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Speaker 1: So we're all being told to upskill educate ourselves on

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Speaker 1: AI because if you aren't using it, your competitors will be,

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Speaker 1: and they'll subsequently be a step ahead of you, more

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Speaker 1: customer centric, more productive, more competitive.

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Speaker 2: So what do you do about AI as a business

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Speaker 2: leader or anyone with some responsibility where an organization is going?

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Speaker 1: Now, there are plenty of paid and free courses available

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Speaker 1: in artificial intelligence. You could literally fill your boots just

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Speaker 1: with the free stuff advertised every day on LinkedIn.

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Speaker 2: Or you could do a mini MBA specializing in AI.

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Speaker 1: This week on the Business off Tech powered by two

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Speaker 1: Degrees Business, we talk to two people who've done just

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Speaker 1: that and developed cases for AI applications that have the

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Speaker 1: scope to make a big impact in their respective organizations.

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Speaker 3: AI is finally allowing us to do what computers were

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Speaker 3: telling us they could do thirty years ago. Computers were

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Speaker 3: going to make everything easier. They were going to do

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Speaker 3: all the drudgery work that we do. It never happened,

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Speaker 3: so it was kind of false promise. But now at

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Speaker 3: repetitive donkey work, we don't have to do that anymore.

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Speaker 4: Can concentrate with all of it.

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Speaker 5: You know, we talk about you know, New Zealand needing

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Speaker 5: a productivity uplift. This is where it can make is

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Speaker 5: huge difference for us. AI can really help us with that.

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Speaker 2: Well, delve into the world of AI and what you

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Speaker 2: can achieve with this mini MBA and the topic shortly.

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Speaker 1: First though, some of the tech issues making headlines this

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Speaker 1: week and a lot of commentary bend from across the

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Speaker 1: Tasman about the big banks painting the so called Consumer

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Speaker 1: Data Right which has been in place for four years

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Speaker 1: now and was hailed as the start of open banking

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Speaker 1: over there. Well, the banks is saying it's been a

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Speaker 1: massive failure.

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Speaker 5: Yeah.

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Speaker 2: The number they're throwing around a zero point three percent

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Speaker 2: of customers are actually using CDR Customer Data Right to

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Speaker 2: access their banking data. It's a little bit of a

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Speaker 2: mislead I think though, if you look into some of

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Speaker 2: the reporting that you sent me, Peter, where it talks

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Speaker 2: about the fact that that's only ongoing active access. There

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Speaker 2: are also one off accessing where for loans and things

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Speaker 2: like that, where they'll just dip in and check and

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Speaker 2: then dip out again. But also we have to remember

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Speaker 2: that the CDR is not just about open banking, and

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Speaker 2: that's a point that's made as well on some of

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Speaker 2: those reports, it's actually much wider than that. The electricity industry,

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Speaker 2: the insurance industry will eventually reach all these other industries

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Speaker 2: that have a lot of data about consumers. It is

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Speaker 2: about your right as a consumer to access that data

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Speaker 2: and to give mission for access to that data. So

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Speaker 2: while I think that's probably true that the role out

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Speaker 2: of open banking in Australia hasn't been as rapid as

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Speaker 2: people would have liked, and the banks maybe feeling a

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Speaker 2: little annoyed they've made all this investment, but the reality

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Speaker 2: is that it is going to take time to grow

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Speaker 2: and there are benefits beyond just these persistent data access

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Speaker 2: rights that are being ed.

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Speaker 1: Yeah. For instance, Deloitte did a report earlier this year,

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Speaker 1: actually commissioned by a bank, which suggested that the Australian

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Speaker 1: economy would be sixteen point seven billion dollars larger by

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Speaker 1: twenty forty three if that consumer data right expanded into retail, healthcare,

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Speaker 1: and other personal and professional services. So this is Deloitte,

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Speaker 1: an accounting company and a technology company saying we think

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Speaker 1: that there is huge value to the economy from expanding this,

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Speaker 1: which is what Australia is sort of on the track

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Speaker 1: to do. The banks are saying, well, it needs to

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Speaker 1: be reimagined because it's too expensive. Some of the customer

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Speaker 1: owned banks, I guess the equivalent of hour sort of

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Speaker 1: co op banks in New Zealand, they estimate that it's

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Speaker 1: cost them one hundred million dollars. They're not happy about

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Speaker 1: it either. But you've seen the startup community and the

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Speaker 1: FinTechs really push back saying, as you pointed out, Ben,

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Speaker 1: that that is ongoing CDR transactions that they're counting, not

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Speaker 1: the one off stuff and all these other sort of

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Speaker 1: lending companies that are using that to check the status

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Speaker 1: of someone's credit status and that type of thing. That's

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Speaker 1: a hugely valuable service. We've also heard from an Australian

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Speaker 1: startup this week's Writing and Startup Daily across the Tasman.

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Speaker 1: It's called Basic. It's a data aggregator startup and it

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Speaker 1: claims it has nine hundred thousand open banking connections between

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Speaker 1: consumers and businesses, helping them with the likes of budgeting,

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Speaker 1: invest in tax reconciliation. But it's using screen scraping, the

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Speaker 1: old preceding technology that a lot of new Zealand companies

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Speaker 1: are using to try and work with bank data to

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Speaker 1: allow that to happen. So for some reason there's a

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Speaker 1: barrier to basic using the CDR infrastructure that's available. Once

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Speaker 1: these sorts of companies start picking it up, I think

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Speaker 1: we'll see a lot more open banking, genuine open banking

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Speaker 1: transactions going on.

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Speaker 2: The reality is is that people are going to go

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Speaker 2: for the best customer experience, and if at the moment

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Speaker 2: the best customer experience is gained through screen scraping, you

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Speaker 2: can't just turn around and say let's ban screen scraping.

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Speaker 2: I mean, sure, there are some maybe some issues with

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Speaker 2: it around security. We know that CDR is more secure,

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Speaker 2: but all that means is that we need to do

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Speaker 2: a better job at making legitimate data sharing really good.

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Speaker 2: I think that's it, because it is about competition. Like

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Speaker 2: the is there are places for regulation, I don't think

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Speaker 2: customer experience is a place where you can regulate your

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Speaker 2: way out of a problem.

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Speaker 1: And the whole point of this was to stimulate competition.

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Speaker 1: This came out of a Royal Commission of Inquiry into

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Speaker 1: misconduct in the banking sector in Australia and really I

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Speaker 1: think inspired the effort here to create CDR legislation, which

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Speaker 1: we've done. The CDR is being rolled out. There was

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Speaker 1: a lot of hoopler about a month ago about the

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Speaker 1: genuinely the start of op and banking in New Zealand.

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Speaker 1: So you've covered this ban, You've talked to a lot

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Speaker 1: of companies about this. What do you think we can

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Speaker 1: learn from this? What should we not be doing that

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Speaker 1: the Australians did as we develop our own CDR and

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Speaker 1: hope to have much better uptake than they have.

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Speaker 2: Part of it goes to accessibility. It needs to be affordable.

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Speaker 2: The accreditation needs to be reasonable. If it's prohibitively expensive,

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Speaker 2: it's going to stop companies, these startup companies from being

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Speaker 2: able to access it.

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Speaker 4: That's a really big point.

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Speaker 2: Also, consistency is really key, so it needs to be

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Speaker 2: the same between different banks. That's part of what the

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Speaker 2: CDR regulation and part of what the payment's New Zealand

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Speaker 2: application to the Commerce Commission all very complicated.

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Speaker 4: You can read some of my reporting on that if

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Speaker 4: you're interested.

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Speaker 2: But they are trying to create a consistent framework so

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Speaker 2: that these companies can become accredited, can access APIs and

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Speaker 2: they don't have to go through all these different negotiations

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Speaker 2: that will be really good, but it also needs to

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Speaker 2: be paired with that accreditation process, that framework being reasonably

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Speaker 2: easy to for FinTechs to actually access.

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Speaker 4: There are a.

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Speaker 2: Lot of dials that need to be set at the

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Speaker 2: right place to actually really get these things moving. But

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Speaker 2: the good news is we haven't actually been waiting. There

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Speaker 2: are a lot of FinTechs now that are doing stuff

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Speaker 2: with open banking. You've got I think Quickpayer one of

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Speaker 2: them that are the furthest along. They have API agreements

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Speaker 2: with three of the big four banks and they're working

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Speaker 2: on the last one. I think blink Pays in the

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Speaker 2: same position. We've got Dash which just announced today as

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Speaker 2: we record, that it is applying for its license to

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Speaker 2: become a digital only bank in New Zealand to try

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Speaker 2: and improve competition in the space. And he said that

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Speaker 2: the banks have been broadly supportive as well of that,

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Speaker 2: because they are feeling the pressure to show that there

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Speaker 2: is competition in the market. I guess the answer is

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Speaker 2: that it's extremely complicated and it is about that customer experience,

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Speaker 2: because otherwise people are just going to go to screen scraping.

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Speaker 1: Yeah, I'm not filled with confidence to be honest, given

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Speaker 1: that we're dealing with a subset of the same big

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Speaker 1: banks that are kicking up the stink in Australia, Can we,

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Speaker 1: with our history of really slow progress on open banking,

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Speaker 1: do any better than the Australians. I'm not hopeful, but

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Speaker 1: I do. I am inspired by the so called rise

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Speaker 1: of neo banks, these digital only banks, and the likes

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Speaker 1: of Dosh saying hey, we want to become a bank

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Speaker 1: and maybe gain access to the banking rails with the

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Speaker 1: Reserve Bank. That's when you can start to facilitate real

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Speaker 1: time transactions and not have to go through a third

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Speaker 1: party bank. The big issue there is the capital requirements.

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Speaker 1: Thirty million dollars in capital to become a bank and

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Speaker 1: then all the other retape you have to go through.

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Speaker 1: It's a big threshold for a small new entrant.

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Speaker 4: It is.

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Speaker 2: But the good news is the Commerce Commission kind of

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Speaker 2: is aware of this. There has been a bit of

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Speaker 2: conversation about rbnz's want for economic stability versus the Commerce

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Speaker 2: commissions drive for better competition and to lower prices and

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Speaker 2: improve the market and so payments. New Zealand is a

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Speaker 2: bank owned company, a bank run company and it has

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Speaker 2: it applied to the Commerce Commission to become the organization

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Speaker 2: that sets the framework for open banking, and it applied

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Speaker 2: for a five year license and said it wants to

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Speaker 2: do all these things. And the Commerce Commission came back

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Speaker 2: and said, okay, yes, you and your API center can

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Speaker 2: set this framework up, but none of the bank representatives

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Speaker 2: on your board can vote, so only independent directors can

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Speaker 2: actually have a vote on payments.

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Speaker 4: And zed's board. Also, we're going to give we'd like.

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Speaker 2: To give you eighteen months, not five years, because we

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Speaker 2: think that CDR is going to come in. Also, everything

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Speaker 2: you do will need to be transparent, will need to

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Speaker 2: have records of all your communication and emails so that

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Speaker 2: when the legislation does come in, we can pick up

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Speaker 2: where you've left off and take things more through the

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Speaker 2: legislative roots. So the Commerce Commission is aware that there

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Speaker 2: is an issue, whether you want to say, conflict of

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Speaker 2: interest in having the banks with as much power in

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Speaker 2: open banking, and is working on doing its best to

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Speaker 2: kind of make it fair.

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Speaker 1: I think, okay, well that definitely sounds promising. We'll keep

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Speaker 1: an eye on that and you'll be reporting on it.

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Speaker 1: And other news been a lot of shadow this week

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Speaker 1: about the power consumption involved with artificial intelligence, the stuff

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Speaker 1: we're going to be talking about with our featured guests.

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Speaker 1: Some interesting stats out in the last week. Microsoft's emissions

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Speaker 1: are up forty percent between twenty twenty and twenty twenty three,

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Speaker 1: and the big component of that is its data centers,

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Speaker 1: and the big component of what's going on in those

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Speaker 1: data centers is artificial intelligence. Google's emissions almost fifty percent

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Speaker 1: higher in twenty twenty three than they were in twenty nineteen.

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Speaker 1: So the Tech Giants twenty twenty four Environmental Report notes

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Speaker 1: that plans and missions were will be difficult due to

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Speaker 1: increasing energy demands from the greater intensity of AI compute.

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Speaker 1: We are facing a big sustainability issue. A lot of

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Speaker 1: those data center makers were investing in clean power projects,

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Speaker 1: whether they were solar or a hydro or whatever, to

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Speaker 1: try and get their carbon footprint down. Then we saw

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Speaker 1: generative AI come along, all of these chips they've been

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Speaker 1: buying from Nvidia that are very power hungry, and suddenly

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Speaker 1: they're blowing away their targets for emissions reduction. It sort

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Speaker 1: of ties into that discussion we had with Rod Drury

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Speaker 1: and the piece you wrote which we'll link to Ben

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Speaker 1: sort of fleshing out his thinking on this, which is,

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Speaker 1: really we have an advantage here. If we can continue

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Speaker 1: to grow our renewable energy and literally be one hundred

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Speaker 1: percent renewable with hydro and solar and geothermal and other

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Speaker 1: stuff here, we become a destination for a lot of

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Speaker 1: those big companies which are building hyperscale data seeners here already.

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Speaker 4: Yeah, definitely.

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Speaker 2: I mean, it's something that has been ongoing for a while.

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Speaker 2: Is an attempt to capture that market a little bit more,

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Speaker 2: and we just do need to go hard on it,

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Speaker 2: like Rodrury said. And one of my earliest articles that

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Speaker 2: I wrote for Business Desk was actually about this issue

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Speaker 2: of we're building more data centers in New Zealand, are

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Speaker 2: we going to be able to actually meet the renewable

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Speaker 2: energy demands? And one of the things that came out

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Speaker 2: of that reporting was this idea that because these because

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Speaker 2: big tech making renewable energy power purchase agreements, they're committing

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Speaker 2: to saying we'll spend this much money, it's allowing the

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Speaker 2: energy generators to actually invest in renewable generation. One of

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Speaker 2: the barriers is that they can't build it fast enough.

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Speaker 2: At this point, we have labor shortages and just trying

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Speaker 2: to get stuff done can be quite a challenge. So yeah,

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Speaker 2: it needs a lot more investment in new to be

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Speaker 2: pushed ned really push the boat out on it. But

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Speaker 2: it is really worrying, I think, like on an existential level,

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Speaker 2: this idea that we had, you know, these companies that

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Speaker 2: collectively make up what eighty percent of the world's resources,

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Speaker 2: like suddenly that they're exploding in the amount of consumption

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Speaker 2: and we don't really know what that's going to mean

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Speaker 2: just yet. Because they have been promising that they're going

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Speaker 2: to be carbon zero or carbon negative by twenty fifty.

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Speaker 2: They've been saying this for the last three or four years,

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Speaker 2: and suddenly it's looking like, well, how are you actually

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Speaker 2: going to achieve that while at the same time achieving

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Speaker 2: the promises that you're making around artificial intelligence? So big questions.

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Speaker 1: Yeah, Bill Gates has come out this week, and he

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Speaker 1: clearly has a vested interestedness as one of Microsoft's ongoing

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Speaker 1: biggest shareholders, so as you know, tens of billions of

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Speaker 1: dollars tied up in this. But he's saying, don't worry

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Speaker 1: about this. Those companies, including Microsoft, will will pay a

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Speaker 1: green premium for access to that power. The market will

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Speaker 1: sort this out. He's also saying that as you build

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Speaker 1: more of this infrastructure with the funding of some of

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Speaker 1: these tech companies, it's sufficient to do other things as well,

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Speaker 1: so to fund a local factory and housing subdivision as well.

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Speaker 1: So he's saying that this will accelerate the greening of

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Speaker 1: the economy in general. So that's yet to be seen.

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Speaker 1: We've got so much to do. Eighty percent increase in

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Speaker 1: electricity consumption projected here in New Zealand, but I think

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Speaker 1: twenty fifty it was, So we've got to get moving

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Speaker 1: on this and stuff is getting commissioned. But at the

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Speaker 1: rate of the digital economy alone growing, boy, we're going

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Speaker 1: to need a lot more than people probably think we do.

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Speaker 2: Yeah, and we still don't have it right on incentives,

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Speaker 2: I think because Billshop Bill Gates can say that they're

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Speaker 2: happy to pay a premium for green, but why would

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Speaker 2: they if it's not cheaper, Like, what are they gaining

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Speaker 2: from it aside from goodwill?

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Speaker 4: And goodwill will.

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Speaker 2: Only get you so far, you know when it comes

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Speaker 2: to pleasing shareholders. So hopefully they really do start to

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Speaker 2: see these really strong incentives foregoing green.

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Speaker 4: That makes sense.

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Speaker 1: Finally, Ben, we delve into the crypto world, and good

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Speaker 1: news for former customers of mount gox, and that takes

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Speaker 1: me back to one of the first crypto exchanges, Japanese

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Speaker 1: based mount gox twenty fourteen. It was hacked quite spectacularly

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Speaker 1: and it was four hundred million dollars off crypto mainly

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Speaker 1: bitcoin I think, on that platform back then. So customers

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Speaker 1: were devastated. That has led to a ten year process

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Speaker 1: trying to get back those funds. Forensic accountants, going through lawyers.

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Speaker 1: It's been going through the courts for a long time.

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Speaker 1: But now the money just from this month is starting

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Speaker 1: to be paid back. And the amazing thing is those

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Speaker 1: customers who were devastated and frustrated that they couldn't access

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Speaker 1: those funds that have been sitting there, they've experienced a

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Speaker 1: massive capital gain. That four hundred million dollars back then

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Speaker 1: it was stolen and it has been recovered, is now

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Speaker 1: worth forty five billion, and they're paying that out to

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Speaker 1: those customers with that gain included. So probably a lot

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Speaker 1: of people will be thinking, oh my god, I would

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Speaker 1: have sold that off probably at a fraction of the price.

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Speaker 1: This has actually worked out.

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Speaker 4: Well for me. Yeah. Yeah.

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Speaker 2: One of the subjects in the reporting was a teenager

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Speaker 2: who put one thousand dollars dollars in and now he's

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Speaker 2: getting seventy thousand dollars backs, so pretty strong return on

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Speaker 2: investment for a lot of them.

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Speaker 1: Yeah, but it was really the wild West days of

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Speaker 1: the crypto exchange sort of environment, and in some respects

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Speaker 1: it hasn't got a heck of a lot better. But

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Speaker 1: we do have big legitimate players now. But New Zealand

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Speaker 1: sort of had a parallel to this with Cryptopia. I

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Speaker 1: think it was twenty seventeen that one was hacked a

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Speaker 1: lot of money, hundreds of millions of dollars. Again, we're

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Speaker 1: effectively stolen from people who had held deposited cryptocurrencies with Cryptopia.

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Speaker 1: There's been some recent developments on that case as well.

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Speaker 4: Yeah, it's quite complicated.

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Speaker 2: So I was writing up the latest liquidators report and

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Speaker 2: from the look of it, they've managed to gain about

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Speaker 2: twenty eight million back of the assets, and a lot

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Speaker 2: of that's been turned into fear I guess to actually

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Speaker 2: pay for the process because it has been a really

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Speaker 2: difficult process, and I think Cryptopia's accounts were not in

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Speaker 2: let's say the best order from the sound of it,

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Speaker 2: So from the liquidator's report that I wrote about, they

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Speaker 2: said that they had managed to get twenty eight million

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Speaker 2: of assets, and it looks like there's about seven hundred

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Speaker 2: and seventy five thousand of it left after paying for

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Speaker 2: liquidator's fees, legal fees, the cost of building the portal

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Speaker 2: where you can apply to be a recognized account holder.

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Speaker 2: So not sure how much will actually end up back

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Speaker 2: in the account holder's pockets in that situation.

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Speaker 1: Yeah, well, that's heartbreaking for those and I guess salient

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Speaker 1: lesson and things have improved since then. But ultimately, if

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Speaker 1: you're with a custodial exchange, you know you're relying on

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Speaker 1: their security and their insurance and their backup measures if

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Speaker 1: that exchange gets hacked.

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Speaker 2: And this is one of the things that people said

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Speaker 2: about cryptocurrencies from the very beginning, right, which is that

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Speaker 2: if you have a decentralized currency, there is no body

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Speaker 2: that is actually overseeing things. So we've learned the usefulness

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Speaker 2: of centralization in some ways.

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Speaker 1: Here and FTX we saw as well, which was you

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Speaker 1: know it means and that's very recent times, so beware

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Speaker 1: out there. Crypto still has those elements unfortunately.

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Speaker 4: Yeah, be careful.

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Speaker 1: Yeah. Now getting onto our featured interview this week, we're

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Speaker 1: delving back into AI.

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Speaker 4: Ben Yes, you.

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Speaker 2: May have heard of Scott Galloway, the marketing guru, tech entrepreneur,

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Speaker 2: and professor at NYU Stern University.

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Speaker 1: He's also the co host with Kara Swisher of one

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Speaker 1: of my favorite tech podcasts, Pivot, and runs an online

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Speaker 1: school called Section that upskills managers with intense four to

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Speaker 1: six weeks so called mini MBA programs. In September, Galloway

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Speaker 1: and former Apple executive and Section CEO Greg Shove launched

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Speaker 1: a mini MBA on the business of artificial intelligence. Galloway

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Speaker 1: takes a fairly jaun this view of many technologies that

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Speaker 1: have been hyped over the past decade. But here's what

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Speaker 1: Galloway had to say last year about artificial intelligence.

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Speaker 6: Virtual reality, three D printing, Internet of things, wearables, all ridiculous,

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Speaker 6: stupid technologies, ridiculous, overhyped license to burn money. I think

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Speaker 6: AI is actually the most significant technology in a long

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Speaker 6: long time, and anyone who plays with any generative AI,

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Speaker 6: it's hard to not acknowledge this is going to have

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Speaker 6: huge impact. I don't think we're over hyping it. What

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Speaker 6: I think we're doing is catastrophizing it. And that is

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Speaker 6: if you look at the history of disruptive technologies, the

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Speaker 6: first stage is we're sort of in awe of it,

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Speaker 6: and a lot of capital rushes in and takes a

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Speaker 6: lot of companies beyond probably a rational valuation. And that's

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Speaker 6: happening here. What also happens is the media immediately goes to, well,

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Speaker 6: this will have very negative impacts on the job ecosystem,

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Speaker 6: and there'll be a lot of people who are laid

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Speaker 6: off and this could result in very bad things. And

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Speaker 6: then what we generally find is that it creates more

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Speaker 6: jobs than it destroys. And I would argue that the

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Speaker 6: whole AI pause catastrophization movement is largely fueled by people

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Speaker 6: who are looking for other innovators to pause while they

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Speaker 6: can catch.

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Speaker 2: Up fighting words for some in the industry. And earlier

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Speaker 2: this year, telecoms company Spark signed up to host and

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Speaker 2: pay for the first New Zealand cohort of students to

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Speaker 2: go through the Mini MBA, making one hundred and fifty

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Speaker 2: scholarships available to business people.

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Speaker 1: Here. We had Spark CEO Jolie Hodson on the podcast

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Speaker 1: back in May talking about that and the company's push

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Speaker 1: to encourage corporate New Zealand to accelerate its uptake of

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Speaker 1: advanced digital tech like AI.

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Speaker 2: So we thought we'd catch up with a couple of

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Speaker 2: those students who have been through the MINIMBA to see

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Speaker 2: what they learned from it. One of them is actually

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Speaker 2: a colleague of ours, Matt Martel, the Managing Editor of

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Speaker 2: Audience and Platform Curation at ENZME, publisher of the New

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Speaker 2: Zealand Herald and Business Desk. He's already been on this

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Speaker 2: AI journey, having led development of projects like BDAI Business

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Speaker 2: Desk's AI Generated Market Reporter.

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Speaker 1: Another student fresh out of Scott Galloway's mini MBA is

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Speaker 1: KP Kirsten Patterson, the CEO of the Institute of Directors,

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Speaker 1: who has huge governance experience and has plugged into boardrooms

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Speaker 1: all over the nation.

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Speaker 2: So what insights did they gain into their strategic approach

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Speaker 2: to AI? This many MBA worth it?

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Speaker 1: And will they just use AI to replace the human workers?

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Speaker 4: Let's find out.

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Speaker 2: Here's our chat with Kirsten Patterson and Matt Martel.

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Speaker 1: KP and Matt, Welcome to the Business of Tech. Thanks

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Speaker 1: so much for coming on because you have just been

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Speaker 1: back to school, both of you. You've done a Mini

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Speaker 1: MBA in the Business of AI AI for Business and

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Speaker 1: Mini NBA. It's officially called This is a program run

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Speaker 1: by a company called Section and it was founded by

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Speaker 1: NYU Stern Professor Scott Galloway, who's a big name in

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Speaker 1: marketing and in the tech world as well, has some

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Speaker 1: very successful podcasts, and he said recently about AI, he said,

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Speaker 1: this is not like all the other technologies we've seen

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Speaker 1: in the last decade, the Internet of Things, you know, wearables,

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Speaker 1: web three, all of these sort of hyped up marketing

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Speaker 1: terms around technology. He truly believes that AI is different.

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Speaker 1: It is transfer formative. So he's a true believer. Interested

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Speaker 1: if we can just start off, kp Let's start with you,

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Speaker 1: what your philosophy was to AI going into this intensive

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Speaker 1: four week course. What was your perspective on it dealing

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Speaker 1: with business leaders as you do at the Institute of Directors,

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Speaker 1: who are probably thinking to themselves, what do I need

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Speaker 1: to do to upskill in this or is it just hype?

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Speaker 5: Yeah? Yeah, thanks Peter. It's a really great question. And

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Speaker 5: actually my perspective was one of uncertainty and curiosity actually,

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Speaker 5: and I was really worried at a personal level about

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Speaker 5: getting left behind, Like this thing was taking off, and

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Speaker 5: how did I keep up to date and how did

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Speaker 5: I kind of grab the tail of this and kind

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Speaker 5: of keep with where it was headed to. And you know,

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Speaker 5: as board members, we're always thinking about risks as well, right,

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Speaker 5: so what are the risks of this? And where could

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Speaker 5: this technology go? But I could also see huge opportunity

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Speaker 5: with it. So you know, I'm not a tech person

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Speaker 5: by background at all. I just wanted to immerse myself

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Speaker 5: in this and I really needed someone to go me

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Speaker 5: through it because I've seen so many technology changes over

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Speaker 5: my lifetime. I didn't want this one to be the

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Speaker 5: one that I'm.

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Speaker 2: Ass So, Matt, you kind of had a different perspective.

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Speaker 2: You've been doing stuff with AI for quite a while now,

439
00:24:12,040 --> 00:24:15,640
Speaker 2: quite publicly on business desks, So where was your mind

440
00:24:15,640 --> 00:24:17,440
Speaker 2: that going into the Mini MBA.

441
00:24:18,680 --> 00:24:20,760
Speaker 3: I agree with KP like I didn't want to get

442
00:24:20,800 --> 00:24:26,080
Speaker 3: left behind. I was around when the Internet started and

443
00:24:26,400 --> 00:24:32,120
Speaker 3: newspapers started to all apart, and it's really taken twenty years,

444
00:24:32,160 --> 00:24:36,160
Speaker 3: for twenty plus years, to get to a stage where

445
00:24:36,200 --> 00:24:39,560
Speaker 3: we're confident in our business models again and I can

446
00:24:39,600 --> 00:24:43,760
Speaker 3: see AI being as big a change. We launched first

447
00:24:43,800 --> 00:24:46,800
Speaker 3: AI product on business desk, which was Enzy Next Summary's

448
00:24:47,560 --> 00:24:49,639
Speaker 3: February of last year after a lot of testing, and

449
00:24:49,680 --> 00:24:53,399
Speaker 3: it works really well. We're building tools for the Herald

450
00:24:53,480 --> 00:24:58,520
Speaker 3: newsroom now around AI, particularly editing content. You can just

451
00:24:58,560 --> 00:25:01,280
Speaker 3: see how quickly it's improved and how quickly it's moving.

452
00:25:01,960 --> 00:25:07,359
Speaker 3: So to me as media, if we don't stay up

453
00:25:07,440 --> 00:25:10,920
Speaker 3: to date with those sorts of trends, we are at

454
00:25:11,000 --> 00:25:16,400
Speaker 3: another extinct extinction level threat. So those who don't take

455
00:25:16,520 --> 00:25:20,719
Speaker 3: part and understand are going to get left behind. And

456
00:25:20,760 --> 00:25:24,680
Speaker 3: so this was a good opportunity to understand where things

457
00:25:24,720 --> 00:25:29,520
Speaker 3: were going. And it was just incredible to see how

458
00:25:29,600 --> 00:25:32,879
Speaker 3: much these sort of AI enabled workers in the US

459
00:25:32,960 --> 00:25:36,080
Speaker 3: are using AI because they've got a tool open the

460
00:25:36,280 --> 00:25:38,639
Speaker 3: entire time. They no longer write a first draft of

461
00:25:38,680 --> 00:25:42,720
Speaker 3: an email or a business document. Everything they're doing is

462
00:25:42,800 --> 00:25:46,399
Speaker 3: using AI and then reviewing it. You think about the

463
00:25:46,560 --> 00:25:51,480
Speaker 3: impact that we'll have as it gets better. It's fundamentally

464
00:25:51,520 --> 00:25:53,640
Speaker 3: different to everything we've done up till now.

465
00:25:54,880 --> 00:25:57,040
Speaker 1: So just as a bit of background here, this was

466
00:25:57,119 --> 00:26:02,720
Speaker 1: four weeks of intensive work by Spark. So Spark made

467
00:26:02,720 --> 00:26:06,160
Speaker 1: available one hundred and fifty positions on this course, which

468
00:26:06,200 --> 00:26:10,280
Speaker 1: is quite significant because I think it costs about three

469
00:26:10,320 --> 00:26:12,520
Speaker 1: thousand dollars US or something like that, so this is

470
00:26:12,600 --> 00:26:15,040
Speaker 1: quite a big investment. It came on the back off

471
00:26:15,080 --> 00:26:19,080
Speaker 1: research that they commissioned from the Institute of Economic Research

472
00:26:19,600 --> 00:26:22,879
Speaker 1: which found that if we did invest in advanced digital

473
00:26:23,320 --> 00:26:27,400
Speaker 1: technologies like AI, literally billions will be added to the economy.

474
00:26:27,480 --> 00:26:30,240
Speaker 1: So this is one of many reports that have suggested

475
00:26:30,280 --> 00:26:32,120
Speaker 1: that we are a little bit behind in New Zealand

476
00:26:32,160 --> 00:26:36,440
Speaker 1: and the uptake of things like AI. They see obviously

477
00:26:37,000 --> 00:26:40,200
Speaker 1: business for them in being a leader in this KP.

478
00:26:40,359 --> 00:26:43,360
Speaker 1: What was it actually like doing this, What did involve?

479
00:26:43,400 --> 00:26:46,480
Speaker 1: What sort of format was it? Presumably it's remote, so

480
00:26:46,560 --> 00:26:49,600
Speaker 1: it was all online. Did you get the PEP talk

481
00:26:49,600 --> 00:26:53,439
Speaker 1: at the start from Scott Galloway and others, Greg Shove,

482
00:26:53,840 --> 00:26:56,040
Speaker 1: who's the leader of that course. How did they set

483
00:26:56,359 --> 00:26:57,080
Speaker 1: the tone for this?

484
00:26:57,560 --> 00:27:00,879
Speaker 5: Yeah, Look, I'm a learner who really appreciates being in

485
00:27:00,920 --> 00:27:03,760
Speaker 5: the class with other students, so I was a bit

486
00:27:03,800 --> 00:27:06,280
Speaker 5: nervous about actually doing it all online to start with.

487
00:27:06,359 --> 00:27:08,680
Speaker 5: So I did go in person to the kickoff session.

488
00:27:08,720 --> 00:27:11,680
Speaker 5: We had that option, and Gregshov was there in person

489
00:27:11,760 --> 00:27:14,000
Speaker 5: and sort of gave us a bit of a challenge.

490
00:27:14,119 --> 00:27:17,159
Speaker 5: And I think that kind of was a really useful

491
00:27:17,200 --> 00:27:19,280
Speaker 5: learning style for me. I'm really self aware about the

492
00:27:19,320 --> 00:27:22,040
Speaker 5: fact that often if I'm doing online content, I'll get distracted,

493
00:27:22,080 --> 00:27:24,119
Speaker 5: you know, I'll have my email on and other things

494
00:27:24,200 --> 00:27:26,840
Speaker 5: kind of going. So we had structured sessions during the

495
00:27:26,840 --> 00:27:29,680
Speaker 5: week where there was some videos and content we were

496
00:27:30,000 --> 00:27:32,000
Speaker 5: looking at online during the week, and then we had

497
00:27:32,040 --> 00:27:33,880
Speaker 5: a couple of touch points during the week where we'd

498
00:27:33,920 --> 00:27:36,639
Speaker 5: get together as a cohort and have a live presentation

499
00:27:36,800 --> 00:27:39,160
Speaker 5: where you could actually learn from others and see their

500
00:27:39,240 --> 00:27:42,800
Speaker 5: questions and sort of have that real online experience as well.

501
00:27:42,800 --> 00:27:44,960
Speaker 5: So I think it was a really good combination of both.

502
00:27:45,359 --> 00:27:47,280
Speaker 5: And it wasn't as hard as I thought it was

503
00:27:47,280 --> 00:27:48,680
Speaker 5: going to be too, so that was a great thing

504
00:27:48,720 --> 00:27:51,359
Speaker 5: about it. I felt that it was achievable and it

505
00:27:51,400 --> 00:27:52,600
Speaker 5: kind of challenged me all the way through.

506
00:27:53,240 --> 00:27:56,480
Speaker 1: And Matt Jolly Hodson said, the reason she wanted all

507
00:27:56,520 --> 00:27:59,399
Speaker 1: these sort of business leaders to go on this is

508
00:27:59,440 --> 00:28:04,200
Speaker 1: to gain strategic comprehension of AI. You know, you've been

509
00:28:04,240 --> 00:28:07,199
Speaker 1: sort of using this technology and conjunction I think with

510
00:28:07,240 --> 00:28:11,159
Speaker 1: a Turkish company that you got to build BDAI at

511
00:28:11,200 --> 00:28:14,280
Speaker 1: business desk, What was the balance like between actually teaching

512
00:28:14,320 --> 00:28:18,520
Speaker 1: you about the technology and other things that are really important,

513
00:28:18,560 --> 00:28:22,160
Speaker 1: such as the so called guard rails putting in place

514
00:28:22,240 --> 00:28:26,760
Speaker 1: the governance and interesting you take on as an executive

515
00:28:26,800 --> 00:28:29,720
Speaker 1: at enzied me and coming out out of Business desk,

516
00:28:30,160 --> 00:28:33,080
Speaker 1: your approach to setting those guide rails when you're using,

517
00:28:33,600 --> 00:28:36,720
Speaker 1: in the case of Business desk, really sort of breaking

518
00:28:36,840 --> 00:28:39,800
Speaker 1: news in the financial realm, which if you get it wrong,

519
00:28:39,920 --> 00:28:41,240
Speaker 1: could move markets.

520
00:28:41,600 --> 00:28:44,480
Speaker 3: Yeah, I mean that it's terrifying that if we get

521
00:28:44,520 --> 00:28:47,080
Speaker 3: an insiet X announcement wrong, we can move the market.

522
00:28:48,920 --> 00:28:51,600
Speaker 3: We haven't got it wrong yet. I've just knocked on

523
00:28:51,640 --> 00:28:55,800
Speaker 3: wood and we've done that by setting quite clear guardrails,

524
00:28:55,840 --> 00:28:59,880
Speaker 3: which is, do not add any information, do not hallose.

525
00:29:00,680 --> 00:29:03,200
Speaker 3: All I'm asking you to do is summarize this as

526
00:29:03,240 --> 00:29:06,800
Speaker 3: a news article for a New Zealand business publication, so

527
00:29:06,880 --> 00:29:09,600
Speaker 3: being really clear, and then testing and testing and testing,

528
00:29:09,680 --> 00:29:14,560
Speaker 3: and for the editing tools we've built for the Herald,

529
00:29:15,120 --> 00:29:18,400
Speaker 3: the amount of work that has gone into the prompting

530
00:29:18,840 --> 00:29:23,840
Speaker 3: of the AI is phenomenal. And then building the pipeways

531
00:29:23,880 --> 00:29:26,560
Speaker 3: to get the Herald content into the AI and back

532
00:29:26,640 --> 00:29:30,240
Speaker 3: has been really important too, and then just having humans

533
00:29:30,320 --> 00:29:32,920
Speaker 3: check everything called it humans in the loop. So the

534
00:29:33,000 --> 00:29:37,480
Speaker 3: tool we're built now, it'll do your style guide'll fix

535
00:29:37,600 --> 00:29:40,400
Speaker 3: up any macrons that are incorrect, it'll even do basic

536
00:29:40,480 --> 00:29:43,120
Speaker 3: fact checking. It's probably thirty or forty prompts that it

537
00:29:43,160 --> 00:29:46,200
Speaker 3: goes through to get there, and again it's just humans

538
00:29:46,240 --> 00:29:51,040
Speaker 3: in the loop the whole way. There's such huge reputational

539
00:29:51,160 --> 00:29:55,040
Speaker 3: risk if we get this wrong and if we make mistakes,

540
00:29:56,560 --> 00:30:00,640
Speaker 3: but there's also just such advantages to us to have

541
00:30:00,800 --> 00:30:04,880
Speaker 3: every story grammatically correct and spelt properly. Is a trust

542
00:30:04,920 --> 00:30:07,200
Speaker 3: issue in there, which Ali can actually help us with

543
00:30:07,880 --> 00:30:11,560
Speaker 3: and allows our editors to go through a story knowing

544
00:30:11,640 --> 00:30:15,360
Speaker 3: that it's grammatically accurate and they can look for look

545
00:30:15,360 --> 00:30:19,160
Speaker 3: at making the story better. It is complex, and it's

546
00:30:19,240 --> 00:30:21,600
Speaker 3: just a mant of giving ourselves time and not setting

547
00:30:21,600 --> 00:30:26,320
Speaker 3: deadlines while we experiment, and also being really comfortable with

548
00:30:26,440 --> 00:30:29,520
Speaker 3: backing out if it's not working. And we have had

549
00:30:29,520 --> 00:30:31,959
Speaker 3: a few instances where we've been trying to do something

550
00:30:32,400 --> 00:30:34,760
Speaker 3: it's too hard for us. Now we can come back

551
00:30:34,800 --> 00:30:38,640
Speaker 3: at that in six months or otherwise we use chat GPT,

552
00:30:39,160 --> 00:30:40,840
Speaker 3: we can look at Claude or one of the other

553
00:30:40,880 --> 00:30:43,640
Speaker 3: AI tools and see if they do a better job

554
00:30:43,760 --> 00:30:46,720
Speaker 3: of what we're trying to achieve. So not being tied

555
00:30:46,800 --> 00:30:51,320
Speaker 3: into one framework, but being adaptable enough that you can

556
00:30:51,640 --> 00:30:52,760
Speaker 3: swap out as needed.

557
00:30:54,120 --> 00:30:55,720
Speaker 2: You said, that's one of the things that you took

558
00:30:55,760 --> 00:30:59,640
Speaker 2: away from the MINIMBA was the awareness that different tools

559
00:30:59,720 --> 00:31:03,560
Speaker 2: are going at different things That could seem quite daunting

560
00:31:03,600 --> 00:31:06,000
Speaker 2: for some people to think, Oh my goodness, how many

561
00:31:06,040 --> 00:31:08,040
Speaker 2: of these tools do I have to run stuff through

562
00:31:08,080 --> 00:31:11,920
Speaker 2: before I find one that works? What is that process

563
00:31:12,080 --> 00:31:14,600
Speaker 2: like for you guys as you develop things? Is it

564
00:31:14,680 --> 00:31:18,160
Speaker 2: quite a laborious thing or have you got a kind

565
00:31:18,160 --> 00:31:19,320
Speaker 2: of down to an art now?

566
00:31:19,720 --> 00:31:22,560
Speaker 3: For what we do, it's actually quite simple where we're

567
00:31:22,560 --> 00:31:29,160
Speaker 3: doing some checks of some text and GPT three point

568
00:31:29,280 --> 00:31:31,080
Speaker 3: five does everything we needed to do.

569
00:31:31,200 --> 00:31:32,120
Speaker 4: We've used four.

570
00:31:32,240 --> 00:31:34,520
Speaker 3: It's more expensive where you don't need it, so we

571
00:31:34,560 --> 00:31:38,560
Speaker 3: went back to three point five. One of the things

572
00:31:38,600 --> 00:31:43,239
Speaker 3: the Mini MBA talked about a lot was using the

573
00:31:43,280 --> 00:31:46,600
Speaker 3: AI as a copilot. So you develop a business plan,

574
00:31:47,000 --> 00:31:50,080
Speaker 3: or you write a board paper, and then you ask

575
00:31:50,200 --> 00:31:53,200
Speaker 3: the AI, as the CFO of a listed company, I'm

576
00:31:53,200 --> 00:31:56,560
Speaker 3: presenting this paper to you, what is your feedback? You'll

577
00:31:56,600 --> 00:31:58,760
Speaker 3: see quite quickly that if you run that through three

578
00:31:58,760 --> 00:32:02,160
Speaker 3: different ais that you'll get a different result, and you

579
00:32:02,320 --> 00:32:05,880
Speaker 3: just become more comfortable with one over the other. Claude

580
00:32:06,000 --> 00:32:08,560
Speaker 3: is the one that I have found as really good

581
00:32:08,600 --> 00:32:11,440
Speaker 3: as a research tool, even things like what are the

582
00:32:11,480 --> 00:32:14,040
Speaker 3: personas of people who might subscribe to the Herald or

583
00:32:14,040 --> 00:32:17,840
Speaker 3: business desk? What are the reasons people make cancel a subscription?

584
00:32:18,280 --> 00:32:20,400
Speaker 3: And just ask it and it will give you information

585
00:32:20,480 --> 00:32:22,520
Speaker 3: you hadn't thought of, but maybe not a lot of it,

586
00:32:22,840 --> 00:32:24,880
Speaker 3: so then you can continue to dig into it and

587
00:32:24,960 --> 00:32:26,360
Speaker 3: just have a conversation with it.

588
00:32:26,760 --> 00:32:29,040
Speaker 5: Yeah, I think it's if we think about the technology

589
00:32:29,040 --> 00:32:31,560
Speaker 5: we use now. You know, we probably had the same

590
00:32:31,560 --> 00:32:34,280
Speaker 5: conversation back when all of a sudden word was released

591
00:32:34,320 --> 00:32:38,680
Speaker 5: and EXL was released in PowerPoint and you know, calendars

592
00:32:38,720 --> 00:32:40,720
Speaker 5: and emails, and we were thinking about all these different

593
00:32:40,720 --> 00:32:43,760
Speaker 5: pieces of technology doing different things and how we're going

594
00:32:43,800 --> 00:32:47,560
Speaker 5: to manage them. And I suspect actually, our AI world

595
00:32:47,560 --> 00:32:49,520
Speaker 5: won't be that much different. We'll get quite used to,

596
00:32:50,080 --> 00:32:52,000
Speaker 5: you know, which tool is going to be the right thing,

597
00:32:52,120 --> 00:32:54,200
Speaker 5: or which kind of app is the right thing for

598
00:32:54,280 --> 00:32:55,920
Speaker 5: which part of our life we're going to need that

599
00:32:56,000 --> 00:33:00,640
Speaker 5: for And like mad I'm already using different AI formats

600
00:33:00,640 --> 00:33:01,440
Speaker 5: in different places.

601
00:33:01,920 --> 00:33:05,280
Speaker 3: Someone said to me the other day. AI is finally

602
00:33:05,360 --> 00:33:07,640
Speaker 3: allowing us to do what computers were telling us they

603
00:33:07,680 --> 00:33:11,280
Speaker 3: could do thirty years ago. Computers were going to make

604
00:33:11,280 --> 00:33:13,120
Speaker 3: everything easy. They were going to lay out on newspapers,

605
00:33:13,160 --> 00:33:15,480
Speaker 3: they were going to write articles, they were going to

606
00:33:15,520 --> 00:33:19,320
Speaker 3: do everything. We know, all the drudgery work that we

607
00:33:19,360 --> 00:33:22,560
Speaker 3: didn't want to do. It never happened. In fact, we

608
00:33:22,600 --> 00:33:24,760
Speaker 3: needed these huge IT departments the one of the things,

609
00:33:24,760 --> 00:33:27,440
Speaker 3: and we all lost a heap of staff because the

610
00:33:27,440 --> 00:33:29,680
Speaker 3: computers and then just had to work harder. So it

611
00:33:29,720 --> 00:33:32,840
Speaker 3: was kind of a false promise. But now it used

612
00:33:32,880 --> 00:33:36,240
Speaker 3: to take us forty five minutes to process and inns

613
00:33:36,400 --> 00:33:38,840
Speaker 3: X announcement, write the article, check out, and put it out.

614
00:33:39,120 --> 00:33:41,680
Speaker 3: We have that to thirty seconds now with the tool

615
00:33:41,680 --> 00:33:43,360
Speaker 3: we've built for the heral. It used to take us

616
00:33:43,400 --> 00:33:46,959
Speaker 3: twelve minutes to take a story from one of our

617
00:33:46,960 --> 00:33:49,840
Speaker 3: syndication partners like the Washington Post or the New York Times,

618
00:33:50,560 --> 00:33:53,720
Speaker 3: feed it into our system and get it ready to publish.

619
00:33:54,640 --> 00:33:56,520
Speaker 3: We can now do that in under a minute, maybe

620
00:33:56,560 --> 00:34:00,680
Speaker 3: two minutes. And it means that that repetitive of donkey

621
00:34:00,760 --> 00:34:03,360
Speaker 3: work we don't have to do that anymore. We can

622
00:34:03,400 --> 00:34:04,560
Speaker 3: concentrate on quality.

623
00:34:05,560 --> 00:34:08,040
Speaker 5: That's where the real productivity gains are, right. You know,

624
00:34:08,080 --> 00:34:11,560
Speaker 5: we talk about you know, New Zealand needing a productivity uplift.

625
00:34:11,719 --> 00:34:13,920
Speaker 5: This is where it can make us huge difference for

626
00:34:14,000 --> 00:34:16,080
Speaker 5: us instead of having to try and put more and

627
00:34:16,120 --> 00:34:19,200
Speaker 5: more people in particularly you know, as we found over COVID,

628
00:34:19,200 --> 00:34:20,680
Speaker 5: we didn't have enough people to be able to do

629
00:34:20,719 --> 00:34:23,440
Speaker 5: the things we wanted to do when the borders were closed.

630
00:34:23,960 --> 00:34:26,200
Speaker 5: AI can really help us with that KP.

631
00:34:26,680 --> 00:34:30,160
Speaker 1: Part of this mini MBA was putting a business case together,

632
00:34:30,280 --> 00:34:34,920
Speaker 1: testing it out. What has the Institute of Director's approach

633
00:34:35,000 --> 00:34:37,920
Speaker 1: to AI been to date? Give us a favor of

634
00:34:37,960 --> 00:34:39,960
Speaker 1: your approach to AI, and if you can take us

635
00:34:39,960 --> 00:34:42,920
Speaker 1: into that business case you worked on over these four weeks.

636
00:34:43,120 --> 00:34:45,160
Speaker 5: Yeah, we were pretty cautious to start with, you know,

637
00:34:45,200 --> 00:34:48,080
Speaker 5: because you know, boards always is said thinking about risk

638
00:34:48,160 --> 00:34:51,080
Speaker 5: and opportunity. In many ways, boards are the responsible adults

639
00:34:51,120 --> 00:34:52,960
Speaker 5: in the room, right, and so we were kind of

640
00:34:52,960 --> 00:34:55,240
Speaker 5: a bit worried about making sure that we were thinking

641
00:34:55,280 --> 00:34:58,839
Speaker 5: about things like bias and hallucinations and making sure that

642
00:34:58,920 --> 00:35:01,359
Speaker 5: you know, that information was going to be trusted and

643
00:35:01,440 --> 00:35:03,600
Speaker 5: so we were pretty cautious and only really at the

644
00:35:03,640 --> 00:35:06,759
Speaker 5: playing stage and had some really pretty tight restrictions as

645
00:35:06,760 --> 00:35:09,719
Speaker 5: we still do around no sort of personal information going in,

646
00:35:09,840 --> 00:35:12,400
Speaker 5: no member data, and nothing you know that we would

647
00:35:12,440 --> 00:35:16,120
Speaker 5: I want to use to either claim ip over those

648
00:35:16,120 --> 00:35:19,360
Speaker 5: from a creation perspective. But my business case in the

649
00:35:19,400 --> 00:35:22,000
Speaker 5: project I worked on was something called Pipper. And the

650
00:35:22,040 --> 00:35:24,520
Speaker 5: reason she's called Pipper is that the core kind of

651
00:35:25,080 --> 00:35:27,360
Speaker 5: principles of best practice at the IOD is called the

652
00:35:27,360 --> 00:35:29,759
Speaker 5: four pillars of the four Piece, and if you add

653
00:35:29,800 --> 00:35:33,520
Speaker 5: AI to four piece, you get Pipper. So now the

654
00:35:33,560 --> 00:35:37,799
Speaker 5: team we're using internally, she's our best practice guide. So

655
00:35:37,840 --> 00:35:40,640
Speaker 5: she's got access to all of our sort of guides,

656
00:35:40,800 --> 00:35:45,000
Speaker 5: our community of knowledge. She's pointed to particular websites, certain

657
00:35:45,080 --> 00:35:47,799
Speaker 5: things that she's allowed to search and not search. She's

658
00:35:47,800 --> 00:35:51,800
Speaker 5: got the NZX listing code rules, and she is the

659
00:35:51,840 --> 00:35:54,359
Speaker 5: best source of information to be able to answer those

660
00:35:54,480 --> 00:35:58,480
Speaker 5: kind of queries member inquiries, kickstart you know, a draft

661
00:35:58,520 --> 00:36:02,760
Speaker 5: of something, and we recently did a quiz about governance rules.

662
00:36:02,760 --> 00:36:05,240
Speaker 5: Twenty out of twenty, Pepper beat everybody in the team.

663
00:36:05,800 --> 00:36:09,920
Speaker 1: Fantastic And have you had to partner with a specialist

664
00:36:09,920 --> 00:36:13,560
Speaker 1: company to implement this or doing it yourself with off

665
00:36:13,560 --> 00:36:14,680
Speaker 1: the shelf sort of tools.

666
00:36:15,239 --> 00:36:18,279
Speaker 5: I delivered it myself as part of my project. It

667
00:36:18,400 --> 00:36:20,640
Speaker 5: was a no code AI bot. They taught us how

668
00:36:20,680 --> 00:36:24,080
Speaker 5: to do that and if I can do it, anyone

669
00:36:24,160 --> 00:36:27,160
Speaker 5: can do it. And just before our AGM a couple

670
00:36:27,239 --> 00:36:29,120
Speaker 5: of weeks ago, in the forty five minutes I had

671
00:36:29,120 --> 00:36:30,960
Speaker 5: sitting at the airport, I was able to put one

672
00:36:30,960 --> 00:36:34,759
Speaker 5: together just on the inenzec on our iod rules, to

673
00:36:34,840 --> 00:36:36,799
Speaker 5: have it running for the AGM, to be able to

674
00:36:36,840 --> 00:36:39,640
Speaker 5: test quorums, to be able to ask queries who can vote?

675
00:36:39,680 --> 00:36:41,320
Speaker 5: What do we do if these sorts of things happen?

676
00:36:42,200 --> 00:36:45,680
Speaker 5: So yeah, they taught us some pretty easy no code

677
00:36:46,080 --> 00:36:47,880
Speaker 5: kind of things to be able to get the basic stunt.

678
00:36:47,920 --> 00:36:49,720
Speaker 5: But what it showed is that even in a small

679
00:36:49,760 --> 00:36:53,400
Speaker 5: not for profit there are some really easy entry points

680
00:36:53,400 --> 00:36:55,799
Speaker 5: for AI to be used in a responsible way that

681
00:36:55,840 --> 00:36:57,040
Speaker 5: could really change your business.

682
00:37:04,239 --> 00:37:06,799
Speaker 2: Matt, your business case is aiming to put an end

683
00:37:06,840 --> 00:37:09,839
Speaker 2: to my job. You want to remove all humans from

684
00:37:09,880 --> 00:37:13,280
Speaker 2: podcasts and only do AI generated podcast Is that correct?

685
00:37:17,239 --> 00:37:22,120
Speaker 3: No, KP, that sounds phenomenal. What your bill sounds as

686
00:37:22,200 --> 00:37:28,040
Speaker 3: absolutely fantastic. So I have this idea to take the

687
00:37:28,080 --> 00:37:31,440
Speaker 3: top ten business articles or sport articles or political articles

688
00:37:31,480 --> 00:37:34,680
Speaker 3: we write each day, use AI to create a radio

689
00:37:34,760 --> 00:37:38,000
Speaker 3: script or a podcast script, put them together and then

690
00:37:38,120 --> 00:37:40,040
Speaker 3: and we're probably a few months away from doing that,

691
00:37:40,440 --> 00:37:44,600
Speaker 3: and you can just see how the source journalism can

692
00:37:44,640 --> 00:37:48,759
Speaker 3: be split into different things once we've done it. I

693
00:37:48,840 --> 00:37:52,560
Speaker 3: don't believe AI is going to steal your job. Ben

694
00:37:53,400 --> 00:38:00,600
Speaker 3: definitely Peters though, And because the origination of content need humans.

695
00:38:01,239 --> 00:38:04,560
Speaker 3: AI can turn a press release. It can't interview like this,

696
00:38:05,000 --> 00:38:07,719
Speaker 3: It can't go off and do an investigative piece. It

697
00:38:07,760 --> 00:38:12,400
Speaker 3: can't go and do a really sensitive interview with a

698
00:38:12,480 --> 00:38:15,640
Speaker 3: victim of sexual assault or something like that. What it

699
00:38:15,719 --> 00:38:19,440
Speaker 3: can do is improve the journalism that we create, and

700
00:38:19,480 --> 00:38:23,440
Speaker 3: then in appropriate cases, can turn it into other forms,

701
00:38:23,640 --> 00:38:27,839
Speaker 3: other formats That might be as simple as a TikTok

702
00:38:27,920 --> 00:38:31,040
Speaker 3: with a caption, or it might be a seven minute

703
00:38:31,080 --> 00:38:34,200
Speaker 3: podcast which we're then able to sponsor. Being able to

704
00:38:34,400 --> 00:38:38,640
Speaker 3: use the AI tools to expand the audience for the

705
00:38:38,680 --> 00:38:42,200
Speaker 3: journalism means we can monetize more journalism, which means we

706
00:38:42,239 --> 00:38:44,040
Speaker 3: can maintain our industry for longer.

707
00:38:45,080 --> 00:38:47,799
Speaker 2: It's interesting, got two different focuses, but they're both on

708
00:38:48,040 --> 00:38:51,160
Speaker 2: specific things that we know that these tools are good at,

709
00:38:51,280 --> 00:38:55,120
Speaker 2: which is taking a corpus of information and digging out

710
00:38:55,360 --> 00:38:59,239
Speaker 2: details from it, and then again taking a corpus of

711
00:38:59,280 --> 00:39:04,000
Speaker 2: information and creating a particular summary that can give you.

712
00:39:03,960 --> 00:39:05,080
Speaker 4: What you need to know from it.

713
00:39:05,239 --> 00:39:08,359
Speaker 2: So when you were in this mini MBA, what did

714
00:39:08,400 --> 00:39:12,640
Speaker 2: you learn about maybe the limitations of AI, because you know,

715
00:39:12,680 --> 00:39:14,560
Speaker 2: over the last year we've kind of gone from this

716
00:39:14,640 --> 00:39:16,520
Speaker 2: it's amazing and it can do everything and it's going

717
00:39:16,560 --> 00:39:18,719
Speaker 2: to solve all of our problems to actually looking at

718
00:39:18,800 --> 00:39:21,719
Speaker 2: the reality of what it can do. What are some

719
00:39:21,760 --> 00:39:24,799
Speaker 2: of the things that you found that you were disabused

720
00:39:24,840 --> 00:39:25,880
Speaker 2: of the expectation.

721
00:39:26,680 --> 00:39:28,520
Speaker 5: I think the description that they used was a really

722
00:39:28,560 --> 00:39:30,520
Speaker 5: great one. It kind of helped me think about it

723
00:39:30,560 --> 00:39:35,080
Speaker 5: is thinking of it as a really keen graduate, right,

724
00:39:35,160 --> 00:39:37,120
Speaker 5: So you know, you get a graduate in and when

725
00:39:37,120 --> 00:39:39,719
Speaker 5: they're new in the business, you have to help them

726
00:39:39,760 --> 00:39:42,040
Speaker 5: with really good questions, and you have to review their

727
00:39:42,080 --> 00:39:44,879
Speaker 5: work regularly, and sometimes they'll get things wrong and they'll

728
00:39:44,880 --> 00:39:46,960
Speaker 5: develop as fast as the amount of time you spend

729
00:39:46,960 --> 00:39:49,279
Speaker 5: with them. So I think that was one of the restrictions.

730
00:39:49,280 --> 00:39:51,040
Speaker 5: I think, you know, expecting it to be at a

731
00:39:51,440 --> 00:39:54,800
Speaker 5: sort of a professor level on everything just isn't reasonable

732
00:39:55,440 --> 00:39:59,200
Speaker 5: but actually a really wonderful, bright, intelligent graduate. It's a

733
00:39:59,239 --> 00:40:02,040
Speaker 5: great thing to hear in your team and to be

734
00:40:02,280 --> 00:40:04,640
Speaker 5: and spending some time and investing in you know. The

735
00:40:04,719 --> 00:40:06,719
Speaker 5: joke going around online is of course that you know

736
00:40:06,840 --> 00:40:09,439
Speaker 5: chat GPT or won't can't tell you how many ares

737
00:40:09,440 --> 00:40:12,160
Speaker 5: there are in strawberry. You know, that's a you know,

738
00:40:12,200 --> 00:40:13,880
Speaker 5: there's a bit of a meme going around about that.

739
00:40:13,920 --> 00:40:15,880
Speaker 5: So there's there are some restrictions and things, and I

740
00:40:15,920 --> 00:40:18,040
Speaker 5: think that's where Matt's saying, you know, human in the loop,

741
00:40:18,400 --> 00:40:20,399
Speaker 5: and that was a real clear thing. Start with a human,

742
00:40:20,560 --> 00:40:22,520
Speaker 5: end with a human was one of the key things

743
00:40:22,520 --> 00:40:24,680
Speaker 5: that came through. But you know, I think we're just

744
00:40:24,719 --> 00:40:27,200
Speaker 5: seeing the beginnings of this, aren't we. So we can

745
00:40:27,200 --> 00:40:29,319
Speaker 5: talk about all of the things that are restrictions and

746
00:40:29,360 --> 00:40:32,480
Speaker 5: not working well now, but I suspect in a very

747
00:40:32,480 --> 00:40:34,319
Speaker 5: short period of time they'll work all all of those

748
00:40:34,360 --> 00:40:35,919
Speaker 5: and it's just going to get better and better.

749
00:40:36,280 --> 00:40:38,440
Speaker 3: Yeah, I think that came through again and again. If

750
00:40:38,440 --> 00:40:40,160
Speaker 3: it can't do it now, it should be able to

751
00:40:40,200 --> 00:40:42,480
Speaker 3: do it in six months. Can't do it in six

752
00:40:42,520 --> 00:40:44,359
Speaker 3: months or be able to do it six months after that.

753
00:40:44,800 --> 00:40:47,160
Speaker 3: Right now, a lot of the effort a ound AI

754
00:40:47,320 --> 00:40:50,640
Speaker 3: is around productivity and improving process and then I think

755
00:40:50,680 --> 00:40:55,320
Speaker 3: we'll see over time AI being used in more creative ways,

756
00:40:55,760 --> 00:40:59,920
Speaker 3: developing business cases or creating movies, sort of media created

757
00:41:00,120 --> 00:41:03,360
Speaker 3: task From where I said, my daughter, who's sixteen, wanted

758
00:41:03,400 --> 00:41:07,759
Speaker 3: to become a video game animator, and she's decided that

759
00:41:08,000 --> 00:41:10,279
Speaker 3: she can't do that anymore because AI will be able

760
00:41:10,320 --> 00:41:12,880
Speaker 3: to do that so easily. There's no skill set in anymore.

761
00:41:13,520 --> 00:41:16,439
Speaker 3: So you're starting to see a generation of kids looking

762
00:41:16,440 --> 00:41:19,759
Speaker 3: at their future and trying to place themselves within the

763
00:41:19,800 --> 00:41:24,840
Speaker 3: context of an AI enabled workforce. And in some ways

764
00:41:24,880 --> 00:41:28,000
Speaker 3: that kind of crushed one of her dreams, and I

765
00:41:28,000 --> 00:41:31,480
Speaker 3: think that's really sad, but I'm really proud of her

766
00:41:31,520 --> 00:41:33,799
Speaker 3: that she's able to see that and move on from it.

767
00:41:34,680 --> 00:41:36,560
Speaker 3: Now she wants to be a professional bass player.

768
00:41:37,000 --> 00:41:39,600
Speaker 4: So it's not all bad things around to us.

769
00:41:39,960 --> 00:41:43,759
Speaker 5: It's my daughter's eighteen and she's studying to become a

770
00:41:43,920 --> 00:41:47,640
Speaker 5: professional dog trainer because she tells me AI won't take

771
00:41:47,680 --> 00:41:50,360
Speaker 5: her job. And during COVID everyone got dogs. You know,

772
00:41:50,440 --> 00:41:52,200
Speaker 5: ten or fifteen years ago, I would have looked around

773
00:41:52,200 --> 00:41:55,319
Speaker 5: ta a dog trainer. But she's probably right, you know,

774
00:41:55,400 --> 00:41:58,160
Speaker 5: she's also looking forward to into a tech future, going, Okay,

775
00:41:58,200 --> 00:42:00,640
Speaker 5: what's a hands on job. It's going to sort of

776
00:42:00,680 --> 00:42:01,880
Speaker 5: have some longevity for it.

777
00:42:02,480 --> 00:42:02,959
Speaker 4: Yeah, KP.

778
00:42:03,600 --> 00:42:07,719
Speaker 1: Your business case is very much a real productivity efficiency play.

779
00:42:07,719 --> 00:42:12,120
Speaker 1: It's making your staff that much more knowledgeable straight off

780
00:42:12,120 --> 00:42:15,000
Speaker 1: the bat by using this chatbot. Do you see scope

781
00:42:15,040 --> 00:42:18,360
Speaker 1: for transitioning that to your membership.

782
00:42:18,480 --> 00:42:18,640
Speaker 2: Oh?

783
00:42:18,680 --> 00:42:20,919
Speaker 5: Absolutely, that's got to be the plan and the longer term.

784
00:42:20,920 --> 00:42:23,279
Speaker 5: And at the moment we're just using it internally and

785
00:42:23,320 --> 00:42:26,040
Speaker 5: with a few of the volunteers. You know, our board

786
00:42:26,040 --> 00:42:29,280
Speaker 5: and volunteers around the branch is kind of testing those results.

787
00:42:30,040 --> 00:42:32,000
Speaker 5: But absolutely, if you're in the middle of a board

788
00:42:32,040 --> 00:42:34,120
Speaker 5: situation where you've got a question or a challenge and

789
00:42:34,160 --> 00:42:36,800
Speaker 5: you need to know the answer and get some trusted advice,

790
00:42:37,280 --> 00:42:38,759
Speaker 5: this should be a really great way to be able

791
00:42:38,840 --> 00:42:41,680
Speaker 5: to do that. And it's not the sort of technical content,

792
00:42:41,760 --> 00:42:44,719
Speaker 5: it's not the sort of specifics of A plus B

793
00:42:44,840 --> 00:42:47,839
Speaker 5: equal C. You know, it's getting some perspective on how

794
00:42:47,840 --> 00:42:50,560
Speaker 5: do I deal with board dynamics, how do I get

795
00:42:50,640 --> 00:42:53,360
Speaker 5: rid of a board member who's not performing those sorts

796
00:42:53,400 --> 00:42:57,080
Speaker 5: of challenges and perspectives where sometimes those are lonely questions

797
00:42:57,160 --> 00:42:59,359
Speaker 5: late at night and you don't want to wait till

798
00:42:59,400 --> 00:43:01,560
Speaker 5: sort of nine o'clo on Monday morning when someone opens

799
00:43:01,600 --> 00:43:03,719
Speaker 5: up their emails to get an answer. So, you know,

800
00:43:03,760 --> 00:43:05,840
Speaker 5: I can see in the future, you know, our position

801
00:43:05,920 --> 00:43:08,400
Speaker 5: at the IOD will be the face of trust and

802
00:43:08,400 --> 00:43:11,040
Speaker 5: the sort of layer of trust over that to be

803
00:43:11,040 --> 00:43:12,920
Speaker 5: able to give people access to great information.

804
00:43:13,760 --> 00:43:16,960
Speaker 1: Yeah, and ten years ago there was everyone wanted to

805
00:43:16,960 --> 00:43:19,520
Speaker 1: do an MBA. That was the thing to do, a

806
00:43:19,640 --> 00:43:23,879
Speaker 1: Masters of Business Administration, and you would try and get

807
00:43:23,920 --> 00:43:25,880
Speaker 1: your company to pay for it, and it was a

808
00:43:25,880 --> 00:43:28,840
Speaker 1: great thing for them. If they saw c suite potential

809
00:43:28,880 --> 00:43:31,440
Speaker 1: in you, they would put you through an MBA, often

810
00:43:31,800 --> 00:43:35,280
Speaker 1: in Australia or remotely to some of the Ivy League colleges.

811
00:43:35,320 --> 00:43:37,080
Speaker 1: You know, it could cost one hundred and fifty thousand

812
00:43:37,120 --> 00:43:41,200
Speaker 1: dollars big investment over two years. What's your take on

813
00:43:41,760 --> 00:43:44,880
Speaker 1: where we're at when it comes to NBAS. Has interested

814
00:43:44,880 --> 00:43:47,000
Speaker 1: in that fizzled a little bit? Are they still relevant

815
00:43:47,400 --> 00:43:50,080
Speaker 1: or are these mini MBAs actually in a very fast

816
00:43:50,120 --> 00:43:53,760
Speaker 1: moving business environment these days? Are they maybe a better approach?

817
00:43:54,000 --> 00:43:56,439
Speaker 5: Yeah? I think they're work incredibly well for currency, right,

818
00:43:56,480 --> 00:43:59,400
Speaker 5: So they give you that intensity and that kind of

819
00:43:59,400 --> 00:44:02,600
Speaker 5: real folk learning and to keep your current. I'm not

820
00:44:02,640 --> 00:44:05,799
Speaker 5: sure that this kind of approach is necessarily going to

821
00:44:06,200 --> 00:44:10,920
Speaker 5: replace your deep academic learning over a longer period of time.

822
00:44:11,920 --> 00:44:13,840
Speaker 5: There's an element of fast food to them, you know,

823
00:44:13,880 --> 00:44:16,640
Speaker 5: in terms of refined sugar, and there is a place

824
00:44:16,680 --> 00:44:19,480
Speaker 5: for that, because you know it's then up to you

825
00:44:19,560 --> 00:44:21,160
Speaker 5: to make the most of that and to then go

826
00:44:21,239 --> 00:44:24,719
Speaker 5: away and make it deeper learning and to embed that

827
00:44:24,800 --> 00:44:27,200
Speaker 5: into your practice. But I think there are a really

828
00:44:27,239 --> 00:44:29,800
Speaker 5: effective way, and we see more and more people wanting

829
00:44:29,800 --> 00:44:34,239
Speaker 5: to sort of micro credential or kind of collect badges

830
00:44:34,280 --> 00:44:36,839
Speaker 5: on the way through and do more of that sort

831
00:44:36,880 --> 00:44:39,480
Speaker 5: of mini learning as we sort of build up. And

832
00:44:39,520 --> 00:44:41,960
Speaker 5: my husband's just done one with he's a funeral director,

833
00:44:42,040 --> 00:44:44,840
Speaker 5: so not a particularly techy kind of a guy or industry,

834
00:44:45,160 --> 00:44:48,239
Speaker 5: and he's just done one with Edex about the digital workplace,

835
00:44:48,280 --> 00:44:51,319
Speaker 5: and all of a sudden, he's creating music videos with

836
00:44:51,400 --> 00:44:54,240
Speaker 5: AI that have written songs about the dogs using photos.

837
00:44:54,280 --> 00:44:56,200
Speaker 5: He's taking it home because that's what he had handy.

838
00:44:56,600 --> 00:44:58,319
Speaker 5: I think that's going to be the way we all

839
00:44:58,360 --> 00:45:01,760
Speaker 5: have to engage dive in the little bits of learning

840
00:45:02,000 --> 00:45:05,320
Speaker 5: and don't be put off by the having to necessarily

841
00:45:05,320 --> 00:45:07,120
Speaker 5: go to university to learn a little bit more about

842
00:45:07,160 --> 00:45:08,640
Speaker 5: keeping up on top of it.

843
00:45:09,200 --> 00:45:11,920
Speaker 3: I was very lucky to have that experience of a

844
00:45:12,000 --> 00:45:14,560
Speaker 3: large corporate paying for me to do an MBA that

845
00:45:14,800 --> 00:45:18,920
Speaker 3: was at the Australian Graduate Graduate School of Management at

846
00:45:19,600 --> 00:45:24,520
Speaker 3: University of New South Wales, and it was not comparable

847
00:45:24,600 --> 00:45:27,880
Speaker 3: to this. It was hugely expensive, it was talk up

848
00:45:27,880 --> 00:45:30,960
Speaker 3: a huge amount of time, and then I was made

849
00:45:31,000 --> 00:45:33,240
Speaker 3: redundant before I finished it, so I didn't even finish

850
00:45:33,280 --> 00:45:35,200
Speaker 3: it because I couldn't afford the forty thousand dollars to

851
00:45:35,200 --> 00:45:38,960
Speaker 3: finish it. I learned a massive amount from that and

852
00:45:39,200 --> 00:45:41,200
Speaker 3: it was as much as anything else as someone who

853
00:45:41,280 --> 00:45:45,080
Speaker 3: never went to university. How to think, and how to

854
00:45:45,160 --> 00:45:48,480
Speaker 3: write essays and how to look at frameworks, a whole

855
00:45:48,480 --> 00:45:51,479
Speaker 3: lot of things like that which a one month course

856
00:45:51,560 --> 00:45:54,520
Speaker 3: is not really going to give you as a micro credential.

857
00:45:54,560 --> 00:45:57,719
Speaker 3: It's really interesting. The one thing I did get out

858
00:45:57,760 --> 00:46:00,120
Speaker 3: of it is I now have a postgraduate certificate and

859
00:46:00,200 --> 00:46:03,880
Speaker 3: change management, which I love because I never graduated from anything.

860
00:46:03,920 --> 00:46:08,160
Speaker 3: To be postgraduate. It's different, it's completely different. The level

861
00:46:08,160 --> 00:46:10,200
Speaker 3: of commitment is nowhere near the same. But I can

862
00:46:10,239 --> 00:46:13,400
Speaker 3: see the advantage of both, and I can see why

863
00:46:13,440 --> 00:46:19,000
Speaker 3: these little micro MBAs or whatever they want to be called,

864
00:46:19,640 --> 00:46:23,280
Speaker 3: help you get a deep knowledge of one small subject,

865
00:46:23,360 --> 00:46:25,160
Speaker 3: like how do work with AI?

866
00:46:26,040 --> 00:46:28,440
Speaker 2: What is the one thing that you've learned, whether from

867
00:46:28,480 --> 00:46:33,160
Speaker 2: the minimba or from your own research following or during

868
00:46:33,480 --> 00:46:36,000
Speaker 2: that you are going to take with you forward for

869
00:46:36,040 --> 00:46:36,880
Speaker 2: the rest of your life.

870
00:46:37,480 --> 00:46:40,640
Speaker 5: First is give it a go. I think that's the

871
00:46:40,680 --> 00:46:43,000
Speaker 5: real challenge. I was kind of waiting and holding back

872
00:46:43,000 --> 00:46:45,360
Speaker 5: a bit, and what the course did. It gave me

873
00:46:45,360 --> 00:46:47,360
Speaker 5: a permission. And I don't know who I was waiting

874
00:46:47,360 --> 00:46:50,080
Speaker 5: for that permission from, but actually it just kind of

875
00:46:50,120 --> 00:46:53,240
Speaker 5: gave felt that I had permission or empowerment to actually

876
00:46:53,320 --> 00:46:55,840
Speaker 5: go and give this a go. So that would be

877
00:46:55,880 --> 00:46:58,080
Speaker 5: my first thing. Make sure that you're in there. And

878
00:46:58,520 --> 00:47:01,759
Speaker 5: the reason we need that is with responsible adults. I

879
00:47:01,760 --> 00:47:04,360
Speaker 5: don't want to have happen with AI. What happened with

880
00:47:04,400 --> 00:47:07,960
Speaker 5: social media. Too many of us, as kind of leaders

881
00:47:07,960 --> 00:47:11,920
Speaker 5: in our communities left that to some of the big

882
00:47:11,960 --> 00:47:14,080
Speaker 5: tech and it got away on us. And I don't

883
00:47:14,120 --> 00:47:17,680
Speaker 5: think it's necessarily been for society's benefits. So you know,

884
00:47:17,800 --> 00:47:20,759
Speaker 5: get involved, be in there. We want responsible AI and

885
00:47:20,760 --> 00:47:22,560
Speaker 5: that's going to take all of us to be engaged

886
00:47:22,600 --> 00:47:25,239
Speaker 5: and informed, so we know what that looks like, what

887
00:47:25,280 --> 00:47:28,359
Speaker 5: trust looks like in those communities. And it's not as

888
00:47:28,400 --> 00:47:29,960
Speaker 5: scary as you think it can be. If I can

889
00:47:29,960 --> 00:47:30,759
Speaker 5: do it, anyone can.

890
00:47:31,000 --> 00:47:32,200
Speaker 3: Couldn't put any better than that.

891
00:47:32,239 --> 00:47:34,760
Speaker 1: Any burning things we didn't ask you that you wanted

892
00:47:34,800 --> 00:47:37,680
Speaker 1: to reflect on, No.

893
00:47:37,880 --> 00:47:39,680
Speaker 5: I think so I've just you know, I think, like Matt,

894
00:47:39,719 --> 00:47:42,560
Speaker 5: I've really enjoyed that Matt's been sharing his articles. I've

895
00:47:42,560 --> 00:47:44,800
Speaker 5: been trying to share it and I've been repeating the

896
00:47:44,840 --> 00:47:47,720
Speaker 5: sessions that we've been doing as a group with my team,

897
00:47:48,239 --> 00:47:50,239
Speaker 5: so you know, trying to spread the word as much

898
00:47:50,239 --> 00:47:53,520
Speaker 5: as we can, but to just kind of think about

899
00:47:53,560 --> 00:47:56,000
Speaker 5: what responsible AI is going to look like. There's definitely

900
00:47:56,040 --> 00:47:58,279
Speaker 5: a few more challenges on that front yet to go right.

901
00:48:00,320 --> 00:48:04,480
Speaker 1: Did you see what other people in that cohort were

902
00:48:04,520 --> 00:48:09,160
Speaker 1: working on? Did you go wows lots of really cool

903
00:48:09,200 --> 00:48:11,280
Speaker 1: ideas here that are AI related.

904
00:48:11,400 --> 00:48:13,520
Speaker 5: Not as much as I would have liked. Actually, I

905
00:48:13,520 --> 00:48:15,839
Speaker 5: think I think there was more opportunity for that man

906
00:48:15,960 --> 00:48:18,760
Speaker 5: or was your view? I would have in those question sessions,

907
00:48:18,800 --> 00:48:19,520
Speaker 5: you've got a little bit.

908
00:48:20,520 --> 00:48:22,560
Speaker 3: Yeah, like there were one or two. There was one

909
00:48:22,800 --> 00:48:25,920
Speaker 3: person in particular who had done some process change management

910
00:48:25,960 --> 00:48:31,320
Speaker 3: around importing of large expensive things and had actually revolutionized

911
00:48:31,320 --> 00:48:36,120
Speaker 3: a business, a large business, and had done that all

912
00:48:36,200 --> 00:48:37,879
Speaker 3: very quietly and then said, oh, here's what I've done

913
00:48:37,880 --> 00:48:40,399
Speaker 3: over the last few weeks. And it was Greg Shove

914
00:48:40,520 --> 00:48:42,360
Speaker 3: was just like, you are the best person in the

915
00:48:42,480 --> 00:48:43,240
Speaker 3: entire universe.

916
00:48:45,160 --> 00:48:46,839
Speaker 5: Yeah. I mean, I think there's some of the people

917
00:48:46,880 --> 00:48:50,200
Speaker 5: have got some pretty amazing examples within their organizations where

918
00:48:50,239 --> 00:48:54,680
Speaker 5: they're making some really deep tech investment about making some

919
00:48:54,719 --> 00:48:57,040
Speaker 5: of those big changes which I was, I was in

920
00:48:57,120 --> 00:48:59,319
Speaker 5: awe of. So I think New Zealand's in good hands.

921
00:48:59,800 --> 00:49:01,839
Speaker 3: I think that's a really good point, KP. And that

922
00:49:02,320 --> 00:49:06,799
Speaker 3: we might not understand how far this has already come,

923
00:49:07,560 --> 00:49:10,840
Speaker 3: because if you look at the relatively public example what

924
00:49:10,880 --> 00:49:14,680
Speaker 3: we're doing in ends in me, that's being replicated across

925
00:49:15,040 --> 00:49:18,920
Speaker 3: so every large company in the country and not just

926
00:49:18,960 --> 00:49:25,240
Speaker 3: companies but associations and government, and there's a quiet revolution

927
00:49:25,400 --> 00:49:27,399
Speaker 3: happening and no one's really talking about it yet because

928
00:49:27,400 --> 00:49:30,640
Speaker 3: everyone thinks that they what they're doing is small. It's

929
00:49:30,680 --> 00:49:32,880
Speaker 3: freaking massive and it's happening really quickly.

930
00:49:33,000 --> 00:49:35,080
Speaker 5: Yeah, the business case is pretty clear, so I think

931
00:49:35,080 --> 00:49:37,799
Speaker 5: we're going to see quite a march shift. We've got

932
00:49:37,880 --> 00:49:40,560
Speaker 5: a AI forum coming up for directors. We're trying to

933
00:49:40,640 --> 00:49:43,799
Speaker 5: expose directors because you know, we really need them to

934
00:49:43,800 --> 00:49:46,319
Speaker 5: be leading this from the boardroom, because there's a set

935
00:49:46,360 --> 00:49:48,160
Speaker 5: on the social media front. You know, they were in

936
00:49:48,239 --> 00:49:52,480
Speaker 5: charge of Facebook and organizations and they never even used it.

937
00:49:52,520 --> 00:49:54,600
Speaker 5: They had no idea what it was, and I'm trying

938
00:49:54,640 --> 00:49:57,920
Speaker 5: to make decisions about how organizations were engaging with social media,

939
00:49:58,080 --> 00:50:00,040
Speaker 5: so can't afford for the same thing to it.

940
00:50:04,960 --> 00:50:07,440
Speaker 2: Sounds like the biggest thing that both of them gained

941
00:50:07,840 --> 00:50:13,160
Speaker 2: is a renewed appreciation for the breadth and depth that AI,

942
00:50:13,600 --> 00:50:16,240
Speaker 2: these new AI tools are going to bring to business.

943
00:50:16,320 --> 00:50:18,080
Speaker 2: So while Matt, for example, had quite a lot of

944
00:50:18,120 --> 00:50:22,160
Speaker 2: experience with AI, he was even more taken with what

945
00:50:22,239 --> 00:50:24,400
Speaker 2: he experienced during the Mini MBA.

946
00:50:25,320 --> 00:50:28,960
Speaker 1: Yeah, and interesting that KP was able to off her

947
00:50:28,960 --> 00:50:33,440
Speaker 1: own bat come up with an internal chatbot that can

948
00:50:33,480 --> 00:50:39,479
Speaker 1: be used based on off the shelf technology to really

949
00:50:39,520 --> 00:50:43,440
Speaker 1: speed up processes within the Institute of Directors. And I think,

950
00:50:43,480 --> 00:50:45,640
Speaker 1: as she said there, yeah, you know, the potential is

951
00:50:45,640 --> 00:50:49,160
Speaker 1: to make that outward facing. And I can imagine if

952
00:50:49,160 --> 00:50:51,239
Speaker 1: you are a board director and you're paying a subscription

953
00:50:51,360 --> 00:50:54,399
Speaker 1: to IOD, how valuable it would be to be able

954
00:50:54,400 --> 00:50:56,359
Speaker 1: to go to a chatbot and go ooh, okay, I've

955
00:50:56,400 --> 00:50:58,399
Speaker 1: got this process. I need to present to the rest

956
00:50:58,400 --> 00:51:00,960
Speaker 1: of the board on what are the rules around this.

957
00:51:01,760 --> 00:51:04,840
Speaker 1: Matt's new products sounds a little bit scary to us,

958
00:51:05,440 --> 00:51:09,200
Speaker 1: but okay, I see the appeal the AI news reader,

959
00:51:09,680 --> 00:51:12,440
Speaker 1: I think is inevitable. We have heard it time and

960
00:51:12,520 --> 00:51:17,279
Speaker 1: time again from everyone from those vested interest in it

961
00:51:17,400 --> 00:51:20,839
Speaker 1: like Spark and the vendors and that saying you've got

962
00:51:20,880 --> 00:51:22,239
Speaker 1: to upscille and we're going to help you on that

963
00:51:22,360 --> 00:51:26,800
Speaker 1: journey through to the government and business people in general

964
00:51:26,880 --> 00:51:31,480
Speaker 1: realize we're not capable enough for this revolution. So here

965
00:51:31,560 --> 00:51:34,600
Speaker 1: is one aspect of it where you can get people

966
00:51:34,640 --> 00:51:38,839
Speaker 1: in leadership positions to take four to six weeks and

967
00:51:38,840 --> 00:51:41,600
Speaker 1: that's just mainly online with a few sort of meetup

968
00:51:41,640 --> 00:51:44,960
Speaker 1: sessions and that sort of thing to start thinking strategically,

969
00:51:45,000 --> 00:51:47,240
Speaker 1: not necessarily about the nuts and bolts of the tools,

970
00:51:47,320 --> 00:51:52,480
Speaker 1: but how does this fit into my business plan and

971
00:51:52,680 --> 00:51:55,080
Speaker 1: how do I make the most of this. That's a

972
00:51:55,120 --> 00:51:57,160
Speaker 1: really useful thing. And if you can do that for

973
00:51:57,480 --> 00:52:00,000
Speaker 1: three four thousand dollars, it's a good investment.

974
00:52:00,480 --> 00:52:02,839
Speaker 2: Yeah, you know, like they said, a good start, I reckon,

975
00:52:02,840 --> 00:52:05,000
Speaker 2: it's just sitting down and actually thinking about it and

976
00:52:05,040 --> 00:52:09,359
Speaker 2: maybe asking your claudes or your chat GPTs or whoever.

977
00:52:09,160 --> 00:52:11,160
Speaker 4: Like what could I do? This is what I do?

978
00:52:11,280 --> 00:52:11,560
Speaker 6: Where do you?

979
00:52:11,600 --> 00:52:11,839
Speaker 4: Reckon?

980
00:52:11,880 --> 00:52:15,920
Speaker 2: AI might be able to assist me and just man,

981
00:52:16,160 --> 00:52:18,920
Speaker 2: you know, figuring out how to train your own GPT.

982
00:52:19,040 --> 00:52:20,279
Speaker 4: I think that's pretty cool as well.

983
00:52:20,760 --> 00:52:24,520
Speaker 1: Yeah, we'll all have to do that within probably a

984
00:52:24,600 --> 00:52:27,279
Speaker 1: year or two. The sooner we get on board with that,

985
00:52:27,400 --> 00:52:28,960
Speaker 1: the more capable we're going to be as a nation.

986
00:52:29,920 --> 00:52:32,240
Speaker 1: That's it for the Business of Tech this week. Thanks

987
00:52:32,360 --> 00:52:35,640
Speaker 1: so much to Matt Martel and Kirsten Patterson for coming

988
00:52:35,680 --> 00:52:36,120
Speaker 1: on the show.

989
00:52:36,400 --> 00:52:38,719
Speaker 2: Check out the show notes for links to information on that.

990
00:52:38,800 --> 00:52:42,319
Speaker 2: Mini MBA KP is also co hosting an Institute of

991
00:52:42,400 --> 00:52:46,960
Speaker 2: Director's webinar with fellow AI MINI NBA graduates David Downs

992
00:52:47,239 --> 00:52:50,120
Speaker 2: and Ada Campbell on what they learned, and that's coming

993
00:52:50,160 --> 00:52:52,160
Speaker 2: up on July thirty first, so we'll pop a link

994
00:52:52,200 --> 00:52:52,480
Speaker 2: for that.

995
00:52:52,640 --> 00:52:55,880
Speaker 1: And two, The Business of Tech is on all major

996
00:52:56,040 --> 00:52:59,000
Speaker 1: podcast platforms, as well as on iHeartRadio, where you can

997
00:52:59,040 --> 00:53:02,440
Speaker 1: stream every epis. Leave us a review and share it

998
00:53:02,480 --> 00:53:03,600
Speaker 1: with your friends and colleagues.

999
00:53:03,800 --> 00:53:07,239
Speaker 2: Get in touch with feedback, ideas, topics, and guest suggestions.

1000
00:53:07,320 --> 00:53:10,839
Speaker 2: Email me ben at businessdesk dot co dot benzed. We'll

1001
00:53:10,880 --> 00:53:13,239
Speaker 2: find both of us on LinkedIn and x.

1002
00:53:13,239 --> 00:53:16,280
Speaker 1: And we'll be back next Thursday with another dose of

1003
00:53:16,320 --> 00:53:17,319
Speaker 1: the business of tech.

1004
00:53:17,480 --> 00:53:19,000
Speaker 2: Until then, have a great week.