Speaker 1: It's been a big year from mergers and acquisitions. As
Speaker 1: a tough economy, competitors to join forces and try and
Speaker 1: achieve scale here and beyond Australasia.
Speaker 2: But what does it mean in practical terms when two
Speaker 2: companies merge? How do you spice everything together in an
Speaker 2: orderly way?
Speaker 1: Well? Two degrees is in the process of doing just
Speaker 1: that following its merger with Vocus to create New Zealand's
Speaker 1: third largest telecoms operator.
Speaker 2: On the sponsored episode of the Business at Tech, Stephen Kerjier,
Speaker 2: two degrees is chief information officer, talks to us about
Speaker 2: the major job of bringing two large businesses together with
Speaker 2: two million users on the platform and what he's learned
Speaker 2: so far in the two Degrees Vocus merger.
Speaker 1: And Stephen joins us. Now, Steven, thanks so much for
Speaker 1: coming on the Business of Tech. How are you doing.
Speaker 3: I'm doing great. Thanks for having me. Yeah, gat to
Speaker 3: be here soon.
Speaker 1: We're going to get into some really interesting things that
Speaker 1: you've been working on around integration of these telecommunications businesses
Speaker 1: from Vocus to two degrees. So lots of interesting stuff
Speaker 1: to cover there. But you're also joining us for our
Speaker 1: usual sort of run around the big tech headlines off
Speaker 1: the week and a Talco story that's a particular interest
Speaker 1: to you. The Commerce Commission is asking our big telcos,
Speaker 1: including two Degrees, One End, Zed and Spark, to start
Speaker 1: showing coverage maps. Now I thought they already did this.
Speaker 1: You go onto your website, you'll see a coverage map there,
Speaker 1: but they're looking for something a bit more extensive. What's
Speaker 1: your take on this.
Speaker 3: Yeah, I think we've all done it, but in different ways.
Speaker 3: So we have a coverage map on our website. I
Speaker 3: think it's actually a really good thing. It provides consistency
Speaker 3: to our consumers on coverage, so when they sign up,
Speaker 3: they know what they're actually getting. I think for two
Speaker 3: Degrees in particular, how we've always been the challenger in
Speaker 3: the market. One of our bigger issues is network perception.
Speaker 3: You know, do you actually have the coverage or not?
Speaker 3: Given our history, and we've invested in our network significantly
Speaker 3: over the last three to five years and we have
Speaker 3: parity now, so we actually think it's a great thing
Speaker 3: for us to show. So, you know, is coverage poor,
Speaker 3: is it good? Is it great? That transparency I think
Speaker 3: is really important for consumers. How hard is to do
Speaker 3: is probably another question.
Speaker 1: That's the thing, because the maps are there, the physical
Speaker 1: you know, your engineers go out and map this is
Speaker 1: where the coverage is. But then putting a rating on
Speaker 1: that is this is really good quality coverage, This is
Speaker 1: a bit miginal. I guess that's where it will be
Speaker 1: a little bit contentious.
Speaker 2: And how do you actually measure it because I'm sure
Speaker 2: there would be fluctuations and it would depend on capacity
Speaker 2: of the towers and things like that. So how are
Speaker 2: you thinking about that nuance?
Speaker 3: Yeah, in one way, you've got kind of network kind
Speaker 3: of measures, you know, signal strength, and those can be
Speaker 3: quite well defined. But I think ultimately its customer experience
Speaker 3: is the most important part of it. So I think
Speaker 3: coverage maps will give you a broad view, but it
Speaker 3: may not show kind of end to end network experience.
Speaker 3: So I think I'll give us enough of a good
Speaker 3: measure for consumers to make the right decisions, particularly in
Speaker 3: the more rural areas. But our focus atally around customer
Speaker 3: experience and how can we measure that kind of that
Speaker 3: true end to end view, which is around collecting the
Speaker 3: right data from the handset, either crowdsourced or you can
Speaker 3: also get some really interesting telemetry from the network as
Speaker 3: well on the user experience and also independent testing of
Speaker 3: the network. So recently we've actually done really well in
Speaker 3: some independent tests, so that's another measure. But from the
Speaker 3: Commerce Commission, it's really just get a baseline on what
Speaker 3: good coverage is and make it consistent, which I think
Speaker 3: is actually a great thing to do, but it is
Speaker 3: probably only one measure. It's not going to be the
Speaker 3: whole story to your question. Yeah.
Speaker 2: Yeah, And the other part of it is that the
Speaker 2: commissioner is looking to have an exit right put into
Speaker 2: contract so that if the customer signs up and they
Speaker 2: find that, like I think it was thirty percent of
Speaker 2: the small businesses that they surveyed that the coverage they're
Speaker 2: not happy with what they're getting, that there is a
Speaker 2: clause saying I want to exit based on this issue. Now,
Speaker 2: two degrees is one of the tolcoes that's already that
Speaker 2: in their contract for some time. Do you find that
Speaker 2: is something that's triggered a lot, or is it just
Speaker 2: gives you the confidence to bring on for customers to
Speaker 2: join you, Like, how does that work out in the field.
Speaker 3: Yeah, well, I say bring it on. Yeah, we've already
Speaker 3: had it. You know, sort of money back guarantee network reliability.
Speaker 3: More recently we've been doing more advertising on that, like
Speaker 3: a network blind test. You can actually try the network out,
Speaker 3: give it a go again. We're really focused on our
Speaker 3: network's fantastic. Now it's parody across all the players, but
Speaker 3: we have this network perception issue so that when customers
Speaker 3: get on our network they actually love it.
Speaker 1: So yeah, that was a big thing, getting to network
Speaker 1: parody as a third player, considering how long it took
Speaker 1: for both Spark and Vodafone to really flesh out coverage.
Speaker 1: There was a lot of complaints about those networks for
Speaker 1: a long time to even in semi rural places, even
Speaker 1: urban areas was patchy coverage. So I guess in some ways,
Speaker 1: coming from behind and as a third player, it was
Speaker 1: a bit easier because you knew some of the pain
Speaker 1: points they'd been through. But still a huge undertaking.
Speaker 3: Significant undertaking, and it keeps going right, it never stops.
Speaker 3: We're investing hundreds of millions dollars in our network every year.
Speaker 3: We've got a five G modernization program that we're well
Speaker 3: in flight. We started late for five G compared to
Speaker 3: our competitors, but we're well on track. I think we
Speaker 3: may be one of the ones to finish first, just
Speaker 3: in terms of our approach. So yeah, it never stops
Speaker 3: and it's never perfect, Like network coverage is always a
Speaker 3: thing where you're always looking to improve. But yeah, coming
Speaker 3: from the origins of basically starting from nothing, right, two
Speaker 3: degrees is a challenger, effectively a startup. We're a really
Speaker 3: big startup now and we still think like a startup,
Speaker 3: but quite an incredible journey from what was a position
Speaker 3: of not owning a network to where we are now,
Speaker 3: where we own over two thousand cell towers, We've got
Speaker 3: fiber assets that are four thousand, six hundred kilometers. The
Speaker 3: network's out of the equation for us, so our focus
Speaker 3: is now on customer experience and software and all that
Speaker 3: great stuff. It takes hard work and a commitment to
Speaker 3: just keep investing for a long term strategy.
Speaker 1: And of course you've got the text satellite stuff comes
Speaker 1: as well, so you've got the coverage map, but then
Speaker 1: you're going to have where the big gray spots are
Speaker 1: at the moment, the ability to send text messages initially
Speaker 1: anyway via satellite.
Speaker 3: That's right, Yeah, and that's the exciting part coming up,
Speaker 3: which we've partnered with link as I think you know
Speaker 3: and still trialing that. And the time for that to
Speaker 3: be effective is just how many birds you can have
Speaker 3: in the sky, how many satellites you can have in
Speaker 3: the sky. So yeah, we're really excited about that to
Speaker 3: really plug those other areas of coverage. And I'll also
Speaker 3: say it's an industry effort as well. You've got the
Speaker 3: Rural Connectivity Group that has been looking at how we
Speaker 3: do things more effective, so it's not just three players
Speaker 3: going out it themselves. We actually do collaborate significantly and
Speaker 3: that's going really well as well. RCG went over five
Speaker 3: hundred sites in terms of the real connectivity areas, so
Speaker 3: that's been really good. And complement that with a low
Speaker 3: Earth orbit for the celt satellite. So I think you
Speaker 3: have a really great story about more ubiquitous coverage in
Speaker 3: New Zealand.
Speaker 2: Yeah, you said you were thinking you'll possibly be completed
Speaker 2: your five G roll out ahead maybe some of the others.
Speaker 2: What does that actually look like. Would it be would
Speaker 2: have equitable coverage to for G or is it separate
Speaker 2: like more specified area rollout.
Speaker 3: Yeah, it's a great question. I think everyone starts with
Speaker 3: the higher population areas, right, that's where you start with
Speaker 3: terms of just needing more capacity, but there's also being
Speaker 3: a program of work to get five G in rural towns,
Speaker 3: working again with the government, so that's been really successful.
Speaker 3: Again that's part of investment strategy to get better coverage
Speaker 3: into rural towns. But yeah, our approach has been basically
Speaker 3: to modernize everything on that cell tart once. So not
Speaker 3: deploying just five G but modernizing four G and five
Speaker 3: G simultaneously means to actually get better four G experience
Speaker 3: and a five G experience at the same time, which
Speaker 3: I think is a really good strategy for us. It
Speaker 3: does mean probably it's a little bit slower initially because
Speaker 3: you're having to do a bit more work, but in
Speaker 3: the long run it's a great strategy for us to
Speaker 3: move forward on.
Speaker 1: So we'll keep an eye on those coverage maps to
Speaker 1: ComCom wants them within a year.
Speaker 3: Yes, yeah, the team are busy already working through it timeframes.
Speaker 1: Yeah, so we'll keep an eye on out. Another big
Speaker 1: story making headlines this week, which is sort of relevance
Speaker 1: to us, the Australian government has proposed mandatory guide rails
Speaker 1: for use of artificial intelligence. They want require testing, transparency
Speaker 1: about how AI is being used, and accountability. There's a
Speaker 1: ten point checklist that they have floated. This is out
Speaker 1: for public consultation for the next month or so. We
Speaker 1: haven't moved to this step yet, and I think the
Speaker 1: government will look quite closely at this. NBA has laid
Speaker 1: out sort of the roadmap that it's following, and Collins
Speaker 1: has said that she would like a risk based, proportional
Speaker 1: and light touch regulatory regime around AI. Here they'll only
Speaker 1: create AI legislation if there's a real need to, So
Speaker 1: there's a lot of parity sort of going on with
Speaker 1: the Australians. What's your take having looked at this already
Speaker 1: using AI at two degrees and the practicalities of trying
Speaker 1: to conform to something like this.
Speaker 3: I think what I've seen was really doing is actually
Speaker 3: really good because it is a risk based approach and
Speaker 3: actually focused on the higher risk areas, but also holistically,
Speaker 3: they're looking to invest in AI and actually accelerate it,
Speaker 3: particularly in government, So I think that's a really great thing. Yeah,
Speaker 3: for us, we've already been doing a lot in this space.
Speaker 3: So when we compare our AI policy and responsible use
Speaker 3: and all the great things around governance for AI. It
Speaker 3: very much aligns to what we're already doing at two
Speaker 3: degrees trust and transparency, having a human loop. Yeah, the
Speaker 3: explainability aspect, which is hard for a black box. But
Speaker 3: I think it's also important to recognize AI of the
Speaker 3: past is very different to AI now with large language models.
Speaker 3: So regulation previously was effectively based on like machine learning,
Speaker 3: are you applying decisions to HR process or to a
Speaker 3: criminal activity or churn predictions and how are you using it?
Speaker 3: But large language model is actually very different to that,
Speaker 3: although I wouldn't use them for actually making autonomous decisions yet,
Speaker 3: but decision support is really great. So I think a
Speaker 3: lot of the regulation is effectively the AI of the
Speaker 3: past that I've actually seen trying to control that. But
Speaker 3: the AI of the future is very different, And what
Speaker 3: the Australian proposal is is actually taking more of a
Speaker 3: holistic view of that, kind of breaking down what is
Speaker 3: a developer of a model, you're training a pre trained model,
Speaker 3: and what you need to do because that's a higher
Speaker 3: risk area. You know, if you don't have the guardrails on,
Speaker 3: you could create an unmitigated AI l em that could
Speaker 3: do anything versus someone who's deploying LM into an organization,
Speaker 3: which is effectively where we're at. And then also just
Speaker 3: users of AI. You know, we're all using it every day.
Speaker 3: I think nearly everyone is, So I think what they've
Speaker 3: done is actually broken it down quite pragmatically into those
Speaker 3: different areas and focusing on the higher risk components. So yeah,
Speaker 3: I think it's actually really good. It aligns with what
Speaker 3: we're doing. I don't like more audits, so I think
Speaker 3: there was a mention in the proposal around actually auditing
Speaker 3: entities around it. So having a more integrated approach where
Speaker 3: it's actually part of existing audits and compliance, I think
Speaker 3: is a better way to go. And I think they
Speaker 3: actually proposed a few options have it more integrated as
Speaker 3: well versus separate.
Speaker 2: It seems like when you look at AI risk, it
Speaker 2: doesn't immediately jump out that Talco would particularly be one
Speaker 2: of the more risky areas we are using AI. But
Speaker 2: then when you add that large language model factor into
Speaker 2: it for things like customer service or whatever else you
Speaker 2: might use for decision making support, that does kind of
Speaker 2: enhance the risk a step up by default of using
Speaker 2: those models. So how are you thinking about that? At
Speaker 2: two degrees in terms of maintaining your level of I
Speaker 2: guess soft compliance because we don't have that regulation, but
Speaker 2: compliance with your own internal guidelines.
Speaker 3: So we did deploy straight away quite early on, obviously
Speaker 3: the explosion of chat gipt and everyone got really excited
Speaker 3: about it, an AI responsible use policy with the intent
Speaker 3: to actually embrace the technology, not to kind of stop
Speaker 3: people using it, but put some guardrails around it so
Speaker 3: people can use it safely. So that was kind of
Speaker 3: immediately done and create more awareness around use of AI.
Speaker 3: So a lot of our work has been just creating,
Speaker 3: just doing lunch and learns and awareness training. So we
Speaker 3: do want to invite people to use it for their
Speaker 3: daily work. I think that's the best way to test
Speaker 3: and learn get ready for what I think is actually
Speaker 3: a huge you know, we're in a new revolution of technology.
Speaker 3: We're just we're trying to get ready and taking many
Speaker 3: different stakeholders on that journey, your board who are probably
Speaker 3: very risk adverse, you know, the use of AI just
Speaker 3: in simple things like if you're putting Microsoft Copilot on
Speaker 3: the teams meeting and the people that you invite into
Speaker 3: it will automatically get all the summarization notes and you
Speaker 3: may not want them all to so just being aware of,
Speaker 3: you know, the technology and what it could do in
Speaker 3: terms of transparency of information, we may not want it,
Speaker 3: and I think ethically we've just our main purpose is
Speaker 3: to put the customer first. So when you think about
Speaker 3: your customer experience as your north star, that's your guiding
Speaker 3: light for anything. It doesn't necessarily have to be AI, right,
Speaker 3: it could be any technology. When you put the customer first,
Speaker 3: that's the most important thing for us. So that's kind
Speaker 3: of our main principle under the use of AIS. You know,
Speaker 3: is it going to actually make a better experience for
Speaker 3: our customers.
Speaker 1: You've got to foster that trust, ye You've it's got
Speaker 1: to be a high trust system and part of that,
Speaker 1: you know, this is an interesting point that they've floated
Speaker 1: across the Tasman be transparent with other organizations across the
Speaker 1: entire AI supply chain. So being able to explain to
Speaker 1: your customers, Okay, we might be using something of AWS
Speaker 1: or Azure, which is underpinned by open AI. We need
Speaker 1: to be able to tell you a little bit and
Speaker 1: have confidence that our suppliers are giving uugh enough information
Speaker 1: to be confident that we're not going to breach privacy
Speaker 1: or that. So I guess that'll be something that increasingly
Speaker 1: you'll be looking at through the entire supply chain. What
Speaker 1: AI are we using and do we know enough about it?
Speaker 3: Yeah, definitely, And how using our data? We use a
Speaker 3: lot of third party systems that probably hasn't changed in
Speaker 3: risk profile. You've got to effectively trust. If you're using
Speaker 3: a cloud provider like Microsoft or Amazon, you have to
Speaker 3: trust what they're saying around their data use even something
Speaker 3: like open ai GPT four, which may be a misconceptionist
Speaker 3: because I think people might think of AI or as
Speaker 3: a entity. So when you put data in there, it's
Speaker 3: just learning everything, but it's still just a computer program
Speaker 3: in some respects, or a data program. And there are
Speaker 3: features in there where you can specifically say don't train
Speaker 3: on my data or use it privacy features like you
Speaker 3: would get with anything like Dropbox or anything. How using
Speaker 3: your email Gmail, for example, you may not trust Google
Speaker 3: to be training on your data either, but there are
Speaker 3: ways to actually opt in or out. So I think
Speaker 3: that's you do have an element of trust in the
Speaker 3: supply chain definitely, which we've already had I think in
Speaker 3: the past. So whether organizations trust these big tech companies
Speaker 3: to do that is another question. You've got themes around
Speaker 3: more sovereign AI and private AI as definitely building momentum
Speaker 3: as well. You're seeing that globally, seeing what France is
Speaker 3: doing that EU are doing, So I could see a
Speaker 3: future actually where we do have more sovereign AI use,
Speaker 3: which I think is actually not a bad thing, because
Speaker 3: the future of AIS, I think will be actually a
Speaker 3: zoo of them. There'll be LMS for various things. You've
Speaker 3: got your obviously your big foundational frontier models, but you've
Speaker 3: obviously got a huge amount of open source models. So
Speaker 3: I think it's going to be more of an ecosystem
Speaker 3: and more of a hybrid approach going forward, which I
Speaker 3: think we have to be ready for and regulation needs
Speaker 3: to make sure that is safe.
Speaker 1: And small language models for very specific purposes, and of
Speaker 1: course we've got the hyperscale is setting up here, so
Speaker 1: the option of keeping all your data within New Zealand's
Speaker 1: borders will be attractive as well from a privacy point
Speaker 1: of view.
Speaker 3: Absolutely, yeah. Are we using Amazon bedrock for a lot
Speaker 3: of our kind of experimentation and what we're doing today
Speaker 3: which has the LMS based in Australia, but you also
Speaker 3: don't get the latest models that often either. That's also
Speaker 3: a challenge with these entities. They do deploy them in
Speaker 3: their main regions first and they come down to our region.
Speaker 3: So we still want to be on the bleeding edge
Speaker 3: as well and experimenting. So yeah, we're also are minting
Speaker 3: with open source models, creating kind of those foundations to
Speaker 3: be able to have optionality. Earlier in the year, we
Speaker 3: released a chatbot, which was a generative AI chatbot to
Speaker 3: our customers in a test and learn approach. We actually
Speaker 3: deployed it into Slingshot, which is one of our brands
Speaker 3: still rather than our bigger two Degree space, just to
Speaker 3: really understand it's actually quite a dangerous thing to do.
Speaker 3: In some respects it can hallucinate. We put a lot
Speaker 3: of effort in testing guardrails and making sure it was
Speaker 3: defined in its own context so it couldn't go out
Speaker 3: of the context. My own team we're testing it. You know,
Speaker 3: you know, what would Steve buy from two Degrees or
Speaker 3: from Slingshot? Would you like a pair of shoes with
Speaker 3: that or something? And we had a lot of learnings
Speaker 3: from that, so we've built kind of an orchestration guardrail
Speaker 3: ecosystem around it to ensure that the chatbot doesn't stray
Speaker 3: off too far, which is a great learning for us,
Speaker 3: and it also taught us many other things like our
Speaker 3: knowledge bases need improving data is a huge aspect to
Speaker 3: get value out of these things. So it's accelerated our
Speaker 3: view on data modernization and knowledge management along the way.
Speaker 3: So it's I think every organization should go on this
Speaker 3: journey and actually test learn scale, just try it out,
Speaker 3: take a few risks, but make sure you've got the
Speaker 3: guardrails in place. That one is an example where it's
Speaker 3: actually customer facing. I think a lot of organizations are
Speaker 3: doing internal based chatbots as well, which is what we're doing,
Speaker 3: which is more is a safe way to do it
Speaker 3: as well.
Speaker 2: Did you have any moments of like real shock when
Speaker 2: a customer tried to do something or found a way
Speaker 2: to break the bot in any kind of way that
Speaker 2: you suddenly you had to deal with.
Speaker 3: I think we got most of the guardrails pretty well
Speaker 3: sorted what it did highlights because we do have the
Speaker 3: human and the loop still and looking at the actual
Speaker 3: responses and actually measuring how effective it is. It just
Speaker 3: really highlighted our knowledge management wasn't as good as it
Speaker 3: should be. So our knowledge base is our help desk articles,
Speaker 3: which is what the model is using to refine its answers.
Speaker 3: So when we had some results that we thought weren't
Speaker 3: quite right, it's actually we're already saying that to our customers.
Speaker 3: So it's actually helped our feedback loop. So not necessarily
Speaker 3: anything too wrong. Yeah, I think there are some cases
Speaker 3: where people tried to say, you know what I like
Speaker 3: some would you like frise with that or you know
Speaker 3: a few extra things, and the bot did actually go
Speaker 3: off and try to help. It really wants to please people,
Speaker 3: you know, that's the thing. So we had a few
Speaker 3: instances where it did stray off a little bit to
Speaker 3: try and please our customers because that was you know,
Speaker 3: it was kind of told to do that. So yeah,
Speaker 3: it's never perfect. I think. I think these things do
Speaker 3: hallucinate a little bit, so that's why it's effect. You'd
Speaker 3: still need the human and the loop, which is part
Speaker 3: of the Australian proposal and human oversight. Do you need
Speaker 3: to pull the trigger as well. We've got a within
Speaker 3: our own policy like a something does terribly go wrong
Speaker 3: with it we can just turn it off immediately. So
Speaker 3: we've got, yeah, the big red button, the control loop.
Speaker 1: Yeah, So that's what the Aussies will decide on. They've
Speaker 1: got three options in terms of how they do this
Speaker 1: from a framework point of view. They might have an
Speaker 1: AI act like the EU, or they might just tweak
Speaker 1: existing legislation. Does it sort of fill you with trepidation
Speaker 1: the idea of dedicated legislation being introduced. Would you prefer
Speaker 1: to see using existing legislation or is it much of
Speaker 1: a muchness to you?
Speaker 3: I don't think there's any right or wrong answer here
Speaker 3: on this one. I generally would prefer it integrated into legislation,
Speaker 3: which I think will need to happen anyway. You've got
Speaker 3: the privacy Acts that we comply with today, so there's
Speaker 3: maybe amendments to that. But it is such a transformational
Speaker 3: technology that may need some different aspects to it. But
Speaker 3: I'd like to see, you know, this kind of happening
Speaker 3: in parallel to the kind of scientific development improvements, rather
Speaker 3: than being kind of at the end of the process
Speaker 3: and being preemptive and blocking technology actually going together on
Speaker 3: the journey, which would be a completely different way of regulation.
Speaker 3: I think technology is going to move so fast anyway,
Speaker 3: and regulation is going to catch up. So yeah, I
Speaker 3: do fundamentally believe that would probably need a different way
Speaker 3: of regulating in the future given the speed of this.
Speaker 3: But yeah, I think getting a few smart people together
Speaker 3: to work it out would be really important in New
Speaker 3: Zealand context. I think jud To Collins is already kind
Speaker 3: of working on that, bringing the experts together. So I
Speaker 3: don't think there's a right or wrong there, but I'd
Speaker 3: prefer integrated. Yeah, And if we.
Speaker 1: Can harmonize where possible with the Aussies, that would be
Speaker 1: ideal as well. We have a lot of businesses operating
Speaker 1: both markets, so it's good. Thanks for your views on
Speaker 1: those two topical stories. And this really speaks to what
Speaker 1: we wanted to talk to you about today, which is,
Speaker 1: you know, there's a lot of mergers and acquisitions happening
Speaker 1: and tough economic times. We've covered at Business Desk a
Speaker 1: lot of takeover attempts of New Zealand companies. Private equity
Speaker 1: out there is looking at New Zealand going Some of
Speaker 1: these businesses are relatively cheap at the moment out of
Speaker 1: economic necessity, Competitors are looking at each other going would
Speaker 1: we be better off have more critical mass merging. You've
Speaker 1: been through this process over the last couple of years
Speaker 1: with Vocus merging with two degrees and really fascinated. First
Speaker 1: of all, if you can tell us where is the
Speaker 1: merger at at the moment thousands of people have been
Speaker 1: brought together, but also all the systems underpinning these customer
Speaker 1: bases coming together. Where are you at in that transformation.
Speaker 3: Yeah, when you ask me that, I reflect back. It's
Speaker 3: been a wild journey. So it's been two years into
Speaker 3: effectively what is a three year strategy for us to integrate.
Speaker 3: Merger happened first June twenty twenty two, and we've made
Speaker 3: a huge amount of progress, and I think it's important
Speaker 3: to kind of reflect. You know, both organizations have been
Speaker 3: built up in different ways from different mergers and acquisitions.
Speaker 3: Like you mentioned, if you look at the history of
Speaker 3: the organizations, we're actually built up over sixty different service
Speaker 3: providers and entities. So in a way, mergers and acquisitions
Speaker 3: are in our DNA, but we're also there's a common
Speaker 3: thread through all of it, which is that we're a challenger.
Speaker 3: And Yeah, if you look back through all the different entities,
Speaker 3: they've had their own stories around challenging the market, disrupting
Speaker 3: the market. You look at two Degrees and the two
Speaker 3: Degrees effect that occurred in two thousand and nine on
Speaker 3: the Vocus side, there are many similar stories. So I
Speaker 3: think when we came into the merger, we had a
Speaker 3: lot of knowledge and how to do it, and we
Speaker 3: had a blueprints and what we needed to achieve. But
Speaker 3: every time it's different. Fundamentally, you can have a great vision,
Speaker 3: but it's how you achieve it that's really important. So, yeah,
Speaker 3: we're two years in and yeah, effectively nearly at the
Speaker 3: end end of our three year integration strategy. It's going
Speaker 3: really well. The main things we've got left kind of
Speaker 3: at a high level from a system's perspective, we had
Speaker 3: these two probably a large business support system which is
Speaker 3: the two Degrees of Origin that's got most of our
Speaker 3: mobile customers in it. We've got this other one called Tahi,
Speaker 3: which is our target state. We rebranded it as part
Speaker 3: of the merger as well, which is part of kind
Speaker 3: of getting people on the journey. And we've been building
Speaker 3: all the mobile journeys in that system, and we've done
Speaker 3: a few trials in the last actually three weeks through
Speaker 3: those mobile journeys and it's night and day. The difference.
Speaker 3: It's not to say the other one was bad necessarily,
Speaker 3: but we've invested so much in the customer experience aspect.
Speaker 3: So while we're integrating trying to consolidate, we're trying to
Speaker 3: improve customer experience. And to give an example of the difference,
Speaker 3: kind of signing up a customer in the retail shop
Speaker 3: that used to take probably around ten minutes on the
Speaker 3: old system is now under a minute, so that's a
Speaker 3: significant change. And the digital experience as well for our
Speaker 3: customers to sign up online and also our digital experience
Speaker 3: for our colleagues to actually interact with our customers is
Speaker 3: night and day. So it's very exciting. So now we're
Speaker 3: focusing on our migration aspect to actually migrate customers without
Speaker 3: impacting the customer experience. So last year we've actually been
Speaker 3: working on a range of techniques. This is more of
Speaker 3: the geeky technology side of it in terms of transitional
Speaker 3: architectures so that we can actually support customers through both
Speaker 3: stacks at the same time, so building effectively transitional overlays,
Speaker 3: so it's cross tax support. So from a customer experience,
Speaker 3: if you've got your mobile app or your web experience.
Speaker 3: It's seamless because it actually is supported across both systems.
Speaker 3: So there's a range of little techniques that we use
Speaker 3: to try and make it as seamless and effective as possible.
Speaker 3: Our main focus, which is, don't treat it like a
Speaker 3: big band kind of big project. Get incremental delivery time
Speaker 3: to value is one of our key principles. So, you know,
Speaker 3: the traditional digital transformation efforts that we go two years in,
Speaker 3: I'm sitting here quite happily. I'd normally, you know, I'll
Speaker 3: be gray by now as a CIO or CTO, and
Speaker 3: normally the half life of a CIO is around that
Speaker 3: two to three year mark. I'm sitting here reasonably comfortably
Speaker 3: touch would. But it's because we've been so focused on
Speaker 3: the incremental delivery. We've got that value and learning often
Speaker 3: we don't want to do big bang approaches, and we've
Speaker 3: seen that over the last two years we've actually had
Speaker 3: value delivered. We are brought in the fixed broadband from
Speaker 3: one system to in to TAHI quite early on, within
Speaker 3: four months, and now the focus on the mobile journey.
Speaker 3: But underpinning that, you know, you've got this business support
Speaker 3: system you've also got operational support systems, you've got ERP systems,
Speaker 3: you've got the billing charging. Yeah, all of that, TAHI
Speaker 3: if it does a lot of that for us, But
Speaker 3: there are hundreds of different systems. Got your data systems
Speaker 3: started warehousing, range of programs. So it is quite a
Speaker 3: diverse thing and it's very very hard to do and
Speaker 3: you have to empower your teams to do it. So
Speaker 3: I guess that's one of our key principles is actually
Speaker 3: empowering our teams. Focus on the outcomes, focus on the why,
Speaker 3: but empower the teams to deliver it. Yeah.
Speaker 2: How do you start to make decisions about which parts
Speaker 2: of your tech stack you've got? So you've got two
Speaker 2: CRMs from different different entities that have come together. How
Speaker 2: do you start to think about which one you're going
Speaker 2: to wind down, which one you're going to build up.
Speaker 2: What is the strategy that you think through there? Do
Speaker 2: you go to your customers and ask them, do you
Speaker 2: just take other metrics to see which are delivering better outcomes?
Speaker 3: What does that look like? Yeah, a really good question,
Speaker 3: and again, yeah, you've got to face it on its
Speaker 3: merits at the time actually making these decisions and when
Speaker 3: you look back at the different entities, each one has
Speaker 3: their own legacy as well. So fundamentally, our approach has
Speaker 3: always been probably to choose the best based on certain metrics,
Speaker 3: but not build something new on top of that. So
Speaker 3: not a green fields approach, actually an evolutionary approach. So
Speaker 3: you're looking at your ecosystem of systems on its merits.
Speaker 3: You do obviously have to factor in commercial constraints as well,
Speaker 3: but basically capability and the people around it that can
Speaker 3: support it. It may not be the system that has
Speaker 3: the most amount of customers or usage or adoption, it's
Speaker 3: really the system that is going to be future proof
Speaker 3: for us going forward. So when I mentioned TAHI, it's
Speaker 3: actually in house built system, which is quite key for
Speaker 3: us because it's basically our intellectual property, our unique proposition.
Speaker 3: I've got a team around four hundred and fifty staff
Speaker 3: in the technology team that crosses digital, cybersecurity, data, network engineering,
Speaker 3: the radio access engineers is quite a big team, but
Speaker 3: we have over one hundred software developers. I consider ourselves
Speaker 3: actually a software company with great Taco assets. So one
Speaker 3: of the decisions that rarely is you know, for TAHI,
Speaker 3: it was a decision around we have people around it,
Speaker 3: we support it, We can innovate every day. You know,
Speaker 3: we deploy features tens of times a day, launching darkly
Speaker 3: into our system. So we have an ability to iterate
Speaker 3: and have really fast feedback on that system. So that
Speaker 3: was one of the key decision points, was actually time
Speaker 3: to value, time to learning versus other systems we might
Speaker 3: be dependent on third parties or vendors, or even the
Speaker 3: system itself might just have constraints. So one of our
Speaker 3: main factors was again back to that time to value.
Speaker 3: May not be the best system in terms of capability,
Speaker 3: but we could back ourselves to build certain capabilities on
Speaker 3: that system due to our ability to deliver quickly. So
Speaker 3: I think that's actually unique for us. Is nothing I
Speaker 3: don't think in the New Zealand markets or even when
Speaker 3: I look globally in terms of that software approach, and
Speaker 3: that means it turns up to customers in terms of
Speaker 3: customer experience.
Speaker 1: That is yeah, I think quite quite unique because we've
Speaker 1: heard so much. A lot of the big software vendors
Speaker 1: have tailored a version of their software for the Talco market.
Speaker 1: They see that as an important market to serve, whether
Speaker 1: it's SAP or salesforce, for instance, you've gone to bespoke
Speaker 1: route is really part of that your legacy as a
Speaker 1: challenger brand. You're one of the originals way back in
Speaker 1: the in the early days of Slingshot and all that,
Speaker 1: with Malcolm Dick and Mike Callender. You know you're still there.
Speaker 1: Is that The mindset really is that if we want
Speaker 1: to be competitive, more competitive than the incumbents, we have
Speaker 1: to be bespoke. We can't just buy something off the
Speaker 1: shelf and have a great experience. We've got to build ourselves.
Speaker 3: Yeah. I thinks, yeah, there is a mindset to do
Speaker 3: things differently. You know. One of our values is say
Speaker 3: no to the status quo. So we like to innovate,
Speaker 3: do things differently Bespoke Yeah, I think I still think
Speaker 3: of it like a product. So it's actually what we're
Speaker 3: building is a product in its own right. So it's
Speaker 3: not a kind of customized you know bespoke aspect. It's
Speaker 3: actually something that's strategically important for us. And we don't
Speaker 3: build everything. We use a lot of open source, We
Speaker 3: do use a lot of other software to complement an ecosystem,
Speaker 3: but the core of it we do actually build and own,
Speaker 3: which I do think is unique. To give you an
Speaker 3: example of you know, we don't necessarily you know, build
Speaker 3: versus by aspect. Right, Building's not always great because it
Speaker 3: can take a lot of time. You don't want to
Speaker 3: reinvent the wheel. So when we were looking at we
Speaker 3: built this actually a few years ago to do network
Speaker 3: as a service. So when a customer, particularly business customers,
Speaker 3: we have a portal called Flex, they can actually log
Speaker 3: in get networks on demand, so you can provision a
Speaker 3: network service from a data center to the cloud as
Speaker 3: your AWS in real time. Typically the approach would be
Speaker 3: to go to a vendor for your network orchestration layer.
Speaker 3: We actually looked across what other big tech companies were
Speaker 3: doing and we saw what Uber we're doing in terms
Speaker 3: of their workflow orchestration to book a taxi we go,
Speaker 3: why don't we look at using that technology for the
Speaker 3: network and we did and that was actually a huge
Speaker 3: game changer for us. So I'm just looking wider than
Speaker 3: kind of a traditional talco. And then we've used that
Speaker 3: same workflow engine that we Uber open sourced it to
Speaker 3: do mobile provisioning as well. So since the merger, we've
Speaker 3: actually been using the same workflow engine to do mobile
Speaker 3: workflow orchestration. So that's one example where you're leveraging technology
Speaker 3: not necessarily building all of the building blocks, which is
Speaker 3: pretty exciting. The team are excited about it, and they're
Speaker 3: all just across the road actually from where we're recording
Speaker 3: this podcast. Most of them they're all in New Zealand
Speaker 3: software developers and they're really bought into the vision of
Speaker 3: two degrees. So I think that's a really important part
Speaker 3: is they're there to fight for fear. They know how
Speaker 3: important it is for two degrees to exist in this market,
Speaker 3: and they come to work every day to actually provide
Speaker 3: value to our customers, and that quite often can be
Speaker 3: you know, as I've been a previously a software developer,
Speaker 3: a network engineer, you can be stuck in the on
Speaker 3: your keyboard, not really understanding why you're doing it. So
Speaker 3: a big part of what we're doing is the cultural aspect.
Speaker 3: It's actually eighty percent cultural. When you kind of break
Speaker 3: it down.
Speaker 2: When you're doing such a big transformation integration project and
Speaker 2: you really hyper focused on trying to go from one
Speaker 2: thing to another, how do you balance just focusing the
Speaker 2: resource on kind of the transformation aspect versus bringing in
Speaker 2: new features upgrading, adding on to create new experiences. How
Speaker 2: do you decide where to go big and new and
Speaker 2: where to just stick to your bread and butter.
Speaker 3: It's a great question, and it's a yeah. We do
Speaker 3: have this discussion a lot. Yeah, So we laser focused
Speaker 3: on some key deliverables and that integration and getting onto
Speaker 3: a single stack is such a game changer for us.
Speaker 3: So we're trying to be really really focused, but we
Speaker 3: see opportunity, we may take it. I guess one example
Speaker 3: we've had of that when we were building some of
Speaker 3: the new mobile capability in TAHI, we saw an opportunity
Speaker 3: to launch a new product out as kind of a
Speaker 3: relatively simple product, a traveler some package. So when overseas
Speaker 3: people come in and they want to roam around New
Speaker 3: Zealand and have the great experience, they could easily just
Speaker 3: get on boarded and have a traveler prepay like a
Speaker 3: thirty day unlimited use, and we built that on the
Speaker 3: new system. In some respects, it was to both test
Speaker 3: the system, but also there was a gap in the
Speaker 3: market we saw and we can actually provide a really
Speaker 3: great digital experience you can sign up with eSIM. So
Speaker 3: there was an example of where it was a win win.
Speaker 3: You could actually test your integration thesis while also building
Speaker 3: something something unique in the market. But we don't do
Speaker 3: that too often because we're trying to stay trying to
Speaker 3: stay focused on what we're trying to achieve. But yeah,
Speaker 3: it's a daily discussion. But I think that's the important
Speaker 3: part is you get the right people together to have
Speaker 3: that intentional discussion around these decisions. I think that's the
Speaker 3: most important part. So where we may deviate, it's for
Speaker 3: a good reason, we're following some sort of value, or
Speaker 3: it's giving us ability to test and learn for where
Speaker 3: our future is. But yeah, there's many many aspects of that.
Speaker 3: You can't really break it down to a recipe or playbook.
Speaker 3: It could just be an opportunity that is in front
Speaker 3: of you. And I think, yes, again, it's really important
Speaker 3: you have a plan, but you have to be able
Speaker 3: to adapt. And quite often over the last two years,
Speaker 3: it's been the actual constraints and the impediments or the
Speaker 3: oppertunities that actually have created our journey for us. So
Speaker 3: not being rigid on a plan, which I think is
Speaker 3: also learning that I've had in my history, and I
Speaker 3: think many other CEOs and many organizations where you do
Speaker 3: follow a plan rigidly, that's where things can go wrong.
Speaker 3: You have to definitely adapt. You know, AI wasn't really
Speaker 3: a thing when we started off on the merger either,
Speaker 3: so that you know that all you know became blew
Speaker 3: up in October November twenty twenty two. Actually, can we
Speaker 3: leverage this technology to accelerate our integration strategy And we've
Speaker 3: been looking at aspects in that area too. One of
Speaker 3: those has really been around software development, which I think
Speaker 3: is a massive game changer for anyone using AI to
Speaker 3: actually improve productivity and software development, which we did invest
Speaker 3: a lot of time in. That's you know one example,
Speaker 3: things change and you've got to adapt to it.
Speaker 1: Yeah, that's a massive one. Andy Jesse, the CEO of Amazon,
Speaker 1: said a couple of weeks ago that using their in
Speaker 1: house sort of copilot for software development, I think it's
Speaker 1: called Q, they were able to save four and a
Speaker 1: half thousand developer years just on upgrading Java apps. At
Speaker 1: Amazon they have tens of thousands of Java apps, so
Speaker 1: this is they said, it's going to save about a
Speaker 1: quarter of a billion dollars a year, not just in
Speaker 1: getting rid of developers, all the infrastructure the apps run
Speaker 1: more efficiently on their big server farms and ad as well,
Speaker 1: so the energy use is lower. So are you seeing
Speaker 1: that you say you're a sort of a software firm
Speaker 1: in many respects, are you seeing big efficiencies and software development?
Speaker 3: Yet we are seeing some maybe not claim so many
Speaker 3: hours in productivity. Yeah, I think early on, yeah, we
Speaker 3: saw around twenty percent as we measured kind of by hours,
Speaker 3: and it's probably got to about ten percent now in
Speaker 3: terms of productivity. But it's hard to measure software development productivity.
Speaker 3: It's not an easy thing to do, and there are
Speaker 3: other constraints. So this is something I think about a lot.
Speaker 3: I've been on the journey for too long. And yeah,
Speaker 3: you've got to think about how you can figure your
Speaker 3: teams and how you can figure your overarching system. An
Speaker 3: organization is actually a complex adaptive system and it can
Speaker 3: be immune and you can poke it and it can
Speaker 3: do things. But you've got to look at the end
Speaker 3: end value. So software development can improve by maybe even
Speaker 3: fifty percent, But if you've got constraints to go to market,
Speaker 3: or you've got constraints at the front end and your
Speaker 3: business casing, your finance modeling, how you actually do business
Speaker 3: cases you won't get the true end to end value.
Speaker 3: So we do think a lot about that idea to
Speaker 3: value and measure that flow. That's a really key part
Speaker 3: of what we look at. So while you can optimize
Speaker 3: and that wedge in the middle for software development, you've
Speaker 3: got to optimize the full end to end, which I
Speaker 3: actually think is the whole thing around generative AI. There
Speaker 3: is a huge software is completely revolutionized. I believe from
Speaker 3: what happened kind of in the nineteen seventies, software actually
Speaker 3: hasn't changed too much and in many respects you're still
Speaker 3: kind of was very traditional, but with the advent of
Speaker 3: large language models, it's turning more into a natural language.
Speaker 3: We have co pilot assistance next to our software developers.
Speaker 3: I really think the multi modal aspect is going to
Speaker 3: be huge. So you can actually have a voice assistant
Speaker 3: while you're coding. That's going to be significant. So yeah,
Speaker 3: it's going to be a huge game changer, and there's
Speaker 3: a whole lot of I did not necessarily threaten my team,
Speaker 3: but I provocated that, you know, software development profession will
Speaker 3: be dead in five years, you know, and think about
Speaker 3: the evolution of it. I don't think that's actually a case,
Speaker 3: but it will definitely adapt and change. So the teams
Speaker 3: are you know, they're just getting ready, getting the skills
Speaker 3: to adapt, but it is the next step. Can you
Speaker 3: have these we call them value streams. Can you have
Speaker 3: value stream agents that can connect the requirements to the
Speaker 3: teams that are doing the work. Can you then get
Speaker 3: strategy agents over the top. So if you can get
Speaker 3: a X improvement and software development, can you get the
Speaker 3: team working better at the value stream? That's another ten X.
Speaker 3: Can you get the strategy connected to execution better. That's
Speaker 3: you know, could be a thousand X improvement, which I
Speaker 3: think would be massive. And then you've got the other
Speaker 3: case where AI is now I play with it all
Speaker 3: the time. You can build apps and software so quickly
Speaker 3: as an individual contributor, So you'll see the advent of these,
Speaker 3: you know, one person companies, which I think it's going
Speaker 3: to be great for startups in the future as well. Yeah,
Speaker 3: but our team's assisting them day to day to write
Speaker 3: better software, also for testing software, doing our reviews of
Speaker 3: software as a complementary like a copilot. And that's something
Speaker 3: we've actually talked about a lot, is around the co
Speaker 3: pilot nature of AI. Actually being an assistant next to you, saying,
Speaker 3: with our care team actually having a co pilot next
Speaker 3: to our agents that are answering the phone to support
Speaker 3: our customers, they've actually got a co pilot that can
Speaker 3: coach them and work with them. That's our next step
Speaker 3: in our journey, particularly in the care environment. So that's
Speaker 3: I think that's the approach I think we'll move forward on.
Speaker 2: So it would be in quotes, listening to the conversation
Speaker 2: as you're having it and say, oh, I noticed that
Speaker 2: you've talked about this. You know, don't forget to tell
Speaker 2: the customer that this is an option that kind of thing.
Speaker 3: Yes, definitely, Yeah, that's it.
Speaker 1: Yeah, ye.
Speaker 3: And we've already got a AI auto summary working with
Speaker 3: our care team, so is actually it's got the real
Speaker 3: time transcription happening. It actually summarizes the entire call for
Speaker 3: the agent. It used to be the human and the
Speaker 3: agent used to type it all out, so they get
Speaker 3: a little bit distracted while they're trying to talk to
Speaker 3: a customer while they're writing notes. So now our humans
Speaker 3: can actually have a real conversation, and then the AI
Speaker 3: is actually writing up the notes in real time, which
Speaker 3: gives us really good data. It actually captures sentiment and intent.
Speaker 3: You can then put that into our feedback loop and
Speaker 3: actually improve the situation. We haven't quite got to the
Speaker 3: point where we actually have a really true co pilot,
Speaker 3: but that's our next step is to have a co
Speaker 3: pilot alongside our human agent to give that sort of
Speaker 3: coaching aspect and go okay, you should maybe think about
Speaker 3: talking about this. It actually a near term for us,
Speaker 3: it's not far away, which is pretty exciting.
Speaker 1: It's pretty cool.
Speaker 3: Yeah.
Speaker 1: So just finally, Stephen, what's your advice to executives in
Speaker 1: New Zealand companies that may have just acquired or been
Speaker 1: a process of merging with another company that might be
Speaker 1: a retailer or a construction company. You've talked about the
Speaker 1: sort of the gradual approach versus big bang. It's nice
Speaker 1: to be on one system from an efficiency point of view,
Speaker 1: so there will be this temptation to get everything onto
Speaker 1: one platform. We've seen where that has gone wrong. Some
Speaker 1: projects have gone off the rails have been paused because
Speaker 1: that's too complex. We've seen Jason Parris, you know, your
Speaker 1: competitor from One Ends, had been very upfront and honest
Speaker 1: about the pain that that organization went through with all
Speaker 1: these old systems hanging around. Even if you can bridge
Speaker 1: through APIs and that you can get data shared across
Speaker 1: those systems, how do you approach what sort of thinking
Speaker 1: do you go through? Everything? Is every situation as different
Speaker 1: as you sort of said, But what are some of
Speaker 1: the fundamental questions you need to ask yourself as a
Speaker 1: leadership team when you're in this sort of merged position.
Speaker 3: Now, yeah, yeah, you've got to have belief in a
Speaker 3: commitment to do it. So it's not easy. So integration
Speaker 3: in particular and legacy has to be a priority. It
Speaker 3: has to be the forefront of your strategy. So one
Speaker 3: of our strategies is that one company strategy. So integration
Speaker 3: is everything that's fundamental. I think sometimes leaders can get
Speaker 3: a little bit maybe just focus on the next thing
Speaker 3: they think it's done, But there's a long term commitment
Speaker 3: and you have to reinforce it, and I think leaders
Speaker 3: go first in that respect. Build the belief, build the
Speaker 3: vision of the future. Don't inflict change on your own
Speaker 3: teams because part of it is also you're not just
Speaker 3: changing technology, you're changing how people work. There's so many
Speaker 3: different aspects to you making change happen. It's like it
Speaker 3: is eighty percent of human endeavor, So you really need
Speaker 3: that cultural aspect. I think that's the critical part is
Speaker 3: having a team that are willing to have each other's
Speaker 3: back and actually, you know, it's okay to make mistakes.
Speaker 3: Actually having a psychologically safe environment where it's okay sometimes
Speaker 3: maybe not do the right thing, and amplify those signals quickly.
Speaker 3: I think that's a key part of it. But you
Speaker 3: have to move fast. You've got there's a pace to it,
Speaker 3: and that can be you know, people are stronger on
Speaker 3: loss of version right, two times more impactful losing something
Speaker 3: than gaining something, So you do have to build that belief.
Speaker 3: You have to create a really safe environment and empower
Speaker 3: people to go on that journey. So I think you
Speaker 3: focus on the outcomes and why incremental is critical. It's
Speaker 3: really easy to follow a plan and might be a
Speaker 3: big modernization plan, but it may feel painful at the beginning,
Speaker 3: actually questioning things, really do we really need to do this?
Speaker 3: You know, is that requirement really necessary. It's quite easy
Speaker 3: for people to add in all sorts of things as
Speaker 3: you go on the journey, so you need to be
Speaker 3: deleting things removing things. The best way to achieve it
Speaker 3: actually not to build anything. So you have to be
Speaker 3: really really focused on it and really ask those tough questions,
Speaker 3: and yeah, focus on the value, focus on the customer.
Speaker 3: That's ultimately why we're doing what we're doing. Yeah, why
Speaker 3: we're doing what we're doing is get onto a single stack,
Speaker 3: be the customer experience for the future for our customers.
Speaker 3: So yeah, easy as that, so simple.
Speaker 2: Right, Well, that's it for this week's episode. Thank you
Speaker 2: to Stephen Kajerv for joining us and to two Degrees
Speaker 2: for its ongoings. Abort the Business of Tech.
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Speaker 3: Catch you then,