Latest / Speaking Your Language / Episode 4 - People, Processes and AI: How to solve the right business problems

Transcript

Episode 4 - People, Processes and AI: How to solve the right business problems · Speaking Your Language

• 0:00
  SPEAKER_01: Welcome to Speaking Your Language, the AI podcast that is focused on how you really implement AI into your business.
• 0:07
  SPEAKER_01: No hype, no noise, no jargon.
• 0:13
  SPEAKER_01: Welcome to this episode of Speaking Your Language.
• 0:16
  SPEAKER_01: I am joined today by Catherine Pratt, who is somebody that I've got to know over the last six to seven months and someone that has been working with us at Configure to help support some of the businesses that we work with.
• 0:28
  SPEAKER_01: Her perspective on operations and communications is really interesting, and we'll come on to all of that.
• 0:34
  SPEAKER_01: And she helps people understand where AI might be needed or not.
• 0:38
  SPEAKER_01: And it's a thing that we look at, and we love working with partners like Catherine.
• 0:41
  SPEAKER_01: But I'm going to let you introduce yourself and then we'll go from there.
• 0:44
  SPEAKER_00: Awesome.
• 0:45
  SPEAKER_00: Thanks, Marco.
• 0:47
  SPEAKER_00: So my background is in IT consultancy.
• 0:50
  SPEAKER_00: I started off on a graduate training program with IBM, learning to code, learning to test, learning the entire end-to-end software delivery lifecycle.
• 0:59
  SPEAKER_00: I don't know how I ended up in technology.
• 1:01
  SPEAKER_00: It's not something that I ever sort of had a drive or a passion to go for, but it's where I've ended up and it's where I quite enjoy.
• 1:09
  SPEAKER_00: I've got sort of lots of different experiences in my background.
• 1:14
  SPEAKER_00: I've worked sort of in large corporations, I've worked a lot in utilities, smart metering solution.
• 1:22
  SPEAKER_00: I've worked ordnance survey, looking at maps, and I didn't know maps could actually be so cool.
• 1:27
  SPEAKER_00: They really are.
• 1:28
  SPEAKER_00: And trying to digit digitalise them is not as easy as you might think it would be.
• 1:42
  SPEAKER_00: It was more than a job, it was a passion, it was a lifestyle, it was it was just brilliant.
• 1:45
  SPEAKER_00: What was that doing?
• 1:46
  SPEAKER_00: It was farming robots.
• 1:48
  SPEAKER_00: Very good.
• 1:48
  SPEAKER_00: So I don't know if you've seen Clarkson's Farm Series 5.
• 1:51
  SPEAKER_00: A lot of the stuff that's sort of being shown there was some of the technical challenges that we were trying to solve nearly 10 years ago.
• 1:58
  SPEAKER_00: Yeah, yeah, nearly 10 years ago now.
• 1:59
  SPEAKER_01: Yeah.
• 2:00
  SPEAKER_01: Planting the onions and aggro bot and all of that stuff is really cool.
• 2:04
  SPEAKER_00: Yeah, it mapping the field, knowing where all your plants are, being able to use AI to identify the health of the plants, to identify what type of plant it was, um, training all the models so that they can do that identification.
• 2:18
  SPEAKER_00: Um we had autonomous robots that we were building, so we're having to uh make sure that the autonomy was okay.
• 2:24
  SPEAKER_00: Uh there weren't sort of the established ground rules as to how autonomous vehicles should should be working.
• 2:30
  SPEAKER_00: So we're trying to influence as well some of the sort of the industry standards as to what an autonomous vehicle not on the road would need to actually adhere to.
• 2:39
  SPEAKER_00: We had uh big robot with what we call the death wand on it, which we used to zap weeds.
• 2:44
  SPEAKER_00: Um sort of taking the chemicals out.
• 2:46
  SPEAKER_00: I could do without my garden, I'm not gonna lie.
• 2:49
  SPEAKER_00: Do you know?
• 2:49
  SPEAKER_00: Everybody we spoke to is like, oh, do you do knotweed?
• 2:52
  SPEAKER_00: Japanese knotweed, you'd make a fortune if you could do that.
• 2:54
  SPEAKER_00: And it's like, no, no, we were just in the farmers' fields.
• 2:57
  SPEAKER_00: Um, my role in that company was was, as I say, it was the most enjoyable one I've ever had.
• 3:03
  SPEAKER_00: I went in as head of delivery.
• 3:04
  SPEAKER_00: Um, it was an eight-person company, small startup.
• 3:08
  SPEAKER_00: Uh, I think by the time I left, we'd scaled it to about 50 people.
• 3:11
  SPEAKER_00: Um, we had we had a real challenge in that we had hardware development going on, RD and innovation, and we had software development going on, so the innovation and AI and AI on the edge as well.
• 3:26
  SPEAKER_00: So we were having to put together a delivery framework that would allow all of those different elements to evolve in the way that worked best for them.
• 3:35
  SPEAKER_00: So you had sort of a conflict between hardware, which is more traditionally uh delivered using sort of more waterfall type um frameworks, and then you had the software side, which was using more agile type frameworks, and then you had the innovation side, which was different again.
• 3:51
  SPEAKER_00: So having to blend all of these frameworks together and put together sort of these great big plans to make sure that we were delivering the right things at the right time.
• 4:00
  SPEAKER_00: We were actually meeting our technical milestones that we're aiming for.
• 4:04
  SPEAKER_00: Um, and I also then had the business side as well that I was involved in.
• 4:08
  SPEAKER_00: Um I promoted to chief operating officer, and that for me is just the most fun role that you could ever want to do.
• 4:15
  SPEAKER_00: Um, looking at sort of the systems across the company, the processes across the company, and how the people interact with those so that you can make a really efficient and effective overall architecture and system that takes the frictions out of the way people work.
• 4:30
  SPEAKER_01: I think that's a really good point because a lot of businesses will have those different areas of their business, they're trying to marry together.
• 4:36
  SPEAKER_01: So I wasn't going to jump in just yet, but that's I think that's a really good point.
• 4:39
  SPEAKER_01: What what did you do that made that effective?
• 4:43
  SPEAKER_00: Looked at it holistically.
• 4:45
  SPEAKER_00: So I think where a lot of people go wrong is they'll have an HR department and they'll say to their HR department, right, you need a system or a tool to manage all of our onboarding, all of our recruitment process, um, and the sort of the life cycle of an employee in the company.
• 5:00
  SPEAKER_00: So have they signed the right policies?
• 5:03
  SPEAKER_00: Um, what happens if they go on maternity leave or something like that?
• 5:07
  SPEAKER_00: So your HR department will go away, pick a system, implement a system, roll it out, and that system doesn't integrate with anything else.
• 5:14
  SPEAKER_00: So your finance system's in silo, your HR system's in silo, you might have a supply chain system then that's in silo as well.
• 5:23
  SPEAKER_00: So I think looking holistically across a business in the early stages, that's that's a real um sweet spot for doing it right.
• 5:33
  SPEAKER_00: Uh and when I say doing it right, I don't mean doing it once and then never changing it.
• 5:36
  SPEAKER_00: I mean getting it right so you set yourself up that if you wanted to change a component in that architecture later, you've you've done the groundwork, your data's aligned, your data's sensible, uh, it's managed in a way that you can extract it from one system and put it into a new system, should you wish to migrate across.
• 5:52
  SPEAKER_01: Yeah, so it's having an overarching strategy that you can track and evolve and ensure you're on top of as part of anything that you bring into the business.
• 6:02
  SPEAKER_01: And that's so important, right?
• 6:03
  SPEAKER_00: Yeah.
• 6:04
  SPEAKER_00: And understanding as well that it's not static.
• 6:06
  SPEAKER_00: So what uh a setup that's right for an eight-person company is probably one that involves spreadsheets, right?
• 6:13
  SPEAKER_00: Everyone has a spreadsheet uh or many spreadsheets, and then they get versions of those spreadsheets.
• 6:18
  SPEAKER_00: But that works for an eight-person company.
• 6:20
  SPEAKER_00: The minute that you start scaling up, the spreadsheets sort of start creaking and things start going wrong, and people start making mistakes, and the spreadsheet isn't updated all the time because somebody's suddenly busy and they're they they don't see that their value of updating that spreadsheet has a downstream knock-on effect to another department.
• 6:38
  SPEAKER_00: So you then have to think about migrating away from these spreadsheets onto sort of your um small company level tooling.
• 6:46
  SPEAKER_00: So, you know, things like zero, I was gonna say zero, but that's got quite quite big now as well.
• 6:50
  SPEAKER_00: But originally it was sort of a smaller accounting package rather than sort of the sages, if you like.
• 6:55
  SPEAKER_00: Um, things like HubSpot and was it MailChimp we were using uh you know 10 years ago to do our CRMs.
• 7:02
  SPEAKER_00: Um those sorts of systems are great, but then again, they'll start creaking, and then you'll need to be looking for something different again in sort of a uh, I was gonna say number of years' time, and it might not necessarily be years, it's it's more down to the people growth and the company size growth.
• 7:18
  SPEAKER_01: And it comes down to having, if you have an overarching strategy for all of this, you've got adaptability on these things, right?
• 7:25
  SPEAKER_01: Because you you can see where the friction might end up coming and you can push that forward, which is, I'm guessing, what's driven you now, because I know you went consulting after that, and now you you've gone on your own, which is exciting.
• 7:37
  SPEAKER_01: Well done.
• 7:38
  SPEAKER_01: And you are now a fractional, what would you class it as?
• 7:41
  SPEAKER_01: Fractional COO?
• 7:42
  SPEAKER_00: It's such a difficult one to answer this question.
• 7:46
  SPEAKER_00: Um, I have classified it as fractional COO, chief operating officer.
• 7:50
  SPEAKER_00: So looking at the business architecture across a business, but really I sort of blend across chief of staff in that you could chuck me a difficult project, you know that you need a new system, or there's a problem in a process, or teams aren't working, or communications across the company aren't working.
• 8:06
  SPEAKER_00: I can pick up those types of activities as well.
• 8:08
  SPEAKER_00: I'm sort of a a bit of a Swiss army knife for a business.
• 8:11
  SPEAKER_00: I've got a problem here, or I've got a special project, I need to get it done.
• 8:14
  SPEAKER_00: I I'm the person that would come along and work with the company and in the company to implement to identify the change and then implement it.
• 8:22
  SPEAKER_01: That's good.
• 8:23
  SPEAKER_01: So if if I was a small to medium business, would you say that was probably your sweet spot or are you happy to Yeah.
• 8:30
  SPEAKER_00: Small small to medium.
• 8:31
  SPEAKER_00: Um so over my career I have worked in larger corporations um and I can see the challenges and I can see the fun of being in those environments.
• 8:39
  SPEAKER_00: The area that I love is the smaller side.
• 8:42
  SPEAKER_00: Um, and maybe that's sort of different to the norm.
• 8:45
  SPEAKER_00: People like to say, you know, I've run a team of 10 people, I've run a team of 100 people, I've run a team of a thousand people.
• 8:50
  SPEAKER_00: I think for me, at the point that we get to that 200 mark, that's probably where I'd I'd lose interest.
• 8:56
  SPEAKER_00: I like the dynamics of the smaller units, I like um I like the companies before they're overprocessed and people can still be empowered to really perform to their the best of their ability.
• 9:08
  SPEAKER_00: And I think that's where people are happier.
• 9:09
  SPEAKER_01: Yeah, definitely.
• 9:10
  SPEAKER_01: Uh so well, not everybody, but not everybody.
• 9:14
  SPEAKER_01: No, you know, people like us like a small business, but you know, every everyone's got their own uh their own favorite sort of size of company to work for.
• 9:22
  SPEAKER_01: And I think it comes down to I'm a small medium business.
• 9:25
  SPEAKER_01: I come to you and say, Catherine, I think we can use your help because we have issues with workflow or delivery, or what's the first thing you go and look at when you get involved with a business?
• 9:38
  SPEAKER_00: Probably everything except the thing that they think is the problem.
• 9:41
  SPEAKER_00: And I'm not being sort of deliberately obtuse when I say that, but what you tend to find is in a in a business architecture where you're feeling the pain and where the pain is coming out often isn't the root cause of it.
• 9:55
  SPEAKER_00: So you you might experience um an issue in production.
• 9:59
  SPEAKER_00: Um, some mistakes might have been made.
• 10:02
  SPEAKER_00: But what you may find is the production processes and systems and tooling is actually okay.
• 10:07
  SPEAKER_00: It's the information going into the production team that isn't isn't free-flowing, which is then creating the production team having to get volumes to be be produced and getting it wrong.
• 10:17
  SPEAKER_00: So when you first go in, you go in in what I call sponge mode.
• 10:22
  SPEAKER_00: There's probably some really posh term for it somewhere.
• 10:25
  SPEAKER_00: I I think of it as being a sponge.
• 10:27
  SPEAKER_00: Um, you talk to as many people across the operations as she can from all different levels, because you'll get a very different uh view from a manager than you will somebody actually do the implementation.
• 10:38
  SPEAKER_00: So finding just listening to people, sort of saying, explain to me what it is you do, what are the processes that you follow?
• 10:44
  SPEAKER_00: And what what I tend to find by asking different people to explain to me the processes that they follow, you naturally start spotting the gaps.
• 10:53
  SPEAKER_00: So you might talk to one person and use that as your baseline, and then the next person you talk to will tell you sort of their perspective of the same process, but they'll expose something that the first person didn't mention.
• 11:04
  SPEAKER_00: So talking to lots of different role types, lots of different people, I use the process as sort of that golden thread to keep the conversation going.
• 11:13
  SPEAKER_00: But what you discover whilst having those conversations is you discover a lot about the people setup in the organization and the communications or lack of communications within like the people layer.
• 11:24
  SPEAKER_00: You do identify then the process layer and you identify the gaps in the process layer or areas where the process isn't optimal, it's slow, it's clunky.
• 11:32
  SPEAKER_00: But also you then get a technology layer.
• 11:35
  SPEAKER_00: So I think of businesses in those those three different layers.
• 11:38
  SPEAKER_00: I think a lot of people do.
• 11:39
  SPEAKER_00: The the layer that I think is missed often is the governance and reporting layer.
• 11:43
  SPEAKER_00: So you have your org structure, you have your processes in place or needing to be put in place or optimized, you have your tooling that sits underneath that.
• 11:51
  SPEAKER_00: You might want to change some systems or integrate some systems or bring in one system that does everything, but you also then have a governance layer.
• 11:58
  SPEAKER_00: And the governance layer is what then you know all about the governance layer, it's what configure does.
• 12:03
  SPEAKER_00: Um so the governance layer is there, it's your dashboards, it's your reporting that shows you how the operation across those other three layers is or isn't working.
• 12:10
  SPEAKER_00: So with a small business, you tend to find that they've put in processes because people know they need processes.
• 12:18
  SPEAKER_00: The tooling is sort of put in but in a um in a way that's evolved rather than has been thought out strategically at sort of set uh milestones throughout the the company's life cycle.
• 12:30
  SPEAKER_00: The people, you sometimes find you've got the right people but in the wrong roles, or um you might have an HR person doing something that really a finance person should be doing.
• 12:40
  SPEAKER_00: So you have to look at all of those different elements.
• 12:43
  SPEAKER_00: But then once you've once you've sort of got those in place, that governance layer leads you to be able to see where it isn't working as you scale up.
• 12:52
  SPEAKER_00: And then you have to make changes across all four of those layers again when you're ready to sort of start growing onto the next stage.
• 12:58
  SPEAKER_01: Yeah, is it is always an interesting one when we speak to businesses because uh a lot of people will say, Well, it takes me, you know, two days to create these reports.
• 13:07
  SPEAKER_01: And then you speak to management or senior leadership teams and they say, Well, they never look at those reports.
• 13:12
  SPEAKER_01: So, you know, there's there's all of that element happening where people are taking time out of their day-to-day to do things that are never looked at.
• 13:19
  SPEAKER_01: You then have, like you say, a situation where people are doing workarounds because systems don't deliver the elements that they want.
• 13:26
  SPEAKER_01: And then you have, like you say, people in roles that don't quite suit them, or being asked to wear multiple hats across roles.
• 13:32
  SPEAKER_01: And that can always, you know, be one of those situations where it can either be a benefit or a massive hindrance for a business.
• 13:39
  SPEAKER_01: So, where do you start?
• 13:41
  SPEAKER_01: What's the one bit, you know, you've listened, you've done all of that.
• 13:45
  SPEAKER_01: What would be your go-to area to be like, do you know what we start there?
• 13:48
  SPEAKER_01: That's the bit we try and fix or at least understand better first.
• 13:52
  SPEAKER_00: I think it varies company to company.
• 13:54
  SPEAKER_00: Um So there's there was one company I was working with where for me, um, after listening to everybody and sort of gathering my thoughts, the the sort of the thing where I saw uh the biggest pain relief would have been in the communications layer.
• 14:10
  SPEAKER_00: So that's in your people layer.
• 14:12
  SPEAKER_00: Um so I suggested sort of putting in a session, a couple of meetings a week, just to have very specific conversations with a sort of a set agenda.
• 14:20
  SPEAKER_00: Um, what I didn't want to do is then fill people's calendars up with meetings, and everybody just has a day of meetings and no time to do anything.
• 14:29
  SPEAKER_00: So it I always go with um sort of baby steps when you're making change, you don't want to go in and absolutely change everything.
• 14:37
  SPEAKER_00: So for that one particular customer, it was putting in mechanisms that's that's facilitated and encouraged conversation to take place.
• 14:45
  SPEAKER_00: That conversation then freed up the blockage that was in the pipeline and and enabled things to work.
• 14:52
  SPEAKER_00: There's other um clients then who you might go into and it it is a a process issue.
• 15:01
  SPEAKER_00: So you'd have people are talking, they want to do the right things, but there's there's a something in the process which is blocking them.
• 15:07
  SPEAKER_00: So then you'd start in the process issue.
• 15:10
  SPEAKER_00: So it really does depend on what you find within a business.
• 15:14
  SPEAKER_00: You could have a business that's got a hero in who's doing everything and not sharing the load.
• 15:19
  SPEAKER_00: So you need to then distribute the workload and sort of clarify roles.
• 15:23
  SPEAKER_00: Um, there's other companies where they've got so many products that everything is a bespoke delivery, um, and they're trying to appease every single customer by doing something different every single time, which is great from a winning work point of view, but it becomes a very expensive, ineffective way to actually then deliver that work.
• 15:41
  SPEAKER_00: So you would start looking for standardizations and reuse and sort of mechanisms and tools that would support that.
• 15:47
  SPEAKER_00: So there's no, there's no default go-to start here.
• 15:52
  SPEAKER_00: It's very much look at what's in front of you, identify where the pain points are, identify where the biggest pain points are, and start with those biggest pain points that you can sort of see immediate um resolution to.
• 16:06
  SPEAKER_00: Look for that low-hanging fruit where you can make some change, but then think about that more strategic roadmap, which might be you need to change out a system, which is then a reasonably big activity.
• 16:16
  SPEAKER_01: Yeah.
• 16:17
  SPEAKER_01: It's really refreshing.
• 16:18
  SPEAKER_01: You haven't mentioned AI once yet.
• 16:21
  SPEAKER_01: No.
• 16:21
  SPEAKER_01: But I say it's refreshing because a lot of the work that we do, and you'll know because we're doing it together in a lot of cases, it starts with looking at all these things first.
• 16:31
  SPEAKER_01: Yeah.
• 16:31
  SPEAKER_01: And it's not coming with oh well, I'll throw AI at that problem and just fix it with AI because it can then have uh unexpected consequences.
• 16:43
  SPEAKER_01: So where do you see things now compared to where you were, let's say, five, six years ago?
• 16:49
  SPEAKER_01: What do you think the shift has been in businesses?
• 16:52
  SPEAKER_01: Do you think that it's a very much a we'll throw AI at it and hope that fixes the main problem?
• 16:57
  SPEAKER_01: Or do you think there is still a mindset of, do you know what?
• 17:01
  SPEAKER_01: We'll we we need to work to get these things fixed first.
• 17:03
  SPEAKER_01: Do you do you think what's your kind of general feel?
• 17:07
  SPEAKER_00: You're gonna hate me.
• 17:08
  SPEAKER_00: It it depends.
• 17:09
  SPEAKER_00: Um that's gonna be my answer to pretty much everything, spot the consultant in the room.
• 17:13
  SPEAKER_00: Um Yeah.
• 17:15
  SPEAKER_00: Yes, it's very much sitting on the fence, Catherine.
• 17:18
  SPEAKER_00: It's fine, it's not a problem.
• 17:19
  SPEAKER_00: No.
• 17:20
  SPEAKER_00: Um I I think some people have got the appetite for it.
• 17:23
  SPEAKER_00: Um, and you go in and and you know, if you don't say AI is the solution, they don't want to know.
• 17:29
  SPEAKER_00: But I think there's other people who are cautious of it.
• 17:32
  SPEAKER_00: Um, you know, not so long ago, cloud was the solution.
• 17:36
  SPEAKER_00: And before that, I, you know, what was the tech advancement then that we all had to jump on?
• 17:41
  SPEAKER_01: I mean, you've had you had ETL move to ELT because then it was, well, well, this is more of a trial thing and reporting.
• 17:47
  SPEAKER_01: Then you had fabric and cloud and all these different elements that Microsoft sort of brought out that people were were kind of getting behind.
• 17:54
  SPEAKER_01: Then you've in the smaller businesses, you have like the automation side where I can just automate this end-to-end, and and these systems now have APIs and people were very much pushing that.
• 18:05
  SPEAKER_01: And now it's well, it's got an MTP server, so I can just throw AI at it and that'll fix the problem.
• 18:10
  SPEAKER_01: But it's like you said, it's very much understanding.
• 18:14
  SPEAKER_01: I the your point earlier was was spot on.
• 18:16
  SPEAKER_01: If you have a problem with communication, whether that be to a customer or client, that might not mean that you need to put an AI system on the communication part of it.
• 18:24
  SPEAKER_01: Yeah.
• 18:25
  SPEAKER_01: It might be a downstream process somewhat.
• 18:27
  SPEAKER_01: Yeah.
• 18:28
  SPEAKER_00: There was, there's a couple of businesses that um I had the privilege of uh of looking at, um, and they were software delivery businesses.
• 18:36
  SPEAKER_00: Um they were they were building their products, they're building their services.
• 18:39
  SPEAKER_00: Uh so they had developers, testers, the the stereotypical agile software delivery teams.
• 18:45
  SPEAKER_00: And they wanted to know, they needed uh AI to improve the way that they were working.
• 18:50
  SPEAKER_00: So they were looking to get AI in so their devs could could code using AI vibe coding.
• 18:56
  SPEAKER_00: Great, happy days.
• 18:57
  SPEAKER_00: So we were looking at the uh looking at all the processes that were taking place, and we we were saying to the the developers, you know, what where are the pain points?
• 19:06
  SPEAKER_00: Where is it not working?
• 19:07
  SPEAKER_00: What are the what are the things that that aren't going so well?
• 19:10
  SPEAKER_00: Where can we, where, where do you think we can make improvements?
• 19:13
  SPEAKER_00: Because these are the people operating these processes, they they probably have an idea.
• 19:17
  SPEAKER_00: Um and every single time it came back, I'll come away from these meetings thinking, you don't need AI.
• 19:23
  SPEAKER_00: AI is going to improve your process by like 5%.
• 19:27
  SPEAKER_00: What you need is a head of delivery to come in and make this team of people talk to this team of people, have a conversation so that tech and product are aligned.
• 19:36
  SPEAKER_00: And then the actual requirement going through, you know, the software delivery lifecycle sausage factory is agreed on.
• 19:43
  SPEAKER_00: And you don't have this butting up of heads of, well, product wanted this, tech delivered that, and the two things don't align.
• 19:50
  SPEAKER_00: Can AI solve that?
• 19:52
  SPEAKER_00: No.
• 19:52
  SPEAKER_00: I mean, maybe one day, but most things I've seen, there's a solution that doesn't involve AI that people can crack on and do.
• 20:03
  SPEAKER_00: I've then seen other businesses who are desperate to embrace AI and that's great, and they're really enthusiastic about it.
• 20:11
  SPEAKER_00: But you might end up with somebody who's sort of a CEO or an MD who's spending an awful lot of time building out a AI app because it's going to improve the way that their teams work.
• 20:24
  SPEAKER_00: And then you sit back and you think about it and you think, well, hang on a minute, you're the MD of a company or the CEO of a company.
• 20:29
  SPEAKER_00: What's your hourly rate?
• 20:31
  SPEAKER_00: How much does that app actually cost you if you factor in your time into that equation?
• 20:36
  SPEAKER_00: Is it right that you are the one that's putting in this AI app?
• 20:40
  SPEAKER_00: Or should you have given it to somebody else in your company to do?
• 20:43
  SPEAKER_00: Have the idea, have the brainwave, but then get somebody else in who can actually deliver it?
• 20:47
  SPEAKER_00: Probably would have been a cheaper option.
• 20:49
  SPEAKER_00: So I think in small business, you've got almost these two groups forming, those who are just throwing themselves at it, embracing it, loving it.
• 20:57
  SPEAKER_00: But with that, I think comes danger.
• 21:00
  SPEAKER_00: Um, if you think back traditionally, if you wanted to uh change a system or implement a technology solution, you had a lot of people doing a lot of thinking.
• 21:10
  SPEAKER_00: You made sure that the requirements included legal constraints, GDPR compliant.
• 21:15
  SPEAKER_00: Now you've got anybody who doesn't have any awareness of what I call these non-functional requirements of how a system should behave creating their own application.
• 21:24
  SPEAKER_00: And that's great.
• 21:25
  SPEAKER_00: So from a functionality point of view, it might work.
• 21:28
  SPEAKER_00: But then from a is it compliant, it probably isn't.
• 21:32
  SPEAKER_00: So I see sort of almost this um danger zone, if you like.
• 21:37
  SPEAKER_00: That sounds very top down.
• 21:40
  SPEAKER_01: It sounds very cool, yeah.
• 21:41
  SPEAKER_00: Yeah.
• 21:41
  SPEAKER_00: So this this danger zone almost appearing where people have gone in really early, have embraced the technology, are building out solutions.
• 21:48
  SPEAKER_00: Functionally, they're great, but with them comes a lot of risk that I think we're going to start seeing further on down the line.
• 21:54
  SPEAKER_01: It's a really good point.
• 21:56
  SPEAKER_01: Two two key things.
• 21:58
  SPEAKER_01: First off, because you can It doesn't mean you should.
• 22:02
  SPEAKER_01: Yes.
• 22:02
  SPEAKER_01: Absolutely.
• 22:03
  SPEAKER_01: And secondly, like you said, if the MD is building something for their team, it's a very strange position because they're not going to know the nuances of what that team does.
• 22:14
  SPEAKER_01: And like you say, the functional requirements might not be there.
• 22:17
  SPEAKER_01: And it's just wasted time and effort in a lot of cases.
• 22:20
  SPEAKER_01: And there's this thing with vibe coding versus, and then, you know, the slightly different variation, kind of, you know, what we can sometimes do for businesses if needed, a genetic engineering.
• 22:32
  SPEAKER_01: Yeah.
• 22:33
  SPEAKER_01: And it's about having and it comes down to the same as a business.
• 22:36
  SPEAKER_01: It has the framework around the coding tools that AI can deliver, but it has to have checks, has to have people, it has to have verification, you know, push requests, actual checks of that push request.
• 22:51
  SPEAKER_01: In vibe coding, you just tell it what to do and it does it.
• 22:54
  SPEAKER_01: Does it and it doesn't account for anything else?
• 22:57
  SPEAKER_01: And it's quite it's exciting for people because they can do things quickly, but it's quite concerning.
• 23:04
  SPEAKER_01: If you put that into a genuine business process, a functional environment, and your team are trying to use that end-to-end, and like you say, you don't know what's happening behind the scenes.
• 23:11
  SPEAKER_01: It could be problematic, right?
• 23:12
  SPEAKER_00: Yeah.
• 23:13
  SPEAKER_00: I mean, if I take uh an example of the the uh robot company I was working in, one of our um early stage sort of tasks was to train an AI model to identify uh wheat, wheat plant in a field, small, tiny wheat plants in a field.
• 23:29
  SPEAKER_00: So we took loads of imagery manually, um, pumped it through the AI, training the model, and we got to a point where the model said um with 100% success rate, so absolutely gonker's success rate, um it could identify wheat plants from all of our pictures.
• 23:46
  SPEAKER_00: And it's like that's too successful, we don't believe that.
• 23:49
  SPEAKER_00: So we then started looking into it, and what we discovered is because we'd only put in pictures that contained wheat plant against brown soil background, it then said anything that was green was a wheat plant.
• 24:03
  SPEAKER_00: And it just so happened that that was the case because we were taking pictures of wheat plants in a field.
• 24:07
  SPEAKER_00: So what we then had to do is go out and take pictures of lots of other green plants.
• 24:12
  SPEAKER_00: So I think our CTO was at a football with Son's football match, taking pictures of the grass, you know, instead of watching the game.
• 24:17
  SPEAKER_00: Um, and then we had to pump those through the the AI.
• 24:20
  SPEAKER_00: Now, that took a human to think, hang on a minute, there's something not quite right here.
• 24:25
  SPEAKER_00: And maybe maybe we'll get to the point where AI can do that, that thinking, go, there's something not quite right here, we don't trust it.
• 24:31
  SPEAKER_00: But it was a human that that did that and said, yes, we're getting a great success rate, but this is too successful.
• 24:38
  SPEAKER_00: We need to go and look into this more.
• 24:39
  SPEAKER_00: If we hadn't done that, we'd have built a product and sold it to people that was misidentifying plants in the field.
• 24:46
  SPEAKER_00: So it might have gone round and you know, been adding fertilizers to weeds accidentally.
• 24:51
  SPEAKER_00: And we could have absolutely annihilated a farmer's crop.
• 24:54
  SPEAKER_00: Well, nobody wants to do that.
• 24:56
  SPEAKER_00: So I think you do still need people in the loop.
• 25:00
  SPEAKER_00: You absolutely need people in the loop.
• 25:02
  SPEAKER_00: You need people making sure that what's going on is sensible and right.
• 25:08
  SPEAKER_00: Um, and maybe over the years AI will be able to do that itself, but I don't think we're there yet.
• 25:14
  SPEAKER_01: No, I I agree.
• 25:15
  SPEAKER_01: People in the business are the most valuable asset.
• 25:20
  SPEAKER_01: Yeah.
• 25:20
  SPEAKER_01: Because they have the knowledge, they have the experience.
• 25:23
  SPEAKER_01: And especially if you use a, in your case, you're using very specific, you know, models that you're building.
• 25:28
  SPEAKER_01: But if you use a general model, like a large language model like Gemini, Claude, whatever, it doesn't know those nuances.
• 25:36
  SPEAKER_01: And it's about understanding where you can leverage it with the expertise to either fine-tune it or help your process and it's spot on.
• 25:44
  SPEAKER_01: It's a really good point.
• 25:46
  SPEAKER_01: What would you say to small businesses when they're looking at the moment?
• 25:51
  SPEAKER_01: A lot of people are on a digital transformation journey.
• 25:54
  SPEAKER_01: I think we're all on it pretty much constantly.
• 25:57
  SPEAKER_01: If they need to take a moment and stop and say, where are we right now?
• 26:01
  SPEAKER_01: What could we do differently that's going to impact the business?
• 26:04
  SPEAKER_01: How do you find the value?
• 26:06
  SPEAKER_01: What's the thing they should be looking for to find value in that?
• 26:10
  SPEAKER_00: It's looking for the pain points in what's currently there.
• 26:13
  SPEAKER_00: Um so trying to identify what things you actually want to use AI for.
• 26:21
  SPEAKER_00: And you sort of asked me a question earlier about what things do you want to use AI for?
• 26:24
  SPEAKER_00: And again, my consultancy answer, it depends.
• 26:27
  SPEAKER_00: So for different businesses, the answer will be will be different.
• 26:31
  SPEAKER_00: Um if you've got a sales and marketing problem, then I'd probably sort of start focusing using AI in in that area.
• 26:39
  SPEAKER_00: If your operations is working okay, why go and poke it?
• 26:43
  SPEAKER_00: Um, you'll then get to a point where you've got significant sales and marketing coming in, and you then need to scale up your operations side.
• 26:50
  SPEAKER_00: And you're you're forever sort of juggling that balance.
• 26:53
  SPEAKER_00: Um, but it would be going into a small business.
• 26:57
  SPEAKER_00: I would go in and I would look for the pain points and start tackling those because those are the things that are causing an issue on a day-to-day basis.
• 27:04
  SPEAKER_00: Um, and I wouldn't always say AI is the answer.
• 27:07
  SPEAKER_00: No, most of the time it's not.
• 27:09
  SPEAKER_00: Um, I'd go in and look to see what are the options about how we can tackle some of those things.
• 27:14
  SPEAKER_00: Um, give some recommendations of you could do something relatively cheap and manual, you could go for an off-the-shelf solution, or you could, you know, custom code something.
• 27:25
  SPEAKER_00: There's sort of those standard principles in IT delivery that you follow anyway.
• 27:29
  SPEAKER_00: You don't you don't just always go in and I'll just build something new.
• 27:32
  SPEAKER_00: If somebody else has done it before, use it, it's cheaper.
• 27:35
  SPEAKER_00: Um, so I'd look I'd look to find the things that are specific to that business that are causing issues, look at recommendations to solve those.
• 27:43
  SPEAKER_00: I think there is an option now that we didn't have before, whereas before you might say, you know, get a tool and automate it and link up the APIs with your existing systems.
• 27:51
  SPEAKER_00: Now we don't need to go down that route.
• 27:53
  SPEAKER_00: Um there's a number of times I've sort of gone into businesses and I know we're partnering, so I feel like I'm hunting for commission here, which I'm really not.
• 28:00
  SPEAKER_00: But there's a number of times I go into businesses and I think, oh, you need configure in there.
• 28:03
  SPEAKER_00: They've got this amazing dashboard that would just show you what's going on across sort of some of your siloed um areas.
• 28:10
  SPEAKER_00: You know, sort your data out, because as we know, if you don't sort your data out, then rubbish in, rubbish out.
• 28:16
  SPEAKER_00: Um but previously, obviously, without AI, you wouldn't have uh you wouldn't have that luxury to have such uh a good technical option at a reasonably cheap price.
• 28:30
  SPEAKER_00: It would have been something that you'd have needed a fleet of software engineers to come in and deliver and you know you're you're talking megabugs.
• 28:36
  SPEAKER_01: Yeah.
• 28:37
  SPEAKER_01: And I think it's a good opportunity right now for businesses to think exactly like you said, where's the pain points?
• 28:43
  SPEAKER_01: But also what's our USP?
• 28:45
  SPEAKER_01: What's our value to the customers?
• 28:48
  SPEAKER_01: Because if we're just fixing things that we feel are problematic, do the customer do the customers care?
• 28:54
  SPEAKER_01: Do the people we work with care?
• 28:55
  SPEAKER_01: Like where's the value going to come from?
• 28:57
  SPEAKER_01: And that's a whole nother angle of what you look at, right?
• 28:59
  SPEAKER_01: Is is going, okay, you deliver this service, but your opportunity is to deliver XYZ service.
• 29:05
  SPEAKER_01: And if you get these things right, you can do all three of them, but that gives you much wider reach and things like that, right?
• 29:10
  SPEAKER_01: That's yeah.
• 29:11
  SPEAKER_00: There's there's a a friend of mine um who works, she she runs her own business, a small, small business, and she works um in the healthcare industry.
• 29:20
  SPEAKER_00: So she um she works with with patients and helps them through rehabilitation and sort of lifelong um health issues.
• 29:29
  SPEAKER_00: And she also does uh a lot of um expert work where she's asked by a legal team to go in and sort of assess what a person might need to live with a particular condition for the rest of their life and sort of price it up so that that person can then get the money that they need to live a decent life going forward.
• 29:48
  SPEAKER_00: Um and I was talking with her, we often we walk our dogs together, so we often talk and put the world to rights, and we were chatting away, and I was sort of saying to her, Oh, there's so many things I can do to help you free up your time.
• 30:00
  SPEAKER_00: Um with AI, you know, the the the plod bangle that just records your your sessions, you could be working with these patients, and as long as you've got consent, you could be recording the session that's taking place.
• 30:12
  SPEAKER_00: Then when you're driving home in your car, hit record and just start chatting away about what you recommend.
• 30:18
  SPEAKER_00: So that dead journey time is now useful time.
• 30:21
  SPEAKER_00: So by the time she gets home, she's then got a report with the the sort of the key points uh sitting in and out waiting for her.
• 30:28
  SPEAKER_00: Um and and it's again not replacing her, she would need to still read through and make sure that it's all correct and it hasn't come up with something absolutely crazy.
• 30:36
  SPEAKER_00: But the number of um man hours that that would save her in a day would be amazing or in her week.
• 30:42
  SPEAKER_00: And what that then enables her to do is to do more reports.
• 30:46
  SPEAKER_00: So whilst you you sort of where's the the saving was the question you asked.
• 30:50
  SPEAKER_00: It might not be an obvious saving, but by freeing up time, it allows you to then do more of the same thing.
• 30:55
  SPEAKER_00: So that's where it's not necessarily a saving, it's a time saving that then becomes an enabler to build up your revenue and definitely process more.
• 31:03
  SPEAKER_01: Yeah, I think some people purely they're overutilized, don't have enough time, need to free up the time, they can then deliver more.
• 31:13
  SPEAKER_00: Yeah.
• 31:13
  SPEAKER_01: Other people, it can be, I have actually a new service line that I didn't even realize was possible.
• 31:19
  SPEAKER_01: Yeah.
• 31:19
  SPEAKER_01: But we have the capability to do it now, and we have the information and we're already doing elements of that service, and it unlocks a whole new pathway as well, which is a really exciting position to be in.
• 31:29
  SPEAKER_01: So we where do you think the next 12 months are heading from your perspective, businesses?
• 31:37
  SPEAKER_00: I think it's a, you know, you get that like um that that calm before the storm, then the storm, then you're in the eye of the storm, and then you're back through the storm, and then you're in the calm afterwards.
• 31:46
  SPEAKER_00: I think we're somewhere in the middle in the eye of the storm.
• 31:50
  SPEAKER_00: I think, you know, the the AI boom started, like I said, you know, we were building robots using or using uh software solutions and robots using AI 10 years ago, but 10 years ago nobody had really heard of AI.
• 32:02
  SPEAKER_00: Not not in the way that it is today.
• 32:04
  SPEAKER_00: So AI has been around for a long time, but people are now just beginning to recognize it and work with it and see what it can do.
• 32:13
  SPEAKER_00: I think we're gonna go through a period where we're gonna see a lot of failure from AI.
• 32:19
  SPEAKER_01: We're already there, I think.
• 32:20
  SPEAKER_01: There's a lot of failure happening already, yeah.
• 32:22
  SPEAKER_00: Yeah, people not necessarily getting the results that they thought they were gonna get.
• 32:25
  SPEAKER_00: Um, so like we're saying, someone who's got no technical background going in, building something, thinking it's great, and then realizing it's not as great as they thought it was, at least.
• 32:34
  SPEAKER_00: Um so I think there's gonna be a lot of that.
• 32:37
  SPEAKER_00: Um I know that sort of we've discussed the cost of AI as well in uh in previous conversations, and at some point that's gonna impact, you know, cheap cheap tokens, cheap tokens, cheap credits, and then the price will go up.
• 32:49
  SPEAKER_00: So I think companies will then be in a position where I what I imagine will have happened is every single department in silo would have done some sort of AI solution.
• 32:58
  SPEAKER_00: So you've probably got in smaller businesses um many different AI tools being used.
• 33:04
  SPEAKER_00: Um, I think there'll come a point where they suddenly get expensive, and businesses are gonna have be having to choose which which solution they stick with and which one they don't.
• 33:12
  SPEAKER_00: And what that's then gonna force is you know migrations from the many AI solutions back into one or possibly two or or a smaller number.
• 33:22
  SPEAKER_01: Yeah, and I think it's the difference between a genic, which is that constantly prompting, asking the questions and using the tokens versus using that capability to give yourself automation or or access to other platforms or tools that don't leverage tokens all the time, but only do that in the right places, right?
• 33:41
  SPEAKER_01: So Yeah.
• 33:43
  SPEAKER_00: And the other sort of thing that I think I think people are jumping in and having a go.
• 33:49
  SPEAKER_00: Which is and which is great.
• 33:50
  SPEAKER_00: And and don't get me wrong, some people are doing it amazingly.
• 33:53
  SPEAKER_00: I was sitting talking to one business owner um a couple of weeks back, and he was showing me what he's developed.
• 34:00
  SPEAKER_00: And hats off to the guy, it was impressive.
• 34:02
  SPEAKER_00: It was it was across his operations.
• 34:05
  SPEAKER_00: Um, it was uh uh an AI app where his team could log in, they could build out quotes, which was uh a lengthy, very manual process.
• 34:14
  SPEAKER_00: Um, but he had taken the data of his previous quotes and he was using that to enable the the tool to sort of predict what the the next quotes would be.
• 34:21
  SPEAKER_00: And this is in a um sort of a a building type environment.
• 34:26
  SPEAKER_00: So there's imagery, you take a picture of of where something's gonna be built or extension, what have you.
• 34:31
  SPEAKER_00: Um and it was interpreting this information, building a quote out, and then he was sort of um benchmarking the the AI quote value against the manual quote value and and sort of training it and then trying to train his salespeople.
• 34:44
  SPEAKER_00: And so he was it was phenomenal.
• 34:46
  SPEAKER_00: It was really impressive.
• 34:48
  SPEAKER_00: And I kept saying, you know, what about the where is where's the data being held?
• 34:51
  SPEAKER_00: Oh yeah, no, no, no, I've checked that.
• 34:52
  SPEAKER_00: That's in UK data center.
• 34:54
  SPEAKER_00: Brilliant, okay, tick.
• 34:56
  SPEAKER_00: Um, what about um, you know, your two-factor authentication?
• 35:00
  SPEAKER_00: Oh, yeah, yeah, no, I'm all over that.
• 35:02
  SPEAKER_00: Yep, tick.
• 35:03
  SPEAKER_00: So that there's somebody who's doing something incredible, streamlining his operations, making huge time savings and in accuracy improvements across his end-to-end um business.
• 35:16
  SPEAKER_00: And he understood technology enough because he came from a tech background to think about all those non-functionals.
• 35:21
  SPEAKER_00: That guy's gonna rock it and good on him.
• 35:23
  SPEAKER_00: I'm I'm right behind him.
• 35:24
  SPEAKER_00: He deserves the success he's gonna get.
• 35:27
  SPEAKER_00: But just my train of thought.
• 35:31
  SPEAKER_01: I think the interesting thing there is that you gave a really good example.
• 35:35
  SPEAKER_01: He's using AI to do the quoting process and constantly look at the data.
• 35:41
  SPEAKER_01: If you change that process to a data repository of the previous quotes, and every new quote that comes in is an automatic process to update that, you can still have the same effect without using AI tokens.
• 35:54
  SPEAKER_01: And that's where you can look for these innovations that are going to keep your cost down in the future.
• 35:59
  SPEAKER_01: But it's very easy to leave them as is because it's already working.
• 36:04
  SPEAKER_01: Yeah.
• 36:04
  SPEAKER_01: And it's those little nuances that are so important for businesses to try and identify and then pick up.
• 36:10
  SPEAKER_01: And like you said, it's also about if that quoting process now works really well and the team can go out and do these things and take the photos, and that's saving them loads of time.
• 36:20
  SPEAKER_01: Is it mean does it mean they can have more quotes?
• 36:23
  SPEAKER_01: Does it mean that they can um be able to actually do other services that they couldn't before because now they can do more quotes?
• 36:33
  SPEAKER_01: And those are the really exciting bits right to be able to work with businesses on and deep dive into and be like, what value does this now give you?
• 36:39
  SPEAKER_01: Yeah.
• 36:40
  SPEAKER_01: And that's what you're doing, right?
• 36:41
  SPEAKER_00: Yeah.
• 36:42
  SPEAKER_00: And it's it's the let's look at what's there today, let's take away the pain from today, and then let's think about tomorrow.
• 36:48
  SPEAKER_00: Yeah.
• 36:48
  SPEAKER_00: And and I think that's where the real um, that's where AI is really gonna take off.
• 36:56
  SPEAKER_00: In in my past, I've worked um in smart metering and I've worked for two different companies.
• 37:01
  SPEAKER_00: One decided that they were going to, they'd heard about the smart metering, they were gonna get involved, and if they were gonna have to do smart metering, then they were gonna get in there early, throw money at it, and understand what that solution needed to look like.
• 37:13
  SPEAKER_00: And and also get in there first to help dictate what that solution's gonna look like.
• 37:18
  SPEAKER_00: There was another company then that was, they called themselves a fast follower.
• 37:21
  SPEAKER_00: So they decided we're gonna sit back, we're gonna watch what the other companies are doing, we're gonna let them solve the problem, and then once they've solved the problem, we'll we'll sort of do the optimum thing.
• 37:31
  SPEAKER_00: So we spend our money in a very optimal way.
• 37:33
  SPEAKER_00: Now, both have pros, both have cons.
• 37:36
  SPEAKER_00: And I think um I think we're gonna see that same sort of principle coming out over the next sort of 12 months, where you've got some companies and people who've gone right in there and and got stuck in, but then you've got other people who've hung back almost because of overwhelm, I'd say.
• 37:52
  SPEAKER_00: Well, I mean, where do you even start?
• 37:54
  SPEAKER_00: Right, which which large language model should I use?
• 37:57
  SPEAKER_00: Uh, what is a large language model?
• 37:59
  SPEAKER_00: You know, somebody who's not in the tech background is probably thinking, well, I've heard of AI, but what actually is it?
• 38:04
  SPEAKER_01: Yeah, we get that, yeah, we get that a lot.
• 38:07
  SPEAKER_01: It's a it's a genuine concern for people, right?
• 38:09
  SPEAKER_01: They they don't know where to begin.
• 38:11
  SPEAKER_00: No, no, they don't know where to begin.
• 38:12
  SPEAKER_00: So I think you're gonna have some people who are naturally holding back, who are gonna be watching what other people are doing.
• 38:18
  SPEAKER_00: And then once they've sort of seen other people make a decision and go, that works, then they'll throw themselves at it.
• 38:23
  SPEAKER_00: And and they will then put in, you know, a a cheaper, more optimal, more efficient solution.
• 38:29
  SPEAKER_01: I love it.
• 38:29
  SPEAKER_01: So we're gonna end with one book or podcast that you would recommend.
• 38:36
  SPEAKER_00: Oh, this is easy.
• 38:37
  SPEAKER_00: Um so I had my mind absolutely blown when I audio booked this one book, and it's a book that I will tell everybody that they absolutely have to read.
• 38:49
  SPEAKER_00: It's um called Outless Shrugged by Ayn Rand.
• 38:53
  SPEAKER_00: She grew up in communist Russia and moved uh across to um the United States, and she wrote the book, I think, in something like 1942, and I audiobooked it, and it was something like 60 hours of audiobook on double speed.
• 39:07
  SPEAKER_00: It's a it's a huge, huge book, but it absolutely blew my brain.
• 39:13
  SPEAKER_00: The only way that you can actually sort of tell that the story, it's it's Utopia America.
• 39:20
  SPEAKER_00: Um, but the only way that you can tell the story was written back in the 1940s was because of their reference to technology.
• 39:26
  SPEAKER_00: Right.
• 39:27
  SPEAKER_00: Other than that, you would swear that it was written in today.
• 39:31
  SPEAKER_00: And it's all about capitalism, communism.
• 39:34
  SPEAKER_00: It's just mind-blowing, changes the way you think.
• 39:37
  SPEAKER_01: Well, okay.
• 39:37
  SPEAKER_01: I'll have to give it that a uh read or listen.
• 39:39
  SPEAKER_01: I'm not sure.
• 39:40
  SPEAKER_01: Well, with me, it'd probably be listening, let's be honest.
• 39:42
  SPEAKER_01: But um, and then one tool that you would recommend people look at or use in in in whatever capacity.
• 39:50
  SPEAKER_00: Pencil.
• 39:51
  SPEAKER_01: Yeah?
• 39:52
  SPEAKER_00: Yeah, whiteboard and a marker, really.
• 39:54
  SPEAKER_00: Um I think uh people tend to jump into the solution uh before they should.
• 40:01
  SPEAKER_00: So in previous companies, people have laughed at me.
• 40:04
  SPEAKER_00: They said, Catherine, you would paint your entire room and house in whiteboards, wouldn't you?
• 40:08
  SPEAKER_00: And it's like, yeah, in my study, probably would.
• 40:11
  SPEAKER_00: I just think getting a group of people around a whiteboard and you know, changing things on the fly, moving things around.
• 40:19
  SPEAKER_00: Virtual whiteboards are great, but they are not as effective as a group of people stood in front of a whiteboard um designing and innovating as a group.
• 40:28
  SPEAKER_00: So it would be definitely a whiteboard pencil.
• 40:30
  SPEAKER_01: Love that market.
• 40:32
  SPEAKER_01: And one piece of advice for businesses right now.
• 40:35
  SPEAKER_00: One piece of advice for businesses now.
• 40:39
  SPEAKER_00: I think everything seems to be going really, really fast.
• 40:42
  SPEAKER_00: We live in a society where there seems to be a rush and an urgency.
• 40:46
  SPEAKER_00: You've got to do this, you've got to do that.
• 40:48
  SPEAKER_00: Have you thought about this?
• 40:48
  SPEAKER_00: Have you thought about that?
• 40:50
  SPEAKER_00: Just take a breather.
• 40:52
  SPEAKER_00: Probably be my advice.
• 40:53
  SPEAKER_00: Take a breather, look around, scan the horizon, don't let the overwhelm, the AI overwhelm, kick in.
• 41:00
  SPEAKER_00: And think strategically about what you're gonna do and how you're gonna embrace AI.
• 41:04
  SPEAKER_01: Love that.
• 41:05
  SPEAKER_01: And I get everyone who comes on the podcast to book.
• 41:08
  SPEAKER_01: So I've got you.
• 41:10
  SPEAKER_01: The connector effects.
• 41:12
  SPEAKER_01: We obviously met at a networking group.
• 41:15
  SPEAKER_01: We did.
• 41:16
  SPEAKER_01: And this is all about how you can maximize working in networker groups.
• 41:20
  SPEAKER_01: It's uh from Ivan Meisner, who's a who founded B and I, which is one of the network groups alone.
• 41:25
  SPEAKER_01: But um, and obviously we didn't meet there, we met at Ultra, but it's uh really good book, and I know that you're building your brand and your network and everything you're doing.
• 41:32
  SPEAKER_01: So I've thought this would be a really useful book for you.
• 41:34
  SPEAKER_01: So that's my Oh, that's brilliant.
• 41:36
  SPEAKER_00: Thank you.
• 41:36
  SPEAKER_00: No problem.
• 41:37
  SPEAKER_01: Genuinely appreciate that.
• 41:38
  SPEAKER_01: Thank you.
• 41:38
  SPEAKER_01: Thank you very much for joining us and joining me.
• 41:39
  SPEAKER_01: It's been it's been a great chat.
• 41:41
  SPEAKER_01: And I'll see you all on the next podcast.
• 41:43
  SPEAKER_01: Thanks for listening.