2996: Human-Centric AI Innovation: Transforming the Modern Workforce

[00:00:01] [SPEAKER_00]: Are you curious about the future of working in an AI-driven world?

[00:00:07] [SPEAKER_00]: Well, according to PWC's 2024 Global CEO Survey, 69% of CEOs anticipate that AI will

[00:00:15] [SPEAKER_00]: require most of their workforce to develop new skills.

[00:00:19] [SPEAKER_00]: But what does this mean for both the employees and the organisations alike?

[00:00:24] [SPEAKER_00]: Today I'm joined by Ash Gorthall, Chief Academy Officer at a company called 1010 and he's

[00:00:31] [SPEAKER_00]: going to be sharing his insights on how AI can be harnessed to drive human-centric innovation.

[00:00:38] [SPEAKER_00]: And I want to explore how AI can augment human capabilities, the importance of comprehensive

[00:00:43] [SPEAKER_00]: training programs ensuring nobody gets left behind and also why demystifying AI is crucial

[00:00:50] [SPEAKER_00]: for organisational success.

[00:00:52] [SPEAKER_00]: Yes.

[00:00:53] [SPEAKER_00]: So, big question.

[00:00:54] [SPEAKER_00]: How can we prepare our workforce to thrive in this evolving landscape, bring people in

[00:01:00] [SPEAKER_00]: from a variety of backgrounds into the tech industry?

[00:01:03] [SPEAKER_00]: These are just a few of the things we're going to discuss today.

[00:01:06] [SPEAKER_00]: Reaching listeners in 165 countries every day is testament to the unwavering support

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[00:02:05] [SPEAKER_00]: That's kiteworks.com to get started today.

[00:02:08] [SPEAKER_00]: Now is the moment you've really been waiting for.

[00:02:11] [SPEAKER_00]: It's time to get today's guests on.

[00:02:13] [SPEAKER_00]: So buckle up and hold on tight as I beam your ears all the way to York here in the UK

[00:02:19] [SPEAKER_00]: where Ash is waiting to join me.

[00:02:24] [SPEAKER_00]: So a massive warm welcome to the show, Ash.

[00:02:27] [SPEAKER_00]: Can you tell everyone listening a little bit about who you are and what you do?

[00:02:31] [SPEAKER_01]: Hi Neil.

[00:02:32] [SPEAKER_01]: Well thanks very much.

[00:02:32] [SPEAKER_01]: It's an absolute pleasure to be invited along.

[00:02:34] [SPEAKER_01]: I'm Ash.

[00:02:35] [SPEAKER_01]: I'm the Chief Academy Officer at 1010.

[00:02:38] [SPEAKER_01]: Within 1010, I work within our 1010 Academy essentially, which is all around bringing in

[00:02:44] [SPEAKER_01]: technologists of the future, attracting them, selecting them, trading them, supporting them,

[00:02:49] [SPEAKER_01]: steering them through roles through the roles which they're going to be best at.

[00:02:52] [SPEAKER_01]: But doing that without necessarily having the technical experience or what you might

[00:02:56] [SPEAKER_01]: consider to be a traditional technical background essentially.

[00:03:00] [SPEAKER_01]: And that's pretty much what I do most of the time today.

[00:03:03] [SPEAKER_01]: We've been running it now for 11 years.

[00:03:05] [SPEAKER_01]: So it's fair to say that over the years we've

[00:03:07] [SPEAKER_01]: changed it quite a lot from what it was in the time we started.

[00:03:11] [SPEAKER_00]: And that's one of the reasons I invited you on the podcast today because I think there's so much

[00:03:15] [SPEAKER_00]: hype around technology, around AI, cybersecurity and so many different fields.

[00:03:20] [SPEAKER_00]: And there's a real shortage of people in these industries and a lot of people from a variety

[00:03:25] [SPEAKER_00]: of different backgrounds almost rule themselves out saying, I'm not techie enough or I'm not

[00:03:30] [SPEAKER_00]: this, I don't fit that.

[00:03:31] [SPEAKER_00]: But the reality is it's a more human centric, the human skills that I really

[00:03:36] [SPEAKER_00]: needed in this industry and also diverse thinking.

[00:03:39] [SPEAKER_00]: We're going to solve complex problems we need people thinking differently and from

[00:03:43] [SPEAKER_00]: diverse backgrounds to solve those problems.

[00:03:45] [SPEAKER_00]: So can you begin by setting the scene for our conversation today by just explaining

[00:03:50] [SPEAKER_00]: the concept of a more human centric innovation in the context of AI and how it can augment

[00:03:56] [SPEAKER_00]: rather than replace these human capabilities which are our biggest strengths, right?

[00:04:01] [SPEAKER_00]: Yeah, yeah, sure.

[00:04:01] [SPEAKER_01]: So I think human centric innovation kind of boils down to a few key points essentially.

[00:04:07] [SPEAKER_01]: Firstly, I think one of the most important things is around transparency and accountability.

[00:04:13] [SPEAKER_01]: So what that means to me is that when organizations create and use AIs, they need

[00:04:18] [SPEAKER_01]: to be very clear on when the AI is being used, how it works, who's responsible for it.

[00:04:24] [SPEAKER_01]: So ultimately comes down to trust.

[00:04:26] [SPEAKER_01]: I mean, there's a great example I can think of where legislation was passed in the state

[00:04:29] [SPEAKER_01]: of New York where AI tools were being used in a great deal by recruitment firms.

[00:04:35] [SPEAKER_01]: And so essentially they were using AI tools to screen people's CVs and resumes,

[00:04:41] [SPEAKER_01]: but also going away to look at their social media posts on the internet.

[00:04:45] [SPEAKER_01]: And off the back of that essentially determine whether individuals would move forward to the next

[00:04:49] [SPEAKER_01]: round. Now obviously that's quite disturbing because one, they won't be clear about that's

[00:04:55] [SPEAKER_01]: what they're actually doing and what these tools are doing.

[00:04:57] [SPEAKER_01]: Secondly, the AI vendors themselves often aren't very transparent around exactly how that tool works,

[00:05:04] [SPEAKER_01]: what data uses, how it comes up with those outcomes.

[00:05:08] [SPEAKER_01]: And so off the back of that they ended up in a situation where they're actually legislating

[00:05:12] [SPEAKER_01]: that an individual now by law if they are putting their resume forward for a role,

[00:05:19] [SPEAKER_01]: the recruiter has to say whether they're using an AI tool, give description of it,

[00:05:23] [SPEAKER_01]: description of how it works. Or crucially the individual has to have the right to be able to say

[00:05:30] [SPEAKER_01]: I don't want my CV to be screened using an AI tool, I would like it to be screened

[00:05:35] [SPEAKER_01]: using a human being. Thank you very much because it's actually as you mentioned there,

[00:05:39] [SPEAKER_01]: you've got a lot of historically marginalized groups and other biases that can creep in

[00:05:43] [SPEAKER_01]: and to have individuals removed from that often very important decisions in their lives

[00:05:50] [SPEAKER_01]: without human being involved is quite troubling. I think the other point is around

[00:05:57] [SPEAKER_01]: treated with security and privacy. You need to understand how that data is being used and for

[00:06:02] [SPEAKER_01]: how well it's been used and for what purpose. And I think as well from a security point of view,

[00:06:07] [SPEAKER_01]: if it's just limited to something that you create that's quite sort of boxed, let's say

[00:06:12] [SPEAKER_01]: using chat GPT to generate a paragraph of text, for example, as long as the text reads fine,

[00:06:19] [SPEAKER_01]: that's okay or if it's generating an image, for example. But if you're using these tools to create

[00:06:24] [SPEAKER_01]: code, for example, there are concerns around whether one weather that could contain any sort

[00:06:30] [SPEAKER_01]: of malicious code which could have some sort of exploit in there from a vulnerability point of

[00:06:35] [SPEAKER_01]: view or even potentially if organizations are uploading developers are uploading source code into

[00:06:41] [SPEAKER_01]: the cloud getting a generative AI tool to be able to rewrite it, for example, or refactor it,

[00:06:46] [SPEAKER_01]: your IP and source code then becomes the training data of tomorrow's model. And once you've put it

[00:06:52] [SPEAKER_01]: up there, you can't get it back. So there's IP considerations and security considerations.

[00:06:57] [SPEAKER_01]: I think maybe the last point, and I think this is by far the hardest ones,

[00:07:01] [SPEAKER_01]: is sort of where it comes from and its purpose really. And this is quite a sort of aspirational

[00:07:06] [SPEAKER_01]: aim, I guess. But if it's done with people for people developing AI apps for the good and

[00:07:13] [SPEAKER_01]: sort of the benefit of a community, which is understood by created by deployed managed by people,

[00:07:20] [SPEAKER_01]: then I think that's a very, very important point. What sadly, I think if we look at humanity as a

[00:07:28] [SPEAKER_01]: whole and kind of look back on what we've done in history, you would I think it's fair to say

[00:07:32] [SPEAKER_01]: that we don't have a great track record of making human centric choices for the right reasons,

[00:07:39] [SPEAKER_01]: essentially. I think there is a risk of it being used to maximize short-term profit, for example,

[00:07:47] [SPEAKER_01]: by replacing junior people within an organization who traditionally have joined that organization

[00:07:54] [SPEAKER_01]: and done a lot of the legwork and a lot of sort of cut their teeth in that industry through doing

[00:07:58] [SPEAKER_01]: that often drudge admin sort of basic legwork to replace those individuals with AI and then

[00:08:05] [SPEAKER_01]: have a management tier, an experience tier that's able to leverage that AI to be able to achieve

[00:08:11] [SPEAKER_01]: a similar level of effectiveness to having a team of juniors beneath them. Yeah, I think at that

[00:08:16] [SPEAKER_01]: point we end up then sacrificing the future of industries for short-term profit essentially,

[00:08:22] [SPEAKER_01]: which is clearly the wrong thing to do. But whether that will end up actually coming

[00:08:27] [SPEAKER_01]: to be a thing or not, I think largely depends on an organizational basis and also

[00:08:33] [SPEAKER_01]: I think whether it can be legislated against us in the case of New York.

[00:08:37] [SPEAKER_00]: It's such an important point you raised there. I've been fortunate to go to a lot of tech

[00:08:41] [SPEAKER_00]: conferences around the world and predictably every single one of them is a vendor on stage

[00:08:45] [SPEAKER_00]: saying AI is a co-pilot saying all the right things. It might replace roles, but it won't

[00:08:51] [SPEAKER_00]: replace people and employees will be liberated freed from doing those repetitive mundane tasks

[00:08:56] [SPEAKER_00]: and do more meaningful work. But of course that only works if employers don't leave staff behind

[00:09:02] [SPEAKER_00]: and invest in re-skilling rather than going for that short-term profit as you just mentioned.

[00:09:07] [SPEAKER_00]: And according to I think it was a PWC Global CEO survey this year reported that 69% of global

[00:09:15] [SPEAKER_00]: CEOs anticipate the need for their workforce to develop new skills due to AI. So how is

[00:09:21] [SPEAKER_00]: 1010 addressing this need with your training programs and what are you seeing any trends you're

[00:09:27] [SPEAKER_01]: seeing around this? Yeah, yeah sure. So when we sort of first started putting this together and

[00:09:33] [SPEAKER_01]: first started seeing it, what we realized quite quickly is that you can talk, well,

[00:09:37] [SPEAKER_01]: Peewee just talked about AI and it's a very, very broad terms which encompasses a very broad

[00:09:43] [SPEAKER_01]: range of disciplines but also a very, very level of skill levels essentially. And after doing a

[00:09:49] [SPEAKER_01]: lot of sort of headscratching, what we kind of came up with is essentially we kind of group this

[00:09:54] [SPEAKER_01]: into three tiers if you like in terms of the skill sets and what they're used for and how

[00:10:01] [SPEAKER_01]: experienced and how much expertise in different disciplines people need to do them. So at the

[00:10:06] [SPEAKER_01]: very bottom we have this kind of level wall essentially, this sort of AI awareness that

[00:10:10] [SPEAKER_01]: says really around what AI can do, the restrictions of it, the considerations around data around

[00:10:18] [SPEAKER_01]: what happens to that data and really around what it can actually do. Now for me this isn't so much,

[00:10:27] [SPEAKER_01]: we do teach this to everybody in our business actually but for me this kind of thing needs

[00:10:31] [SPEAKER_01]: to go much wider than that Neil. It needs to be at some sort of government level essentially

[00:10:36] [SPEAKER_01]: when the kind of things that we're talking about are things which can have a huge impact

[00:10:42] [SPEAKER_01]: at a very high sort of population level. There are certain sacred cows that we sort of

[00:10:48] [SPEAKER_01]: taken for granted for decades that just are no longer true essentially. So for example,

[00:10:54] [SPEAKER_01]: if we take something like the judicial system for a long time, if you had video evidence of

[00:10:59] [SPEAKER_01]: actually somebody committing a crime when you're catching them red-handed or you had

[00:11:03] [SPEAKER_01]: an audio recording of somebody admitting to that crime then that was submissible as evidence

[00:11:08] [SPEAKER_01]: and that was kind of as good as it got. The fact that these things can now be created

[00:11:14] [SPEAKER_01]: and can be spoofed could have a huge impact and everybody needs to be aware of that and what

[00:11:19] [SPEAKER_01]: it's capable of because otherwise if people see things on social media and that can be a huge

[00:11:24] [SPEAKER_01]: problem if they see somebody saying something they just assume that that person really said

[00:11:28] [SPEAKER_01]: that but that's kind of level war and that's sort of the grounding if you like. We're then

[00:11:32] [SPEAKER_01]: going to the middle tier. So this is essentially about augmenting existing workflow and the roles that

[00:11:39] [SPEAKER_01]: people do with AI tools but also crucially the ability to be able to evaluate and differentiate

[00:11:48] [SPEAKER_01]: between different tools because as you mentioned there, you go to a lot of vendor conferences

[00:11:54] [SPEAKER_01]: and every vendor is talking about their latest AI tool. I think sometimes you've got to take

[00:11:59] [SPEAKER_01]: that with a pinch of salt. I think there are a lot of tools that are badged as AI but actually

[00:12:02] [SPEAKER_01]: aren't under the bonnet essentially they're more sort of traditional sort of rules-based

[00:12:07] [SPEAKER_01]: software and not really and also they need to be able to understand how to bring it into

[00:12:13] [SPEAKER_01]: their daily work and use it within the constraints of data privacy, security to make

[00:12:19] [SPEAKER_01]: sure that they don't put themselves in danger or the organization that they're working for

[00:12:24] [SPEAKER_01]: in danger essentially. So that's about how to do what they do the different roles that they do

[00:12:30] [SPEAKER_01]: within the organization but how to use existing and future AI tools to be able to augment that.

[00:12:35] [SPEAKER_01]: We then kind of have a third tier if you like which is all about individuals who are able to

[00:12:41] [SPEAKER_01]: implement new AI tools and models. Now obviously not everybody does this, everybody in the

[00:12:48] [SPEAKER_01]: business does that sort of middle tier but as top tier is for individuals that throughout the training

[00:12:53] [SPEAKER_01]: have shown an aptitude for those sort of very strong technical aspects and have an interest in

[00:13:00] [SPEAKER_01]: this and sort of want to take it to the next level essentially. So to some extent all three tiers of

[00:13:06] [SPEAKER_01]: those individuals are working in AI but there's a very, very different level of expertise required

[00:13:12] [SPEAKER_01]: and a very, very different use case in terms of what they're actually doing with it.

[00:13:16] [SPEAKER_00]: And if we have anybody listening in the corporate space maybe they have transferable

[00:13:21] [SPEAKER_00]: skills wanting to reskill and enter a tech career or someone just starting out at the beginning of their

[00:13:27] [SPEAKER_00]: career thinking about entering a tech career. What are they foundational AI knowledge and

[00:13:31] [SPEAKER_00]: specialized pathways does 1010's training program include because it feels like there's

[00:13:36] [SPEAKER_00]: a lot of opportunities here so I'd love to hear more about your programs and how they cater

[00:13:41] [SPEAKER_00]: to employees at a variety of different skill levels because we're not just talking about

[00:13:45] [SPEAKER_00]: techies here are we we're bringing more people into the industry.

[00:13:47] [SPEAKER_01]: Well absolutely there's a great phrase I don't know who came up with this but essentially

[00:13:51] [SPEAKER_01]: not all roles in tech require tech and in my mind there's a lot of the sliding scale between

[00:14:00] [SPEAKER_01]: a lot of those business spacing to softer skills if you like and a lot of the

[00:14:05] [SPEAKER_01]: extended to be the more hard technical skills and everything every role exists on a

[00:14:10] [SPEAKER_01]: spectrum essentially with a variety of different levels of those two things involved.

[00:14:15] [SPEAKER_01]: And you mentioned about people cross training and coming into the industry I think lots of people

[00:14:19] [SPEAKER_01]: when they want to get into tech they're very much aware of the things that they don't know but

[00:14:23] [SPEAKER_01]: they often forget a lot of the important and good skills that they have expertise in that

[00:14:28] [SPEAKER_01]: they kind of rid with them particularly on soft skills side. But essentially our training

[00:14:32] [SPEAKER_01]: it starts at the most basic level we don't mandate any academic qualifications from a

[00:14:39] [SPEAKER_01]: tech degree or any degree perspective individuals have to pass our own assessments

[00:14:45] [SPEAKER_01]: our own technical assessments which gauge technical aptitude but don't assume any prior

[00:14:50] [SPEAKER_01]: tech experience that allows us to determine people's aptitude for it and really it's

[00:14:55] [SPEAKER_01]: about them demonstrating towards their passion for tech and their desire to want to get into

[00:15:00] [SPEAKER_01]: the industry and also assess around a lot of the soft skills which is so so important in

[00:15:06] [SPEAKER_01]: modern collaborative tech roles. In terms of the actual training we don't assume any prior

[00:15:12] [SPEAKER_01]: technology we start from the very very basics and that builds over a number of months essentially

[00:15:19] [SPEAKER_01]: in terms of complexity in terms of the scope of what we do and I think part of this then is

[00:15:25] [SPEAKER_01]: really around helping people identify what they're good at or what they like because there

[00:15:29] [SPEAKER_01]: are so many different roles in tech and people find a home where they're much better at

[00:15:34] [SPEAKER_01]: some roles than others. It turns to some of that foundational AI knowledge. One question that we

[00:15:40] [SPEAKER_01]: kind of struggled with a little bit at the start was just how do you approach that? How do you sort

[00:15:45] [SPEAKER_01]: of get into that AI training? Now traditionally the approach is that you take a bottom up

[00:15:52] [SPEAKER_01]: approach so you start off with individuals who are at school who enjoy maths they do a GCSE in

[00:16:00] [SPEAKER_01]: it they're good at it they do an A level in it then maybe go do a degree in it or either pure maths

[00:16:05] [SPEAKER_01]: or in some sort of aligned subject like engineering or something similar and they teach all the

[00:16:11] [SPEAKER_01]: basics if you like the fundamental mathematical building blocks of AI machine learning so probability

[00:16:16] [SPEAKER_01]: statistics linear algebra calculus all that good stuff and then that's built on top of until

[00:16:22] [SPEAKER_01]: you get to a point where you're able to understand a deep and fundamental levels of

[00:16:28] [SPEAKER_01]: how machine learning and AI works that does work but that takes a long long time and you lose a lot

[00:16:34] [SPEAKER_01]: of people along the way and from my own experience I was one of those individuals where I did an engineering

[00:16:39] [SPEAKER_01]: degree electronic engineering degree I always wanted to work in that field but I hated pure

[00:16:44] [SPEAKER_01]: maths and so I just had to sort of put the blinkers on essentially it just say this is a

[00:16:48] [SPEAKER_01]: means to an end and stick it out. There are a lot of individuals and lots of people who don't

[00:16:53] [SPEAKER_01]: have that endpoint in their mind and that end game so they just do maths at school it bores them rigid

[00:16:59] [SPEAKER_01]: to be honest and then they're gone and very hard to get them back in again so we kind of took

[00:17:05] [SPEAKER_01]: an alternative approach which is more sort of top down essentially um so you show people what it can

[00:17:11] [SPEAKER_01]: do you show people the end game if you like you show sort of the fun stuff from what it

[00:17:17] [SPEAKER_01]: can achieve and then you kind of go down that tree to a certain level and you go down as

[00:17:22] [SPEAKER_01]: far as people need to know it so maybe a good example here is possible in that with just a few

[00:17:28] [SPEAKER_01]: lines of Python code you can build a neural network and a sort of form of generative AI again the

[00:17:35] [SPEAKER_01]: generative serial network that's able to do quite cool things it's able let's say one example it's

[00:17:42] [SPEAKER_01]: able to generate hand-drawn numerals between zero and nine so you start off and you're viewing

[00:17:48] [SPEAKER_01]: the output of it as this thing's being trained and at the start it just looks like an old

[00:17:52] [SPEAKER_01]: detuned analog TV if you know what I mean it's static essentially black and white static

[00:17:57] [SPEAKER_01]: and then as it's trained over the course of minutes or hours it actually um you start to see

[00:18:05] [SPEAKER_01]: patterns within the output and then after a while you get to a point where it's actually

[00:18:09] [SPEAKER_01]: producing every time recognizable digits between zero and nine for example so that's that's cool

[00:18:16] [SPEAKER_01]: that's impressive it's got a low overhead people are seeing that running on their laptop

[00:18:23] [SPEAKER_01]: so it kind of makes them feel like I did that that that's something that I've done and they

[00:18:27] [SPEAKER_01]: don't understand the intricacies of it but they're aware of the fact that they were able to do

[00:18:31] [SPEAKER_01]: something that resulted in that which a lot more to you is much more accessible than having

[00:18:36] [SPEAKER_01]: somebody spend it five six seven years study a math to get to a point where they could start

[00:18:42] [SPEAKER_01]: so I thought that grounding of having somebody building something and seeing something is really

[00:18:48] [SPEAKER_01]: really fundamental important to to building confidence in terms of ultimately what roles

[00:18:54] [SPEAKER_01]: people end up doing as I mentioned they'll end up doing a role which is the role that they do

[00:19:00] [SPEAKER_01]: but it is augmented by AI a little bit now but more and more in the future

[00:19:05] [SPEAKER_01]: or we have people who are involved specifically in roles around AI and ML so whether that's

[00:19:11] [SPEAKER_01]: choosing models training data deploying those models managing them or working on the data side

[00:19:17] [SPEAKER_01]: around trying to understand the scope of data how long it can be used for I mean when you kind of

[00:19:23] [SPEAKER_01]: look at it you know one analogy that springs to mind I think of that you remember there were

[00:19:27] [SPEAKER_01]: adverts a few years ago where they had a picture of a fighter jet there with the RAF pilot next

[00:19:32] [SPEAKER_01]: to and then the next scene cut to the back there was a team of 50 around the aircraft

[00:19:37] [SPEAKER_01]: and then those were sort of the supporting crew if you like that pilot couldn't fly without those

[00:19:42] [SPEAKER_01]: guys and I think it's very very much the same in a lot of these industries you think of one

[00:19:47] [SPEAKER_01]: particular role but in actual fact it's supported by a lot of people doing a lot of different things

[00:19:52] [SPEAKER_00]: I remember that advert it's such a powerful message and one of the things I love about what

[00:19:57] [SPEAKER_00]: you're doing here is are you're helping people yes gain those technical skills to get that

[00:20:01] [SPEAKER_00]: advantage but also the soft skills to succeed so can you tell me a tell me a bit more about that and

[00:20:07] [SPEAKER_00]: how 1010 equips employees with both those technical and soft skills so they can succeed in this AI

[00:20:14] [SPEAKER_00]: driven world because it's not just tech it is a combination of both yeah I saw so I think

[00:20:20] [SPEAKER_01]: it comes down really to what we found in experience as it comes down to practice

[00:20:26] [SPEAKER_01]: it's not just about knowledge you can tell somebody something you can teach them it

[00:20:31] [SPEAKER_01]: but then actually but the practice is actually about them applying it in a situation that's

[00:20:38] [SPEAKER_01]: just does this real well that you can possibly make it allowing people the freedom to make mistakes

[00:20:43] [SPEAKER_01]: in that sort of environment when it's when it's not really when it does account and in fact

[00:20:48] [SPEAKER_01]: proactively encourage them to make mistakes and see how quickly they can do because that's

[00:20:52] [SPEAKER_01]: one of the best ways the best ways to learn essentially I mean one question in the industry

[00:20:58] [SPEAKER_01]: I think a lot that there's a lot out there is there's a lot of organizations that offer

[00:21:02] [SPEAKER_01]: sort of certifications and certificates in particular levels and particular tools and

[00:21:07] [SPEAKER_01]: particular technologies and I think a lot of people think to themselves well if I

[00:21:13] [SPEAKER_01]: study for that exam and get that certification then that will enable me to do the job

[00:21:18] [SPEAKER_01]: I think it kind of works the other way around if you have the skills and experience

[00:21:23] [SPEAKER_01]: then you could probably sit the exam tomorrow and pass it in many cases

[00:21:27] [SPEAKER_01]: but it doesn't necessarily work the other way around there's a world of difference between

[00:21:31] [SPEAKER_01]: studying for an exam and actually being able to do it in practice so so much of what we try

[00:21:38] [SPEAKER_01]: and do in training is to make it practical and frankly to make it to make it not work

[00:21:42] [SPEAKER_01]: you come across these environments where things are so contrived and they really work

[00:21:48] [SPEAKER_01]: nicely and you just follow one step after the other and it all works perfectly

[00:21:52] [SPEAKER_01]: and then they come across a scenario in the real world where they get to step one and you can't

[00:21:56] [SPEAKER_01]: go to step two because it fails for some reason those problem-solving techniques

[00:22:01] [SPEAKER_01]: having given people a broad grounding across the different tech disciplines in terms of how

[00:22:06] [SPEAKER_01]: they solve it is fundamentally important in terms of what they're doing and it also allows

[00:22:11] [SPEAKER_01]: to build confidence as well and a lot of this is also around getting them to be able to communicate,

[00:22:18] [SPEAKER_01]: to be able to collaborate lots of people don't feel very confident and being able to stand up

[00:22:22] [SPEAKER_01]: in front of a group and present their ideas but when you do that in a group and you do it in a

[00:22:27] [SPEAKER_01]: group that they're familiar with a cohort that they get to know you're able to sort of help

[00:22:32] [SPEAKER_01]: people with that to bring them out of their shell and to allow them to do that in a

[00:22:36] [SPEAKER_01]: safe environment and build the confidence. Maybe one good example just to mention

[00:22:43] [SPEAKER_01]: so much of this as well the industry is around being able to navigate your way through

[00:22:47] [SPEAKER_01]: different teams and different organizations and they have

[00:22:50] [SPEAKER_01]: offering different biases, different views, different things that frankly keep them awake at

[00:22:56] [SPEAKER_01]: night and so if you're able to get an insight into that at some level then that will definitely

[00:23:02] [SPEAKER_01]: help you when you can land so as part of that we have areas of the training where we sort of role

[00:23:07] [SPEAKER_01]: play the different roles that people will find or likely to find when they land on a project

[00:23:12] [SPEAKER_01]: to their tech company and what makes us people tick, their roles and responsibilities, what their

[00:23:18] [SPEAKER_01]: fears are, what their concerns are and pretend to be those different individuals.

[00:23:22] [SPEAKER_01]: Yeah another example where when we're doing a part around the business analysis side and

[00:23:27] [SPEAKER_01]: requirements, solicitations or working out what it is that a system needs to do

[00:23:31] [SPEAKER_01]: will have one stakeholder come in and they'll say well this system needs to do X, Y, Z, A,

[00:23:37] [SPEAKER_01]: B, C which you know is notably everything's captured and that's all good and then

[00:23:41] [SPEAKER_01]: in the afternoon we'll have somebody else drop in another stakeholder and say

[00:23:45] [SPEAKER_01]: don't listen to that person this morning they're an absolute idiot. What the system

[00:23:49] [SPEAKER_01]: actually needs to do is 1, 2, 3, 4, 5, 6 and so then that's practical that's real world

[00:23:55] [SPEAKER_01]: and how do you kind of understand that and sort of move forward with that and that's

[00:23:59] [SPEAKER_01]: so much of part of it really when we built this we cut out all of us had experience ourselves

[00:24:05] [SPEAKER_01]: in delivering projects in tech and so we're really said to ourselves what can this

[00:24:12] [SPEAKER_01]: training look like that doesn't teach you the stuff that you learn in university

[00:24:16] [SPEAKER_01]: in the way that you learn at a university, how can we sort of distill that time to its essence

[00:24:20] [SPEAKER_00]: to give people the stuff that they really need. Absolutely love that and another thing I just

[00:24:26] [SPEAKER_00]: to expand on that I'd love to find out more about how your approach to hands-on training

[00:24:32] [SPEAKER_00]: helps employees evaluate pilot and test tools or AI tools safely ensuring that they can implement

[00:24:39] [SPEAKER_00]: AI tools without compromising things like privacy and security because responsibility

[00:24:45] [SPEAKER_00]: around this tech is something that is incredibly important and something that's

[00:24:50] [SPEAKER_01]: important to you when teaching your students too. Yeah absolutely I mean I guess it's really

[00:24:55] [SPEAKER_01]: around the methodology and the approach that's really important to this and so to that is around

[00:25:03] [SPEAKER_01]: understanding how you implement at any new technology I guess across a variety of different

[00:25:09] [SPEAKER_01]: variables across the tool itself across people across processes and really that teaching that

[00:25:15] [SPEAKER_01]: approach of you start small with something, you build confidence with it, you demonstrate the value

[00:25:22] [SPEAKER_01]: in a low risk environment, you don't sort of bet the farm on this thing, you demonstrate it early,

[00:25:29] [SPEAKER_01]: you demonstrate it often, you sort of win hearts and minds and then once you've done that you've

[00:25:34] [SPEAKER_01]: then kind of won the right to be able to then take it forward and to be able to spin another

[00:25:39] [SPEAKER_01]: pilot or to be able to be able to make it larger than that and I think one also would

[00:25:45] [SPEAKER_01]: very important point is not to just focus on and as technologists this is often the case to just

[00:25:50] [SPEAKER_01]: focus on what it will do sort of what the functional sort of aspects of this are in terms of what value

[00:25:56] [SPEAKER_01]: will it bring, what will it look like, what will it create. Just to the point earlier around the

[00:26:00] [SPEAKER_01]: annual fighter jet there, you're having to implement this within an environment where there

[00:26:06] [SPEAKER_01]: will be teams of people involved whose job it is is to look after privacy, security

[00:26:13] [SPEAKER_01]: and if you rock up there having explained to a number of senior stakeholders and covenants then

[00:26:18] [SPEAKER_01]: that this is amazing and it's going to be brilliant and let's say right we just need to put this into

[00:26:22] [SPEAKER_01]: life and we haven't thought about any of the considerations around privacy or data security

[00:26:27] [SPEAKER_01]: that obviously isn't going well so it's about also getting teams and individuals on board early

[00:26:34] [SPEAKER_01]: on in that process who are responsible for those things outside of what you might consider

[00:26:38] [SPEAKER_01]: to be ultimately what the tool is doing and if you do all those things and then essentially you've

[00:26:45] [SPEAKER_01]: got a much better chance of buy-in and it's succeeding and severely limiting the damage you can do.

[00:26:52] [SPEAKER_00]: 100% with you on that and something that I know as you're also passionate about is emphasizing

[00:26:57] [SPEAKER_00]: the importance of demystifying AI by understanding its basis in mathematical algorithms so can you

[00:27:04] [SPEAKER_00]: elaborate on that approach, how you're helping employees but also stakeholders,

[00:27:08] [SPEAKER_00]: grass-bay eyes capabilities and limitations because it's no longer just the tech department or the

[00:27:14] [SPEAKER_00]: IT department that's dealing with it, it's stakeholders right across the business isn't it?

[00:27:18] [SPEAKER_01]: Yeah absolutely I think that demystification of it is just really really important and fundamental

[00:27:24] [SPEAKER_01]: because if you don't do that then people just see it as this sort of black box that's

[00:27:30] [SPEAKER_01]: this magic essentially that's able to perform these amazing feats and if you do that then you often

[00:27:37] [SPEAKER_01]: miss the constraints of it and its limitations so one example of that that I remember from the

[00:27:46] [SPEAKER_01]: university essentially was to do with, if he is a classic example of so it was a military application

[00:27:53] [SPEAKER_01]: what they wanted to be able to do was to be able to take satellite images of the forest

[00:27:58] [SPEAKER_01]: and determine whether there was a tank in it or whether there wasn't a tank in it

[00:28:02] [SPEAKER_01]: and so the question they asked was can a machine do this, can we actually have a machine that is

[00:28:08] [SPEAKER_01]: able to look at thousands and thousands of satellite images and with a strong likelihood say whether

[00:28:13] [SPEAKER_01]: there's a tank there or not so they produced a team to do it so they gave them images

[00:28:17] [SPEAKER_01]: and these images essentially were pictures of a tank in a forest and they were tagged as a tank

[00:28:22] [SPEAKER_01]: or pictures of a forest with no tank and they were tagged as no tank essentially so they built

[00:28:28] [SPEAKER_01]: the model, they trained it using that data set and it worked really well you know it was able to

[00:28:34] [SPEAKER_01]: say with a very high degree of accuracy on the examples that they had whether there was a tank

[00:28:39] [SPEAKER_01]: in the forest or not they then took it out to the field and they said okay right let's see how

[00:28:44] [SPEAKER_01]: it works and it was awful, utterly awful. You didn't mean just as well off with it with a coin

[00:28:49] [SPEAKER_01]: and tossing it and say whether there was a tank or not off the back of that and obviously that caused

[00:28:53] [SPEAKER_01]: some embarrassment when they dug into it what they realized is that from the data set they'd use

[00:28:58] [SPEAKER_01]: all the images that had the tank in the forest were taken on a sunny day

[00:29:02] [SPEAKER_01]: and all the images that didn't have the tank in the forest were taken on a cloudy day

[00:29:07] [SPEAKER_01]: so what they'd actually built was an amazing machine which could detect

[00:29:10] [SPEAKER_01]: whether this whether the sun was shining or whether it was cloudy essentially

[00:29:14] [SPEAKER_01]: so what's really going on here is that the great thing about AI like that type

[00:29:20] [SPEAKER_01]: was mentioned is that it's able to find its own classification criteria by you just tagging those

[00:29:26] [SPEAKER_01]: images and then it ultimately works out whether it thinks there is something in that image

[00:29:31] [SPEAKER_01]: determined to the tag or not and whilst it finds its own classification criteria the lesson,

[00:29:36] [SPEAKER_01]: the hard lesson is that it might not be the one that you want if it's got no context of

[00:29:40] [SPEAKER_01]: what a tank is or what a tree is or anything like that and if you assume it's magic and you don't

[00:29:45] [SPEAKER_01]: know how it works then you're far more likely to just imagine that it's able to work in the way

[00:29:51] [SPEAKER_01]: that our brains do with context and everything else around that and it's not and I think that's

[00:29:57] [SPEAKER_01]: brilliant. Or then back to the points earlier around you're grounding this in something not

[00:30:01] [SPEAKER_01]: necessarily from the ground up in terms of maths but actually an approach that people understand

[00:30:06] [SPEAKER_01]: because you can demonstrate the concepts to people and have them understand the concepts

[00:30:13] [SPEAKER_01]: intuitively and in the language I don't think that requires the level of maths that traditionally

[00:30:17] [SPEAKER_01]: it's taught in. You can show somebody something and say if you eyeball a set of data points and

[00:30:23] [SPEAKER_01]: say okay can you see how those are clustered can you see that grouping there? Yes, okay I can.

[00:30:28] [SPEAKER_01]: That's very different to having to do the maths from first principles and prove how that works.

[00:30:34] [SPEAKER_00]: Those two things are very different. Something else I was reading about before you came on

[00:30:39] [SPEAKER_00]: the podcast when I was doing a little research on 1010 is how you do prioritize things like

[00:30:44] [SPEAKER_00]: holistic development and also emphasizing technical proficiency alongside communication,

[00:30:51] [SPEAKER_00]: collaboration and adaptability skills it seems incredibly well-rounded approach that you've

[00:30:56] [SPEAKER_00]: got here absolutely love it. Can you tell me a bit more about how you do that at 1010?

[00:31:00] [SPEAKER_01]: Yeah it's a combination of things really. It's training initially to determine where people's

[00:31:07] [SPEAKER_01]: attitude line and working out how we can identify any areas that we need to strengthen or sort of

[00:31:12] [SPEAKER_01]: find more training in and then it's about really working with the individual and ultimately

[00:31:17] [SPEAKER_01]: align to the role that they end up working in around what sort of training is required

[00:31:23] [SPEAKER_01]: and that's across the board so that's hard skills, that's soft skills and essentially

[00:31:27] [SPEAKER_01]: there's a number of mechanisms to do that so we have teams within 1010 who are there to

[00:31:32] [SPEAKER_01]: train individuals to be able to assist behind the scenes. We've also got a more senior consulting

[00:31:39] [SPEAKER_01]: team of several hundred individuals who were able to if they have questions or if they have

[00:31:44] [SPEAKER_01]: queries to be able to help out and answer those and then it's also a case of I think

[00:31:50] [SPEAKER_01]: holding people not accountable necessarily but what I found in my experience is that when

[00:31:57] [SPEAKER_01]: you say to people we start a lot on this program tell us where you think you want to get to will

[00:32:02] [SPEAKER_01]: help steer you towards that goal and give you guidance along the way but you need to do this

[00:32:08] [SPEAKER_01]: work, you need to do these things and everybody always starts off with the best of intentions

[00:32:13] [SPEAKER_01]: and sometimes just human nature is that those things fall by the wayside. People are busy,

[00:32:17] [SPEAKER_01]: they've got busy lives, they're doing a full-time job as well asking them to do something

[00:32:21] [SPEAKER_01]: outside of that it's often the first thing that slips because frankly nobody's jumping up

[00:32:26] [SPEAKER_01]: and down at them saying you must do this in the same way as they are saying you need to come to

[00:32:31] [SPEAKER_01]: work, you need to do this for work, you need to do the washing, do the laundry, mow the lawn,

[00:32:35] [SPEAKER_01]: whatever else that looks like and so it's really helping them put a framework in place

[00:32:39] [SPEAKER_01]: to kind of make sure that they are doing it and that's done through review but regular review.

[00:32:45] [SPEAKER_01]: I've worked with organizations before where you have like the dreaded annual performance review

[00:32:50] [SPEAKER_01]: where bring practice often not much happens for 10 months and then suddenly there's this mad

[00:32:55] [SPEAKER_01]: scramble in the last two months to kind of do it that doesn't help if it comes to 10 months in

[00:32:59] [SPEAKER_01]: and you haven't done the things that you hope to do it's much better to be able to check people

[00:33:03] [SPEAKER_01]: regularly or often in an environment which doesn't feel like it's that sort of adversarial

[00:33:09] [SPEAKER_01]: well have you done this haven't you done that environment and get people to talk them through

[00:33:13] [SPEAKER_01]: it. I think the other thing is that you can set a sort of development plan in terms of what

[00:33:18] [SPEAKER_01]: people want to do but so often it changes, things come along out of left field and

[00:33:23] [SPEAKER_01]: other things need to change so I think that's fine as well that's perfectly okay to recognize that

[00:33:29] [SPEAKER_01]: you thought you were going to be doing this and you're not going to be doing this that's just an

[00:33:33] [SPEAKER_01]: age of it and the way that things change. So that's fine plans adapt and as long as you

[00:33:37] [SPEAKER_01]: continue to learn and you are learning I think that's the most important thing so

[00:33:43] [SPEAKER_00]: as incremental steps. 100% with you there and everything is going to continuously change

[00:33:49] [SPEAKER_00]: we are going to have to continuously adapt so looking ahead how do you see that role of continuous

[00:33:54] [SPEAKER_00]: learning in posturing a culture that drives meaningful innovation within organizations

[00:34:00] [SPEAKER_00]: particularly in this rapidly evolving AI landscape that's only going to continue this way

[00:34:04] [SPEAKER_00]: anything you can share and how you see this evolving in the future.

[00:34:08] [SPEAKER_01]: Yeah I think like I think like learning anything there's a there's a few key themes here so

[00:34:14] [SPEAKER_01]: firstly if you want to if you want to learn you do have to create a habit of it I think that's kind

[00:34:23] [SPEAKER_01]: of fundamentally important really that you need to set aside time to do it so it almost just

[00:34:30] [SPEAKER_01]: becomes autonomous and there are ways and means to do this so you can there's some forms of

[00:34:36] [SPEAKER_01]: learning which you can do whilst you are doing something else there's some forms of learning

[00:34:40] [SPEAKER_01]: that demand your absolute attention and focus and let's say that's the hardest stuff to fit in for

[00:34:46] [SPEAKER_01]: short but I think as well it's hard to do alone I think if you get together with a group of people

[00:34:53] [SPEAKER_01]: where you're able to share your thoughts share your discoveries and share your failures frankly

[00:34:56] [SPEAKER_01]: and then sort of make it fun I think that can help massively with motivation I think it was

[00:35:01] [SPEAKER_01]: just you doing it yourself and you're not really being held to account or nobody's really

[00:35:10] [SPEAKER_01]: ensuring that you do it that that can be a problem not always but it can be and I think

[00:35:16] [SPEAKER_01]: if that happens then really it's a question of setting a target at a sort of weekly monthly

[00:35:21] [SPEAKER_01]: levels to say I'm going to do this but then equally not beat yourself up about it if you find that

[00:35:25] [SPEAKER_01]: one day in you you don't manage to do it as long as you pick it up and and the

[00:35:30] [SPEAKER_01]: some incremental gains over the time are are really important I would also say that there is

[00:35:35] [SPEAKER_01]: a huge amount of content out there available on the web the challenge is navigating your way

[00:35:41] [SPEAKER_01]: through it and actually working out what's good and what's fit for purpose I think that's

[00:35:46] [SPEAKER_01]: that that's a real skill I think in terms of approaching it mind you has always been that

[00:35:52] [SPEAKER_01]: people can pretty much do anything if they set the mind to it it grows mindset of

[00:35:56] [SPEAKER_01]: I can't do it yet but at the same time you need to be realistic you need to recognize

[00:36:01] [SPEAKER_01]: that it will take time but you need to invest time in it you can't expect things to be

[00:36:07] [SPEAKER_01]: instantly amazing very quickly there's an element of mastery behind anything that you want to do

[00:36:13] [SPEAKER_01]: and that frankly does involve you having to do the hard yards on the grind before you can get

[00:36:17] [SPEAKER_01]: to that point of mastery so an element of tenacity and actually sticking at it I think

[00:36:23] [SPEAKER_00]: is fundamentally important as well. 100% with you and of course what we're talking about here

[00:36:29] [SPEAKER_00]: is this real pressure on every one of us to be in a state of continuous learning we all fail it no

[00:36:35] [SPEAKER_00]: matter what background we have and I'd love to turn the tables here and ask you where or how do

[00:36:40] [SPEAKER_01]: you self-educate yeah sure so I think for me there's two sides to this really there's two types

[00:36:47] [SPEAKER_01]: of learning that I tend to do there's a part which doesn't really require my full attention

[00:36:53] [SPEAKER_01]: so that's things such as listening to podcasts listening to audiobooks for example now this can

[00:37:01] [SPEAKER_01]: be done doubling up on the back of something else and James Clear in his book of Tommy Cabitz

[00:37:07] [SPEAKER_01]: he makes a really good point around this the fact that habits and learning should be a habit are

[00:37:12] [SPEAKER_01]: really hard to form and really easy to sort of put the wheels off the wagon and to stop doing

[00:37:18] [SPEAKER_01]: it so if you can piggyback off the back of an existing habit that makes life so much easier

[00:37:22] [SPEAKER_01]: so for example that type one learning not requiring my full attention I'll do that whilst

[00:37:27] [SPEAKER_01]: I'm commuting if I'm in the car I can listen to audiobooks if I'm on the train I can actually

[00:37:32] [SPEAKER_01]: view things as well you obviously if you're walking you're exclusive to stuff I guess if you

[00:37:37] [SPEAKER_01]: cycle into work and don't do this it's probably should be good but you know then things all

[00:37:41] [SPEAKER_01]: the adventacity is there they're crutching gling mowing the lawn and it's an opportunity

[00:37:46] [SPEAKER_01]: to be able to do this in the gym as well and then because you're doing those things already

[00:37:52] [SPEAKER_01]: you've pre-established that habit and you're able to leverage that to be able to do this

[00:37:57] [SPEAKER_01]: and the second part which frankly is much harder I'm not perfect at this by any stretch of the

[00:38:03] [SPEAKER_01]: imagination but this is a part that requires that absolute attention so it requires who in

[00:38:07] [SPEAKER_01]: front of a pc or a machine with complete focus studying something making notes really really

[00:38:14] [SPEAKER_01]: thinking hard about it practicing something harder to fit in but the only way to do it is

[00:38:20] [SPEAKER_01]: just you've got to make it non-negotiable you've got to schedule it in and have it as a thing as

[00:38:24] [SPEAKER_01]: important as anything else because sadly if you don't do that it will fall by the wayside because

[00:38:29] [SPEAKER_01]: there's always there's always the urgent but less important things that will demand your attention

[00:38:34] [SPEAKER_01]: and this stuff is really important and can make a huge difference in terms of people's

[00:38:38] [SPEAKER_01]: outcomes and where they go in life and what they achieve but it requires that self-discipline

[00:38:44] [SPEAKER_01]: because frankly no one's going to chase you down on it. What a beautiful moment to end on thank you

[00:38:49] [SPEAKER_00]: so much for sharing your insights today I'm sure we've left a lot of people listening with a lot

[00:38:54] [SPEAKER_00]: more questions maybe they want to continue this conversation so where is the best place for those

[00:38:59] [SPEAKER_00]: people listening to find you or your team online and you know find out more about anything

[00:39:04] [SPEAKER_01]: we talked about today so our website is 1010.com so that's t-e-n-1-0.com and relating to some of

[00:39:13] [SPEAKER_01]: the stuff we talked about today we specifically have an insight section within that if anybody

[00:39:17] [SPEAKER_01]: wants to call time we directly I'd absolutely welcome that I'm there on LinkedIn down as

[00:39:21] [SPEAKER_01]: ash as h course of g-a-w-t-h-o-r-p that no e on the end yeah should be quite easy to find out

[00:39:28] [SPEAKER_01]: from that many people with that name so you're not gonna have you're not gonna find 50 people with that

[00:39:32] [SPEAKER_00]: well I will add links to everything you mentioned there just to make it easy for people to find

[00:39:38] [SPEAKER_00]: everything and love discussing the importance of developing a human-centric perspective on AI

[00:39:44] [SPEAKER_00]: empowerment today demystifying AI instead of viewing it as AI magic and I just love what

[00:39:50] [SPEAKER_00]: you're doing here in driving this human-centric innovation and how the true power of AI lies

[00:39:56] [SPEAKER_00]: in its ability to augment human capabilities not replacement replace them it's such a powerful

[00:40:02] [SPEAKER_00]: message I think the key takeaway is by harnessing AI as this tool for empowerment organizations can

[00:40:09] [SPEAKER_00]: enable their employees to unlock their full potential and drive innovation within their domains

[00:40:14] [SPEAKER_00]: and again this is something we don't talk about enough so thanks so much for shining a

[00:40:18] [SPEAKER_01]: lie on this today Ash thanks for your mercy it's been absolute pleasure thank you so a big

[00:40:24] [SPEAKER_00]: thank you to Ash for talking about the transformative potential of AI in the workplace

[00:40:30] [SPEAKER_00]: and driving human-centric innovation and also talking about the importance of holistic development

[00:40:36] [SPEAKER_00]: and continuous learning for me I think Ash provided a roadmap to leveraging AI to empower

[00:40:42] [SPEAKER_00]: employees and ultimately foster a culture of innovation but what are your thoughts on

[00:40:48] [SPEAKER_00]: integrating AI into your professional environment how do you see it shaping the future of work

[00:40:54] [SPEAKER_00]: I'd love to hear your perspectives and invite you to join the conversation and share your

[00:40:59] [SPEAKER_00]: insights with me and you can do that by simply emailing me techblogwriteroutlook.com

[00:41:05] [SPEAKER_00]: twitter linked here in instagram just out neil cq's so thanks for tuning in and until

[00:41:12] [SPEAKER_00]: next time let's keep exploring the possibilities of technology speak with you all in the morning

[00:41:17] [SPEAKER_00]: bye for now