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In this week’s episode of all about business, Microsoft UK’s Chief Digital Officer Jed Griffiths shares what he’s seeing at the front lines of AI adoption. From how enterprises are deploying tools like Copilot to what’s coming next in automation and strategy, Jed offers a rare inside look at how the most powerful companies are thinking about the future of work.
We break down what AI is really doing under the hood, and why it's more than just a productivity booster. Think smarter decisions, faster execution, new revenue models, and a whole new way to lead teams and run businesses.
If you’re building something and want to stay ahead of the curve, or just want to stop feeling behind, this episode gives you a clear, honest view of where AI is going and how to get there first.
01:37 introduction
03:20 developing a company AI strategy
07:54 what is Microsoft Copilot?
09:08 productivity - businesses don't use the term right
12:09 2025 the year of more AI start ups
14:17 the valley of death
18:26 mentoring startups
20:59 before Microsoft - high tech weapons
27:37 The Four C's
38:24 will AI humanise work?
41:07 will AI change recruitment? what happens to Reed?
49:41 Microsoft's six AI principles
01:03:39 what gets Jed up on Monday mornings?
01:04:45 where Jed will be in five years
Check out Microsoft Copilot’s website: https://copilot.microsoft.com/
Follow Jed Griffiths on LinkedIn: https://www.linkedin.com/in/jedgriffiths/
Follow James Reed on LinkedIn:https://www.linkedin.com/in/chairmanjames/
James: Well, today on all about business, I'm really delighted to welcome Jed Griffiths. Um, I. One or two people have said, can we have someone from a really big company who knows a lot of stuff and I think I've delivered today, or the team certainly has. 'cause Jed, he comes from Microsoft, which is the most valuable company in the world at the moment, and he's the Chief digital officer for the uk, Microsoft, uk.
James: And Jed is also a scientist, a proper scientist. He's a physicist. And we're gonna be exploring what Microsoft is doing in the world of ai. Um, his views on startups. Um, I'm gonna try and get some information out of him about his time at the Atomic Weapons Institute, but he's been pretty cagey in the warmup.
James: And, uh, and we'll see, um, what we can learn. But I know it's gonna be a lot. So thank you very much, Jed, for coming in this afternoon. Thank you very much for having me. Yeah. Looking forward to, to, to sharing [00:01:00] some insights. So let's just begin, what do you see in the sort of tech landscape at the moment?
James: What's going on out there? What should be re. We'd be really paying attention to.
Jed: Yeah, it's a great question. It's, it's actually, uh, the number one question I get asked by pretty much every executive board or senior leader I speak to these days. Um, so, so in my day-to-day role, uh, I spend a lot of time talking to companies from different sectors, sort of various sizes, but mainly enterprises.
Jed: Um, and the number one thing that every board is asking themselves right now is, how do I get the most value out of this new generative AI thing that's hit the market? Because if you look at, uh, you know, what's happened since November 22 when chat GPT exploded onto the scene, you know, millions of users in a matter of weeks, um, pretty much every board is now asking their que the question, what does this do for my business?
Jed: What does this do for my strategy? And I think it's caught perhaps a lot, uh, you know, a lot, a lot of CTOs napping in terms of their technical strategy. And so a lot of businesses are pivoting. So the number one [00:02:00] thing is how do I make the most of ai?
James: Right. And that's, that's right in your sort of. Yeah, cross hairs, isn't it?
James: That's what your job is about. That's helping businesses do
Jed: that. That's absolutely right. Yeah. So, um, my role at Microsoft really is to, to bring the, the sort of the breadth of Microsoft, of, of what we have in the UK from our sort of, uh, our products and services. You might have heard about copilot, you know, obviously we've, we've met a lot of those.
Jed: You use copilot. That's fantastic. We, yeah. So, uh, so copilot, you know, but also our, our data and AI services. Um, and then, and then even in some of our more innovative sort of products and services that you can access through Microsoft, but it's to bring that whole plethora of capabilities to an organization to say, how can we help you innovate and change and, you know, create new value for your organization?
Jed: So for people who don't use co-pilot, just tell us a little bit about what it
James: is, what it does
Jed: well in a line. You know, uh, I, I love this piece of marketing actually. It's the, we call it the UI for ai. So it's the new user interface for ai, uh, and co-pilot's. Your, it's your companion. It's an AI companion that gets to [00:03:00] know you, your context in the workplace, documents you access, uh, people you collaborate with, and then it allows you to have a natural language, or even you can even talk to it, right?
Jed: You can just converse with this tool in a way that you would an employee, and then it reaches out into your digital library. You know the files, you have access to emails, you've sent teams, messages you have. And it helps accelerate your day, right? So it can sort of summarize documents that you've been, uh, sent recently.
Jed: It can look at your calendar and help you prioritize your week. It can, uh, summarize your emails with you and, and sort of outlook and really help you, you know, respond to long, complicated email change without having to read through them. So it's really your companion for sort of the modern workplace.
James: What I was thinking was like assistant in a way, but everyone has an assistant now with, with copilot. That's right. So is this making us all much more productive? Well, that is the aim. So there's, there's two things that's really, that's still an aim, isn't it? Yeah, that's still the aim. So what's going on there?
James: Because people saying, oh, we're not more productive, we're actually less productive. Especially the public sector gets blamed for this. [00:04:00] So what's going on? Why, why is that not, well,
Jed: a little bit of a personal point on this one. I, I really don't like the term productivity. I think not many industries really quantify what that means for them.
Jed: Right. So if we look at, um, maybe you don't like it because it's not quantified or because Yeah. I don't think organizations a concept. Yeah. I don't think organizations quantify productivity Well, I dunno what it means. Right, right. So if you've got a, um, and, and I'll, I'll qualify what I mean by that. So if you've got a bunch of job roles.
Jed: And they're using copilot and they're getting time back. Uh, that's great. Right? That's great. From a personal productivity perspective, you know, I get maybe half an hour, an hour, maybe more back by using these tools. Um, and you, what you do with that time, you know, you might choose to invest in a bit of learning.
Jed: You know, you might, uh, take on that stretch project you never had time to do before, and that's great from a personal productivity perspective. But when you look at the organizational productivity, that might be slightly different. And I think the, the challenge is for organizations to think about. Can we quantify what this extra time back means for us as a business?
Jed: What are we gonna do with that? Does [00:05:00] that mean that we change our processes in some way? Or do we give back to the employee experience of the wellbeing? I see organizations saying, look, you know, you can have that time back, right? Invest in that as you will to, well, you could just go home early.
James: Well, I dunno, maybe that's what people do.
James: Well, maybe. I mean, if they've done the work, I suppose it's a different way of thinking about it.
Jed: Yeah. But it all comes down to what this, this hangs off really, is your organizational strategy. So how you're gonna use AI and, and how this, uh, how it's supercharging, both that personal productivity, but also adding organizational productivity.
Jed: You know, is it changing the fundamental processes of how you do your work and how you, you know, how your
James: business gets paid. So if you are running an organization, if you're a manager or a CEO mm-hmm. Or you're running a team of people, you should be thinking about that. You are saying, oh yeah, the number one.
James: So these people are gonna have a bit more time because of these new tools. How can we most productively use that time?
Jed: Absolutely right. Yeah, I think that's essential. And that, that's for sort of two reasons. One, it helps with the adoption. You know, it's gonna help people, um, understand why they should [00:06:00] use a tool like copilot.
Jed: Why should they sort of invest in learning about AI skills and how to prompt, you know, many people've heard about prompt engineering or, you know, writing good quality prompts. How do I get the most out of this tool, both to give me that personal productivity, but also then to be more creative for the business, to, to think about new avenues of maybe revenue generation or new growth, whatever it might be.
Jed: And I think that's the transition point. Then organizations need to then, from a top down perspective, have a clear strategy for what they want to do with ai. I think that's the key. A lot of organizations are sort of stuck in neutral and trying to scale their AI pilots, and it's because they don't have a clear strategy for what they actually want to do with, with ai.
James: Right. Mm-hmm. We had two, uh, young entrepreneurs in the podcast studio a few weeks ago. Richard Archie Hollingsworth. Mm-hmm. And, and they've developed an AI assistant, it's called Fixer. And they were telling us about it, and they'd actually gone to America and been sort of encouraged and cajoled and sort of boosted in all sorts of ways by that experience.
James: But they've just raised [00:07:00] 10 million, uh, dollars. Amazing. For this venture, which is about five people, I think, at the moment. So that's, and one of the things they said, and I want to test this with you, is that they think 2025 is the year where you're gonna see a lot more AI startups. You know, we've been talking about companies and how they deploy it.
James: Mm-hmm. But, you know, new businesses coming into the scene with new offerings, is that something you are seeing or as Microsoft, 'cause you'll be, you'll be at the forefront 'cause you'll be supplying on the tech.
Jed: Yeah, I think that's absolutely true. We're seeing that number increase. I mean, we have, um, at Microsoft we have a, a Microsoft for Startups program that helps people, you know, build with our platform and build these new, exciting, differentiated AI solutions.
Jed: But, um. Yes, they're on, they're on the increase. I think, um, the UK's a great place to be a tech startup. Um, uh, it definitely, you know, people are looking at this technology and looking for avenues to disrupt incumbent businesses. You know, think about new business models, new ways of bringing, uh, data together in, in exciting ways to create, you say you create new services or new products.
Jed: So I think it's a really vibrant, you know, it's definitely a [00:08:00] vibrant scene around now. Why do you think
James: the UK's a good place then to do this?
Jed: Uh, I think that the UK's got a really high propensity for sort of creativity and innovation. So if you look at some of the, the, the, you know, the benchmarks and metrics over the years, you know, in terms of ip, we are right up there, I think in the top three or top five countries in terms of IP generation.
Jed: Um, you know, our education system is really, really good. We attract talent into sort of, uh, um, uh, into zones like London and Manchester particularly where there's a, a strong concentration of technical and digital skills. So I think, um, you know, it's a good place. It's, it's got good networks, you know, it's, it's got good infrastructure, so it's a good place to, to begin a
James: startup.
James: So it's like a cluster effect where you get lots of people coming together. Yeah. I suppose in London from all over the world, really.
Jed: Yeah. Ex exactly right. You know, the UK has this reputation, but then I think, uh, you know, the challenge might be scaling. So perhaps, you know, a lot of the conversations I've had over the years with, with, uh, uh, either technology startups or in, you know, before joining Microsoft, I've done a lot with the [00:09:00] Institute of Physics in supporting more deep tech or physics-based startups and, um, the challenge has always been scaling.
Jed: So getting that, you know, uh, sort of seed funding and accelerator funding, or working with networks to get you going, that's one thing and that's, you know, you can do that relatively easy in the uk. The challenge is how do you move from, uh, technology or initial sort of technology focused offering into a much more product based offering where you can get over the, you know, the traditional value of death, right?
Jed: Where you, you, you, the value of death. The value of death. That sounds bad. Well, it does sound bad, doesn't it? So the value, how you get
James: through the value of death. Yeah.
Jed: Come on, tell me. Yeah. You need to know this. Yeah. Right. So, so the value of death really is a, is where you have your initial, sort of your minimal viable product, right?
Jed: So you've, you've done your initial prototyping, you've got your, uh, uh, initial perhaps product. Um, but you need to, you need to scale, right? You need to sort of reach new markets. You need to get that recurring revenue, you need to sell it to some people. Exactly right? You need to start getting some market penetration and, you know, for various reasons.
Jed: Either it's cashflow reasons or you know, especially in the, in deep text, [00:10:00] you see this where your, where your product is tangible, where you're building a physical thing that's much tougher, right? Because you, you know, you've gotta go and manufacture that. Then you need to distribute that. Um, you know, maybe you're a, a, a biomedical devices supplier or you know, you've got a great new handheld kit for the GP surgery.
Jed: Um, that's gonna be hard, right? That's a regulated industry. So there are gonna be some delays and some challenges to getting that product out to the market and penetrating the market. And if those challenges are too great to, you know, surmount, then you end up falling into the valley of death where the funding doesn't support you to, to know, really reach that scale and really penetrate and, and, and sell into the market you're looking for.
James: So there's a lot in this valley that can kill you by the same things, but I mean, two things that struck me when you were saying that one is there's maybe not a hinterland of, of manufacturing and design capacity that you can go and get new things made at volume quickly and the other maybe there isn't the money.
James: Is that a fair summary of that?
Jed: Yeah, I think so. And I think, [00:11:00] I think the, um, uh, there was a great actually report that came out around, uh, venture capital in, in, in physics, uh, that the Institute of Physics published recently, which is a, which is a pretty good read and talks about some of these barriers.
Jed: But one in particular is, um, around. You know, for Deep tech especially, you know, I talked about these tech, you know, you see this, what do you mean by Deep Tech? Explain. So, so Deep Tech would be science-based startups, right? Where you're infusing some kind of digital AI or, or technology product with a physical product.
Jed: So it might be, um, uh, some kind of, um, augmented reality or virtual reality, or it might be drone technology or automotive technology or, you know, uh, quantum. Quantum, yeah. Something, it's something, uh, that relies on a, a physics or chemistry or some kind of engineering principle or, or IP that then is scaled through some product, right?
Jed: That's how I see, see Deep Tech. And the challenge there, of course, is a lot of your costs will be in extended sort of research and development cycles. So when you think about, if you perhaps contrast that with maybe a FinTech or a [00:12:00] finance tech business, then it's predominantly software based and the.
Jed: Thresholds for moving from that series A to series B and getting your funding? Um, it is, it's very, they're very well quantified and you know, your revenue can come quite quickly. You can go to market quite quickly as a deep tech, as an r and d. With that long r and d cycle, you're still looking for funding as you're refining your product and you're seeking new markets and you're looking to expand.
Jed: So there's some, so you need very patient investors definitely who know what they're doing. Yeah. Yeah. And investors who really understand what the commercialization routes are for deep tech products as opposed to more traditional and
James: there's a shortage of
Jed: them in the uk, is
James: that the case?
Jed: I don't know if there's a shortage.
Jed: I just think perhaps, um, from some of the sort of. Uh, you know, anecdotes from speaking to startups in this space and some of the, you know, the reports I've read things like, you know, Hurst and PitchBook and, uh, you know, some of the, the recent, um, uh, government papers that have been issued around this so that there's an, there's an issue, there's something that we're perhaps not doing.
Jed: There's a number of factors, but there's something we're not quite getting right in the UK to really [00:13:00] maximize on that scaling factor for some of this technology. So what
James: happens then? So you, so these new, these, these new businesses are heading off and they get to this place, the value of death, as you call it.
James: Uh, what happens? Do they sell out? Do they go bust or accommodate? I mean, do they, do, do these entrepreneurs get out too soon? What, what? What's the sort of outcome of this?
Jed: Uh, yeah. Well, I think all too often they, perhaps they, they, they collapse, right? They're unable to sustain, disappear. Sustain. Yeah, I think so.
Jed: I mean, um,
James: uh, I know that some, well, there's a sort of Darwinism about that as well, isn't it? You might not have been a very good idea. Well, potentially, and I think that's one of the, you're saying it might be killing good ideas as well? Well,
Jed: it could be, and I think that's right. You know, I, I, I couldn't say I'm not, I'm professed to be an, you know, an expert in the, the whole sort of startup ecosystem, but certainly from some of the ones that I've spent time either mentoring or um, uh, are supported through the, the Institute of Physics.
Jed: You know, they all report these challenges on the deep SEC side, especially of being able to overcome these, uh, these scaling challenges. Right. In getting those products
James: out there.
Jed: Yeah. Yeah. [00:14:00]
James: So you mentor startups.
Jed: I do. Yeah, I do, I do. How does Few I've mentor, I think every
James: startup should have a mentor. We were talking to a young woman who said she got such valuable advice from her mentor that saved the company the other Oh, day.
James: Wow, okay. Yeah,
Jed: but I'm not, I'm not, I'm not sure if anyone would report that. I've, I've, my advice is directly saved the company. You don't know that. I don't dunno that. Right.
James: So go on. What, so how does it work? How do you do your mentoring of startup?
Jed: Uh, so, so I, I think sometimes through networking, um, mainly, you know, uh, somebody might say, Hey, you know, J Jed's done a few of these.
Jed: Go and talk to Jed or through the work. So you're available for people Deep tech. I, I'm, I'm available certainly for Deep Tech, I think because I'm a physicist and I look at that. But I think there
James: might be someone out there be interested in your support.
Jed: Yeah, absolutely. Right. And I'm, I, I'm gladly offer that.
Jed: In fact, the, the Institute of Physics has got, um, uh, you know, a mentorship scheme that, uh, you know, accesses the sort of member networks and enables deep tech or physics-based startups to get this kind of scaling advice. But there's a load of this out, out in the UK as well. You know, there are a lot of networks and, um.
Jed: Uh, you know, everything from our kind of catapult centers right through to [00:15:00] some of the regional funding, uh, and network support that we have in the UK for startups. You know, you can get a lot of advice. Um, I think the, um, you know, the key thing that I would, that I tend to offer is how to, how to go from technology to products.
Jed: So how to think around, could you think of the, the sort of the anatomy of a, of a tech founder is probably someone PhD, maybe they've spun out of a university or they've, you know, organically come up with a great idea. Yeah. Um, founders tend to be hyperfocused on that idea, right? Yeah. Which is great. You know, they've got that singular vision.
Jed: This is gonna be great, you know, this is the technology, you know, we need to invest in researching that. But there's a long way from that thinking to creating a product that someone can realize the value of, invest in and scale and get some return. And I think sometimes that's a hard barrier for some founders to cross, which is why mentors and networking and, and having a good support.
Jed: System around you of people who've done it before and navigated those challenges is really important
James: and you are well placed. You are, you are like the sort of bridge between the [00:16:00] science and the commerce in a sense.
Jed: I, I find myself being in that space
James: at the moment.
Jed: Yeah. And I think, uh, I think that's probably
James: very
Jed: important.
Jed: Yeah. I think the transition into, over the last few years, moving into Microsoft and seeing what's happened with ai, spending a lot of time with large enterprises, spending time with, um, with startups. I was at a, um, the Barclays Eagle Labs event, uh, a few, few weeks, a few months ago. Um, and, uh, spoke to a lot of, you know, tech startups there and talked to 'em about, you know, what challenges they're facing, what's the market like?
Jed: Uh, I think it's been an explosion. Right? And so having people close to you who can help you navigate that is essential at this time. 'cause there's so much, yeah, there's so much movement in the, in the, especially in the AI market right now.
James: You said you'd moved relatively recently to Microsoft, which I know.
James: Yes. Yeah, yeah. Give us a quick heads up what you were doing before.
Jed: Uh, well, I, I could tell you, but I'd, I'd have to kill you. No, uh, it's a stupid joke. You're not gonna get away with that. We've got
James: witnesses here. Yeah.
Jed: So come on. Open up. Yeah, no, it's fine. So, uh, so [00:17:00] yeah, my, I have a, sort of an eclectic journey into Microsoft and I, I don't mind talking about this because I think, um, squid, the concept of a squiggly career I think is really important these days.
Jed: Yeah. There's a book called Squiggly Career. Yeah, there is, right? Yeah.
James: And it's very successful.
Jed: And, um, so yeah, so I, I spent sort of 19, 20 years or so in, uh, nuclear defense, um, you know, before coming into nuclear defense. Yeah, that's right. Atom bombs. Uh, yeah. Essentially, essentially in, in and out of that world, 19 years, arro.
Jed: Yeah. Around about that 19, almost, almost two decades. Yeah. And, um, so I, so that's
James: pretty different to
Jed: Microsoft, is it? Oh, it's, it's very different. Yeah. Absolutely. Completely different. Yeah. Different, uh, I mean, not many people do sort of almost two decades of work and then completely pivot their career into something very different.
Jed: That's, that's a d thing to, to. To pull off. I'm, I'm, I'm quite, quite surprised to managed it. But, um, uh, but it was good fun and I think, um, you know, I was characterized myself even in the role I'm in now as a, a technologist, as a, as a physicist within essentially a, you know, a senior software sales role, right?
Jed: Uh, you know, if you think about it, if, if I look at, if I'm [00:18:00] objective and I look at my skillset and who I am, I, I approach the world. And the way, I think as a physicist, it sort of approaches the world still.
James: Yeah, yeah. So the, the, the atomic weapons space, you know, we've seen the film Oppenheim and you are like one of those people in the white coats doing that.
James: I used to wear a white coat, but I used to, I, I, uh, yeah, I, I would've done genuinely. So you're a real scientist working, I mean, and this is, you know, you can't make mistakes in that sort of environment, can you? Yeah. It's to put it mildly. That's right. Yeah. I think so. That's quite a different one with other sort of trial and error that we have in
Jed: startups.
Jed: That's a really good, a really good point to make. Yeah. Because, um, you know, if you think about, um, the, the world of, of, of nuclear weapons and defense, you know, that's a world that, that absolutely. You, you need to be a hundred percent correct. You know, everything needs to run, as you say, it needs to run, you know, you need to have a high, um, uh, high process control.
Jed: You need to make sure that, that, that everything is operating as it should. And so, um, because of that high risk hazards, you know, you, um, if you think about the usual mantras of [00:19:00] innovation, you know, let's move fast, let's break things, let's be creative. That, that really doesn't fly in certain industries.
Jed: And, and I say it's not just in, um, uh, in nuclear defense. You know, one of the key learnings that I've taken into my role now is actually when I work a lot with other regulated industries like finance or the energy sector, where they also have very strict rules and regulations about what they can and can't do with technology.
Jed: For instance, um. Those learnings have done me well in how to think about driving innovation in heavily regulated environments. So, so, um, you know, there, there are still things you can do though, right? Just because something is, is locked down, it doesn't mean that you can't build mechanisms for, for innovation.
Jed: Interesting. But you can't test the product? Uh, no. Luckily we can't test, and luckily we don't test the product. I think that's, that's probably the, the, the, the best way to say it, or at least we don't, we don't. So no AB testing? No,
James: no, that's right. Yeah. Which is probably a good thing for world, I think it is a thing.
James: I'm really pleased that there's no ab testing. And, but then you, you mentioned that you pivoted and you, you modestly said you were quite [00:20:00] surprised. I'm not surprised that you'd done successfully in the new role. What, for someone thinking about pivoting, what, what, what should they be reflecting on? I mean, we talk a lot about transferable skills in recruitment.
James: Yeah. But what were you thinking about and, and how did you make it work? So,
Jed: so I think, yeah, you know, my, my career was quite an interesting one. You know, being a physicist, working in research and development, then a bit of manufacturing and then moving to sort of a, a, a, a technical policy and advisory role, um, you know, to policy makers and government around, around the sort of nuclear program.
Jed: And then transitioning into organizational strategy and innovation thinking, you know, working with lots of other organizations on, on what we just talked about, you know, how to create innovation in a, in a, in a regulated space. Um, and then moving into Microsoft, you know, that changed. The, the reason I looked at that was, um.
Jed: I could see where the world was going in terms of how prevalent digital technology and, you know, AI and, and how that was changing [00:21:00] how businesses were thinking around running their business and driving innovation. But this was
James: before chat
Jed: GPT. Oh yeah, that's, yeah, that's before it landed. Right. But, but actually, but you could see it coming.
Jed: Yeah, you can see it coming. And, you know, being a bit of a nerd, you know, I'd read these sort of, uh, uh, um, journal of computer science type articles around the, the nature of, um, you know, what were then sort of adversarial, generative adversarial networks or gans, you know, where that technology was going.
Jed: Transformer technology, you know, this was sort of the 2017 mark you could sort of see on the horizon. Wow. This is pretty. This is different. You know, this is a different, uh, technology base and that's gonna evolve and it's going to accelerate and it's gonna be really, really important. Uh, and it has Right?
Jed: And it's ultimately led to, so you wanted to be part of the, so I wanted to be part of it. Yeah. That's basically what happened. And so I realized, um, going back to your question, you know, what is it that I could offer? And it, and it boiled down really, you know, it was my approach to thinking. And, and that's if I think now where we are with AI as well, um, some of the core skills that.
Jed: You know, I certainly look for in candidates or [00:22:00] that I always try to put forward in, in myself. And you know, when I, when I made that pivot was, how do you think about the world? Can you, how do you assess information? You know, can you, can you navigate complex environments and someone's telling you one thing, how do you check That's true?
Jed: How would you, you know, how would you set about, I can experimental thought, right? Depend on think how would I set up testing if you've told me something that's. That's right. That's why I'm interested. You,
James: you, you described your approach to thinking Yeah. I want to know what that
Jed: is. Yeah. It's, it's rooted
James: in the scientific method, so on.
James: So, so it's rooted in the scientific Yeah.
Jed: Yeah. I think so. You
James: don't believe anyone you want to go and check it? Well, that's not, that, is that unfair?
Jed: Yeah,
Frankie: that's, it's
James: not that I don't believe anyone, but,
Jed: but you are, you are always testing. It's just a mindset. I think, I think something, one of the wonderful is that the scientific mindset.
Jed: Yeah. I think it is. It's one of the wonderful things about why I think people who are STEM professionals who have had a career in, you know, science tech, um, engineering mathematics is where stem, um, it, it that the scientific methods is a wonderful way of framing your thinking for life because it gives you that rigorous process of essentially [00:23:00] critical thinking.
Jed: And as we move now into an era of AI where information could be at your fingertips, you know, uh, um. Summarize or synthesized from huge volumes of other information sources. It's the number one skill, right, of how do I think through this? Is that likely to be the right answer? You know, what, what might I do or how might I probe and test this, and how do I need to, you know, turn that into something useful so I can, I can do something with it.
James: It does seem particularly important at this. Point, especially mm-hmm. Critical thinking with these new emerging technologies. Yeah, I think it is. Think someone told
Jed: me recently from LinkedIn, um, there was, um, sort of the top sort of 15 skills or so on LinkedIn. I think, uh, strategic thinking, adaptability, critical thinking are now up in the top five, I think, or top 10 of skills that employees are looking for.
Jed: So that suggests to me, more young people should be doing STEM subjects or they should be, um, thinking around what I call the, the kind of four C's, right? You know, the, the four C's in collaboration, communication, critical thinking, and creativity. Right. How do [00:24:00] you maximize, say them again? Yeah, you're gonna collaboration, communication, critical thinking and uh, uh, creativity.
Jed: The four C's. I love the four C's. Right? So, so, no, that's good. I like that too many, many organizations or, or many disciplines I should say. Um. You, you can, you can leverage the four Cs. And they've, they're totally transferable and I think they're really, really, really good skills to, to, to maximize on in the era of ai, because now, um, you've got this incredible tool set that's gonna be able to reach into, you know, uh, data applications or, you know, huge fields of complex data or massive amounts of documentation.
Jed: Pull all this insight out inside out, and you need to figure out what you gonna do with that. How are you gonna make the value of that? What, what are you gonna combine that with to do something useful? Right? And those, you know, that's a, that's a skillset set. And I think that's where, you know, where the, the kind of the job market's going to shift is employers are gonna be looking increasingly for people who can think, you know, think a little bit bigger, bigger picture, and understand how they can leverage [00:25:00] these tools to do the more sort of lower end, mundane information finding, but actually turn that into value.
Jed: How, how, how do you learn those skills do you think? Well, that's a good question. I think they can be taught for sure. Uh, you know, there's, like I said, that's why, going back to a question about the scientific method, that's one of the things that it sort of gives you, well, you to Yeah. Studying science.
James: Absolutely. I mean, there's quite a lot online now about communication skills. Mm-hmm. You can do courses that are very modestly priced. Yeah, yeah. That's right. And we've got lots actually on our courses page on Red. Yeah. co.co uk. So those, so those are things that anyone can go and learn. You don't need to be a physicist, do you?
Jed: No, not at all. I, I just think by, by, you know, I guess I'm lucky, really. I think, uh, I never really had a life plan. I just always, uh, said I, Hey, look, I'm really interested in science. We're gonna do that. But over the course of my career, I've discovered that actually a career in physics has stood me in really, really good stead for.
Jed: Uh, you know, these are, these are sort of adaptable skills that I can apply virtually anyway, really. And, and it's essentially, it's thinking, it's sort of teaching you critical thought. Um, again, [00:26:00] philosophy, actually, I've, I've got a few friends who, who've got, um, you know, degrees in philosophy and, and then the old joke when we were in university about the, the applied stem or the, the arts degree and so on and so forth.
Jed: That was the joke. Oh, dear. Um, I, I always remember there was a, um, uh, maybe I shouldn't say this one on the podcast, but I always remember in the, on, on the hand, on the hand dryer in the, in the toilets, in the physics department, it was someone had. It's sketched with a pen, the permanent pen arts degree pull here.
Jed: And it was just the, the kind of paper towel and that. Okay. And obviously for the purposes of listeners, no, well, you had the Dyson one for the scientist. Yeah, that's right. Yeah. Dyson one. Exactly. Yeah, yeah, please. But, um, but, but I think, uh, you know, you know, I don't prescribe to that at all, but the, um, uh, but what's really, really, what's the truth in a joke?
Jed: I perhaps I was a bit jealous 'cause my, my course was like 30 odd hours a week and I, I'd look at my colleagues in philosophy and doing three hours and spending lots of time paid off, didn't they? Did? Yeah. So where are they now? Something I, but actually they, they're laughing now because if you think about, um, [00:27:00] you know, go back to the four C's.
Jed: Mm-hmm. Right. And you think about this one around critical thinking. Uh, hey, it doesn't get much more, um, critical thinking than philosopher right, than, than the kind of philosophy, uh, uh, the, um, uh, the, the sort of art of a philosopher, right? The way they think, the way they approach problems. So when you, you're looking at ai, some of the people I've seen do really well with prompting with, uh, you know, getting AI to do what they want and using multiple tools.
Jed: Well, now agents, as you, you probably would've heard around, you know, um, the sort of agentic era they're coming from more of those communicative, uh, you know, disciplines where they're used to structuring an argument. Yes. Right? Because that's, AI likes that. If you can structure an argument, AI likes a philosopher, doesn't it?
Jed: Yeah, it does. Ai, thank goodness, because that's what I studied.
James: I pleased hear there's some hope. You called it agen. The age. Age, yes. Tell me what's that? What's that? That's, is that the age of the agent? The age of the agent? That's right. Yeah. So, so
Jed: what's that mean? How does that look? Uh, so, so this has been the holy grail, I guess, of, of AI [00:28:00] researchers for decades, which is can we create a digital system that can take action on our behalf, you know, given some instruction, then go away and, but your holiday, well, yeah, right?
Jed: I mean, you can do that now, right? So, uh, but kind of do sophisticated things for us, you know, given some basic instructions and then go away and complete a task and, and, and come back. And we're starting to see that now with generative ai. And it's, uh, in part because you've got this new, when you
James: say sophisticated things, what, what are you thinking?
Jed: Uh, well it might be, um, uh, it might be that it goes into, let's say in a business context. Um, you, uh, you know, let's take an example. So, um. I dunno. Ordering, ordering a laptop in your business, right? If you break that down, I think many people out there have been in a, in a work environment where the laptop goes wrong.
Jed: So you go to IT support and there's probably many, many steps right in, in that process to get that to replaced.
James: It doesn't bode well. It doesn't, you're not thinking this is gonna a good experience, I suppose.
Jed: Yeah. Sometimes that can
James: happen, not in Microsoft. I should not, not in read either, but um, but so you go to IT [00:29:00] support and so what will happen in this new agent?
Jed: So, so you can imagine, you know, if you think about, uh, you know, we, we certainly at Microsoft, we see this scale of, of agents, okay? So at the, um, one end you have this sort of retrieval aspect of an agent. So very simply, um, this agent might know the it, uh, help desk process really, really well. Um, and so it's got all the documentation in there.
Jed: And so usually the user say, my laptop's broken, and you'd press enter and the agent would look at that and assess that. And then I. Find out and tell you, just spit back some information. Okay. You need to do the following things. Mm-hmm. So that would be a very basic sort of query based agent. Mm-hmm. Right.
Jed: And, and it might, it might use your personal circumstance to do that. Maybe, maybe say, you know, if you're a director or you work in a field engineer, it might give you a different process. Then you might go on to, uh, sort of a, a little bit more sophisticated sort of a task-based agent. So in that same example I gave there, you say, oh my, my laptop's broken.
Jed: Um, so what that agent might do is then not just give you the process, but it might reach [00:30:00] out and pull the forms that you need to complete, or it might complete them for you. Right. And it might execute a set of. Tasks based on your context and get you some of the way there so that you know, you can start to, to action this problem.
Jed: And then you might go on to a much more sophisticated, or, or these kind of more autonomous agents where it says you might put in a prompt sale, my laptop seems to have stopped working. I can't do the following things, and press enter. And then that agent might go, okay, what's the problem here? Looks like there's a problem with a laptop.
Jed: Maybe I need to look the laptop procedures. Maybe it calls another agent that. Um, it goes to the, uh, uh, the sort of, um, the stock or the warehouse for instance, or the ordering system orders you a new one, right? Or maybe it triggers a help desk ticket for you or sort of solves that, you know, with you. So it will go and execute.
Jed: It will assess by itself and execute a number of times. So you, you
James: are seeing AI do
Jed: some or all of this already then, are you? That's right. Yeah. You know, across organizations, um, one of the key things, going back to what I was saying about that personal productivity and that business value, [00:31:00] a lot of organizations now are saying, well, hold, hold on a second.
Jed: Um, this means that these agents are now really, really closely aligned to our business processes. So what are the business processes that I can get the agents to do so that I can get my human operators really to focus on the more value add parts of the work, right? 'cause nobody really wants to spend their whole day filling in spreadsheets or doing it service test tickets, right?
Jed: No. Hate doing forms. Yeah. Everyone does, right? Nobody wants to fill informed, but you like. Interacting with your staff and you like talking and solving people's problems. Yeah. And they are more inherently human based activities, whereas the whole filling in the documentation and doing the ordering, well, nobody really wants to do that.
Jed: That could be infinite in its application. Right. Which is why I think there's just so much value to what's going on with AI right now. Um, I love this quote, actually. I, I'm not sure if I can take credit for it because, um, I, I think, uh, go on. Yeah. What is quote, quote, I didn't take credit. Yeah. Take credit.
Jed: What is it? Yeah. Um, so, so the, um, coming out of, you know, being a physicist, working in [00:32:00] manufacturing, you know, and, and, and coming out in my sort of, really part of my career, um. I, if you're aware of how manufacturing processes work, they have this thing called lean, right? Yeah. So if you've got any manufacturer and you think they'll have some kind of lean or, or, or the kind of six sigma, if you're aware of that as well, right?
Jed: Process optimization, removal of waste, optimizing the factory floor, making sure everything is in the right place, the right time. And this sort of came out of the, uh, you know, the nineties really, really big. Yeah, I remember studying it, right? Exactly right. It was transformational. It was indeed. Yeah. So, so you think about what Lean did to manufacturing in the 1990s process, as, you know, uh, measure your process, put metrics in place, eliminate waste, drive through value, all that good stuff, right?
Jed: Um, what ai, now, what generative AI and agents are doing, in my view for the knowledge work is basically what Lean did for manufacturing, right? So if you think about knowledge work and professional services, or auditing or tax or legal, um, consultancy, a lot of that work is in the IP of humans. Yeah. Okay.
Jed: It's tied up in [00:33:00] insights in, um, tacit information, hard to measure sometimes in documentations, in advisory, you know, complex rules and we never really had a tool set that could access that before. Right. You know, it was difficult. You could do it, but it was difficult. But now with generative AI and its ability to go through a lot of that documentation to reason with you and you can converse with it, suddenly you can start to put shape around that.
Jed: You know, you can put metrics around that. And I think the, the exciting thing for me is AI is gonna start doing a degree of lean and process, automate optimization now in knowledge work, you know, in, in knowledge
James: workers. That seems to be happening. But I mean, when I was listening to you, I was thinking about manufacturing and how it's changed.
James: Not many people work in manufacturing now. I mean, lean did result and lots of people leaving manufacturing going on. Is that something we should be concerned about now in knowledge work? I mean, all these people at home with their laptops doing stuff and [00:34:00] I mean if, if what you say is correct, that's gonna be.
James: Very disruptive to the labor market, isn't it? I think it could be
Jed: disruptive, but I think, um, I, I know, you know, you, you read in the press around, you know, we're looking for headcount reductions and, and job losses, you know, because of ai. Um, I actually think, um, it, it won't be all that bad. In fact, if anything, I think it will allow us to really humanize work.
Jed: Um, I think a business that really thinks about this in the right way is able to push the, the mundane and the, the, the not engaging aspects of those knowledge tasks.
James: Yeah. Some, some business leaders, they're probably not the most sort of empathetic. So, so we are gonna go from a 15,000 person organization to two or something like that.
James: And, and you think, well, is that really the best way to motivate your team to embrace these new technologies? Perhaps? Yeah. Perhaps not. Probably not, right. No, but the, the, so that's odd to me that people would sort of pronounce that maybe they're trying to get their share price up or something. I dunno. Um, but then there is this.
James: Experience that we've all had, that with more technology, there seems to be more [00:35:00] work, not less, you know, over the, certainly my career, lots of marvelous innovations. I'm busier than ever and our business is busy. Mm-hmm. So that's our lived experience. But this technology's different, isn't it? Maybe it would be different in that respect.
Jed: I'd like to think that it will be different because, um, you know, it all goes back to what I was saying about when you give people time back in a work context, what, what do you, what is your expectations of what they do with that time? And this is why it's so important, I think, to do hand in hand the. The optimization, you know, using AI to optimize people's workflows, but then that time used productively, right in the right way.
Jed: So is it, is it opening up new business models? You know, is it created, is it changing the way you operate or is it an opportunity to say, excellent, we can give that time back to our employees, you know, no more 14 hour days in some, you know, certainly in some professional services, you know, long, long working days and Sure.
Jed: Burnout in many sectors. I mean, the legal sector [00:36:00] especially suffers from this. And so, um, you know, I think there's a real opportunity to change the way we think about work. And, you know, my personal hope actually for AI is it diffuses more broadly through the economy is, um, we actually humanize work. You know, going back to what you were saying around, around the, you know, redistributing the, the workforce as it were.
Jed: Very few people's jobs are just one task. Right. You know, AI's good at task-based work. Yeah, absolutely. Fantastic. Um, but there's still some tasks that it's not ideally suited for. That's what humans are for. Right? So what we're really looking at is how do we rejig it so that AI does the tasks that it's really good at and we rehumanize work.
Jed: Right? And I would love to see that, you know, people spending and investing a lot more time in the four Cs building those collaboration, those, you know, those networks with people, spending more time with customers, spending more time and employee wellbeing. You know, spending more, being able to spend more time, perhaps your children, your family, because you've really implemented AI well and you've been able to strike a balance between a good business model [00:37:00] and a work life balance.
Jed: For me that's like, no, that's a really positive message. You know, that's really where can go. I mean, that's
James: exciting. That's game changing. That's a life improving, uh, offer. If we can make it right, that's it. Come through like that. Now I'm, I'm in recruitment and, um, been in recruitment for a long time and there's a lot going on in the sort of AI recruitment space.
James: What are you seeing what I mean? What, what do you. How do you think it might change recruitment? I'm just, I'm just being cheeky here, but I want to hear what you think because Yeah, you are, you're in the Yeah. Sharp end.
Jed: So, um, yeah, it's, it's a good question because, um, I think a, a lot of organizations in, in your sector are, are starting to really think seriously about this technology and, and what it does.
Jed: And actually there are, there are probably three, three broad areas really, uh, that, that it's really having an impact on. Um, the first is the traditional back office processes, you know, invoicing, finance, sort of onboarding, those kind of things, right? Um, uh, you know, the, being able to sort of automate and speed up those [00:38:00] processes, being able to use much more kind of predictive analytics.
Jed: Uh, so that's, you know, that's really important. Those are those sort, the generic, in fact, they're quite generic, right? Those kind of back office processes. Yeah. Mean that was the first
James: place we introduced AI years ago. Yeah, exactly. Our ca cash allocation function, I think it was. Yeah. It just went through the work like a.
James: Data sorts. Absolutely.
Jed: Right. And those sort of processes now become supercharged. So what's happened now is, um, uh, you know, one of the things that, that generative AI is doing in this sort of back office process is, is, um, allowing people who don't necessarily have the sort of data science or engineering skills to work with, you know, large volumes of organizational data that you would have in sort of your ERP or enterprise tooling.
Jed: You can now sort of push some of those tasks out to people who don't have that skillset. So you can put AI in between. And so people much closer to the work can ask questions of maybe organizational resourcing or costing or whatever it might be, and get that data, you know? Right. And get that insight from without, you know, from the, um, uh, the, the kinda source of truth, the organizational data without having to task a separate team and get them to do that analytics for [00:39:00] them.
Jed: Right. So that's, that's great. Right. 'cause that's gonna just, um, bring those data driven insights of an organization straight to the people who need them in, you know, in the front line. Um, the, for recruitment, I think, um, in particular, one of the things I think it's going to do is really help the consultant in terms of that time for profitability.
Jed: 'cause as I understand, there's quite a high, um, turnover in, in this industry as well. You know, it's a long time to sort of onboard a consultant, you know, they bring their network and the knowledge that, that they have Yeah. At their area. Um. Uh, and so then you're, you're looking at, you know, your client base, you know what, what's in the market right now?
Jed: What are you trying to match in terms of roles and candidates? Um, and so there's quite a long sort of onboarding process, bringing a new consultant in. And then of course, if they leave after a matter of months, um, that's quite a problem. So I think one of the things that the recruitment industry is gonna gain from AI is reducing that time to profitability.
Jed: When you're onboarding a new, uh, you know, a new consultant, they'll be able to access training candidate databases quicker, you know, job spec [00:40:00] creation, those kind of things, and be able to do a lot of that much, much faster. Uh, and then be much, much more effective, you know, much quicker in the organization, which would change the economics.
Jed: Right. And going back to that business model thing I was talking about, right? Mm-hmm. So you're changing the personal productivity of the consultant. The role of the business leaders is to think about how that translates into adjusted business models for value. Right? And that's, that's the AI strategy bit.
Jed: And then this final one, I think is the, um, the client engagement, right? The candidate engagement. So, um, and, um, you know, as a piece of anecdote, I know many know many recruiters lament not being able to go back to all the unsuccessful candidates and tell them, Hey, look, really sorry you didn't make it this time.
Jed: Or, you know, this was the, these were the things that perhaps, you know, would've been good to improve on. Well, AI is a great tool there for providing a much more personalized, uh, set of responses or engagement. Right. You know, you can, I. You, you would be able to sort of create those, uh, feedback mechanisms as much more readily for the candidates.
Jed: You might be able to create personalized sort of journeys for candidates [00:41:00] so that they can navigate a recruitment process much more smoothly, you know, and have that sort of Yeah, because there's a lot of disappointment involved, right? Yeah. When you apply for
James: jobs, you don't get them. You go for an interview you don't hear.
James: Yeah. Or you don't get the job and Yeah. And we want to place everyone clearly. So exactly the way maintaining good relations is, is very important. You could see AI supporting that.
Jed: Yeah. I, I, I really could. I think, I think, you know, the recruitment industry's actually got, um, uh, there's some real innovation that, that could be made in this sector.
Jed: Right. You know, if you think No, I agree. I like to think sort of blue sky. So, you know, let's imagine a world where everyone has their own kind of personal AI career. Tooling, right? So, you know, the recruitment industry or creates a kind of this competitive world where you're, you're offering to, you know, I pay, I dunno, a subscription a month or something like that.
Jed: And I, I get this like personalized AI tooling that's guiding me through my career advising what's coming up and what should I be thinking about what, what skills. I mean, that'd be amazing, right? So there's real opportunity to create these personalized candidate and career experiences based on the knowledge and the [00:42:00] experience that recruiters have in the market and job market.
James: We're seeing, we're seeing people use AI to. You know, create their cvs, shall we say. Mm-hmm. And it's used for creating job descriptions. And then the job description goes online and someone applies. You get this situation where it's sort AI's talking to ai. Yeah. Who's, you are smiling. I am, because I've read a few of these
Jed: perfect cvs recently.
Jed: We put out a job spec and expect then that the candidates replies. Exactly. Natch
James: outstanding cv. Yeah. Yeah. And the AI thinks, wow, that's the perfect person. I mean, are we kidding here? I mean, maybe the AI is better at choosing people. I don't know. But it's sort of, I mean, it's quite easy to game it, isn't it?
Jed: Oh, well this, this is really interesting. So this is like the, you know, going back to things that I, I talked to boards about. This has been kind of the recruitment industry's number one, like. Pain point at the moment in that, you know, it's very, it's, you have to stay one head, one step ahead of the game in terms of how you're doing candidate preselection and what you're looking for now, and, and be quite creative in that because, well, [00:43:00] we have a screening business Exactly.
Jed: Right. That's really busy. Yeah. Because more and more people wanna have people double check. Yeah. And I think this is just one of the, you know, one of the things that happens when a technology like this hits the market, the, there are waves of disruption for a little while while everyone reconfigures.
Jed: Okay, how are we going to updates their cv? Yeah, yeah. Right. How are we gonna deal with this? Are gonna, that we're got a perfect cv.
James: But we used to say, I mean I've written a book about CVS and, and, and it was considered fine to tailor your cv. So if you, if you were going for a sales job, you'd emphasize your sales experience.
James: Yeah, of course. If you're going for an admin job, you'd emphasize your admin. But now I think the people just said, look, this is the job I'm applying for. Chat, what should I say? Yeah, that's right. And it comes back and
Jed: it's sort of, and, and it's become difficult, right? It has. And I, I think that's where the, the innovation needs to, to come from is, okay, how can you then use the technology to have a much more, um, uh, engaging interview process, right?
Jed: How do you change the selection interview process with this technology? Right? So to really, for me, you know, the things that I [00:44:00] would be looking for is how do you find, has this candidate got the four C's I'm looking for? Right? Well, you, you could get AI to check for that. Well, I don't know, maybe, maybe you could, that, that's, maybe you couldn't, creativity.
Jed: I don't know. Right. Well, this is the innovation. I think that the sector's, it's, you know, the sector's got a huge opportunity to invest and think about, well, what does that look like? You know, I'm not a, an expert, but some people
James: are using it to screen people out as well, aren't they? You know, they, they use AI to.
James: So sort of assess people on a sort of video interview, ask set questions, right. Looks at you. You are perspiring a bit or your eyes are dilating. No, you know, this is what I'm hearing. I dunno if it's true, but it frightens me that that might be happening. And I've heard similar things too. It's a randomness and craziness of that.
James: Yeah. Um, it doesn't bo well, but you've heard similar things. I have. I have. And, and what do you think about that in other
Jed: industries as well? You know, you hear people saying, well I could use AI for this and, you know, I could use AI to, you know, make decisions about whether someone's eligible for, for this program or not.
Jed: And, and then I always, my little internal alarm bell goes off at that point [00:45:00] because, um, one of the things that's exceptionally important when you think about AI is how you approach it from an ethical and responsible point of view. You know? Yes. Where will you use ai? Where won't you use ai? That's where you need your philosopher.
Jed: That's right. Well, and that's where you need for an organization. And I think this, you know, going back to, so
James: how does that work? How do people deal, how do companies. Address that.
Jed: Yeah. So, so that, that, that's right. So, so I think, you know, for your listeners, if there's one piece of advice I could give, whether you are an entrepreneur, you know, setting up a business, or you are, you know, an established business set about creating some form of ethics or governance board for ai, so that you really have this multidisciplinary senior team of people whose role it is to dictate this is where we're gonna use it, and these are the reasons why, and this is where we're not gonna use it.
Jed: You know, these, these use cases are deemed too sensitive or, um, you, you know, prohibited Yeah. In our industry and we, we just don't feel comfortable. And then build a degree, uh, you know, a set of principles and a degree of transparency and accountability around that. That is the, if you are embarking in the world of AI and you're [00:46:00] dealing with people's.
Jed: Personal and private data, and you're making decisions about the, you know, the, the kind of future prospects you need. Something like that,
Frankie: you know, well,
Jed: you need to be able to justify decision, I suppose. Well, not just that, but I think, I think people will expect now society will expect, um, organizations to operate with degrees of transparency and trust.
Jed: And they will, you know, they will need to, you know, certainly at Microsoft that's something we, we treat extremely seriously around our, you know, how our six AI principles then turn into how we build. You have six AI principles. We, we do, yeah. We have, um, uh, uh, six, six principles that govern everything we do.
Jed: What are, do are, can you remember that? Yes. It's not a question. I'm just interested.
James: Six is a lot.
Jed: So Yeah. So what, what are they? Yeah, so it's, uh, uh, our sort of principles around, um, safety and security mm-hmm. Around, uh, um, around privacy and, and trust with AI around, uh, inclusivity of ai. Um, and then we've got our, um, uh, transparency and accountability that sort of underpin that.
Jed: Mm-hmm. Um, and, uh, all of these principles then translate. And, and guide everything that we do [00:47:00] and think about, you know, in terms of, um, how we build AI systems and how we, um, you know, how we, uh, turn that, those principles into actionable things that we do in terms of software development, but also, um, where we will and won't put products into the market.
Jed: So it's really, really important. You know, we have, um, uh, an annual AI transparency report that we do now, an office of responsible AI that is, you know, responsible for. So what's in the transparency report? What would be in that? Uh, quite a lot actually. It's, it's a big read. I think that the, the one released, uh, last year was, um, you know, 60 plus pages.
Jed: You know, it's a big report. It talks about our journey, really about generative ai, things that we've been doing in our, our tooling to, um, you know, things like copilot, how we approach things like sensitive uses of our technology. Um, uh, but yeah, it's, it's a, it's a really, you know, if you're really into.
Jed: How, you know, how an organization like Microsoft is being transparent with its use of ai. Then our transparency report is a, is a good
James: read to, to understand that. So you, are you saying, just so I understand that any company that's doing this at any scale should have a sort [00:48:00] of. Policy of that sort with a board or sub board that oversees this.
Jed: I think that's important. I think, yeah, you know, obviously depending on what you're doing and what industry you're in de you know, will dictate the level of which you go to. But I think it's very, very important because, because of the nature of generative ai and it's how, how very powerful it is in creating these human-like responses.
Jed: You know, there's a duty of care that if you're dealing with a, if you're, if you're a B2C company and you're dealing with, uh, you know, you know you're going out to the mass market, you need to make sure that the people interacting with your product, you know, are, are clear where you are using ai. You know, James, they should know it's ai.
Jed: They should know it's ai. I think that's right. Yeah. You know, we, we certainly, we make that clear. And also if you're using the Microsoft products, you'll see occasionally AI does occasionally make mistakes. You know, generative AI by its very nature, is a probabilistic tool set, so it does occasionally.
Jed: Return. It does make mistakes isn't, yeah, people say hallucinate, right? Don't like the term hallucinate. I think people hallucinate AI makes errors, but um, uh, you know, it will, it will occasionally make [00:49:00] mistakes. Um, why does it make mistakes? Oh my goodness.
James: Uh, how, how, how long have we got? No, essentially, I dunno, I'm interested, but you are the scientist.
James: What why's it? Because seems like it. So when you think about it is almost making stuff up
Jed: in a, in a, if we look at the kind of cool technology, um, so the transformer based technology that it, that, uh, things like, you know, the GPT for instance, generative pre-trained transformer. What it really does, um, in, in it sort of base form is it's kind of predicting.
Jed: The next word in a, in a sentence, you know, pretty much like auto complete on your phone. Okay. But it's doing so on on steroids, right? Yeah. So it's doing that, but it has a great deal of context about that sentence or that paragraph or the, you know, the, say the prompt or the input that you've put in. So it's just got a lot more context than your standard sort of auto complete word.
Jed: But in, at its heart, what it's doing is it's, it's predicting the, the likelihoods. Of the next words or the next string in the sentence. Um, now of course, you know, much more innovation's gone on since the sort [00:50:00] of first of the transformer technology landed, you know, a couple of years ago. And then now it does quite a lot.
Jed: It's actually quite complicated and a lot of the, what we call reasoning inference time, so that, how the models are able to make decisions around that, that context when you, when you ask it something. But in, in essence, that's what it's doing. So, um, occasionally when you've got, um, uh, sort of subjects where it doesn't have a huge amount of data on or, you know, um, where it, it, um, it, it perhaps hasn't got that, let's call it statistical background of information to rely upon.
Jed: Occasionally it will hallucinate as they say, it will create, um, the next word or next string of words, or it'll create references perhaps that, that aren't true. Now that's the model in its sort of raw form. How
James: can you check it
Jed: then? Right, exactly. So that's the model in its raw form, what you do, if you think about, um.
Jed: How you use generative AI in practice, you actually surround those systems with a huge number of safety systems. So other, um, uh, sort of technical measures that you put around the model to check, [00:51:00] right? So, you know, groundedness, uh, you know, you know how how well rooted is this response in the kind of the user's query.
Jed: Um, and you can look at, um, sort of harmful or bias information as well. You can do a lot behind the scenes to make sure that the output of that model is actually getting pretty consistent and safe and everything. That said, occasionally it does make errors, but you can do quite a lot in the safety systems to cut that down.
Jed: And that's where actually we're a tremendous amount of innovation at Microsoft is, is spent in doing that.
James: Yeah. So the cost of ai, I mean it started off pretty expensive, you know, to run these great computer systems. It's coming down, isn't it?
Jed: Yeah, that's
James: right. Yeah. Like any technology, so where you see that, yeah.
James: But that quite dramatically and quite surprisingly, yeah, it's, I mean it's, it's,
Jed: it's dropping, you know, massively. And I think, I think that's one of the, and where does that
James: leave you at Microsoft? 'cause you, you've got this huge infrastructure, haven't you? The support.
Jed: Yeah. And, and, and we're improving that in, you know, infrastructure sort of every year.
Jed: Absolutely. I mean, in the UK we've got massive, you know, multi-billion, uh, pound investments in building new data centers in the UK to make [00:52:00] sure that we are providing the, you know, the right, uh, you know, the sovereign capabilities that in, in the data centers in the UK to, to, to serve the UK market, you know, high.
Jed: Is that what people want? They want a data center market. Well, it gives you that low latency and performance. You can deploy the models closer where the people are. Right. Ultimately, you still have to move electrons over cable. So, uh, uh, to, you know, to provide this capability, you have to keep these centers nice and cool.
Jed: Yes. Yeah, absolutely. So, so UK's good
James: for that.
Jed: Well, you know, not, not as most, we could
James: serve
Jed: the whole world. It could be a new USP, sadly. Sadly. No, but, but, uh, oh, that's a shame. Um, but, um, but you're right actually talking about boat. About data center technology, there's a huge amount of innovation in, in that space.
Jed: You know, you, you people look at, um, the sustainability of data centers. Um, what I, what I love about what we've done at Microsoft and, and, and how we've done that is, um, the, the type of data centers you'll see will vary depending on their geography, right? So they will make use of the natural environment.
Jed: You know, in the, in the Nordic ions we've got data sensor. They use geothermal power and [00:53:00] then they use much more of a natural environment to cool. Um, you know, and obviously that's different in hotter climates where, you know, they haven't got that and they need different sort of cooling requirements. And there's a huge amount of innovation in terms of chip cooling and things, and even using AI within the data center to optimize the chip usage as well.
Jed: So it is a, a tremendous amount of innovation going into data centers. So this
James: reduction in cost, uh, and the, the huge infinite. Number of applications from what we're hearing means that the agent age is truly upon us, doesn't it? I mean, it's gonna be, yeah. This is gonna be huge. It's gonna be massive.
Jed: Yeah.
Jed: I think it, I think it really gonna massive. It's gonna change everything, isn't it? Well, it change a lot of things. I mean, I think, I think to a degree where we won't, it's very difficult to forecast exactly
James: what impossible, I thought. Yeah. I mean, I mean, when we started out on the worldwide web, you know, in the early nineties, yeah.
James: I mean, no one thought of social media, did they? The consequence of that are huge. So there'll be a lot of things that I guess no one's even I think that's thought of.
Jed: I think that's right. Yeah. And that's like with any, you [00:54:00] know, AI is a general purpose technology. Um, it's a general purpose technology. So, um, uh, you know, what that means is it can be applied in, you know, many different industries in many, many different ways.
Jed: And, uh, and some of that sort of unforeseen, but like many general purpose technologies, it creates new jobs and new roles Yeah. That nobody thought of before. Right.
James: So if you are a young person listening to our conversation. How, how would you advise them to sort of get to grips with ai? What sort of things should they be doing?
James: Because it seems to be pretty obvious. I mean, you left a very good job at Atomic Weapons Institute to get into AI pivoted successfully. 'cause you could see this is where the action is, right? I mean, I think people listening to this was, will be doing the same thing. This is where the action's gonna be for, I'd have said decades to come, but certainly the next couple of decades.
James: Um, how do you get started? What's the, 'cause this is all new.
Jed: Yeah, I think, and it's really interesting 'cause the, um, maybe the answer to that a number of years ago would've been, you know, go straight into sort [00:55:00] of AI development and code developments. Yes. But actually that, that might, this is different then that might not necessarily be the answer.
Jed: Yeah. And, and it's, um, uh, you know, I'm, I'm not saying that we're not gonna need coders, by the way, you know, it certainly no AI can write good code. AI can write code. Yeah. But you still need coders. You still need to know what good looks like. You know, um, just, just a quick aside on that, I, um, a friend who's an author.
Jed: Uh, you know, obviously authors getting very worried about, you know, around AI's capability to, to sort of write, write copy and text. And, but you still need authors, right? You still need people who know what good writing is to, to make sure that we're still, you know, doing that. You know, that that role won't, that disappear.
Jed: It will change, but it won't disappear. No, no, no. I'm just writing a book. Yeah, exactly. Right. But that's, you've gotta have something to say. Yeah. These things are important. I mean, look at what, um, you know, what, um, uh, sort of mass manufacture did over, over the centuries. Um. We still have artisan furniture makers.
Jed: Right. You can make, you can make every piece of furniture machinery. Well, not, not as many, right. Not as many things change. Not many will rights. That's right. No, exactly. [00:56:00] Yeah.
James: But a lot of these, a lot of the roles and the, the jobs will change. Yeah. So that's gonna con, so the will could be a continuation, but I'm, I'm thinking, you know, if I want to get started, so I should be using these apps a lot.
James: Yes. Yeah. I should be thinking about how to interrogate them and learning by trying.
Jed: Yeah. Going, going back to your question, I got sidetracked. Yeah. Going back to your question. Yeah. The, um, the sort of skills that you, you really need to think about are, um, how can you, um, how, how to, how do you use the technology?
Jed: Right. So unless you, I guess it depends if you want to go into sort of AI coding program. Absolutely. Then, you know, the, those traditional routes are still. Sure. Absolutely. Right? Completely valid. But if you're thinking more around the application of ai, then it's perhaps it's a bit different, right? And perhaps you need to be thinking more around, well, what do I know about particular industries or problems out there?
Jed: You know what, how do I get more immersed in the capabilities of this technology? So a lot of that will depend on you being much more experiential. So the number one thing that I say to anyone who perhaps hasn't ventured into AI is get hold of some tooling and start playing. Because it's the kind of [00:57:00] skillset that is learned from experience.
Jed: You know, you, you don't sort of sit down and read a manual about gens AI and no. And then get good at using it. You have to use it to get good at using it. And, and that's because of the, the nature of the tooling. So, you know, so if you're looking at, um, you know, you've got some great business ideas or you think that you can really disrupt your industry or create new value with this tool, then the best advice is to try and do that.
Jed: Right? Try and spend time playing with the technology. Look at some, maybe some of the solutions that are out there. Maybe look to create some new solutions with, again, the, um, it's lowered the barrier, right? The skills barrier, that's what it's done. You know, in terms of writing code, I mean, I certainly, as a, as a physicist, I wish I had GitHub copilot, uh, you know, 10, 15 years ago when I was writing some code.
Jed: Um, I, I would've killed for it. I wasn't a great coder as it was, but, um, now it would've been so much easier to just, to use the tool to accelerate my learning. Um, so there's all these, you know, it's an amazing learning tool.
James: Yeah. So I, one of our guests said, you know, that AI wasn't gonna take a job, [00:58:00] but someone who knew how to use it might.
James: Yeah. And so it's about learning how to use it. Yeah.
Jed: And I, I think that's true. But that, but again, you know, I, I like to, the way my mind works, I like to think by kind of big picture stuff. And if you look back over, um, you know, look back over time and the diffusion of these general purpose technologies, that's, that's always been true, you know, whenever the new technology comes along.
Jed: So you think of
James: it as a general purpose technology. It's very
Jed: much so. Yeah. I think it's a great book. What are other
James: general purpose technologies?
Jed: Well, you know, electricity. Yeah. Right. Electricity, you know, the internet. Those kind of things. You know, very general, where you can apply them in many different ways.
Jed: I think Jeffrey Ding has written a great book on this, actually, the Diffusion of General Purpose Technologies Over Time. It's a fantastic read. Okay. Uh, you always like to do a recommendation, but, um, that's good. Yeah. We'll try and get Jeffrey into Yeah. The studio it and, and it's a great, it's a great read 'cause it talks about this, right.
Jed: You know, one thing history can teach us is how these sorts of. Big technology inflection points affect society and, and the kind of things we need to do and, and, and what we can learn from that. Right. And it's the same. Yeah. Yeah.
James: Well, it's exciting to be [00:59:00] living in this moment and you've certainly lifted my spirits around all of this, and I think it's, yeah, the opportunities are vast.
James: So thanks so much for coming to talk to me today. Yeah, pleasure. Thank you so much for having me. Congratulations. Thank you. So thank you much. I'm gonna ask you two questions though. Okay. Which I ask all my guests. Well, and, and the first question, um, is what gets you up on a Monday morning? 'cause we love Mondays here a week.
James: So what gets you up on a Monday morning?
Jed: Well, it's normally one of my young children. That's a good answer. But, uh, but no really, um, what time do they get up? Oh, uh, varying. Yeah. Normally, normally before 6:00 AM some of them, but, um, so they'll be entrepreneurs. Yeah. But, uh, no, what, what gets me up on a Monday morning is, um, you know, I.
Jed: I feel very privileged to be in the position I'm in now, to be able to work with business leaders and, you know, really clever technical leaders from, you know, all across the uk and to be able to help and steer and advise how they navigate AI in their industries, you know, as their experts in their [01:00:00] industry.
Jed: So that's an amazing privilege, I think, and I, you know, I wanna make the most of that to make sure that I'm. Adding as much value as I can in, in, in helping them do that. So that, that definitely gets me outta bed in the morning knowing that, you know, piece of advice or some guidance that I can give can help members of a, an industry change a business and, and, and do some great work.
Jed: So that's, you know, that's, that's pretty cool.
James: Yeah. And the last question is, where do you see yourself in five years time? Oh my goodness. This is, this is literally particularly interesting in this space. Well,
Jed: I think, I think earlier I said, you know, I, I've never really had a life plan. Uh, I've always sort of just gone, oh, that's interesting.
Jed: I'll do that. I have that kind of a mind. I, I'm sort of attracted to things I find interesting in the moment. And so, so never really had a life plan and sort of looked at things and said, oh, that's interesting, right. To be part of that. So I, I don't actually know, I dunno where I'll be in five years. I'd like to think that I'll be able to, um, you know, learn from the experiences I've had now in this sort of career pivot I've had in the last few years.
Jed: And then. Move on to something that, that, that caches that in and, and provides more value somewhere else and hopefully [01:01:00] doing something, something interesting.
James: Yeah. I've got a feeling you'll be somewhere interesting in five years time. We might have to invite you back to find out where that is. Yeah. Okay.
James: Let's do that. Put a date in the calendar, what the next five years we'll get AI to. Yeah. Yeah. Thanks very much, Jen. That's pleasure talking to you.
Jed: Thank you.
James: Great. Great. Really good. Really interesting loads of stuff. Learned so much.
Frankie: Yeah. Loads of stuff there.
James: Um, I, at the age, agent age, I haven't heard that.
James: Yeah, I know that's the phrase phrase. As an employment agent, I feel my time has come the age. That's a really good phrase. I like it. Yeah, yeah, yeah. Is that what they call it?
Jed: That's, well, that's how it's age. I, I've gone from age, age, age. Yeah. A lot of people don't like, uh, Luke don't like it. It's, it's like Marmite.
Jed: I think Sev coined the phrase and did he Oh, he coined it. He coined it. You gonna like it? I quite like it. And a lot of, a lot of people, oh, what is this? Age agentic nonsense. And then, uh, a lot of people Oh, I love it. Agen. So it's become a bit of a Marmite phrase. A gen. Yeah. Oh, I'm glad we heard it here first.
Jed: Yeah. I,
James: I thought it was good. Yeah. Um, that was [01:02:00] interesting. Did we cover everything? Is there anything I should ask Jed?
Frankie: Yes. You know, when you were talking about, um, AI strategy, yes. You should plan ahead. Um. Yeah, because AI updates so quickly. Yeah. How, how should you treat that sort of like, uh, OKRs in the same way, so you have like sort of, uh, like a two year plan, but you, you have like short, shorter goals within that 'cause it could like completely change.
Frankie: Like, so thinking about chat GPT coming out. Yeah. That's just surely anything before that, that strategies have to completely change
Jed: this. This is a, so this is an interesting one. So it depends on, um, there's a whole answer to that that's rooted in your organizational AI strategy, your, um, approach to governance, your data strategy.
Jed: You know, how are you managing your data in your organization? 'cause of data is the fuel for ai. With, with poor data, you don't really get the AI [01:03:00] outcomes that you are Yeah. That you're looking for, right? So you need to make sure that you've, you've, you've got the right data or the right access to. You know, align to that business goals.
Jed: Yeah. Because
Frankie: for, you know, thinking about like startups and who don't necessarily have the board you were talking about. Yeah. Yeah. So
Jed: that's a good point. 'cause I, I made a note here actually that we, we didn't quite get to it, which is perhaps one of the things that um, uh, well we can do that now. Yeah. I think, what shall I
James: ask you about then on that?
Jed: Uh, how, like yeah, sort of a question around how to plan and predict ahead in terms of your AI strategy. Um, what,
James: because you said it's important to have one, it's important to
Jed: have, yeah.
James: Okay. That's, so we should have a follow up question on that. Yeah.
Jed: So if the question was something like, you know, you talked about, um, the importance of having an AI strategy, what are the core, core, core components of that?
Jed: And then what I'll do is I'll break that out into, uh, yeah, that would be really helpful. Yeah. Business alignment, technology platform, um, you know, governance. Yeah. What are the transfer? Yeah. What are the things that you need? Can, yeah, yeah, yeah. I made, um, [01:04:00] I made a bit of a note on that. I'm not sure. I, there it is.
Jed: It's on there. There we go. Yeah. Is there anything else you feel you didn't get to that you could just carry on? I, I forgot our AI principles, which is embarrassing because I'm a of the six. Yeah. Um, do you wanna do that again? I did, I did five. Um, I counted four. Yes.
James: Yeah. But it's always impossible to get, that's why I asked you.
James: 'cause it's easy to remember three. I know. More
Jed: than that becomes invisible. So what's terrible is, is because, uh, any other day of the week, I remember those. 'cause I, I talk to 'em all the time. We'll take them. Don't worry about it. We'll redo that if you want. So it's, it's, yeah. It's accountability and transparency.
Jed: I'll ask the one. And then you've got trustworthy. Uh, safety and safety and security. Privacy. There's two of them are linked. I always
James: case Do you want, do you wanna leave
Jed: it? I mean, we don't need to put that bit in. Yeah, I just can't, I can't remember the,
Frankie: would they like you to say that?
James: Uh, they'd like me to get it right.
James: Would would it be good for you to have it in, I mean, if we do it again, if you wanna have it in, I mean, I dunno. So I,
Frankie: they're listening to it [01:05:00] first, so if they, they're expecting to hear it, then
Jed: let me, let me just get over this brain fart that I've had, um,
Frankie: that I've got the time code, so I have know exactly where to put it in.
Jed: Uh, here we go. Hang on. Um, yeah, so it's, that's, it's quite, quite hilarious they called the six principles. Yeah. Yeah. Here we go. So the reliability, that was the one I, that's the one I was forget. So it's, it's fairness, reliability, and safety. Yeah, I know. Um, privacy and security. 'cause I always go by safety, but it's, it's, uh, it's pretty similar, but it's not saying.
Jed: Yeah, it's,
James: it's,
Jed: we group them because, um,
James: oh, you call 'em six principles for ai. Yeah, we do. Yeah. So they came. Okay, I'll ask you about that again, and then I'll ask you how to do a strategy. Excellent.
Frankie: Great. And also just the last one very quickly when you were talking about learning about. Getting on tooling and, and teaching yourself about
Jed: Oh, yes.
Jed: Yeah,
Frankie: yeah. Just what tools, like if you know me?
Jed: Yeah, just, okay. That's, that's a good
Frankie: shout. Just, just throw a few names out there about where [01:06:00] to get started. Yeah.
Jed: So people
James: will have a look at
Jed: stuff. Yeah, that's good. In fact, in fact, I think, um, Charles was speaking about doing some right stuff with, with Reid.
Jed: 'cause I don't know, we have our AI 1, 2, 3 program as well, um, that I learned about yesterday. So that's, that's something where we we're partnering with organizations and they can sort of provide volunteers to do skilling and, and things like that. So there's, there's that program, but, but what, what we have in, in Microsoft, we have the get on skills program.
Jed: So I might, I might talk about that because that's, I mean, we've hit our target now. We've skilled over a million and a half people in the UK on AI skills. So, so how you were talking about that
Frankie: made me want to exactly. Immediately jump on and like upskill, but I'm like, I dunno what to type in. So that would
Jed: be great.
Jed: Yeah. Okay. That's cool then. So let's, so let's, let's revisit those. Yeah. So I've got three questions here. We'll just go straight back in.
James: So, so Jed, you said how important it's for company to have an AI strategy. How do they go about that? What, what does an AI strategy look like in your, in your view? Yeah,
Jed: thanks.
Jed: So it's, it's very important I think, and there are a number of components. You know, it's easy to say AI strategy, but to make it actionable, let's, let's turn that into [01:07:00] things. So the first one really is to make sure that you've got a really clear, uh, business alignment to, you know, why you're going to use ai, right?
Jed: So where, where are we gonna use this technology? Um, rather than just sort of spin up some pilots and not really make the most of them, how are you actually going to leverage AI to provide, you know, real business value? And that's really, really important because it's not necessarily, um, you know, it's not cheap necessarily to go and put AI everywhere in your business, and you need to make sure that you're, you're doing it in the right parts.
Jed: So there's that business strategy in alignment. Um, the second thing then that comes very quickly after that is making sure that you've got a really good set, perhaps technology and data strategy that underpins that. And what I mean by that really is, um, you know, data is the fuel for ai. So if you've got poor data, then the AI you put over the top of that data won't be that great.
Jed: Okay? You won't get the great outcomes. So you need to make sure that you've got, you know, the right data that you've got, that data is governable, that it's accessible, uh, you know, it's usable by people, um, and that that really fuels and gets better AI outcomes. Um, and then I think for entrepreneurs and where [01:08:00] they perhaps have a benefit over some of the more incumbent businesses is that they tend to be what we call digital native or cloud first businesses.
Jed: So they don't necessarily have this technical debt, you know, from, from, from years of operating. They're able to sort of have that proposition and very quickly, you know, move to perhaps a cloud platform and get the most of the benefits of cloud to scale, right? So they have that technology platform. And that's really, really important because it allows them to be, uh, you know, to, uh, expand quickly, you know, the scalability of cloud serve lots of people and if you're building an application, whatever it might be, um, but also it allows you to, um, uh, it allows you to leverage the improvements in AI so you're not having to do so much of the, the, that's kind of infrastructure, estate maintenance yourself.
Jed: You're able to make the most of like new models and new capabilities as they come out and then build on a platform. And, you know, Microsoft that for that platform for us is our Azure Foundry platform, which allows you to get the most of, of AI quickly. Uh, and then two other components of that AI strategy is, um, is what we've talked about, which is the AI governance and [01:09:00] ethics, which is very, very important.
Jed: Uh, making sure that you, you know, you will know. Where you want to and not want to use ai. And you'll be transparent with your customers as well. You know, how, where you are infusing AI in that, in that products or service. Um, uh, yeah, and I think, I think that's sort of finally, I guess part of your strategy is your skilling is your people, of course.
Jed: You know, how are you giving people, um, the right skills and tools that they need to use these capabilities to grow as this technology's moving so quickly, you know, how do they learn about, um, about AI and, and
James: how do they keep current? It's interesting thinking about our business. You know, we've been around in business for quite a long time, so we've got a lot of data.
James: Um, but you know, we've probably got some legacy tech, so we might be in that camp. If you are a new startup, I mean, you can put all the new cloud stuff in place. But you haven't got the data right. So you know who's gonna win. I mean, it's sort of interesting, so I love that. I suppose you wanna put the data into a new cloud situation if you can.
Jed: Yeah, so I, I love that actually. It's really good because there's this viewpoint, I think, with a lot of founders I [01:10:00] speak to, that they're out there, they're out to, you know, build a product and disrupt a market and go, you know, and some do, right? You know, we have unicorns that, that eventually reach that status of, of being very disruptive and creating a strong following quickly.
Jed: But actually many startups don't go that go there that quickly. And actually one of the things that I would recommend is don't necessarily discount the kind of the big incumbents, right? Maybe you need to partner with them because as you say, they've got the existent client base, they've got the knowledge within the sector.
Jed: You might have a great products and a great. Disruptive, you know, avenue in that sector, wherever it might be. But perhaps you need that big incumbent to work with you on something. Maybe there's a partnership there. Right. And there's mutual value. So never discount as a founder, you know, you've got that strong vision, you got that vision to be disruptive, but sometimes the partnership angle might be the one to go down.
Jed: And, and actually in lots of enterprises I talk to, many of them now are trying to be intrapreneurs, right? So they've got this viewpoint that they wanna disrupt themselves. And so there could be a natural partnership there looking to work with, with startups and, [01:11:00] and, you know, do, do it together.
James: I think that's really interesting and, and a huge opportunity for everyone actually.
James: Absolutely. Yeah. Everyone benefits, right? Yeah. Um, you, you talked about the importance of the principles you have at Microsoft, the six principles for ai. Yes. Yeah. It would be helpful just to hear what they are. So just in summary. So if other people are thinking how they want to, you know, ensure or protect, you know.
James: Secure their business, what they might consider.
Jed: Sure. Yeah. So, so the six principles of, of, of ai, of, um, AI principles at Microsoft, they came from our Ether committee, which is, um, uh, sort of AI ethics and, um, uh, in, you know, Microsoft, how we think about, um, you know, what's the current state of the art is with, with ai and how we should think about it.
Jed: And, and sort of back in 20 17, 20 18, when we began this, this journey, um, we said, look, if we're gonna do this generative AI thing, or if we're gonna do ai and we're gonna, you know, serve these products to the world, we need to make sure that we're doing in a responsible way. And so, um, so our six, um, principles are fairness, uh, reliability and safety, [01:12:00] uh, privacy and security.
Jed: Inclusiveness and transparency and accountability. So those are the, the six principles. Um, and those guide everything we do. Um, those principles are then, um, sort of translated into operational things that we do at Microsoft to make sure that the products and the services that we build, our software development, you know, how we partner, how we build solutions, we can make sure that we're adhering to those principles in how we want to bring AI to the world.
Jed: And, and they're sort of constant. They haven't changed. Yes. Those principles are, are constant. That I think that's actually, it's a good point. Um, the, the principles have been constant. How we've operationalized them has shifted and changed in feedback and, you know, from our own learnings and also from, uh, from the market.
Jed: So we, we actually have something called the, um, responsible AI standard that we, we had a first iteration of this in. 2019. Um, and where we talked about the principles and, and ai and actually the market gave us some very good feedback in saying, look, you know, essentially these are great principles, but they're not very actionable.
Jed: And so in [01:13:00] 2022, we had a second release of the AI standard. Um, and um, uh, and what we did there was go a little bit deeper into those principles. So they break down into a number of goals that sit underneath each of those and some sort of guidance, uh, to help build sort of metrics and guidance for engineering teams.
Jed: Say, well, okay, if I'm looking at fairness and I'm building a system, how can I make sure that I'm aligning to that, that that goal of, of fairness, right? I think
James: when so much is changing, which is at the moment, it's good to have some constant mm-hmm. It really helps people definitely, yeah. Anchor to certain key things.
James: Um, you've used the, the phrase agen. The agen. This is sort of stuck in my mind, is that a Microsoft word? Uh, I think, where does it come from?
Jed: I, I think, I think, uh, Satya Nadella coined that one in our, in our ceo. Yeah. I think he, he's very good at coining things. He's, I have the hugest respect for Satya.
James: I come to some of your seminars and he's always so interesting.
James: And I, and I like the fact that he opens up to customers and tells us what he's thinking.
Jed: Yeah. He's some, so he's come up
James: with Argentic.
Jed: Yeah, he has. Right. [01:14:00] I've, I've had the pleasure of meeting him a few times and he's, um, he has a, a talismanic quality to him. I think he's, you know, he's a fantastic leader who's able to bring that clarity of, you know, of, of business and direction, but also, um, really champion the importance of a good corporate culture.
Jed: You know, an organizational culture that underpins good performance. And I think Satya really embodies that. I think that's why he's been such a successful CEO and why, you know, why people, he has such a strong following,
James: right? Microsoft has turned around really under his leadership. Which I find very interesting.
James: So he's written a book, hasn't he? Yes, he has. Yeah. Uh, hit refresh. Hit refresh. Yeah. Well we should all read that, I think. Yeah. De definitely, definitely business. Mandatory reading for, for everything. Is it mandatory? Yeah, that's right. When you joined Microsoft, you, you forced to read it. Have to hit refresh.
James: Okay, well I'm gonna read it 'cause I want to. Um, and then lastly, um, we were just talking about how, you know, people perhaps coming into the job market might begin to access AI or start experimenting with it. You know, what [01:15:00] tools. Might you recommend for people who wanna teach themselves a few things?
Jed: So that, it's a great question actually at the moment.
Jed: There's just so much out there, you know, that's actually free to, to access. Um, you know, even on, um, on, on Microsoft's own pages, actually we have something called Microsoft Learn. We've had that for a very, very long time. And, and actually what people don't realize is that it's free. You know, you can get a lot of this content, uh, learning about, you know, our tools and products, but also what generally about, um, about ai, AI products.
Jed: Um, and um, uh, additionally as part of our sort of commitments in the UK and, and, you know, in supporting the, the growth of the UK economy and, and the, and the diffusion of ai, we had something called the Get on Skills program. Um, and uh, you know, we pledged to, to sort of skill over sort a million and a half people in AI skills over the last few years.
Jed: And I think, I think we've hit our target now, which is amazing and we're continuing to do that. But
James: if you want to get on, get on skills, yeah. How do you do that? You just go
Jed: online? Go on. If you go online and look up Yeah, exactly. You know, get on or AI skills learning, you'll see a number of sort of free programs that are, are out there.
Jed: There is actually a tremendous amount of free learning. On, on [01:16:00] AI out there. Even, um, uh, LinkedIn do some amazing courses as well that are, that are free. There's a really good one, um, for business leaders on, you know, if, if, if generative, if you know, it's two years in and if you're embarrassed as a senior leader to say, Hey, look, I, I haven't really been paying much attention to this, but there's a great grounding course on LinkedIn for specifically for leaders.
Jed: It's about, I think about four or five hours long. It's a free course and yeah, there's so much great content out there to give you a good, um, starting point for how to think about this technology. And I think that's the key part.
James: Thank you.
Jed: Pleasure.
James: I think that's everything we wanted to catch up on.
Frankie: Just the tool, the tools.
Frankie: Just listing
James: a few d why did he just said that the get into, what was it? Yeah, and you,
Frankie: the top one.
James: Are you thinking we there are more? You wanna Yeah, I'm
Frankie: thinking more like, um, other. App, like apps, this I might be totally
James: Oh, I see. Yeah. Me. These might be competitive products. Yeah, they, well, so I, he might get in trouble.[01:17:00]
James: He just said Taio as SAT's, talent, value list whole. If I was gonna go really junior
Frankie: and then, and then step up to the, um, Microsoft one, that might not make any sense. You
Jed: know? Oh, it's, it's very, yeah. So it's, it's really entry level. I thought you said that. Get it's, I thought that was fine. So the way, that's fine.
Jed: The way that, um, Microsoft Learn structured is it will take you from a zero understanding point right through to advanced. So it's, it is in the, in the hierarchy. Everything's into, we have leads like 1, 2, 3 star type accreditations. So like, beginner is like literally beginner, like, I don't know anything about this subject.
Jed: And it, and then you, you can get, I think we've made Jed work hard enough, like ai now he needs a bit of downtime. No, that's right. Are
James: you looking
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