpt for teams. Lock it down so you've got a good element of control over it, which is, that's why you pay the money. So you've got some control.

And at that point you're in a safe [00:52:00] space, you know that your data's not leaking outside an environment. You've got a work play to really push this technology quite hard. Yes, it's not an industrial strength application, but it does get your team starting to really start to think about, hang on a minute, the, these two documents that I used to spend an hour comparing, I can now do that in Lightspeed and, prepping for a meeting, doing transcripts, all these things, they just become.

Asked as opposed to work, which is great. And that's a great place to, to really start. That is level one getting going and play playing around. Outside of that, I would say look, just be really curious about that question. What we were designed to do and what your value proposition is as a business.

And that doesn't necessarily mean thinking about getting started with using ai, but as a employee, and I'm coming at this from an employee lens as opposed to a citizen lens, is that you know your role, you know what you're accountable for, [00:53:00] and no one better than you knows the trials and tribulations you go through on a daily basis just to meet your obligations as an employer.

Oh, sorry, as an employee. 

So therefore start to be that ideas person, start to be that individual that's going, Hey, there is a better way to do this. And be that advocate for a little bit of change, not the change, not the big ma monolithic one, but hey, look, we can just have better outcomes if we go down this road and this road might be a tiny little process change.

Or it might be, look, we are just gonna do this in a slightly different way using some of these tools, which aren't a big investment to the business. And just start that ball rolling. And why I advocate for not just picking up a tool and using it, but starting to think about what you do as a human. And what you shouldn't be doing is firstly unlocks value.

Because if you do that over a lot of people, that's quite powerful over, over time. But secondly, it becomes a really fascinating breeding ground for just ideation and that [00:54:00] whole scenario of if we could do we've never been able to do this before, but now we can. That starts to get people excited.

It means that just momentum builds. And the biggest weight for I've seen to drive change is when the whole of the ground floor is onboard from the get go. 'cause they're normally the ones that get consulted last. It's normally a top down. We're doing this thing and everyone has to last to the party.

And it's amazing how those small flaps of ideas can actually iterate through once management get get wind of it. Yeah. The final point on that is, or what does a good relationship look like with your employer look like when there's a lot of change coming up and I can guarantee you this now as an employer of a considerable number of people, i'm constantly looking for people that, in the organization that really get what we do, they want to be here, and they've got capacity to go on the journey with us. And that means they're not just doing their job, but they're thinking about how we can deliver better for our customers and do a better job.

And I as we go through the next five years, your employer is gonna be [00:55:00] screaming out for people that aren't just there collecting a paycheck, whether they're here to help you go through this navigation. And you'd be surprised how few of those people actually exist. And therefore I think there's opportunity for those that are curious and prepared to start coming up with the ideas and and leaning in.

But start off with, yeah. Start off with something small and yeah, get curious, get playing. Yeah, that's the one I every, everything else starts costing a little bit too much money. So start off smaller and then go from there. 

Designer Peter: Love it. 

Dr Dani: Love that. 

Designer Peter: That is a, an awesome recipe, Dave. Love it.

Guest Dave Howden: we are definitely the first to say we've not got everything. Yeah. If you, when you look at your recipe's a good one. We talk about the recipe for a successful AI approach and we think about it very much as that which is the power of AI plus your world, which is your in intellectual property and knowledge does start to equal some magic.

Yeah. We talk about that, that a lot. If people start to get that in their minds, then yeah. Just get into it. Love it.

Dr Dani: I love talking about that as magic. 'cause I mentioned earlier, like I dabble with [00:56:00] ai particularly feeding it some of my research and getting it to do things that. Or getting it to see it or look at it, or processes in different ways that would take the human brain so much longer to do or picking up on patterns that we just don't. Sometimes it's not always accurate or Right. But it does do things that get you curious and get you questioning. And so I think there is a little bit of, I love framing it as AI and your IP can create some magic.

'cause there's there's huge potential there. 

Guest Dave Howden: Yeah. Yeah. The consumer grade entry point AI is just, it's really good at getting the juices going. It's industrializing, it becomes the next leap. And it's how you take one thing and make it distribution ready to a whole company.

And but without the idea, there is nothing.

Dr Dani: Yeah. 

Guest Dave Howden: So where do we start? Let's start with the idea,

and the [00:57:00] idea comes from pain, it comes from problem, it comes from opportunity, it comes from creative thinking. And that. So like I said, start at the beginning, go to the middle, and then go to the end. 

Dr Dani: Love it. 

Guest Dave Howden: Wow. 

Dr Dani: Thank you, Dave.

This has been a fantastic conversation so as we're wrapping up, what we usually like to do is we each share something we're taking away from the conversation. So as our guest, you get to go first.

Guest Dave Howden: Yeah. Okay, cool. I, much of my key takeaway is not one related to AI and tech, but I think your observation, Dani, is, you started in a workplace where, you were, the change had already happened. That was your starting line. I was probably a tiny bit before that, but actually no, you're probably right.

I was main digital when I started in the workplace. And I do think the key takeaway from me from this session is we have to have empathy that every part of an organization's on a different timeline of the journey [00:58:00] about what's gonna be accepted and what's gonna be not. And a one size fits all approach to change management with these things isn't always going to be a successful outcome from Gen Z through to, people entering into retirement years.

There's all that, but there's value as all parts of that chain. And we have to take everybody on the journey to create the change that we need to with this stuff. That was more of a reflection for me because I hadn't thought about it that way. So yeah, thank you for sharing that.

That nugget with me. 

Dr Dani: I love that. This is why as much as we talk about, we shouldn't be labeling generations, one of the reasons we label generations is because. What defines you as a generation are the things that were happening in the world that has now shaped you. So for millennials, most of us entered the work.

Actually, I think all of us entered the workforce after it's been digitized. Which means we had a very different experience than boomers did, or Gen X did. So that still has a relevancy in why we talk about it. I don't like the fact like, we shouldn't be labeling, oh, all boomers. Hate [00:59:00] technology because that's not the case. But those things are still relevant. 

Guest Dave Howden: Yep. Yep. What about you, Peter? What's what's your takeaway? 

Designer Peter: What's my takeaway? Trying to narrow it down it's this idea that actually AI is a technology that can help us be human. Is it be more human or be human again in a number of different ways. And it maybe it starts with , that kind of fundamental question of what are we designed for? What are we here for? And that could be, in, in a big W what is life ought for?

What is the point of being human in a very positive and optimistic way. Or it could be in an organization. So what is your role in an organization? And if AI can do so much, then actually use that as a creative departure point for what is our purpose as an organization, as individuals?

And then, what is our value proposition? Yeah, I think it's the, so rather than seeing AI as this, and I think it's a lot of the kind of the hype and the headlines are to blame as this, existential threat to people's jobs, [01:00:00] actually. AI can be, and another human created tool that helps us.

Realize who we are, what we're here for and, move forward to a better future. In that way. 

Guest Dave Howden: There's so many threads that come off that narrative of if that's the case, and AI does put a lot of time back in everyone's days because, in agentic world where everything's take taken care off for us.

What does that mean for the current societal norms that we have? Yeah. Around it's good to work because it gives you p purpose and it's good to work 'cause you're connecting with other human beings and universal basic income and all these things which kind of flow off the back of, actually if if robots are building houses, who, what happens to all the builders?

Like there is a, and it's just because the money's turning. Just that people don't want to work. Like it's, there is something quite animalistic about going, yeah, you know what, I wanna put my skills out there.

But [01:01:00] again, that is a whole other two hour deep dive session, which, that's not for right now, but I look at that 'cause this isn't slowing down.

And right now AI is good, but we're not angen future yet. We've not got to a GI where everything is, considered interwoven. There's a whole lot of technical substrate to go and infrastructure to be deployed to get to that point. And we may even get there, but if we do, what does that actually, what does that actually mean?

That, that's where no one knows the answer to that. The casting the future is not written yet. So we're just figuring that one out. So everything's just more an opinion than a, from an expert view. But yes, it's why I'm in the game,

Designer Peter: yeah. Nice.

How about you, Dani? What what's your takeaway? 

Dr Dani: Yeah. I also just wanted to comment. Something that we have to remember through all of this is that humans also have a fundamental need to be useful. So that is why work is important. Like you said, we, we have a need to, want to go to work, and there's lots of good aspects of that.

And perhaps what needs to shift is how we think about our work. My takeaway is, and this comes back to what [01:02:00] you said earlier, Dave, which was like, invest in the $20 a month for your employees. And get them to play with it. Every other technology that has been rolled out in organizations, it had to be done in a way, top down. The decisions was made the requirements were defined. This is probably the first time in history where we could actually drive technology change from the ground up at scale. Yes. It democratizes the process of technology change in that every. Human that works in an organization can have access to this technology and propose ideas and bring their ideas to improve it creates a different contribution model, and a whole playground of and probably rethinking and reshaping how we do technology change in organizations. 

Guest Dave Howden: I'm trying to find an elegant way to explain this, but this the politics of that democratization get in the way at larger organizations very quickly.

That's been [01:03:00] our experience because there has genuinely been an objectivity, loss of sight about what people are there to do in their role. And just because AI is such a kind of. Interesting topic. Right now everybody wants to be the chief AI officer and know I'm doing this thing and that happens is more prolific in larger enterprises and actually the ground up scenario just gets, is likely to get drowned out by corporate enterprise politics. And it'll just get stifled out, which actually I think creates a whole nother paradigm, which is people will just leave.

They'll just go 'cause they'll go where they can be. They again, who wants to work in an organization where they can see that their competitors are not doing the tedious work and where, those things those reputations move pretty, pretty quickly. And the humans will get in the way I believe in enterprise.

Dr Dani: Totally agree and this brings back to another thing that you said, which is it's a leadership moment. Because I think the responsibility is on leaders to recognize [01:04:00] this potential and go, how do I. Not let this get drowned out in my organization. And I think 

Designer Peter: yeah, 

Dr Dani: that's gonna be a key success factor there.

Designer Peter: Good stuff. Love it. 

Dr Dani: Thank you Dave for joining us. This has been such a great conversation. Thank we, thank you for making the time and thank you everyone for tuning into this episode. 

Designer Peter: Yeah. Thanks everyone. Thank you Dave. Thanks 

Dr Dani: everyone. Thanks Dave. 

Designer Peter: Pleasure. Bye.