What can data insights reveal about people and their potential?

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[SPEAKER_00]: Welcome to Sensing the Signal.
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[SPEAKER_00]: I'm Terry Petart from Atradies.
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[SPEAKER_00]: We're exploring the overlap between data, technology, and intelligence in our increasingly volatile and complex world.
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[SPEAKER_00]: What this means for national security and how deeper insight can help organisations make better decisions.
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[SPEAKER_00]: I've spent the past 20 years helping organisations improve their intelligence capabilities.
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[SPEAKER_00]: Now I get to talk to some of the leading voices who I've learned from myself.
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[SPEAKER_00]: This time we're joined by two guests.
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[SPEAKER_00]: See you recuperate us, CEO of Greenbeam, and Sally Walker, founder of Human Digital Thinking.
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[SPEAKER_00]: Welcome to the show.
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[SPEAKER_02]: Thank you.
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[SPEAKER_00]: Sally, you were actually the guest on our first ever episode of Sensing the Signal, and so if anyone hasn't listened to that episode, I would encourage you to go back and listen to it because we talked about essentially information integrity and how we know whether we can trust information or not.
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[SPEAKER_00]: And that's still still really pertinent now, I think, and I still have conversations with people where I end up referencing that episode.
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[SPEAKER_00]: So I fully encourage people to go back and listen
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[SPEAKER_01]: Thank you very much for having us.
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[SPEAKER_00]: And see you're welcome to the show.
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[SPEAKER_00]: It's great for you to be here for the first time for us talking and meeting.
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[SPEAKER_00]: But I really want to delve into a lot of the work that you've been involved in.
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[SPEAKER_00]: And specifically the topic of the show where we're really talking about how to use information and data, also how organizations can use information and data to better understand people.
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[SPEAKER_00]: And really, that's the most important point I think of this discussion is people.
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[SPEAKER_00]: And how do we get?
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[SPEAKER_00]: people who are maybe from more diverse backgrounds and thinking different ways into organizations in roles which really benefit them and benefit the organizations and so maybe you can sort of lead off with that and talk to us a little bit about your work and what you do.
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[SPEAKER_02]: thank you Terri and it's a conversation I'm deeply deeply passionate about.
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[SPEAKER_02]: So yes on the CEO of Greenbeam and we exist to help human capability be seen.
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[SPEAKER_02]: So for far too long I think people have been distilled down to what they've done before we talk about it as their past experience but for the first time organizations are being really challenged because
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[SPEAKER_02]: What we need from people is not what they've done before, it's adaptability, it's the ability to evolve and to move into new roles, to address new challenges, and it's essentially at the very heart of what we do, which is how do we start to see what people are truly capable of?
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[SPEAKER_00]: Well, I mean, that's hopefully something that's of interest to everybody in every organisation, but are there particular sectors you're working in, where you're sort of really focused on developing organisations or is this a cross different sectors?
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[SPEAKER_02]: Look, I think it's across every sector today, so a real challenge for organizations is that the way that they've traditionally looked at people is not fit for purpose anymore.
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[SPEAKER_02]: We have AI and technology that is coming in and changing the very fabric of work, and at the same time we don't have any information that is really robust about what our people can do.
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[SPEAKER_02]: and we often talk about how human decisions have traditionally been made of proxy, so things like resumes, what someone has done in the past, whether they went to university before, and that doesn't really stand up in the world today when you've got evolving needs and capabilities for technology, but also what humans need to do in that new modern workplace.
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[SPEAKER_02]: So,
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[SPEAKER_02]: We do a lot of work in national security and intelligence, and I actually think that sector was one of the most progressive adopters of thinking differently about people, based off the basic need of needing diverse people to support a diverse range of challenges.
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[SPEAKER_02]: But equally we're starting to see sectors like banking, retail, medicine, all of these sorts of areas across society come to the
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[SPEAKER_02]: We need to really understand people if we are going to be effective in what we do with technology and with AI into the future.
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[SPEAKER_00]: And, Charlie, that's something you've done a lot of work with before when you were heading the National Cyberforce in UK.
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[SPEAKER_00]: Maybe you can talk a little bit about that for anyone who didn't listen to our first episode.
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[SPEAKER_01]: Look at they've not been listening to the first episode.
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[SPEAKER_00]: So yeah.
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[SPEAKER_01]: So yeah, we built the National Cyberforce, my goodness, more than a decade ago now.
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[SPEAKER_01]: And we were building it at the same time that we had established the National Cybersecurity Center.
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[SPEAKER_01]: And the National Cybersecurity Centre went out and took all the national cyber experts.
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[SPEAKER_01]: Not an unreasonable thing to do.
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[SPEAKER_01]: They are a rare breed.
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[SPEAKER_01]: They are extremely confident individuals.
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[SPEAKER_01]: And they had the credentials to prove their worth to that organisation and to the UK and our allies.
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[SPEAKER_01]: But when we were trying to establish a new capability with sort of went to a more bare bucket of people and so it possibly wasn't a deliberate act but it wasn't extension of how I have always operated which is to think
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[SPEAKER_01]: who could help.
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[SPEAKER_01]: And we went out almost with that question, off the back of the London Olympics and other things and just said who wants to work in this new organisation and the people who came forward were not the traditional cyber specialists with their extensive credentials.
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[SPEAKER_01]: There were people who were interested or passionate or curious.
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[SPEAKER_01]: They were people who
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[SPEAKER_01]: I'd like to echo back to Bletchley Park where, you know, let's face it, under wartime conditions, we faced existential challenges, looked to the pool of talent in mathematics and so on that was needed for Bletchley, and that bucket was dry.
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[SPEAKER_01]: and in those circumstances the first thing was to see who was good at crossroad puzzles and then we ran out of those and then to go to the academics or possibly that was the other way around and they all gone to the front or to other critical roles and so you know heaven help us we had to ask women.
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[SPEAKER_01]: and the women who were called to be actually with those who were seen to be trusted.
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[SPEAKER_01]: I either families were known, but trust, I think, still underpins or capabilities and our information environment.
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[SPEAKER_01]: We could come back to perhaps later.
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[SPEAKER_01]: But those women offered a really different insight into the world of communications.
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[SPEAKER_01]: They recognized it wasn't just what I have said to you or you have transmitted to me.
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[SPEAKER_01]: It was me as a person and you as a person and if you understood that, you understood the social ecosystem.
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[SPEAKER_01]: Then you got deeper rich insight into the data that was in front of you and that's how they cracked the Enigma Code.
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[SPEAKER_01]: All that was part of how they cracked the Enigma Code.
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[SPEAKER_01]: So that sense of cognitive diversity or thognorthinking different approaches to problems in information in technology has resonated down the decades with me and I think is profoundly important and I'm just so thrilled that Sierra and her organization are taking that approach into the world of business where it's equally vital to our economic security in our wellbeing and our growth of capable organizations who are making most of humans.
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[SPEAKER_00]: Yeah, and you mentioned Bletchie Park, and we're recording this shortly after International Women's Day.
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[SPEAKER_00]: So it seems really pertinent to touch on that aspect, because we're 80 years on now from those events, essentially, or that team of people, and that organisation, effectively being disbanded, and losing that capability,
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[SPEAKER_00]: So I guess a question for both of you really is how do how do you go about building the capability in organisations where they do recognise talent and expertise from a diverse array of people but then keep that there and keep that going beyond let's say an initial crisis situation that might lead to a team or unit being formed that is you know drafting in those
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[SPEAKER_02]: It's a great question, Terri, and I think the challenge is it has to be a systemic rewiring of the very employment system that we're all used to dealing with, so people traditionally get jobs because they have followed a pathway that we think will deem appropriate for getting that job.
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[SPEAKER_02]: A lot of the time, that is a cater to 12 education, followed by university, and
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[SPEAKER_02]: followed by experience stepping into that role.
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[SPEAKER_02]: What we have seen is that it is locking huge portions of society out and that is making it very limited in terms of the talent pools that we can be tapping into these systems.
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[SPEAKER_02]: You mentioned in the spirit of international women's day we've seen in technology there still exists a significant imbalance between the genders in the tech workforce and many other work forces for that matter.
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[SPEAKER_02]: And we need to change that by changing how we're looking at people, so instead of looking at their historic experience in the workplace, we can start to look at people from a capability perspective in terms of how do those skills that they might have honed from whatever background translate into the corporate world.
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[SPEAKER_02]: But even more importantly than that, from a potential, a potential perspective.
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[SPEAKER_02]: So you may not have had the opportunity to do something before, but it doesn't mean that you don't have the potential to pick up on those skills.
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[SPEAKER_02]: And I think Sal, you touched on it before in terms of identifying people who may never have worked in cyber before, but it doesn't mean that they don't bring a diversity of thinking of experience of cognition to be able to successfully move into a future career in that perspective.
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[SPEAKER_01]: And I think, see a touched on locking out that extraordinary talent pool that's available that doesn't realise it's capable.
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[SPEAKER_01]: There's two other problems in modern society.
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[SPEAKER_01]: There's locking in.
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[SPEAKER_01]: So we were talking to someone in defense actually just last week who was looking at the kind of insight that we can get into aptitude and motivation and purpose.
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[SPEAKER_01]: and they, that and looked at me and I was expecting them to talk about the importance of development conversations and you know making the most of people and they said, I think you've just shown me the age percent of my work forces in the wrong job.
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[SPEAKER_01]: because they're in the job that they have historically been grown towards as a product of their education, as a product of their training, and as a product of the system that was designed whether it's 10 years ago or 10 months ago or 100 years ago, the systems haven't changed.
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[SPEAKER_01]: and we are expecting our people to adapt to fit, that is not the best use of our talent.
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[SPEAKER_01]: We need to be able to take people and say, what are you good at?
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[SPEAKER_01]: How are we going to make the most of that?
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[SPEAKER_01]: And that's a totally different approach.
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[SPEAKER_01]: So that's one aspect of what I think is really important.
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[SPEAKER_01]: The other aspect, so we've got the locking out and the locking in, we've also got the aging off and I think this is a conversation we all have.
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[SPEAKER_01]: People are recognizing that artificial intelligence is going to
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[SPEAKER_01]: They're fearful their jobs are going to go, they're fearful they're training pods are going to change and they're fearful that they are going to be without meaningful employment.
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[SPEAKER_01]: Possibly mid career possibly at a point way before they are ready to say goodbye to the world of work.
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[SPEAKER_01]: And our ability to recycle talent, that's pretty much where when we met you started see it was military veterans coming back from theatre with this extraordinary set of skills, capabilities and experience, who when they put the words on a CV couldn't frame that in the language that was appropriate to the job boards that were in the commercial world and they were being left on the scrap heap and that desire to solve under employment was such a powerful motivation
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[SPEAKER_01]: for the journey that we've shared as we've got to know each other.
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[SPEAKER_01]: So that ability to refocus talent, to recycle talent, and just to play and put the spotlight out there and identify people who are capable competent and sometimes fiercely ambitious but have never had that and file it under them because they didn't realise that they were able to
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[SPEAKER_01]: to contribute in the way they're so powerful.
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[SPEAKER_02]: I think Salio mentioned the word fear, which is a really important one because individuals are fearful as you said in terms of what is the impact of AI going to have on me and my career and my job opportunities.
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[SPEAKER_02]: But I think you've also got organisations who are really scared.
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[SPEAKER_02]: They don't understand the impact.
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[SPEAKER_02]: And
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[SPEAKER_02]: There's a little bit of a knee-jerk reaction in terms of defaulting to traditional ways of trying to solve problems when new technology and new situation means we need to absolutely fundamentally shift the way that we're thinking about building our teams and supporting our people to be the best that they can be.
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[SPEAKER_01]: And that's I think where some of our conversation in this area has started when I joined the World of National Security.
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[SPEAKER_01]: Information was at the core
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[SPEAKER_01]: But because we had to work so hard to get those insights into what our adversaries were thinking and saying,
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[SPEAKER_01]: We treated the information that we were privy to, like the crown jewels, but we also didn't see it as an answer.
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[SPEAKER_01]: We saw it as the start to a set of questions.
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[SPEAKER_01]: So it triggered curiosity as to about what we didn't know and what we couldn't see yet and where we needed to go next to really get a better understanding because we knew that we only ever had a partial picture.
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[SPEAKER_01]: I was reminded last week that in the year 2000, which feels like yesterday, but apparently it's quite a long time ago.
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[SPEAKER_00]: Oh, don't say that.
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[SPEAKER_01]: I know that we had to deal with the emergence of the concept called email, and we were talking sort of megabytes and gigabytes of overwhelm, or what we would do with volume.
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[SPEAKER_01]: But in the...
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[SPEAKER_01]: signals intelligence world, I think we, I think we missed a trick.
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[SPEAKER_01]: I think we stopped thinking about information and we started treating data as if it was a subject in its own right to be put over in a box and study and we forgot that actually less than that data belongs to people and gives you insight into people.
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[UNKNOWN]: Yeah.
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[SPEAKER_01]: And that problem has got profoundly more difficult as we face the tsunami of social media of material that's available on the internet.
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[SPEAKER_01]: Just a little bit, I think we read somewhere where we're generating the equivalent of the Library of Alexandria of knowledge every second, like how much information can humanity cope with and the answer is a lot less than we've actually got available to us.
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[SPEAKER_01]: So we are starting to view information and information flows and we're boiling it down to narratives and we're boiling it down to things that we can cope with and that reductionist approach stops us being curious and stops us asking questions and stops us generating insights.
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[SPEAKER_01]: So in the same way that an organisation can't boil down it's data to have I got enough money in the bank and what am I financial flows it's got people people are real and difficult and messy But we could use our technology to understand them differently and better But we also need to be profoundly aware of our information environment and what we're operating in that environment, how we're generating insights from it, where we're focusing, why, what we're paying attention to, what we're investing in
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[SPEAKER_01]: we're not investing in the data, we're investing in people, in human connection, in human experience, in humanity.
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[SPEAKER_01]: Are we getting that right?
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[SPEAKER_00]: Interesting question.
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[SPEAKER_00]: It is.
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[SPEAKER_02]: And to go on to the data point, because what I find really interesting about what you said there's is the financial data point.
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[SPEAKER_02]: We spend so much time and money to make sure that we have the information we need to be making business decisions and finance decisions and asset decisions.
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[SPEAKER_02]: But nowhere along the line, have we actually thought about creating a system that captures the data we need on our people, which is, I would not even say, arguably our greatest resource.
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[SPEAKER_02]: People are humanity is our greatest resource for organizations.
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[SPEAKER_02]: It's the biggest piece that we invest in as well, yet we don't typically have the systems that we need to truly understand what someone is capable of apart from that shallow simplistic view of what they've done before.
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[SPEAKER_00]: And it's interesting picking up the two points that you've both made there.
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[SPEAKER_00]: You know, from the perspective of my company, a trade-ease, we deal with better-bytescale data, we deal with masses of data.
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[SPEAKER_00]: And we try and make it useful for people.
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[SPEAKER_00]: But making it useful for people is about highlighting interesting behaviours within there that tell us something.
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[SPEAKER_00]: Ultimately, about humans and humans or collections of humans are doing.
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[SPEAKER_00]: But at the same time, we probably don't do a lot of looking inside the organisation at our own people and how can we help people best make their use of their skills and experience.
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[SPEAKER_00]: And so, you know, and the point you made about helping military veterans is really pertinent because a lot of my colleagues are military veterans.
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[SPEAKER_00]: And I wonder if, you know, they could have been potentially pigeonholed by
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[SPEAKER_00]: some of the experience they did have or some of the experience they didn't have coming out of the military that would have sort of shunted them towards careers that ultimately were unfilling or making the best use of their talent.
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[SPEAKER_00]: So, you know, these really, you know, what you're talking about here is, you know, front and centre really have a lot of the things that I'm thinking about in our organisation.
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[SPEAKER_04]: Yeah.
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[SPEAKER_00]: But see what would be great to get from you, like a sense of how did you get into this and, you know, what led you to developing all of this?
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[SPEAKER_02]: Yeah, look, I think it's the military veteran point as a great one.
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[SPEAKER_02]: It was actually how our organization was founded, which was this idea that you had incredible people doing incredible things in service of their country.
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[SPEAKER_02]: But they would stop that and a lot of the time only be able to get jobs doing security because military equal security.
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[SPEAKER_02]: No one was looking at the broad depth of knowledge and experience and wisdom that they had underneath the surface.
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[SPEAKER_02]: So we were founded to solve that issue, to solve it by helping people map transferable skills and experience into a corporate world.
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[SPEAKER_02]: But also starting to look at the human brain and their cognition in terms of what are the skills you might be able to excel at, even though you haven't yet had the opportunity to do it.
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[SPEAKER_02]: And what we found very early on was that military personnel had an incredible tendency to excel in technology and in particular cybersecurity, which is how Sally and I connected early in the in the piece.
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[SPEAKER_02]: And that doesn't surprise you when you actually think about, you know, the nature of being in the military and hunting threats and thinking analytically and reacting quickly.
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[SPEAKER_02]: So there is certainly that aspect.
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[SPEAKER_02]: For me, in my own personal journey, it was a profound personal story that drove me to where we are today.
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[SPEAKER_02]: And I've seen firsthand the impact that not having people's capabilities in their skills visible can have on individuals.
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[SPEAKER_02]: My story is that I had a wonderful brother, Ben, who was 60 years younger than me, and I
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[SPEAKER_02]: Ben was a fiercely intelligent, beautiful human who always thought very differently.
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[SPEAKER_02]: I've reflect, you know, a decade ago he would be reading the papers and telling me, see her, you need to be investing in gold and silver.
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[SPEAKER_02]: I can see something happening there.
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[SPEAKER_02]: If I only had listened to him, I could be in a very different position to him now.
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[SPEAKER_00]: And was he kind of picking that up into it, just from what he was reading?
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[SPEAKER_02]: Yes, he was, and that he had this incredible way of just analyzing things, seeing through the noise and coming up with his own perspective.
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[SPEAKER_02]: Now, that didn't translate to his experience in our education system.
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[SPEAKER_02]: Ben is dyslexic and
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[SPEAKER_02]: he did not thrive in a traditional classroom environment.
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[SPEAKER_02]: I think we see in here about that all the time.
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[SPEAKER_02]: If you're not thinking in a certain typical way, you can often be challenged in the education system.
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[SPEAKER_02]: And as a result, he finished school when he was in the attend to start a trade, which we thought was wonderful, but then a global financial crisis hit and he was never able to step into that trade.
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[SPEAKER_02]: He was made redundant before he even started.
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[SPEAKER_02]: Anyway, Ben struggled as a result, his whole life, from that he could never really find his footing again.
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[SPEAKER_02]: He always felt less than he applied for jobs all the time, but did not often make it to the interview round, because he didn't have that background that people were looking for.
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[SPEAKER_02]: He hadn't gone to university, he hadn't finished grade 12, and it just beat him down.
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[SPEAKER_02]: He died when he was 29, and
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[SPEAKER_02]: that story haunts me to this day, it haunts my family, and it's not a nice-ladered experience.
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[SPEAKER_02]: We have people whose lives and livelihoods are being impacted because of the way that we have valued human potential, and it frustrates to me me to know and that I'm in this role and with a company that is doing really incredible things in terms of helping organisations.
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[SPEAKER_02]: see potential and understand capability and elevate people to where they need to be.
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[SPEAKER_02]: And he's not here to see that.
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[SPEAKER_02]: It's that irony in that bittersweet irony because if he was around today, I have no doubt that he would be in an extraordinary position.
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[SPEAKER_02]: And for me, that drive to help organisations understand
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[SPEAKER_02]: the impact that you're having on people is not just the impact that is happening to your bottom line.
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[SPEAKER_02]: You can do the right thing by people and you can do the right thing by your business.
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[SPEAKER_02]: And in fact, I would argue that if you do do the right thing by people, your business will thrive.
21:52.165 --> 21:59.676
[SPEAKER_02]: So for me, I'm fundamentally driven to reshape the way that we think about work forces and rethink about people.
21:59.656 --> 22:10.943
[SPEAKER_02]: not just for my brother, but for all the brothers out there, for all the people we all know people with cognitive diversity and with different strengths that deserve to be recognized and given an opportunity to step into work.
22:11.725 --> 22:12.687
[SPEAKER_02]: So that's my passion.
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[SPEAKER_00]: See, I mean, thank you so much for sharing that, so no, it's really difficult to talk about some of these things and especially when it's so personal, but I think that will really resonate with a lot of people.
22:25.695 --> 22:30.287
[SPEAKER_00]: You know, like you said, there's really a lot of people in the same position who have
22:30.470 --> 22:32.574
[SPEAKER_00]: through no fault there, and haven't been given the opportunity.
22:33.035 --> 22:34.959
[SPEAKER_00]: Somebody maybe hasn't taken a risk on them.
22:35.600 --> 22:48.546
[SPEAKER_00]: And is it, I mean, you mentioned there that the world has changed somewhat, and now we've got a better understanding of, you know, people who may think differently and how we can make the most of some of those skills.
22:48.526 --> 22:55.281
[SPEAKER_00]: But thinking back to the example of pleasually park is that potentially something that's easily lost again.
22:55.682 --> 23:04.982
[SPEAKER_00]: If we don't keep working on trying to bring out those sorts of skills and experience and find ways for organisations to work with people who think differently.
23:05.165 --> 23:09.612
[SPEAKER_02]: I think so, and Terry, I say, you know, it's a bit different now, the different is not the norm.
23:09.752 --> 23:22.612
[SPEAKER_02]: There are some organizations that are doing extraordinary things in this space and earlier doctors to be sure, but I think people still default on viewing and judging, human capability in a very rigid way that is not fit for purpose.
23:23.354 --> 23:28.742
[SPEAKER_02]: And for me, I think one of the best examples that we can have.
23:28.722 --> 23:40.946
[SPEAKER_02]: for how we should be running our businesses is absolutely putting humans at the center because we are all living this experience where AI is changing the way that our organizations are running.
23:41.386 --> 23:49.482
[SPEAKER_02]: And if we don't find a way to elevate people and get the very best of humanity, we are never going to be thriving in the way that we need to with that technology.
23:50.288 --> 23:57.539
[SPEAKER_00]: And Sally and you know, you both mentioned the fear factor for my eye, how challenging is it to deal with that?
23:57.579 --> 24:05.651
[SPEAKER_00]: Because of the uncertainty right now, we don't know really what the impact of AI and the AI tools that have been created are going to have.
24:07.975 --> 24:13.523
[SPEAKER_00]: How do you get people to start sort of thinking about how they're going to deal with that in a positive way?
24:13.623 --> 24:15.486
[SPEAKER_00]: So not from a perspective of fear.
24:16.529 --> 24:24.641
[SPEAKER_02]: I find it a really interesting question, Terry, because I had a conversation with the customer last week who was talking about data analytics.
24:24.681 --> 24:29.628
[SPEAKER_02]: And they were saying, it's so great, seeer now that we have, I think it was Claude that they were using.
24:30.249 --> 24:36.137
[SPEAKER_02]: We don't really need data analysts, because we can be uploading all of our data, and we ask Claude what to do, and it's telling us what to do.
24:36.798 --> 24:39.462
[SPEAKER_02]: And for me, that was a quite a terrifying thing to hear.
24:39.502 --> 24:41.845
[SPEAKER_02]: And it's misinformation, because
24:41.825 --> 24:51.600
[SPEAKER_02]: Now, more than ever, you need people with the human ability to conduct data analytics at a higher level, to be able to scrutinize and challenge and test what the AI is saying.
24:51.620 --> 25:00.593
[SPEAKER_02]: And I think the thing with AI, the fundamental impact it's having on roles and workplaces, is it's taking the very basic manual of repetitive tasks.
25:00.573 --> 25:17.991
[SPEAKER_02]: Now, humans at their very best should be lifted up to be able to do those tasks at a higher supervisory level and question what's happening and I think for me that misinformation is going to cause real problems down the track because if we get rid of our data analysis, that one example.
25:17.971 --> 25:26.680
[SPEAKER_02]: says you're going to be relying on information that is not necessarily accurate, that's not verified, that's not asking the questions or answering the questions that you haven't asked it.
25:27.702 --> 25:30.244
[SPEAKER_02]: So I don't know what you're perspective on that itself.
25:30.925 --> 25:35.450
[SPEAKER_01]: I give right back to those basic tenants of being an intelligence analyst.
25:35.470 --> 25:36.932
[SPEAKER_01]: Which sounds like a very glamorous job.
25:37.452 --> 25:39.895
[SPEAKER_01]: It's just asking the right questions in the right order.
25:40.836 --> 25:43.759
[SPEAKER_01]: And it's not stopping asking questions.
25:44.937 --> 25:46.299
[SPEAKER_01]: is the source trustworthy?
25:46.499 --> 25:48.943
[SPEAKER_01]: Do I understand where this came from?
25:49.144 --> 25:54.472
[SPEAKER_01]: What was the purpose that was intended by putting the question into generate this data in the first place, et cetera, et cetera?
25:55.193 --> 26:02.885
[SPEAKER_01]: And a relentless focus on integrity, on why, on what are we missing?
26:03.045 --> 26:04.287
[SPEAKER_01]: What do we not know yet?
26:04.587 --> 26:04.988
[SPEAKER_01]: Now,
26:06.115 --> 26:06.896
[SPEAKER_01]: don't get me wrong.
26:06.997 --> 26:20.120
[SPEAKER_01]: Artificial intelligence is brilliant and I've stood in a room and asked them to people to go to one side of the room or the other depending on whether they think it's going to kill us or save us and there are people who literally are trying to touch both walls at the same time because we don't know yet.
26:20.801 --> 26:27.313
[SPEAKER_01]: There are people who've got hearts in their bodies that are beating because of the advances in medical technology that is amazing and brilliant and wonderful.
26:28.492 --> 26:33.003
[SPEAKER_01]: and there are software engineers who are recognized in the fact that their families really really quite scared.
26:33.404 --> 26:39.177
[SPEAKER_01]: So in this wonderful world of ambiguity, we can be both things in the same space.
26:40.034 --> 26:46.863
[SPEAKER_01]: But artificial intelligence has very, very different capabilities and strengths from human intelligence.
26:47.624 --> 26:55.355
[SPEAKER_01]: And I do, I am concerned that in this extraordinary world that is moving so quickly, we forget to go back to those basic principles.
26:56.336 --> 26:58.359
[SPEAKER_01]: I've do we know where our data came from.
26:59.040 --> 26:59.761
[SPEAKER_01]: Is it true?
27:00.242 --> 27:04.287
[SPEAKER_01]: Do I understand when I am being influenced rather than informed?
27:04.267 --> 27:22.504
[SPEAKER_01]: and I think I am also concerned that we're supplementing in our language and our approach to life, a value system that is impressed by the power of computers and forgets that we should be impressed by what humans can do.
27:22.564 --> 27:25.009
[SPEAKER_01]: So we're valuing speed.
27:26.086 --> 27:29.510
[SPEAKER_01]: speed is not necessarily a good quality, right?
27:29.771 --> 27:31.373
[SPEAKER_01]: And like, I get it.
27:31.473 --> 27:37.761
[SPEAKER_01]: If you're running an Amazon work warehouse, you want to know how long it takes people to get from one side to the other.
27:38.542 --> 27:42.367
[SPEAKER_01]: But then if someone needs to take a phone call because their kids sick, it doesn't make them a bad worker.
27:43.688 --> 27:49.375
[SPEAKER_01]: And yet the data can over, it obliterates that humanity.
27:50.457 --> 27:53.781
[SPEAKER_01]: And that's not a good
27:53.761 --> 28:00.179
[SPEAKER_01]: That is a poor approach, unfair to name a particular organization, it's a generic story.
28:00.199 --> 28:03.207
[SPEAKER_00]: Sure, there's other, yeah, in similar positions using data in the same way.
28:03.268 --> 28:07.720
[SPEAKER_01]: And I read something yesterday saying the problem with the large language models is...
28:08.037 --> 28:12.126
[SPEAKER_01]: They actually code confidence from probability.
28:13.188 --> 28:24.291
[SPEAKER_01]: This is not necessarily a very good thing, and I think we are seeing some of the consequences of that flawed approach in judgment that is not being subject to human checking.
28:25.165 --> 28:37.066
[SPEAKER_01]: AI is not yet supremely accurate, it is not yet supremely consistent, it is not yet supremely reliable and actually it might never be because it is coding from probability.
28:37.728 --> 28:43.478
[SPEAKER_01]: And it can't ask what do I not know, what do I not see, whose voice is not represented in this data.
28:44.892 --> 28:49.741
[SPEAKER_01]: I was talking some of one of the big tech companies yesterday who said that is a simplistic approach to AI.
28:49.761 --> 28:57.855
[SPEAKER_01]: But I think in the world and I recognise that there are some super-neus sophisticated capabilities being developed.
28:58.637 --> 29:01.602
[SPEAKER_01]: But let's look at my core point is that
29:01.582 --> 29:19.908
[SPEAKER_01]: artificial intelligence cannot do some of the things that humans can do and therefore how we practice being human, how we focus on our human value and skill set and how we talk about the importance of humanity and why we're using tech.
29:20.089 --> 29:28.821
[SPEAKER_01]: We're using tech to make the world better in some way aren't we?
29:29.948 --> 29:31.291
[SPEAKER_01]: Money makes the world go round.
29:31.492 --> 29:44.481
[SPEAKER_01]: I know, but there is so much more to the world of work and to the world of business and to the world of decision making and leadership than can we make money and to your point to what technology can bring us.
29:44.630 --> 29:54.611
[SPEAKER_02]: Yes, and this is where I love to explore the relationship between humans and technology because we're all jumping to technology and AI and what can we automate and what can we build?
29:55.593 --> 30:03.550
[SPEAKER_02]: Not having the foundation we need in place underneath which is the human foundation and understanding the capability of our workforce and of our people.
30:03.530 --> 30:10.720
[SPEAKER_02]: And what I constantly see is organizations and governments spending millions on digital transformation and new projects.
30:11.321 --> 30:20.473
[SPEAKER_02]: And it falls over because the people are not ready to adopt it or they haven't been brought on the journey or an organization doesn't have the capability in place they need to then execute with that technology.
30:20.614 --> 30:25.200
[SPEAKER_02]: And it baffles me, I understand how people get to that position, but it baffles me that.
30:25.180 --> 30:50.462
[SPEAKER_02]: We are not placing more of an emphasis on the human aspect, and how can we ensure that as an organisation or as a country or anything, how can we ensure that our people are lifted to do what they need to do to make their system successful, because if you're implementing a technology platform, that has no human power behind it, to Sally's point, it's an echo chamber, it's never going to be able to do what you need it to do.
30:50.999 --> 30:52.060
[SPEAKER_00]: Yeah, absolutely.
30:52.100 --> 30:55.104
[SPEAKER_00]: And there's a couple of interesting points you've both made there.
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[SPEAKER_00]: One, Sally, you're point about asking questions.
30:58.087 --> 31:05.335
[SPEAKER_00]: I think connects to C is point about, you know, companies that maybe use looking AI tools and thinking, oh, we don't need data analysts anymore.
31:05.855 --> 31:16.247
[SPEAKER_00]: But it's the data analysts who will ask the right questions of what the AI tool is producing or think about how to prompt it in the right way to get the best outputs from that tool.
31:16.227 --> 31:21.282
[SPEAKER_00]: So if anything, I think there's more need for data analysts to be able to use these tools effectively.
31:21.322 --> 31:26.638
[SPEAKER_00]: Well, even if AI is the answer, what was the question?
31:26.658 --> 31:28.844
[SPEAKER_01]: Only humans can ask the questions.
31:28.864 --> 31:31.131
[SPEAKER_01]: Only we can pose the problems that we want to be solved.
31:31.212 --> 31:39.804
[SPEAKER_00]: Yeah, and otherwise, as you mentioned, we lose the humans in the data instead of finding the humans in the data.
31:39.844 --> 31:56.427
[SPEAKER_00]: Instead of finding the human stories, you know, what is it that the data represents that tells us about what people are doing, rather than actually this just being a massive data that tells us about productivity, what tells us about efficiency, but doesn't really tell us about what's going on behind that.
31:56.407 --> 32:01.014
[SPEAKER_01]: And we talk right at the start about locking out, locking in, aging off.
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[SPEAKER_01]: I mean, the same's true of data, depending on your data strategy, you're going to end up with a version of the world that reflects actually the decisions you made about what to lock in and what to look out, what to refresh, what to delete.
32:14.855 --> 32:21.705
[SPEAKER_01]: And what gets aged off and what's no longer relevant, but those are human choices.
32:21.955 --> 32:25.802
[SPEAKER_01]: And so our data environment is something that we generate.
32:26.283 --> 32:28.246
[SPEAKER_01]: We decide where the spot like those.
32:28.907 --> 32:30.650
[SPEAKER_01]: We decide what's too old.
32:30.730 --> 32:32.413
[SPEAKER_01]: We decide what granularity it has.
32:32.473 --> 32:34.797
[SPEAKER_01]: We decide when it's not sufficient and we need more.
32:35.960 --> 32:47.119
[SPEAKER_01]: And without those conscious choices and very deliberate strategies within our organizations or our countries or our schools or education systems,
32:48.230 --> 32:54.081
[SPEAKER_01]: we will just replicate the flaws of the past rather than learning from the history of the past.
32:54.502 --> 32:55.664
[SPEAKER_01]: What a tragedy that would be.
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[SPEAKER_01]: We're missing opportunity day and day out.
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[SPEAKER_00]: That's absolutely.
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[SPEAKER_00]: And see you how do organizations go about addressing this challenge.
33:06.016 --> 33:09.885
[SPEAKER_00]: I'm going to ask a big question, but it's a big question.
33:09.986 --> 33:11.168
[SPEAKER_00]: How do you engage with them?
33:11.369 --> 33:15.198
[SPEAKER_00]: And what kind of approach is can organizations take it?
33:15.238 --> 33:18.646
[SPEAKER_00]: It's going to be different depending on size the organization where they operate.
33:19.047 --> 33:23.097
[SPEAKER_02]: I think fundamentally you have to start thinking and capability and skills.
33:23.077 --> 33:35.783
[SPEAKER_02]: So, first of all, an organization needs to understand what capability do we actually need to deliver on our objectives, and that's the very first hurdle, because a lot of the time, particularly in this AI world, organizations don't know.
33:35.823 --> 33:42.537
[SPEAKER_02]: They've got a structure they have people and roles, but they don't actually know the specific capability they need to execute on a goal.
33:42.517 --> 34:00.919
[SPEAKER_02]: The next thing they need is to understand the capability that they currently have in place and I use the term capability and skills very specifically because that's not the language that organizations have previously looked at their people at And with they've, you know, relied on those proxies like, you know, who do we have in the role?
34:00.939 --> 34:02.441
[SPEAKER_02]: How much experience have they have had?
34:02.481 --> 34:04.243
[SPEAKER_02]: What's their educational background?
34:04.303 --> 34:11.832
[SPEAKER_02]: But if we switch that to capability, we start to unlock that diverse range of experience that might exist beyond the current role someone is in
34:11.812 --> 34:20.102
[SPEAKER_02]: and then from that you get that very rich view as to where we at versus where we need to be and what can we do to close that gap.
34:20.523 --> 34:34.140
[SPEAKER_02]: And I think if organizations start thinking in capability and skill as opposed to the traditional way of job titles, resumes, they can really start to unlock how they lift everyone up and look at everyone in that same perspective because
34:34.120 --> 34:43.293
[SPEAKER_02]: Until you actually change the way your entire organization is thinking about its people, you're never going to solve the problem of bringing people in from diverse backgrounds.
34:43.353 --> 34:53.889
[SPEAKER_02]: Because if you have someone apply for a job that comes from a non-traditional background, maybe they haven't been to university, maybe they went straight into work, and built a diverse career in there.
34:53.909 --> 34:58.896
[SPEAKER_02]: Versus a Ivy League graduate who has been working for the past 10 years within your organization.
34:58.876 --> 35:03.303
[SPEAKER_02]: How is an average manager meant to judge them on an equal playing field?
35:03.363 --> 35:10.576
[SPEAKER_02]: They're not, you know, this is the thing, humans will base the decision on what they've seen and done and how they think someone should be selected for a position.
35:10.676 --> 35:21.053
[SPEAKER_02]: So we need to actually reshape the way with thinking as entire organizations to uplift people to capabilities and to make decisions that way.
35:21.590 --> 35:27.724
[SPEAKER_00]: And if they're trying to assess people's potential, when you have maybe haven't seen.
35:27.784 --> 35:32.555
[SPEAKER_00]: And because there is always this thing of, you've got to show a proven capability.
35:32.575 --> 35:39.430
[SPEAKER_00]: You've got to show that you've done something because you've got to show that you have experience of doing something which
35:39.967 --> 36:00.237
[SPEAKER_02]: It's one part, that is one part, but to your point, potential is equally critical, and that's because we know that people grow and they pick up new skills every day, I look at technology in particular with this problem and half the jobs, at least half the jobs that are on the table today, didn't exist five ten years ago yet we're still asking for people with five to ten years experience.
36:00.217 --> 36:15.439
[SPEAKER_02]: And it is, it's funny, but you know, the reality is you absolutely need to understand how someone might lift to the occasion and when we talk about potential, we like to look at two separate data points in particular, one being cognitive aptitude and one being psychometrics.
36:15.459 --> 36:22.950
[SPEAKER_02]: So your cognitive aptitude is going to be able to indicate the type of skills you might excel at learning, even if you've never done them before.
36:22.930 --> 36:29.677
[SPEAKER_02]: And your psychometric profile is going to show the kinds of skills that you would enjoy practicing based off how you show up in life.
36:30.238 --> 36:47.976
[SPEAKER_02]: And if we take those two data points because humans need data to make decisions and particularly when you're making a decision that might not be the traditional decision and you need the data to back it up, those two data points give you a very compelling view into whether someone would likely excel and enjoy doing a new job if they haven't done it before.
36:49.120 --> 37:04.902
[SPEAKER_01]: And the extraordinary thing is, if you move from a system that promotes based on experience to one that promotes based on potential, you eliminate a lot of your biases and
37:06.097 --> 37:07.158
[SPEAKER_01]: non-diverse points.
37:08.239 --> 37:12.864
[SPEAKER_00]: Yeah, looking for looking at people from one perspective, I suppose, if you're looking at them from a different perspective.
37:12.884 --> 37:21.673
[SPEAKER_01]: My evidence base for that comes from one of the very, very large US institutions, government agencies, that moved to that approach in about the year 2000.
37:22.574 --> 37:32.003
[SPEAKER_01]: And went from having no women in senior management to almost 50-50, because suddenly the fact that you hadn't been through the traditional route brackets you hadn't been able to.
37:31.983 --> 37:38.170
[SPEAKER_01]: was no longer a barrier to getting to the next step of the ladder and so they would genuinely looking for potential.
37:39.411 --> 37:46.118
[SPEAKER_01]: So a lot of the systemic imbalance and bias in the system is eradicated by that once and pull move.
37:46.618 --> 37:50.002
[SPEAKER_00]: And it's part of that also changing people's perceptions of themselves.
37:50.282 --> 37:59.492
[SPEAKER_00]: When they start thinking of themselves, not in terms of what they've done and the experience they've built, but in terms of their potential and what they want to do
37:59.523 --> 38:05.093
[SPEAKER_02]: And they've been told their whole life, they can't do something because they don't look like the picture of what we deemed nothing looks like.
38:05.533 --> 38:13.086
[SPEAKER_02]: A great example is we do a lot of work with the GED testing service in the United States, which is the high school equivalency diploma, essentially.
38:13.587 --> 38:18.055
[SPEAKER_02]: More than 700,000 adults in the U.S. take the GED ECHR.
38:18.035 --> 38:25.124
[SPEAKER_02]: And our job is to help them understand what they could potentially thrive at, even though they've never done it before.
38:25.304 --> 38:34.956
[SPEAKER_02]: And to your point, most of these individuals left school, not by choice, but because of life circumstance, a lot of them had to go straight into work to start supporting their families.
38:35.476 --> 38:46.810
[SPEAKER_02]: They work in relatively low-level roles, and they've been locked into that because society has told them that they do not have what it takes to step up into that next line of study or employment.
38:46.790 --> 38:56.107
[SPEAKER_02]: And what we have found is that in helping them understand their potential and what they naturally excel at, they're starting to consider roles that they never deemed possible.
38:56.588 --> 39:02.419
[SPEAKER_02]: A lot of individuals would not natively think that they could step in to be a cybersecurity analyst.
39:02.399 --> 39:15.030
[SPEAKER_02]: but if you do an aptitude and cognitive assessment and psychometric assessment, you can start to understand that many of the traits you have as an individual, align with others who are already acting in this industry and doing extraordinary things, and that's self-belief.
39:15.490 --> 39:26.560
[SPEAKER_02]: It's actually called the pig mailing effect, interestingly, which is this idea that if you're told that you could do something and you could excel at it, then you're naturally going to be inclined to go and do that thing and to excel at it.
39:27.381 --> 39:28.481
[SPEAKER_00]: But the opposite could be true.
39:29.022 --> 39:31.444
[SPEAKER_00]: If somebody sort of says,
39:31.542 --> 39:55.491
[SPEAKER_02]: Yeah, you listen, and it's this self-doubt and the self-talk, and I often say this is why it's so important that we look to data because to make a decision and to do something different, it's great to have evidence as to why you're doing that because you listen to it as an individual, organizations listen to it, leaders listen to it, governments listen to it, if you can find the data to justify the decision, that's how we start to change the system.
39:55.522 --> 40:02.651
[SPEAKER_00]: When we say data there, is this about applying principles of psychology to understand people and turning that into data points?
40:02.991 --> 40:09.800
[SPEAKER_02]: There's a whole range of data, I think, that's required when you're looking at changing these issues, not at the organisation, but also societal level.
40:10.260 --> 40:13.484
[SPEAKER_02]: The data we talk about is specific data about the human.
40:13.624 --> 40:18.070
[SPEAKER_02]: So what kind of data points can we start to bring in to give you confidence
40:18.050 --> 40:39.669
[SPEAKER_02]: is going to do well at XYZ and Sally's experience is certainly one important data point but there is so much more about Sally that we don't know and if we can bring in different assessments different ways of looking at Sally it's how you start to create the some total of everything that she is so that you can start to you know get the very best out of her lift her up.
40:40.370 --> 40:40.971
[SPEAKER_02]: Thank you Sally.
40:42.392 --> 40:48.057
[SPEAKER_02]: I'm
40:48.037 --> 40:57.230
[SPEAKER_00]: This is fast, so I'm going to try and ask this question without making myself sound old and poorly in a fire, but are there generational differences as well that come into play?
40:57.390 --> 41:05.602
[SPEAKER_00]: Because one of the things I've done over the last couple of years is provide some leadership mentoring into government and the department in the UK.
41:06.343 --> 41:08.526
[SPEAKER_00]: One of the things managers have told me is,
41:08.506 --> 41:32.725
[SPEAKER_00]: they found that some of the younger staff they have, they find it difficult to know how to not necessarily motivate them but find the right fit for them or find the right tasks for them to do because there seems to be this perception that they only want to get involved in work that will have an impact or when they feel like they can show they've done something and there's less willingness than
41:32.705 --> 41:41.056
[SPEAKER_00]: maybe in previous generations to deal with work that is, you know, more bog-standard, the drogery of some of the tasks you have to do.
41:41.076 --> 41:52.570
[SPEAKER_00]: And I'm obviously saying this generalizing wildly, but is that something that you've seen in terms of generational gaps as well as kind of thinking gaps in terms of different personality styles and things?
41:52.550 --> 42:11.942
[SPEAKER_02]: Yeah, it is, and actually, that is an excellent example of what we've seen with a program in Australia, which is we work with the Australian government to try to support and create opportunities for young people to start to enter defense industry, which is the industry and ecosystem that supports national security.
42:11.922 --> 42:25.309
[SPEAKER_02]: And through that work, we did some national research that found only 5% of young people were considering careers in defence, even though it's one of the fastest growing industries in the world and it's certainly the most future proof I would say.
42:25.349 --> 42:33.205
[SPEAKER_02]: And one of the reasons behind that was because there was a perception that it was not doing good and there was no purpose.
42:33.185 --> 42:34.969
[SPEAKER_02]: Really identifying that.
42:35.130 --> 42:50.265
[SPEAKER_02]: I know it's it's interesting, but this is a problem that exists with their younger generation They do not understand that broader perspective largely because of you know misinformation and media and an education in this space and it kind of runs counter to what government is saying to us at the moment certainly in the sort of
42:50.245 --> 42:57.692
[SPEAKER_00]: You know, UK, Australia, where we're based, where we are trying to take that whole of society approach to natural security and defense and getting people on board.
42:57.832 --> 42:58.112
[SPEAKER_00]: Yes.
42:58.653 --> 42:59.534
[SPEAKER_02]: And that's to your point.
42:59.574 --> 43:08.102
[SPEAKER_02]: This is why we really need to understand motivation because purpose, there is a lot of research that shows purpose is an intrinsic driver for the younger generation.
43:08.142 --> 43:17.991
[SPEAKER_02]: And even having that data point in terms of understanding what we as leaders as organizations need to do to drive the best from that generation is really important.
43:18.443 --> 43:21.890
[SPEAKER_01]: I wonder if there's another point here relevant I see that you and I've talked about.
43:22.031 --> 43:26.640
[SPEAKER_01]: So how we engage with information, how we engage with our wide recode system.
43:27.121 --> 43:32.513
[SPEAKER_01]: I was a university last night talking and eager students keen to enter the world of work.
43:33.174 --> 43:38.325
[SPEAKER_01]: And they know almost as little about it as I did and I came through that a very long time ago, sadly.
43:38.305 --> 43:50.967
[SPEAKER_01]: You know, you would imagine that in this extraordinary environment of ours where you've got text startups left right in center, like every problem in the mansion of all is multifaceted and somebody's coming up with an idea as to how to fix it.
43:51.729 --> 43:52.430
[SPEAKER_01]: It's global.
43:52.991 --> 43:57.158
[SPEAKER_01]: Like, this should be a job for everybody, but it feels like for...
43:57.138 --> 44:08.031
[SPEAKER_01]: from what I was getting yesterday, it's still in that sort of very traditional professions, a currency banking consultancy, and so we've not changed the dialogue.
44:08.351 --> 44:10.674
[SPEAKER_01]: So that's really strange to me.
44:10.894 --> 44:23.128
[SPEAKER_01]: I don't understand why we are failing to connect the richness of life with the channels of education.
44:25.083 --> 44:26.866
[SPEAKER_01]: I don't have a second point, but I'll come back to it.
44:27.306 --> 44:30.010
[SPEAKER_02]: Can I say though, what I find interesting there, Sal is well.
44:30.130 --> 44:31.352
[SPEAKER_02]: And I know I've been guilty of it.
44:31.392 --> 44:33.254
[SPEAKER_02]: We're talking in jobs and in roles.
44:33.935 --> 44:37.080
[SPEAKER_02]: And the modern world will not stay jobs and roles forever.
44:37.260 --> 44:39.263
[SPEAKER_02]: It's going back to this point on capability.
44:39.844 --> 44:46.273
[SPEAKER_02]: If you understand all of the different capabilities that you have, you don't need to be locked into specific jobs.
44:46.293 --> 44:47.214
[SPEAKER_02]: You should be fluid.
44:47.234 --> 44:50.479
[SPEAKER_02]: You should be able to move to different projects and priorities as it happens.
44:50.499 --> 44:52.862
[SPEAKER_02]: And I know with young people, and this is where you were going,
44:52.842 --> 44:57.969
[SPEAKER_02]: As you're starting a career, there is this tendency to think, what is the job that I want to be when I grow up?
44:58.069 --> 44:59.611
[SPEAKER_02]: And that's not what it looks like anymore.
45:00.052 --> 45:06.080
[SPEAKER_02]: There's clusters of skills and capabilities that are going to be needed in the future for a broad range of things.
45:06.180 --> 45:18.296
[SPEAKER_02]: And we look in this room and you look at our career pathways, none of them are linear, and knowing that the future for our young people is not necessarily going to be a standard job that they move up through the ranks in.
45:18.336 --> 45:21.060
[SPEAKER_02]: It's going to be the old zigzag that we always hear about.
45:21.040 --> 45:33.059
[SPEAKER_01]: But more from a capability perspective, that's the second point I was going to make, which I think my concern is that our young people are becoming a product of their information environment, which is a reinforcement of what they already know.
45:33.960 --> 45:39.589
[SPEAKER_01]: So the scrolling habit, combined with the algorithms, combined with...
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[SPEAKER_01]: not necessarily having a clear version of the new world.
45:45.338 --> 45:55.114
[SPEAKER_01]: Means that if you think you want to be a doctor, it's quite easy to find information about being a doctor and then your social media is going to confirm that you want to be a doctor and this is how you do that.
45:55.094 --> 46:00.385
[SPEAKER_01]: But it doesn't tell you there's a world of biotech out there and there's a world of and so on and so on.
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[SPEAKER_01]: Because that's not how algorithms work.
46:03.672 --> 46:09.925
[SPEAKER_01]: Algorithms are trying to reduce the information noise down to something that is relevant to your interests in this moment in time.
46:10.306 --> 46:15.617
[SPEAKER_01]: And it doesn't ask you the question, have you thought about what you haven't seen yet?
46:15.597 --> 46:31.092
[SPEAKER_01]: and so we need to use technology differently to understand motivation, to understand attitude, to understand potential, so that we can map this extraordinary world that is available to people.
46:31.072 --> 46:32.134
[SPEAKER_01]: with the people.
46:32.415 --> 46:43.576
[SPEAKER_01]: It is the tech and the people and our humans interfacing in different ways and not through the reductive approach of an algorithm that is saying, you're interested in defense.
46:43.636 --> 46:49.367
[SPEAKER_01]: Here's the defense stuff because that's not I think what your average 17 year old is necessarily going to be asking.
46:49.347 --> 47:00.365
[SPEAKER_00]: And also how do we balance that against, that say the information environment is geared towards making certain careers or certain pathways look really cool and really attractive.
47:00.766 --> 47:08.318
[SPEAKER_00]: So everyone wants to go down those routes and nobody wants to do the boring work or the really difficult work or you know
47:09.260 --> 47:16.333
[SPEAKER_01]: we were talking about this again, it's that reductive approach, a status you should have a status edition on next to it.
47:18.096 --> 47:32.162
[SPEAKER_01]: But if you understood the world of statistics, I'm sure you could make this point more eloquently than I can, but if we are reductive, if we try and categorize people in, you know, let's say we're extravert or introvert, and you put people in those pigeonholes.
47:32.142 --> 47:33.685
[SPEAKER_01]: you get very specific answers.
47:34.105 --> 47:40.657
[SPEAKER_01]: I sure what we want to do is not pigeonhole people but open doors so that people can navigate their own path that is rich and varied and diverse.
47:41.478 --> 47:50.033
[SPEAKER_01]: And give them a guiding thread, not of where have you been and what have you proven, but what are you interested in?
47:50.013 --> 47:51.435
[SPEAKER_01]: where are you going to make a difference?
47:51.936 --> 47:59.989
[SPEAKER_01]: And then I think we might capture your gen whatever on quite a lot after mine sadly of I want purpose.
48:00.049 --> 48:15.092
[SPEAKER_01]: Because of course we all want purpose like that is so important to anybody going to work and if again going back to last night I was talking to people saying why what do you want to do and why do you want to do it and it
48:15.967 --> 48:18.558
[SPEAKER_01]: I felt that that was the first time some of that had been asked that question.
48:18.578 --> 48:20.165
[SPEAKER_01]: I said, you need to want to get up every morning.
48:21.189 --> 48:25.808
[SPEAKER_01]: So to work on your why, you had to see as why am I goodness it's so powerful.
48:26.395 --> 48:33.867
[SPEAKER_01]: But that's what navigates you through the hard times and the world's a bit tricky and rocky and crazy at the moment.
48:34.648 --> 48:46.366
[SPEAKER_01]: So wherever you are, whatever you want, think you want to do, you're going to have to be a little bit flexible and a little bit fluid because there's not a linear career out there anymore.
48:46.447 --> 48:46.767
[SPEAKER_00]: Yeah.
48:46.747 --> 48:52.879
[SPEAKER_00]: And I think there's always an element of, well, we all need to get up in the morning to pay the bills and go to work and answer money, etc.
48:53.581 --> 49:05.785
[SPEAKER_00]: But ultimately, to get the best out of people in whatever roles or organisations they're in, it's about giving them that motivation, which is not just about, well, you just turn up, clocking your hours and pay the bills, etc.
49:05.765 --> 49:13.898
[SPEAKER_00]: Because that's where fear and uncertainty starts to creep in when people think, well, hang on, my job is at risk here because of all the things that are out there in the world.
49:14.779 --> 49:30.203
[SPEAKER_00]: But actually, if you focus on skills and capabilities and where people can contribute, those skills, then actually you get a much better sort of motivation, but you get a much better effect, I suppose, once people come together.
49:30.436 --> 49:47.424
[SPEAKER_02]: Well and everybody is different and I think this is the key point that if we spend the time to understand the individual and get the data on the individual, we can help them be the best that they can be and going back to the point around motivation and the pigmanly in effect that I brought up before.
49:47.484 --> 49:56.038
[SPEAKER_02]: Part of our data has found that if someone has been identified as having a high potential for a certain type of training or learning or role,
49:56.018 --> 50:01.366
[SPEAKER_02]: They're 250% more likely to complete that than someone who has not had that information.
50:01.426 --> 50:03.950
[SPEAKER_02]: And that is incredibly powerful.
50:04.131 --> 50:06.775
[SPEAKER_02]: When you actually think about how can we motivate people?
50:06.815 --> 50:10.220
[SPEAKER_02]: And it speaks to the heart of, you know, am I going to be good at this?
50:10.320 --> 50:11.742
[SPEAKER_02]: People just want to be successful.
50:11.782 --> 50:12.744
[SPEAKER_02]: They want to contribute.
50:12.804 --> 50:14.006
[SPEAKER_02]: They want to feel sane.
50:14.547 --> 50:16.149
[SPEAKER_02]: How do we help people be seen?
50:16.209 --> 50:17.792
[SPEAKER_02]: And you've got to find the data.
50:18.112 --> 50:21.317
[SPEAKER_02]: You've got to find the data as an organization and as human kind.
50:22.175 --> 50:32.272
[SPEAKER_00]: I suppose I've got one last question, which is probably a big question to finish on, but I was talking to some of my colleagues last week before we came in here and started having this conversation.
50:32.292 --> 50:40.065
[SPEAKER_00]: I said, we're going to have this really interesting conversation about these topics, and it's part of a really interesting discussion with everybody there.
50:40.045 --> 50:52.536
[SPEAKER_00]: where we were talking about sort of neurodivergence and bringing in people who think differently into an organization and how you know in our organization we probably have got a lot of military veterans and people who maybe on neurodivergence.
50:54.258 --> 51:07.690
[SPEAKER_00]: How do we as an organization bring those skills in and bring together people who think differently and maybe perceive the world differently to be successful when working together collaboratively.
51:09.374 --> 51:10.697
[SPEAKER_01]: Good question, Sal.
51:11.498 --> 51:13.582
[SPEAKER_01]: I think neurodiversity is a superpower.
51:14.645 --> 51:24.885
[SPEAKER_01]: I think it is an insight from an individual that offers them huge strength in navigating their own journey.
51:24.865 --> 51:32.017
[SPEAKER_01]: If it becomes a label and it is used by an organisation, it drives perverse behaviours and can be really uncomfortable.
51:32.097 --> 51:41.693
[SPEAKER_01]: I think there is a subset of our population who have a disability defined by the wiring of their brain and we can put labels on that.
51:43.242 --> 51:45.948
[SPEAKER_00]: in order to give them the right help I guess.
51:46.589 --> 51:56.812
[SPEAKER_01]: In order to sign it, it should be seen as in order to sign post support, not in order to define categorize and put in a particular pigeonhole.
51:56.832 --> 51:58.235
[SPEAKER_01]: We are all neurodiverse.
51:58.276 --> 52:00.100
[SPEAKER_01]: That is the definition of humanity.
52:00.160 --> 52:03.006
[SPEAKER_01]: We are all wired subtly differently.
52:02.986 --> 52:13.216
[SPEAKER_01]: And understanding that we have those traits to be obsessive-compulsive or to have limited focus or to be lacking in empathy.
52:13.556 --> 52:21.123
[SPEAKER_01]: That does not make us the label associated with that pathology, the disability.
52:22.244 --> 52:31.373
[SPEAKER_01]: Those are profound difficulties in interacting day-to-day and those individuals need all support they can get.
52:31.353 --> 53:01.208
[SPEAKER_01]: but all of a son or a diverse and I do think there's a tendency in society to label and pigeonhole and for all of us to take on a diagnosis when it suits us and say, but I can't do because some of our can't do's, we have to work into can do's and we'll try harder, some of our can't do's, my son's very dyslexic, like Ben, my son's very dyslexic, telling him to write faster is not something he's ever going to be able to do because his processing,
53:01.188 --> 53:17.597
[SPEAKER_01]: But I would rather focus on what he can do, which is, again, very like that, that extraordinary ability to see the world differently, to observe things that nobody else has observed, to think, to strategize.
53:19.140 --> 53:25.812
[SPEAKER_01]: And if we talk about neurodiversity as a strength and a superpower, it is an enabling conversation.
53:25.792 --> 53:34.832
[SPEAKER_01]: We do, of course, have to have the conversation around neurodiverse, diversity and neurodivergence as something where support is absolutely necessary and required.
53:34.872 --> 53:37.338
[SPEAKER_01]: It's complex.
53:38.300 --> 53:42.990
[SPEAKER_01]: That's the beauty of being human as it's always a little bit messy and a little bit difficult.
53:42.970 --> 54:00.215
[SPEAKER_01]: But I think there's a pathway we can navigate, having really strong powerful, empowering conversations in our workplaces around how to bring the strength out of people, how to bring people to their best, how to ensure that we understand what their capable of and what their potential is.
54:01.437 --> 54:03.740
[SPEAKER_01]: I'm not sure we always do that at the moment.
54:04.722 --> 54:04.962
[SPEAKER_01]: See you.
54:04.942 --> 54:07.265
[SPEAKER_02]: Yeah, I think that's a great point, Selen.
54:07.345 --> 54:12.732
[SPEAKER_02]: I always like to go back to the data on this one as well, because I agree that we are all different.
54:12.792 --> 54:13.993
[SPEAKER_02]: And we all think differently.
54:14.033 --> 54:19.360
[SPEAKER_02]: We have, I think, almost 30,000 neurodivergent individuals who have gone through our assessments.
54:20.041 --> 54:28.992
[SPEAKER_02]: And the way that we structure our potential assessments is very purposefully to support neurodivergent individuals to understand and uncover what they're going to be great at.
54:28.972 --> 54:44.888
[SPEAKER_02]: and the way we do that is taking a traditional cognitive aptitude assessment, which typically rounds things up into a cumulative school, which means if you score highly in one area, but poorly in another, you're in Thai averages brought down, which is how the system is at fault in terms of locking people out.
54:44.868 --> 54:50.517
[SPEAKER_02]: We keep seven different constructs, very independent, so that you can score highly in one area.
54:51.238 --> 54:57.147
[SPEAKER_02]: And you'll be matched to potential roles or skills or training that might pick up on that strength.
54:57.628 --> 55:01.153
[SPEAKER_02]: And if you score very poorly in another area, doesn't matter, we just block that out.
55:01.554 --> 55:07.703
[SPEAKER_02]: Me, I score highly in abstract reasoning, very poorly in spatial reasoning, which my husband would not be surprised about.
55:07.683 --> 55:14.553
[SPEAKER_02]: And, you know, if I was to do a tradition, when I say very poorly, very poorly, spatial reasoning, which brings my entire average down.
55:14.633 --> 55:25.269
[SPEAKER_02]: But if you start to match me to roles and learning opportunities that is focused only on my strengths, it's giving me that confidence to pursue a different path that traditionally I would have been told I couldn't do.
55:25.309 --> 55:35.243
[SPEAKER_02]: So I think when it comes to genera diversity, it's really important to empower people to recognize and be celebrated for the unique
55:35.223 --> 55:48.203
[SPEAKER_02]: skill potential aptitude they bring to the table and that's how we do start to build these workforces of rich diverse fabric and if we don't look at people for all that they can do and bring, we're never going to get that.
55:48.942 --> 55:52.147
[SPEAKER_00]: That's a really positive uplifting note to finish on.
55:52.207 --> 55:54.169
[SPEAKER_00]: So this has been amazing.
55:54.290 --> 55:58.856
[SPEAKER_00]: I'm so glad you made the time to have you both here and talk about this stuff.
56:00.178 --> 56:01.340
[SPEAKER_00]: Thank you so much for your time.
56:01.400 --> 56:05.545
[SPEAKER_00]: Where can I sign post people towards the fact that more about your work?
56:06.246 --> 56:17.262
[SPEAKER_02]: Beautiful, well, I'm very happy to have people connect with me on LinkedIn, but also thegrainbeam.com website has all of the information about what we do on how we support organizations and government and individuals.
56:17.360 --> 56:18.983
[SPEAKER_00]: and I will mention you've got a great podcast as well.
56:19.645 --> 56:20.086
[SPEAKER_02]: Thank you.
56:20.186 --> 56:21.609
[SPEAKER_02]: Yes, it's called Employee Activation.
56:21.649 --> 56:23.453
[SPEAKER_02]: You are very welcome to go on listen to that one.
56:23.974 --> 56:24.735
[SPEAKER_00]: I highly recommend it.
56:24.835 --> 56:25.196
[SPEAKER_02]: Thank you.
56:26.619 --> 56:30.868
[SPEAKER_01]: And I'm also linked in my human digital thinking work is available through there.
56:31.529 --> 56:33.153
[SPEAKER_01]: I don't have a website because it's just me.
56:35.057 --> 56:35.377
[SPEAKER_00]: Excellent.
56:35.397 --> 56:36.520
[SPEAKER_00]: Well, thank you so much, Paul.
56:36.540 --> 56:37.261
[SPEAKER_01]: Thank you Terry.
56:37.281 --> 56:38.203
[SPEAKER_01]: Thank you Terry.
56:41.710 --> 56:48.378
[SPEAKER_00]: I've never recorded a podcast episode before that made me think so much about my colleagues and the work that we do other tradeies.
56:50.080 --> 57:03.517
[SPEAKER_00]: The conversation I had with them before we had this episode where I told them I'm going to be talking to see and go took to Sally about this area of how do we get the most out of people in an organisation where everyone thinks differently.
57:04.238 --> 57:11.667
[SPEAKER_00]: It really sparks interesting conversations, especially when we combine it with thinking
57:11.647 --> 57:14.011
[SPEAKER_00]: you know what a company that deals with matters of data.
57:14.492 --> 57:18.539
[SPEAKER_00]: We have some really interesting diverse thinkers in our organisation.
57:19.200 --> 57:30.921
[SPEAKER_00]: How do we bring those things together to not only create a more positive impact for individuals, or for our organisation, but to the points both Sally and CMA.
57:30.901 --> 57:41.778
[SPEAKER_00]: how do we actually bring that impact out into society and create a better work environment but also a better social environment for humanity as a whole.
57:42.379 --> 57:45.684
[SPEAKER_00]: We have the opportunity, the technology gives us this opportunity.
57:46.265 --> 57:47.687
[SPEAKER_00]: We just have to use it in the right way.
57:48.529 --> 57:50.592
[SPEAKER_00]: That's my big takeaway from this conversation.
57:50.612 --> 57:56.301
[SPEAKER_00]: I really hope others who are listening will be able to think about how you might apply some of these principles.
57:56.281 --> 58:03.118
[SPEAKER_00]: to the organisations you're working in, and together we can collectively make the world better.
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[SPEAKER_00]: Sensing the signal is brought to you by a Trades, the behavioral analytics company.
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[SPEAKER_00]: If you enjoyed this conversation, you can find out more about what we do at atradies.io