Latest / The SME Stream / Frontier or followers? How NZ can catch up on AI

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Frontier or followers? How NZ can catch up on AI · The SME Stream

• 0:04
  Speaker 1: Welcome to the Business of Tech powered by two Degrees Business.
• 0:08
  Speaker 1: I'm Peter Griffin, back with Season four off the Business
• 0:11
  Speaker 1: of Tech. Thanks for joining me once again for a
• 0:14
  Speaker 1: weekly dose of tech through a Kiwi lens, making sense
• 0:18
  Speaker 1: of the big tech related issues affecting business, the economy,
• 0:22
  Speaker 1: and society. And twenty twenty six is off to a
• 0:25
  Speaker 1: very interesting start from a tech point of view. The
• 0:28
  Speaker 1: big tech story over the holiday period was the hacking
• 0:32
  Speaker 1: and ransom attack on patient portal Manage my Health. I'm
• 0:36
  Speaker 1: a user of Manage my Health, but thankfully it appears
• 0:41
  Speaker 1: didn't have my data released onto the dark web. At
• 0:44
  Speaker 1: least I didn't get a notification to say that my
• 0:47
  Speaker 1: data had been compromised. It doesn't get much more serious
• 0:50
  Speaker 1: than sensitive health data being breached and stolen, So that
• 0:55
  Speaker 1: story has serious implications not only for trust in Manage
• 0:58
  Speaker 1: My Health, but our relationships with patient portals, particularly as
• 1:03
  Speaker 1: AI looms large in the health sector. Speaking of AI,
• 1:08
  Speaker 1: the big story so far of twenty twenty six is
• 1:11
  Speaker 1: confirmation that Apple will lean on Google's Gemini AI models
• 1:15
  Speaker 1: to inform its own foundational models. Siri and Apple Intelligence,
• 1:21
  Speaker 1: long rumored, now confirmed. A five billion dollar a year deal.
• 1:25
  Speaker 1: According to the Financial Times, it's a huge win for Google,
• 1:29
  Speaker 1: which was seen as losing the race with Open AI
• 1:32
  Speaker 1: on generative AI. That move seates it in the box
• 1:35
  Speaker 1: seat when it comes to powering the AI that billions
• 1:39
  Speaker 1: of people use on their phones.
• 1:41
  Speaker 2: Gemini, after all.
• 1:42
  Speaker 1: Is already the default AI assistant one hundreds of millions,
• 1:47
  Speaker 1: if not billions, of Android devices. So as usual in
• 1:51
  Speaker 1: the tech space, there's a lot happening. It's quite complex,
• 1:55
  Speaker 1: it's fast moving, and the premise off the business of
• 1:58
  Speaker 1: tech remains the same. In twenty twenty six, I want
• 2:02
  Speaker 1: to bring you interviews with interesting and informed people who
• 2:06
  Speaker 1: know a lot about technology to help you make sense
• 2:08
  Speaker 1: of what's going on in the world of tech, what
• 2:11
  Speaker 1: it means to you, particularly if you're in business and
• 2:14
  Speaker 1: these are mission critical decisions you have to make. But
• 2:17
  Speaker 1: for all of us who are using, consuming applying tech
• 2:21
  Speaker 1: in our day to day lives. This year, I'll double
• 2:25
  Speaker 1: down a little bit more on the idea of what
• 2:27
  Speaker 1: it means to us, unpacking what is driving the developments
• 2:31
  Speaker 1: in tech.
• 2:32
  Speaker 2: And crucially, how we should respond to them.
• 2:35
  Speaker 1: The case for a social media ban implemented as a
• 2:39
  Speaker 1: world first in Australia last month is, for instance, a
• 2:42
  Speaker 1: perfect example of an issue that needs careful unpacking to
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  Speaker 1: understand the implications and what's involved.
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  Speaker 2: I'm not going to chase the biggest.
• 2:51
  Speaker 1: Names in tech, the best known people, necessarily, I want
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  Speaker 1: to talk to people who have insights and a way
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  Speaker 1: of explaining tech that you experienced that AHA moment and
• 3:03
  Speaker 1: hopefully used what you've learned to inform your own decisions
• 3:06
  Speaker 1: about how you interact with tech to kick off in
• 3:10
  Speaker 1: that vein episode one of season four returns to the
• 3:13
  Speaker 1: topic of artificial intelligence.
• 3:15
  Speaker 2: Look.
• 3:16
  Speaker 1: I did about thirty episodes on AI last year. It
• 3:19
  Speaker 1: was the topic dajore and will remain so in twenty
• 3:22
  Speaker 1: twenty six. New Zealand loves to talk about being a
• 3:26
  Speaker 1: world class tech nation, but when it comes to AI,
• 3:29
  Speaker 1: the numbers tell a very different story. Just over half
• 3:33
  Speaker 1: of Australian and New Zealand organizations say they're using AI today,
• 3:37
  Speaker 1: compared with.
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  Speaker 2: More than two thirds worldwide.
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  Speaker 1: Only eight percent of our firms qualify as true frontier
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  Speaker 1: companies that are using AI to transform how they work,
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  Speaker 1: versus twenty two percent globally. That's according to IDC Research,
• 3:53
  Speaker 1: who surveyed four thousand business leaders late last year on
• 3:57
  Speaker 1: their approach to AI, and a study commissioned by Microsoft,
• 4:01
  Speaker 1: which with copilots and its Azure AI tools, has a
• 4:05
  Speaker 1: big stake in boosting AI uptake. That gap that IDC
• 4:10
  Speaker 1: identified really matters. IDC estimates AI could account for three
• 4:15
  Speaker 1: point seven percent of global GDP by twenty thirty, and
• 4:18
  Speaker 1: the frontier firms that are out there in front are
• 4:22
  Speaker 1: already seeing roughly three times the return on their AI
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  Speaker 1: investments compared with slower adopters. If New Zealand keeps treating
• 4:30
  Speaker 1: AI as a fancy search engine or a personal productivity
• 4:34
  Speaker 1: hack while others rebuild whole processes and industries around it,
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  Speaker 1: we risk baking our low productivity problem into the next decade.
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  Speaker 2: So why are we lagging?
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  Speaker 1: A mix of caution, skills, gaps, patchy governance and culture
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  Speaker 1: is what it boils down to. We're curious enough to
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  Speaker 1: play with AI on the side, but two risk averse
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  Speaker 1: and underprepared on data, leadership and change to go big
• 5:00
  Speaker 1: in the core of the business. In this episode, Microsoft's
• 5:04
  Speaker 1: National Chief Technology Officer for Australia and New Zealand, Sarah Coney,
• 5:09
  Speaker 1: joins me to unpack what frontier firms are doing differently,
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  Speaker 1: why so many KIWI companies are stuck in pilot mode,
• 5:16
  Speaker 1: and the concrete steps we need to take in twenty
• 5:19
  Speaker 1: twenty six to bridge the AI divide and turn our
• 5:22
  Speaker 1: application building strengths into real competitive advantage. Here's my interview
• 5:27
  Speaker 1: with Microsoft's Sarah Conney. Sarah Knney, Welcome to the Business
• 5:42
  Speaker 1: of Tech. How are you doing very well?
• 5:44
  Speaker 3: Thank you for having me.
• 5:45
  Speaker 1: Yeah, great to have you on. We're just into the
• 5:47
  Speaker 1: new year now, another year of what's probably going to be,
• 5:51
  Speaker 1: you know, a wild ride in the world of artificial intelligence.
• 5:55
  Speaker 1: Twenty twenty five was was massive on all sorts of levels,
• 5:59
  Speaker 1: from the infrastructure investment that's gone on in AI through
• 6:03
  Speaker 1: to the actual development of agentic AI. It was really
• 6:06
  Speaker 1: the year of AI agents and that's only going to
• 6:09
  Speaker 1: accelerate in twenty twenty six.
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  Speaker 4: But just before the break, Sarah.
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  Speaker 1: Microsoft commissioned IDC to do some research, so we're going
• 6:19
  Speaker 1: to have a look at that some really interesting results.
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  Speaker 1: This was four thousand businesses across the world that were
• 6:26
  Speaker 1: surveyed about their uptake of AI, and it's quite good
• 6:29
  Speaker 1: because there was a decent cohort of Australia and New
• 6:32
  Speaker 1: Zealand organizations and business people surveyed there as well. I
• 6:36
  Speaker 1: guess the headline figure there is, you know you're really
• 6:40
  Speaker 1: interested in because Microsoft Commission is in those frontier firms,
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  Speaker 1: who are the ones that are leading the way on
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  Speaker 1: uptake of AI IDC identified around the world. Globally, twenty
• 6:52
  Speaker 1: two percent of firms could be considered frontier firms. Now
• 6:56
  Speaker 1: when you drill down to Australia and New Zealand are
• 6:59
  Speaker 1: part of the world, only eight percent frontier firms. To
• 7:03
  Speaker 1: start off, describe for us what actually a frontier firm is.
• 7:07
  Speaker 3: Yeah, it's an interesting concept. And you know, when we
• 7:09
  Speaker 3: think about frontier and you've just talked to all the
• 7:12
  Speaker 3: different evolutions we've seen over the last twelve months, in
• 7:15
  Speaker 3: the last three years in terms of AI, it's those
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  Speaker 3: organizations that are looking at where can we use AI,
• 7:21
  Speaker 3: all the different places that we can put it in
• 7:24
  Speaker 3: a really intentional way. And so we often think about
• 7:27
  Speaker 3: frontier through three phases. You know. The first phase is
• 7:30
  Speaker 3: where most of us are right now, where where we
• 7:33
  Speaker 3: have some sort of AI assistant working alongside us, so
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  Speaker 3: a human with an AI assistant. The second frontier is
• 7:41
  Speaker 3: where you have agents as part of that team. And
• 7:44
  Speaker 3: then that third frontier is your entire functions or departments
• 7:48
  Speaker 3: that are operating using agents with human oversight over the
• 7:51
  Speaker 3: top of it. And so that frontier firm is one
• 7:54
  Speaker 3: where they are looking at every place they can use
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  Speaker 3: AI to optimize operations and move faster, get that of
• 8:00
  Speaker 3: customer outcomes, that whatever the focus of that organization is.
• 8:03
  Speaker 1: And look, we do in New Zealand have some frankly
• 8:07
  Speaker 1: world leading companies using AI. I'm thinking of one end
• 8:11
  Speaker 1: Z which I went to Salesforce their big Dreamforce conference
• 8:15
  Speaker 1: last year and they were held up them and Fisher
• 8:17
  Speaker 1: and pikel and zero as companies that are on the
• 8:20
  Speaker 1: forefront of rolling out agentic AI. So we do have
• 8:25
  Speaker 1: a handful of companies that have really picked up that
• 8:28
  Speaker 1: technology and run with it.
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  Speaker 4: But by and large we're trailing.
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  Speaker 1: Another stat from the IDC research fifty three percent of
• 8:36
  Speaker 1: Australian and New Zealand organizations are actually using AI today.
• 8:39
  Speaker 1: So that's trailing the world wide stats which is sixty
• 8:43
  Speaker 1: eight percent. So you know, just when you look broadly
• 8:46
  Speaker 1: across business, we have less businesses taking up the technology.
• 8:51
  Speaker 1: I guess the big question is is this a problem.
• 8:54
  Speaker 1: We've seen this trend and had this discussion around for instance,
• 8:58
  Speaker 1: cloud computing. Our firms here in New Zealand were a
• 9:01
  Speaker 1: bit late to migrating their applications and data to the
• 9:05
  Speaker 1: cloud and we were relatively late. Was great when we
• 9:08
  Speaker 1: got Microsoft's Hyperscale data center here in AWS, but we
• 9:12
  Speaker 1: were sort of late to the party on that as well.
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  Speaker 1: We have low productivity in New Zealand compared to other
• 9:19
  Speaker 1: countries in the OECD. So a lot of people, including
• 9:22
  Speaker 1: people from Microsoft, have been saying there is a link
• 9:24
  Speaker 1: here between our slow uptake of these advanced technologies and
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  Speaker 1: our progress on productivity. Yeah, keen on your insights, as
• 9:32
  Speaker 1: some of you've been at Microsoft ten years, Telstra before that,
• 9:35
  Speaker 1: and senior tech roles that link and the evidence that
• 9:39
  Speaker 1: sort of underpins it between slower uptake off technology and
• 9:44
  Speaker 1: what it means for a firm's productivity.
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  Speaker 3: Actually, you just now the thing that we did another
• 9:50
  Speaker 3: report on some of your reports at the moment. There's
• 9:52
  Speaker 3: ai diffusion report that we we put out and what
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  Speaker 3: it looks at is where does value set as technology
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  Speaker 3: to fuses across a country, And so that's what you're
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  Speaker 3: actually talking to there is, if we don't have this uptake,
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  Speaker 3: if you know, New Zealand organizations don't use AI, we
• 10:09
  Speaker 3: will not realize the value as a nation, and therefore
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  Speaker 3: we will have lower productivity, will have lower GDP when
• 10:16
  Speaker 3: you look at those comparative countries globally, and so we
• 10:20
  Speaker 3: are lagging behind. And I think what that stat actually hides,
• 10:23
  Speaker 3: that you know, sort of fifty three percent start or
• 10:25
  Speaker 3: whatever it is, actually hides is the way we're using
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  Speaker 3: AI as well. So I don't know that we're using
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  Speaker 3: it well like that is the other problem is that
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  Speaker 3: we are using it like fancy Google is my son's
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  Speaker 3: way of talking about it. We're just using it to
• 10:40
  Speaker 3: search for things as opposed to those higher level tasks.
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  Speaker 3: And so I think when you look at this report
• 10:47
  Speaker 3: and global uptake, I think the actual fundamental difference I
• 10:51
  Speaker 3: see as well is really advanced use cases, really advanced
• 10:54
  Speaker 3: ways of leaning into AI globally that perhaps across Australia
• 10:59
  Speaker 3: and New Zealand we just aren't they. And so I
• 11:01
  Speaker 3: think that number actually masks how far behind we may
• 11:04
  Speaker 3: well be because we're not using it in sophisticated ways.
• 11:08
  Speaker 1: Oh absolutely, I think you know a good chunk of
• 11:11
  Speaker 1: that fifty three percent, and the AI Forum, for instance,
• 11:14
  Speaker 1: has it much higher that figure. I think they have
• 11:16
  Speaker 1: like eighty percent of using AI, but they don't actually
• 11:19
  Speaker 1: define what is use of AI. So it could be
• 11:21
  Speaker 1: that they've subscribed to Copilot from Microsoft and that's all
• 11:25
  Speaker 1: well and good, but that's really supercharging your personal productivity
• 11:30
  Speaker 1: when it comes to email and document management and that
• 11:33
  Speaker 1: sort of thing. But it's very different to agentic AI
• 11:36
  Speaker 1: and bundling AI into customer service and call centers and
• 11:41
  Speaker 1: that sort of thing. Do you see any sort of
• 11:44
  Speaker 1: structural reasons why New Zealand in particular has sort of
• 11:47
  Speaker 1: fewer frontier firms. Is it our the size of our firms,
• 11:51
  Speaker 1: is it our capital intensity?
• 11:53
  Speaker 4: Is it a skills problem?
• 11:54
  Speaker 3: All of those things play a role in this moment.
• 11:57
  Speaker 3: I think across Australia and New Zealand, we are inherently
• 12:00
  Speaker 3: cynical and so we ask a lot of questions and
• 12:02
  Speaker 3: I'm seeing that actually now almost holding us back because
• 12:05
  Speaker 3: organizations aren't trusting any of the systems at all, and
• 12:10
  Speaker 3: so they're not even leaning into try and find out
• 12:13
  Speaker 3: what's possible. And I think so that piece is holding
• 12:15
  Speaker 3: us back. That links to skills, skills and understanding. How
• 12:18
  Speaker 3: do we understand these systems and how they work? How
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  Speaker 3: do we then build trust across this ecosystem of AI
• 12:25
  Speaker 3: and what that means? And so I think all those
• 12:27
  Speaker 3: things you talked to play a part of the role.
• 12:30
  Speaker 3: There is no one silverbullet. If there was, we would
• 12:33
  Speaker 3: have already fixed it, right. So it's a really nuanced
• 12:36
  Speaker 3: and challenging problem. And I think people just taking the
• 12:40
  Speaker 3: time to educate themselves as a really important piece of this,
• 12:43
  Speaker 3: not relying on their employers or their educational institutions where
• 12:47
  Speaker 3: they may be to teach them. They need to have
• 12:50
  Speaker 3: that personal curiosity. How do we create national level personal
• 12:53
  Speaker 3: curiosity in this moment so that everyone individually actually has
• 12:58
  Speaker 3: an understanding and takes the time because that curiosity is
• 13:01
  Speaker 3: what will serve you so powerfully as you start using
• 13:04
  Speaker 3: the tech as well. It always fascinates me what people
• 13:07
  Speaker 3: really care about in this moment, and the big question
• 13:10
  Speaker 3: they have is either governance, like where does my data go?
• 13:14
  Speaker 3: How do I know what the system is doing? How
• 13:16
  Speaker 3: can I trust it? Like? All of those questions are
• 13:18
  Speaker 3: still top of mind. But the second piece, and I
• 13:21
  Speaker 3: think this is where you perhaps go with some of
• 13:23
  Speaker 3: the questions is around that productivity piece and people's jobs.
• 13:26
  Speaker 3: People are really concerned about what does this actually mean,
• 13:30
  Speaker 3: and so that's holding people back. It's this wonderful dualism
• 13:33
  Speaker 3: that we have right which is everyone is using AI.
• 13:36
  Speaker 3: You hit a great number there, eighty percent. I think,
• 13:38
  Speaker 3: let's be honest, I think everyone is trying to use
• 13:41
  Speaker 3: it in their personal lives at least and maybe secretly
• 13:43
  Speaker 3: at work. We all have this feeling that it might
• 13:45
  Speaker 3: help us, but we're really scared that if we use
• 13:49
  Speaker 3: it effectively, we lose our jobs. And so this cognitive
• 13:52
  Speaker 3: dissonance is perhaps one of the biggest things that's holding
• 13:56
  Speaker 3: us back, because people don't have confidence that if they
• 13:59
  Speaker 3: use it and they're really at it at ed, job
• 14:00
  Speaker 3: will still exist.
• 14:01
  Speaker 1: Yeah, and look, twenty twenty five, we didn't really see
• 14:06
  Speaker 1: swathes of jobs going you know, to AI. What we
• 14:10
  Speaker 1: when I spoke to firms, and this was reflected in
• 14:13
  Speaker 1: AI forum research, is that a growing number of firms
• 14:17
  Speaker 1: are hiring less or they're leaving vacancies unfilled because they've realized, oh,
• 14:24
  Speaker 1: we can actually productively sort of use AI to do
• 14:26
  Speaker 1: some of these things. So there's a bit of a
• 14:28
  Speaker 1: sinking lid in some organizations and in New Zealand and
• 14:32
  Speaker 1: a soft economy. You know that I think is a trend.
• 14:35
  Speaker 1: You know a lot of firms have held off hiring,
• 14:38
  Speaker 1: but we haven't seen the swathes of jobs go.
• 14:40
  Speaker 4: What do you think in.
• 14:41
  Speaker 1: Twenty twenty six, particularly with agentic AI ramping up, where
• 14:46
  Speaker 1: you know, for instance, like one in zet has five
• 14:47
  Speaker 1: or six agents behind the scene doing if you want
• 14:50
  Speaker 1: to have a prepaid mobile accounts change, before you might
• 14:55
  Speaker 1: have had to go into a shop or talk to
• 14:56
  Speaker 1: someone on the phone, or even done it yourself on
• 14:58
  Speaker 1: the website. But now you can just go to a chatbot,
• 15:01
  Speaker 1: get the options and change. So that is actually cutting
• 15:04
  Speaker 1: out a number of steps in a process that will
• 15:07
  Speaker 1: only accelerate.
• 15:09
  Speaker 4: How do we deal with that?
• 15:11
  Speaker 1: And our employers and the workforce is sort of ready
• 15:14
  Speaker 1: for that acceleration in the impact of AI on labor.
• 15:18
  Speaker 3: So that example you've given is the perfect way of
• 15:22
  Speaker 3: thinking about the opportunity of AI. I think, like I think,
• 15:26
  Speaker 3: we all start off by getting AI assistance to help
• 15:30
  Speaker 3: us personally do things. But the example you've given is
• 15:32
  Speaker 3: where an organization has looked at an end to end
• 15:34
  Speaker 3: process and thought about what's the outcome. We're trying to
• 15:37
  Speaker 3: achieve faster service, better service, better customer outcomes, and how
• 15:42
  Speaker 3: can we do that differently? And they are using AI
• 15:44
  Speaker 3: to achieve that. That is the goal for a frontier
• 15:48
  Speaker 3: firm or any firm, which is how do we lift
• 15:51
  Speaker 3: the load from our people, which, let's be honest, so
• 15:53
  Speaker 3: they didn't have enough people to be able to service
• 15:55
  Speaker 3: all those requests anyway. So this now lifts the load
• 15:59
  Speaker 3: from the individuals their organization and enables them to refocus
• 16:02
  Speaker 3: on the really hard things, so things that AI can't
• 16:04
  Speaker 3: take care of, the really complex things that we love
• 16:07
  Speaker 3: that challenge us. And so if we come back to
• 16:09
  Speaker 3: the start of the question around what disruption will we
• 16:12
  Speaker 3: see in jobs? I think organizations that do this really
• 16:15
  Speaker 3: well will be looking at processes, how do they change
• 16:18
  Speaker 3: processes using agentic AI That then enables their teams to
• 16:22
  Speaker 3: pivot to different things. And I think that's really important
• 16:26
  Speaker 3: that they're intentful about that, because what do you do
• 16:30
  Speaker 3: when you have more spare time? We fill it with
• 16:33
  Speaker 3: rubbish like human nature, as we fill it with more
• 16:35
  Speaker 3: email and more teams calls. So if you have an
• 16:38
  Speaker 3: organization that's giving you great direction around what they want
• 16:42
  Speaker 3: you to now do with that time. It gives me
• 16:45
  Speaker 3: that safety, cognitive safety in that my job isn't being
• 16:50
  Speaker 3: taken away, it's being changed and changed in a really
• 16:53
  Speaker 3: great way, which is I'm pivoting to those strategic things
• 16:56
  Speaker 3: I always wish I had time for but never do,
• 16:58
  Speaker 3: And AI is now taking the load of things that
• 17:00
  Speaker 3: I just didn't really want to do. Anyway, I don't
• 17:03
  Speaker 3: have to triage all of my inbox to understand which
• 17:06
  Speaker 3: is the most important thing. AI has already been through
• 17:08
  Speaker 3: that and it's taken action on it. Or I don't
• 17:11
  Speaker 3: have to work out do I have the purchase limits
• 17:12
  Speaker 3: to buy a laptop for my new employee? Like AI
• 17:15
  Speaker 3: can do that thing for me, and I can actually
• 17:17
  Speaker 3: then focus on onboarding experiences for my team. So I
• 17:20
  Speaker 3: think if we summarize that, thinking about processes is where
• 17:24
  Speaker 3: the gold really sits for frontier firms, Like how do
• 17:28
  Speaker 3: you choose a business problem and solve that instead of
• 17:30
  Speaker 3: just throwing AI and everything hoping something sticks. And then
• 17:33
  Speaker 3: how do we help people think about their jobs in
• 17:35
  Speaker 3: a different way Because we all think about our jobs
• 17:38
  Speaker 3: and I have these ten tasks and now AI does
• 17:40
  Speaker 3: four of them, and that's where that feeling of AI
• 17:42
  Speaker 3: is taking my job comes from. But the reality is
• 17:46
  Speaker 3: like I now get to create the next four things
• 17:48
  Speaker 3: that I can do as part of that role. I
• 17:49
  Speaker 3: get to create the next evolution of this job. And
• 17:52
  Speaker 3: I see that particularly as these you know, the conversation
• 17:55
  Speaker 3: often goes to graduate roles. There's a lot of concern
• 17:58
  Speaker 3: there I be any entry level roles available. I think
• 18:01
  Speaker 3: it's such a fallacy, which is there'll be different entry
• 18:04
  Speaker 3: level roles, and those new graduates from universities and from
• 18:08
  Speaker 3: TAFE and college, they get to create what that new
• 18:11
  Speaker 3: entry entry level looks like with the assistance of AI.
• 18:14
  Speaker 3: What does that now become.
• 18:15
  Speaker 1: Yeah, and we saw just before Christmas the CEO if
• 18:19
  Speaker 1: Amazon said, you'd be absolutely crazy to stop hiring or
• 18:23
  Speaker 1: radically reduce hiring of graduates because they are the ones
• 18:26
  Speaker 1: that have gone deep on AI very quickly.
• 18:29
  Speaker 4: They're the most skilled in AI at the moment. They're
• 18:31
  Speaker 4: going to bring those skills into the business.
• 18:33
  Speaker 1: So I think that would be a mistake if we
• 18:35
  Speaker 1: were to do away with our hiring programs or reduce them.
• 18:40
  Speaker 1: But you know, the IDC research shows that those frontier
• 18:43
  Speaker 1: firms that you're talking about are getting roughly three times
• 18:47
  Speaker 1: the returns from AI compared with the slow adopters. So
• 18:51
  Speaker 1: that's significant. And what does that gap sort of translate
• 18:56
  Speaker 1: to in real terms for businesses? Are we talking here
• 18:59
  Speaker 1: about the frontier firms are having higher revenue growth, they're
• 19:03
  Speaker 1: gaining market share, they've got better margin, they're getting better
• 19:06
  Speaker 1: customer service. Are you actually seeing those sorts of results
• 19:10
  Speaker 1: when people adopt Microsoft's AI products?
• 19:14
  Speaker 3: Absolutely, but only if you were doing it in that
• 19:16
  Speaker 3: really intentional way. And so I think that's the thing
• 19:19
  Speaker 3: with frontier firms is you can't just accidentally become frontier.
• 19:22
  Speaker 3: It has to be intentional. You have to have a
• 19:25
  Speaker 3: planned process towards it. And the reason why they're actually
• 19:28
  Speaker 3: getting that kind of value is because they've chosen a
• 19:32
  Speaker 3: business problem. They haven't just bought AI and because AI
• 19:36
  Speaker 3: is cool and everyone should have AI. They've thought about it.
• 19:38
  Speaker 3: What is the problem we're trying to solve? Or imagine
• 19:41
  Speaker 3: if we could ten times the benefits in this space,
• 19:45
  Speaker 3: or what if we could grow our revenue by ten times?
• 19:48
  Speaker 3: What would that look like. They're challenging themselves to think
• 19:51
  Speaker 3: differently as opposed to simply adding AI to the thing
• 19:55
  Speaker 3: they do now. And I think that's the really important
• 19:58
  Speaker 3: thing is often people are great, I can build an
• 20:02
  Speaker 3: agent to do the thing we do today, and that's fine,
• 20:05
  Speaker 3: except that's just doing the same thing faster, and so
• 20:08
  Speaker 3: there is a point at which you cannot go any
• 20:10
  Speaker 3: faster doing the same thing. And that's the difference with frontier.
• 20:14
  Speaker 3: Frontier firms are now trying to do different things. They've
• 20:18
  Speaker 3: rethought what the process could be, they've rethought what the
• 20:21
  Speaker 3: outcome is that they're looking for, and they're using AI
• 20:24
  Speaker 3: to now actually fill gaps or help lift or create
• 20:28
  Speaker 3: seamless experiences like those examples that you had for one ends.
• 20:31
  Speaker 3: It like, it's a really great way of doing that thing,
• 20:34
  Speaker 3: completely better outcomes for everybody. But they've rethought that entire
• 20:39
  Speaker 3: experience versus just adding AI to the thing that is
• 20:41
  Speaker 3: already being done.
• 20:42
  Speaker 1: And only a third of organizations in Australia and New
• 20:45
  Speaker 1: Zealand are using AI for what IDC terms as industry
• 20:50
  Speaker 1: specific workflows. So you know, for instance, that one the
• 20:53
  Speaker 1: one ends and customer service in the telco sector, they've
• 20:56
  Speaker 1: got a whole process that's underpinned by agents. And I've
• 21:00
  Speaker 1: spoken of New Zealand firms who are doing similar things
• 21:03
  Speaker 1: and other industries as well. But we may be using
• 21:06
  Speaker 1: general use generative AI systems to maybe even to query
• 21:11
  Speaker 1: our own documents, our databases of information to extract information
• 21:16
  Speaker 1: from them, and that's helping a lot of firms, But
• 21:18
  Speaker 1: when it comes down to healthcare or manufacturing, we've been
• 21:22
  Speaker 1: a lot slower than other parts of the world and
• 21:25
  Speaker 1: actually applying AI to a specific industry use case.
• 21:30
  Speaker 3: Some of my favorite use cases are from New Zealand
• 21:33
  Speaker 3: and this is favorite as in all the ones I
• 21:35
  Speaker 3: know of and my absolutely favorite at the moment is
• 21:38
  Speaker 3: like the National Tale Health Service. Their ability to lean
• 21:42
  Speaker 3: into this space is just phenomenal to me because it's healthcare,
• 21:46
  Speaker 3: so you think about the privacy aspects. It's really important
• 21:51
  Speaker 3: you think about what they're doing, like they are serving
• 21:53
  Speaker 3: people in the toughest moment they're facing into. You don't
• 21:56
  Speaker 3: call Tallyhealth because everything's fine. You're calling because you have
• 21:59
  Speaker 3: an emergency of some kind. And the way they are
• 22:02
  Speaker 3: are leveraging AI to support their team. You know they
• 22:05
  Speaker 3: have a limited number of clinicians and their concern is
• 22:10
  Speaker 3: what about the call that we haven't yet got to?
• 22:13
  Speaker 3: What about the call in the queue, What about the
• 22:15
  Speaker 3: people who maybe in a really dark place? How do
• 22:19
  Speaker 3: we support them? And so they've been exploring agents to
• 22:22
  Speaker 3: help hold those people, to provide empathetic engagement and responses
• 22:27
  Speaker 3: to not clinical advice, none of those things, but just
• 22:29
  Speaker 3: to hold those people until a human can take them
• 22:33
  Speaker 3: and care for them and provide the support they need.
• 22:35
  Speaker 3: And I think that, to me is such a powerful
• 22:38
  Speaker 3: use cases, Like you're talking about industry use cases, that
• 22:41
  Speaker 3: to me is just gold. It is such a beautiful
• 22:44
  Speaker 3: way of leaning into this and finding a way of
• 22:48
  Speaker 3: bringing that human and AI moment together. Like it's just
• 22:51
  Speaker 3: it's not replacing the human care, but it's holding those
• 22:53
  Speaker 3: people until a human can be there for them.
• 22:55
  Speaker 1: I think it's brilliant and like, these are really compelling
• 22:58
  Speaker 1: stories and I've been writing about them for them couple
• 23:00
  Speaker 1: of years in business desk. But there's a sort of
• 23:02
  Speaker 1: a disconnect between being inspired by an understanding the potential
• 23:08
  Speaker 1: of these use cases to boost productivity and revenue and
• 23:13
  Speaker 1: customer satisfaction and our ability to actually do that. And
• 23:17
  Speaker 1: what do you think it primarily comes down to? Is
• 23:19
  Speaker 1: it a sort of an AI skills gap? Is that
• 23:22
  Speaker 1: the key thing. We just don't have the capability and
• 23:24
  Speaker 1: the confidence to roll this stuff out beyond sort of
• 23:28
  Speaker 1: an occasional pilot project in one part of the business.
• 23:31
  Speaker 3: Yeah, I think there are some key elements that I
• 23:33
  Speaker 3: see organizations really struggling with, and skills is a really
• 23:37
  Speaker 3: important piece about have we enabled the entire workforce to
• 23:41
  Speaker 3: step into this moment? And that's not just what is AI?
• 23:45
  Speaker 3: And I think there are two pieces. So one is
• 23:47
  Speaker 3: everyone needs to understand what AI is. We said that
• 23:49
  Speaker 3: at the start, but the other one is specific to
• 23:52
  Speaker 3: my role, like, well, how do I use this to
• 23:54
  Speaker 3: actually be effective in the job I'm doing? So to me,
• 23:56
  Speaker 3: there are two pieces that organizations need to do around
• 24:00
  Speaker 3: skilling for their teams, but a lot of it is
• 24:03
  Speaker 3: leadership as well, which is people. You need an organization
• 24:07
  Speaker 3: that has clear leadership. They know why they are doing this,
• 24:12
  Speaker 3: there is a purpose to it, like we're not just
• 24:13
  Speaker 3: doing it because because AI, We're doing it because it
• 24:16
  Speaker 3: actually serves something for the business. And so those tend
• 24:21
  Speaker 3: to be the two things that organizations struggle with. And
• 24:24
  Speaker 3: then the final piece would be I'd wrap it all
• 24:26
  Speaker 3: in under governance, how good is your data? How is
• 24:29
  Speaker 3: your security, who's responsible? What do you do when something
• 24:33
  Speaker 3: goes wrong? So to me, I often talk about like,
• 24:36
  Speaker 3: if you could do one thing in this moment, don't
• 24:39
  Speaker 3: do too One is that cultural skills piece and leadership,
• 24:43
  Speaker 3: and the other one is governance. If those are the
• 24:45
  Speaker 3: two things you do, you actually will be setting your
• 24:48
  Speaker 3: organization up for real success because those are things that
• 24:51
  Speaker 3: will hold you back.
• 24:52
  Speaker 1: And yeah, I wonder how much culture comes into play here.
• 24:56
  Speaker 1: Maybe we're too risk averse in Australia and New Zealand
• 25:01
  Speaker 1: because look, there are lots of surveys that that do
• 25:05
  Speaker 1: reveal that consumer trust and AI is pretty low. You know,
• 25:09
  Speaker 1: a lot of people have been burned by their first
• 25:12
  Speaker 1: experiences with chatbots. The first wave of chatbots weren't particularly effective.
• 25:17
  Speaker 1: That's improved a lot, So I think there, you know,
• 25:20
  Speaker 1: a lot of the public is coming from a point
• 25:22
  Speaker 1: of view if I don't know if I really trust AI,
• 25:25
  Speaker 1: and I'd rather deal with a human anyway. But on
• 25:29
  Speaker 1: the flip side of that, everyone hates sort of waiting
• 25:31
  Speaker 1: on a phone line for a bank or an airline
• 25:34
  Speaker 1: to answer their call. And if there is a number
• 25:37
  Speaker 1: of queries that can be handled through a chatbot interface,
• 25:41
  Speaker 1: it's going to save you a lot of time and frustration.
• 25:43
  Speaker 1: So I think people relate to that, but do we
• 25:46
  Speaker 1: sort of need to get a bit more on the
• 25:48
  Speaker 1: front foot and accept that, you know, this is a
• 25:50
  Speaker 1: reality now AI is a bit of a race. You know,
• 25:54
  Speaker 1: competitors are going to be adopting that as well. That
• 25:56
  Speaker 1: we need to have a higher appetite for risk.
• 25:59
  Speaker 3: I always coach that governance framework piece, which is you
• 26:02
  Speaker 3: don't want to take risks where you don't know what
• 26:05
  Speaker 3: the implications could be, Like, you want to understand what
• 26:07
  Speaker 3: the risk is that you're taking. And so an organization
• 26:09
  Speaker 3: should just be marching blindly into this moment. They need
• 26:12
  Speaker 3: to really understand what it is, the systems are capable of,
• 26:16
  Speaker 3: how they would operate across their data. All of those
• 26:18
  Speaker 3: things are really important. But great governance builds confidence and trust.
• 26:22
  Speaker 3: I realize how boring this sounds, and often I think
• 26:25
  Speaker 3: governance gets thrown in that red tape bucket. You don't
• 26:28
  Speaker 3: do governance, it'll slow us down. Irackon governance right now
• 26:31
  Speaker 3: is the biggest accelerator for innovation, like it just it
• 26:35
  Speaker 3: helps people feel like they can step in because they
• 26:39
  Speaker 3: know their organization has thought about the risks. They know
• 26:41
  Speaker 3: their organization has considered what might happen, and that they
• 26:45
  Speaker 3: feel protected and safe. And so for me, great governance
• 26:48
  Speaker 3: actually feeds that culture and innovation piece because you've wrapped
• 26:53
  Speaker 3: it all securely. It's the guardrails you need to know
• 26:55
  Speaker 3: that you can innovate quickly because everything has been considered
• 26:59
  Speaker 3: and taken care of. You have to think about a
• 27:01
  Speaker 3: lot of the organizations that we operate within have always
• 27:05
  Speaker 3: had this sense of, you know, we can't possibly get
• 27:08
  Speaker 3: things wrong, we can't afford to take chances, and that
• 27:12
  Speaker 3: culture that we have built over time is actually what's
• 27:14
  Speaker 3: holding us back right now because in order to use
• 27:17
  Speaker 3: AI really well, you have to experiment. You have to
• 27:20
  Speaker 3: try things. We've all had a chat with any of
• 27:22
  Speaker 3: your chosen chat pots and it's been terrible. And you
• 27:25
  Speaker 3: have to keep iterating and coaching it. Like that's not
• 27:28
  Speaker 3: a muscle we have because we're used to Well, I
• 27:30
  Speaker 3: tried it and it didn't work, So I'm going to
• 27:32
  Speaker 3: stop that now and try another thing. Like we have
• 27:35
  Speaker 3: to build a muscle of iteration and pushing and trying.
• 27:38
  Speaker 3: And I think that's just a little bit of a
• 27:40
  Speaker 3: shift that we need to try and build in as
• 27:42
  Speaker 3: part of that culture change.
• 27:43
  Speaker 1: I guess the good thing that the survey reveals is
• 27:46
  Speaker 1: that sixty six percent of A and Z organizations plan
• 27:50
  Speaker 1: to increase their AI budgets in the next twenty four months.
• 27:54
  Speaker 1: So that's good, although we are sort of more cautious
• 27:57
  Speaker 1: still compared to the rest of the world twenty seven
• 27:59
  Speaker 1: percent off our firms expect flat budgets around AI versus
• 28:03
  Speaker 1: twenty one percent, So we are lifting our spending, but
• 28:07
  Speaker 1: not as much as the rest of the world. So
• 28:10
  Speaker 1: interested in any thoughts or advice you have about how
• 28:14
  Speaker 1: to best allocate the dollars you do have potentially for
• 28:17
  Speaker 1: AI budgets. Are we typically overinvesting in some tools and
• 28:21
  Speaker 1: getting disappointed when we don't see the return under investing in,
• 28:25
  Speaker 1: for instance, getting our data house and order. You know,
• 28:27
  Speaker 1: where's the best bang for bucket the moment?
• 28:29
  Speaker 3: Yeah, And we're in such a crunched environment at the
• 28:32
  Speaker 3: moment that no one has spare budget to just throw
• 28:35
  Speaker 3: at things and hope that'll work, Which is why I
• 28:37
  Speaker 3: come back to that really intentional approach. Why are you
• 28:41
  Speaker 3: doing this? Then? If you have a really finite budget
• 28:44
  Speaker 3: to spend on AI, what are you going to spend
• 28:47
  Speaker 3: it on. And we often think about what is the
• 28:50
  Speaker 3: business value, like where is the real return in this moment?
• 28:53
  Speaker 3: And I know we've talked a lot about productivity, but
• 28:56
  Speaker 3: I think we use this term productivity and people have
• 28:59
  Speaker 3: different interpretations of what it is. When we Microsoft think
• 29:03
  Speaker 3: about business cases for AI right now, we look for
• 29:07
  Speaker 3: things that aren't just productivity, Like our return on investment
• 29:10
  Speaker 3: has to have something beyond productivity as a return, because
• 29:14
  Speaker 3: productivity is not experienced evenly across an organization, Like you
• 29:19
  Speaker 3: and I could have the same tool, Peter, and we
• 29:21
  Speaker 3: would have fundamentally different experiences of that because our individual
• 29:25
  Speaker 3: approach to it would be different. But if you change
• 29:28
  Speaker 3: a business process that is experienced evenly, like you and
• 29:32
  Speaker 3: I have exactly the same experience if we add AI
• 29:34
  Speaker 3: to a business process. So that's why we think of
• 29:37
  Speaker 3: Microsoft of that, what is the business viability in this
• 29:40
  Speaker 3: moment and finding things that go beyond productivity is really important.
• 29:44
  Speaker 3: But the other piece of that, it's a puzzle is desirability.
• 29:47
  Speaker 3: Do people actually want this thing? Because I think where
• 29:50
  Speaker 3: organizations end up wasting money as we build something because
• 29:52
  Speaker 3: it seems like a really cool idea, but no one
• 29:55
  Speaker 3: actually wants it, and why would anyone stop doing it
• 29:59
  Speaker 3: the way they've always done it? Use the new tool,
• 30:01
  Speaker 3: like you have to think about is there actually a
• 30:03
  Speaker 3: desire for this thing? And then the technology sits underneath that.
• 30:06
  Speaker 3: So those are the three lenses if you're trying to
• 30:09
  Speaker 3: find the golden place to spend your money, they are
• 30:12
  Speaker 3: the things that I would look at. Is there an
• 30:13
  Speaker 3: actual business viability beyond just productivity metrics? Is there a
• 30:17
  Speaker 3: desire for this single anyone actually use it? If you
• 30:19
  Speaker 3: build it not just because it's cool, but because it
• 30:21
  Speaker 3: solves the real problem and it helps people shift, and
• 30:24
  Speaker 3: can the technology actually help support that. Those things mean
• 30:28
  Speaker 3: you're not then just throwing money to the AI gods
• 30:31
  Speaker 3: and hoping something works like that to me, is how
• 30:33
  Speaker 3: you find those real solve a business problem, real business use.
• 30:38
  Speaker 1: Case and being really deliberate about measurability as well, actually
• 30:43
  Speaker 1: gauging this was the investment we put in the time
• 30:46
  Speaker 1: and effort, and this was the output, and can we
• 30:49
  Speaker 1: then scale that across the business and achieve even more
• 30:52
  Speaker 1: so having really good measurement and revisiting it? Is this
• 30:56
  Speaker 1: going on plan? Is this achieving the goals that we
• 30:58
  Speaker 1: set out to achieve.
• 31:00
  Speaker 3: Absolutely, and I think the piece that you just talk
• 31:02
  Speaker 3: to there a little is chain. The thing that gets
• 31:05
  Speaker 3: overlooked a lot is the technology piece of this is
• 31:09
  Speaker 3: really easy. And I know I'm a technologist saying that,
• 31:12
  Speaker 3: but honestly, trust me, the technology piece is the easy part.
• 31:15
  Speaker 3: The hard part is people, Like eighty percent of the
• 31:18
  Speaker 3: challenge you are going to face into is people change.
• 31:21
  Speaker 3: And so if you're not putting in place a proper
• 31:24
  Speaker 3: change program, you will not get value out of this
• 31:27
  Speaker 3: because you're trying to fundamentally shift how people work. And
• 31:33
  Speaker 3: you cannot do this, so you can't expect it to
• 31:35
  Speaker 3: work if you do it the way we've always done it,
• 31:37
  Speaker 3: like well, it's the traditional way of training people on
• 31:38
  Speaker 3: a new tech system. We roll out a thirty minute
• 31:42
  Speaker 3: video and everyone puts it on two times speed and
• 31:45
  Speaker 3: clicks next, next, next. We know that's what you will do.
• 31:48
  Speaker 3: We don't do the training in this moment, like this
• 31:52
  Speaker 3: is a technology that needs you to be using it
• 31:54
  Speaker 3: time and time again. You're trying to build a muscle.
• 31:57
  Speaker 3: And so if you haven't built a change program and
• 32:00
  Speaker 3: you haven't thought about how you move people from where
• 32:02
  Speaker 3: they are now, to where you need them to be.
• 32:05
  Speaker 3: Then all the money in the world won't actually get
• 32:07
  Speaker 3: you that return on investment because you could still find
• 32:09
  Speaker 3: the perfect use case, you could still deploy it really nicely,
• 32:12
  Speaker 3: but if you haven't shifted people, none of that return
• 32:15
  Speaker 3: comes back to you.
• 32:24
  Speaker 1: And just finally, Sarah, you know, IDC estimates that AI
• 32:28
  Speaker 1: could contribute three point seven percent of global GDP by
• 32:31
  Speaker 1: twenty thirty. That's sort of in line with what other
• 32:34
  Speaker 1: sort of analyst groups and other companies and economists have
• 32:40
  Speaker 1: been saying.
• 32:40
  Speaker 4: So that's great.
• 32:41
  Speaker 1: By implication, we're going to miss out on maybe some
• 32:44
  Speaker 1: of that if we don't have the same appetite for
• 32:47
  Speaker 1: uptake of AI and have as many frontier firms. What
• 32:51
  Speaker 1: can we do as a small sort of open economy,
• 32:54
  Speaker 1: like you know, New Zealand. Are there any levers you
• 32:57
  Speaker 1: think we should be pulling maybe as a nation at
• 33:00
  Speaker 1: national level to try and catch up, to try and
• 33:04
  Speaker 1: encourage our firms to become frontier firms when it comes
• 33:08
  Speaker 1: to AI.
• 33:08
  Speaker 3: You know what really fascinates me is that when I
• 33:10
  Speaker 3: look at governments across the region, I actually love the
• 33:13
  Speaker 3: way the New Zealand government is leaning in here like
• 33:15
  Speaker 3: they are leading. They've created leader boards for each of
• 33:19
  Speaker 3: the agencies and departments to measure how they are using AI,
• 33:24
  Speaker 3: which is a really powerful way of role modeling expectations.
• 33:29
  Speaker 3: They're putting out all sorts of capabilities across government. So
• 33:32
  Speaker 3: I think the environment is there, and now it's just
• 33:35
  Speaker 3: how do we create the appetite nationally? Like how do
• 33:38
  Speaker 3: we help shift the population? How do we think about diffusion?
• 33:41
  Speaker 3: And so for me that's like, how do you help
• 33:44
  Speaker 3: build national curiosity? How do we help make sure this
• 33:47
  Speaker 3: skilling available and that there is no one left behind,
• 33:50
  Speaker 3: that every element of the nation actually has an opportunity
• 33:54
  Speaker 3: in this moment, because that diffusion piece is where you
• 33:57
  Speaker 3: get that GDP uplift. Everyone understand standing and having an
• 34:00
  Speaker 3: opportunity results in GDP. And so for me, that's the
• 34:04
  Speaker 3: piece that I think perhaps there is more to be
• 34:06
  Speaker 3: done is skills and on national scale, and not just
• 34:11
  Speaker 3: for those people who already are in education, employment, in
• 34:13
  Speaker 3: training they will get given AI skills, Like how do
• 34:16
  Speaker 3: we find the people who sit outside of that? How
• 34:18
  Speaker 3: do we help find those groups who could get so
• 34:22
  Speaker 3: much more from this moment if only they had the opportunity.
• 34:25
  Speaker 1: Yeah, hey, and look maybe our LaGG eyed status could
• 34:28
  Speaker 1: be turned into an advantage. You know, when you're on
• 34:30
  Speaker 1: the bleeding edge or an early adopter, you can waste
• 34:33
  Speaker 1: a bit of money, you can make mistakes. So if
• 34:37
  Speaker 1: we at least learn from what some of those frontier
• 34:39
  Speaker 1: firms are doing around the world. Because when I talk
• 34:43
  Speaker 1: to companies here in academics and say, what is our
• 34:47
  Speaker 1: edge in AI when it comes to the application of AI,
• 34:51
  Speaker 1: because we're never going to build large language models, but
• 34:53
  Speaker 1: what can we do differently with AI? And everyone says, oh,
• 34:57
  Speaker 1: we'll high trust here, particularly a use of Mari data
• 35:01
  Speaker 1: and the work that's been done around that, and Microsoft
• 35:04
  Speaker 1: has been involved in that, so we're high trust. We're
• 35:07
  Speaker 1: really good on language and all of these and integrity
• 35:10
  Speaker 1: of data and sovereign data and all those sorts of things. Well,
• 35:14
  Speaker 1: maybe being a little bit later, but doing it really
• 35:16
  Speaker 1: well when we do it is actually a big advantage.
• 35:19
  Speaker 3: Absolutely. And you know what we think about the economics
• 35:22
  Speaker 3: of AI, like you think about the stack of things
• 35:24
  Speaker 3: you talked about, New Zealand will probably not build large
• 35:26
  Speaker 3: language models. The biggest economic opportunity is in applications. Building
• 35:31
  Speaker 3: applications and what is New Zealand great at building applications,
• 35:35
  Speaker 3: like you think about the innovation that comes out of
• 35:37
  Speaker 3: New Zealand, like this opportunity really sits here for you,
• 35:40
  Speaker 3: and so yes, leverage all that learning that has come
• 35:43
  Speaker 3: and lean into the thing that you are great at,
• 35:45
  Speaker 3: which is building applications. And this is a huge moment
• 35:48
  Speaker 3: for I think New Zealand.
• 35:49
  Speaker 4: To lean into great advice.
• 35:51
  Speaker 1: Sarah, thanks so much for talking us through that research.
• 35:54
  Speaker 1: We'll post links to all of that so people can
• 35:56
  Speaker 1: have a read and hopefully be inspired to build capability
• 36:00
  Speaker 1: in twenty twenty six. So thanks so much for coming
• 36:03
  Speaker 1: onto Business of Tech.
• 36:04
  Speaker 3: My pleasure, thank you so much for having me.
• 36:10
  Speaker 2: So there you have it.
• 36:11
  Speaker 1: My key takeaways from that chat with Sarah Coney about
• 36:14
  Speaker 1: uptake of AI and a difference between being a frontier firm.
• 36:18
  Speaker 2: And one that lags on AI uptake.
• 36:21
  Speaker 1: First thing, frontier firms treat AI as a business transformation,
• 36:26
  Speaker 1: not a tech bolt on. Senior leadership directly sponsor and
• 36:30
  Speaker 1: own the vision and the outcomes. Frontier firms start from
• 36:35
  Speaker 1: a specific high value use case. It might be productivity,
• 36:38
  Speaker 1: it could be customer experience, or it even could be
• 36:41
  Speaker 1: cost reduction.
• 36:42
  Speaker 2: They design AI around those.
• 36:44
  Speaker 1: Rather than playing with tools and hoping that value emerges
• 36:48
  Speaker 1: from the experimentation. They typically run multiple AI pilots in parallel,
• 36:54
  Speaker 1: learn across them, and iterate quickly, which gets them to
• 36:59
  Speaker 1: value faster than doing one cautious pilot at a time.
• 37:02
  Speaker 1: They go wide on AI quickly, and they describe themselves
• 37:06
  Speaker 1: as AI driven organizations. The ultimate aim is to have
• 37:09
  Speaker 1: AI infused across every aspect of the business. We have
• 37:14
  Speaker 1: less frontier firms for a complex mix of reasons. Many
• 37:18
  Speaker 1: organizations still frame AI as a technology project run by
• 37:22
  Speaker 1: it rather than a business lead change. So those sorts
• 37:26
  Speaker 1: of initiatives lack strong executive ownership, funding and clear links
• 37:31
  Speaker 1: to the core strategy of the organization. And as Sarah
• 37:35
  Speaker 1: pointed out, there's a real fear of job displacement.
• 37:38
  Speaker 2: It's a big blocker to AI.
• 37:40
  Speaker 1: You know, people worry about becoming visibly great at AI
• 37:44
  Speaker 1: could mean that they become redundant, or that AI automates
• 37:50
  Speaker 1: aspects of their job that they consider really important to
• 37:53
  Speaker 1: their identity in the organization, so that dampens down appetite
• 37:57
  Speaker 1: for adoption and structured GILLS programs. And there's a cultural
• 38:02
  Speaker 1: element as well. We are as Sarah puts it, a
• 38:05
  Speaker 1: cynical bunch. We see hype and run in the other direction.
• 38:08
  Speaker 1: But AI isn't going away. We need to embrace it, experiment, upskill,
• 38:13
  Speaker 1: and ultimately adopt it. The AI return on investment is
• 38:17
  Speaker 1: an interesting one. It's hard to achieve. Firms struggle with
• 38:21
  Speaker 1: ROI because they deploy tools like a copilot without deciding
• 38:25
  Speaker 1: up front what success actually looks like or how they'll
• 38:28
  Speaker 1: measure it. They roll out GENAI it might be copilot
• 38:32
  Speaker 1: or chat GPT enterprise, then later try to justify more
• 38:36
  Speaker 1: funding without baseline metrics, making it really hard to prove
• 38:40
  Speaker 1: the impact retrospectively. Then is it a biggie for New
• 38:44
  Speaker 1: Zealand organizations? Change and adoption work is too often just undercooked.
• 38:50
  Speaker 1: Without time for people to learn, practice and embed the
• 38:53
  Speaker 1: new ways of working, usage remain shallow. The organization never
• 38:57
  Speaker 1: realizes the productivity or innovation gain, just needed to show
• 39:01
  Speaker 1: a clear return.
• 39:03
  Speaker 2: So yeah, lots of good stuff there.
• 39:05
  Speaker 1: Thanks to Sarah Coney for delving into that IDC research,
• 39:09
  Speaker 1: which I'll put links to in the show notes. I
• 39:11
  Speaker 1: think she really put her finger on the issues holding
• 39:14
  Speaker 1: us back from the actual, tangible, true impacts that AI
• 39:18
  Speaker 1: can deliver. Next week, I'll talk to two outstanding key
• 39:22
  Speaker 1: we start up founders doing very well in the US
• 39:25
  Speaker 1: have raised a lot of money applying AI to robotics
• 39:28
  Speaker 1: with the aim of enabling faster and smarter automation and
• 39:32
  Speaker 1: everything from factories to driverless cars. That's next week on
• 39:37
  Speaker 1: the Business of Tech. I'll catch you then, have a
• 39:39
  Speaker 1: great week.