The Stirling Business Podcast
The Stirling Business Podcast is recorded at Studio King Street in Stirling and produced by Johnston Media (Crieff)
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The Stirling Business Podcast
From Data Foundations To Real-World AI Wins
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Most teams don’t need more AI hype; they need better decisions. We sit down with data expert and founder of Head for Data, Colin Parry, to strip AI back to what actually moves the needle: clean inputs, clear processes, and tools that match the job. Colin’s path from renewables and wind turbine analytics to leading data science teams gives him a rare field‑to‑boardroom perspective on how to build systems that work in the real world.
We start with the foundations: why data only exists to improve decisions, and how a centralised platform plus solid governance turns scattered spreadsheets into a reliable source of truth. Colin breaks down the difference between deterministic tasks that deserve automation and ambiguous work where AI’s probabilistic strengths shine. He explains why ChatGPT is just one tool in a larger AI family, and how to pick the lightest‑weight solution that solves the real problem instead of forcing everything through a language model.
The episode’s centrepiece is a practical case study with a major property factoring firm. By defining a true unit of work, cleaning their data, and building an optimisation algorithm that accounts for geography, travel time, and seniority, Colin’s team rebalanced workloads across 40,000 properties. Then they layered fees over effort to expose profitability by development, empowering leaders to adjust pricing, retain the right clients, and drop the wrong ones. The result: fairer teams, sharper unit economics, and faster, more confident decisions.
If you’re wondering where to start, we share a simple path: run a focused gap analysis, centralise your data, automate the deterministic steps, and apply AI where intent or inputs are genuinely messy. Want a partner for that first step? Colin offers a free half‑day Intelligent Futures workshop to identify quick wins. Subscribe, share with a colleague who’s drowning in spreadsheets, and leave a review to tell us which process you’d automate first.
Hello, welcome back to Studio King Street. Uh I'm Neil Munday, the host of the Sterling Business Podcast. I've got a fantastic guest today to talk about uh a topic that I think that we're all interested in, uh which is artificial intelligence, uh AI for short. Um so without further ado, I'm going to introduce my guest, uh Colin Parry. Welcome to the studio today. Thanks very much. Thanks for having me.
SPEAKER_01:Yeah, so your local guy? Um, yes. So I grew up in Stirling and went to Stirling High, but these days I reside in Falkirk.
SPEAKER_00:Okay. But you've travelled around a bit, have you? And you you ended up back back in the Fourth Valley?
SPEAKER_01:Yeah, so we lived in Inverness for um just over six years, my wife and I, um which was great, and we lived it up there. And then more recently, when we had our children, we came back down to live in Falkirk to be closer to family and friends.
SPEAKER_00:Okay, so so we're gonna talk about artificial intelligence or AI today, but before we do that, why don't you just tell the audience a little bit about your background and where you came from? Sure. In terms of the your working working history.
SPEAKER_01:Yeah, sure. So um I I studied physics at university originally um at Strathclyde, and like many people, I wasn't really sure of what I wanted to do after that. So I went to uh Dundee and did some work in renewables, so I did a master's in renewable energy, and then I went to work for a company in Inverness who did wave power. So they were called Wave Gen, they were uh they don't exist anymore, sadly, but they were owned by Voigt Hydro, so Voigt are the people who made the Three Gorges Dam uh turbines. Um worked in uh Isla on their wave power plant and then I worked for a a wind power company, an Italian wind operator, who are now called Nadara. Uh I worked with them for three years doing automation of wind analysis, so I was analysing data from turbines.
SPEAKER_00:So were you like a data scientist? Was it that kind of role you were doing?
SPEAKER_01:Yeah, exactly. It was kind of more a data analyst, probably at that point, but we were automating the reporting, so we were looking at power curves, we were looking at uh availability of turbines, so lots of like very technical uh stuff and a lot of contractual management things on the contract, so you know who was at fault if something went off. I also did my wind turbine climbing licence when I worked there, so I was up climbing turbines, calibrating anemometers, and um north alignment of turbines and things like that, just to improve the data quality. That's how committed we are. And the and the thing about data is, and we'll probably come on to this later, is if you live and breathe it, it's much easier to do the analysis, you know. You know, the the the the people who want to just sit at a computer are the ones that don't really understand the problems they're solving. And so to get out in the field and get your hands dirty is definitely the way to go.
SPEAKER_00:Yeah, so it's kind of understanding the um you know what's behind the data in the first place and what you're trying to get from it, if you know that or you have a good feel for what you're trying to get from the information that's coming in, then you're gonna be more successful. Yeah, yeah.
SPEAKER_01:When you when you're looking at a signal coming in from a wind turbine, if you've been out and seen that sensor and where it is, and okay, that data's not great, okay. And now I've seen it and I see okay it's in a really dirty environment, it's covered in grease, it's you know, then you understand why. And then when we came back down here, I went to work for Agreco in Glasgow, uh, where I was originally doing um business intelligence development, and then I became their first data scientist, so I founded their data science team. I was doing predictive maintenance of engines on looking at battery failures and also at fires. And then I left there to join a SaaS company, software as a service company in Glasgow, who were analysing energy data from commercial buildings. That was my job. So I was looking at the time series data from that. Um that work, some of that work in I'm on uh patents. I've got four, I'm an inventor on four patents related to energy management at commercial buildings. And then I got made redundant from that role, sadly, in 2023, and then I founded Head for Data.
SPEAKER_00:Wow, okay. So you're definitely a data guy.
SPEAKER_01:Yep.
SPEAKER_00:There's no question. You know a little bit about data. So Head for Data is the name of your company, um, a local um institution in the Fourth Valley here. So tell us a little bit about Head for Data and then we'll get specifically into AI and what that means to the audience.
SPEAKER_01:Sure, thanks. So Head for Data is a a a technology company, right? But the way I always try and sell it to people is we I like to think of us as we are business reverse engineers, right? That's basically what we are here to do. So our goal is to come into an organization, and you know, lots of organizations now are looking at data and AI and technology and how they can use it as an enabler to make their business more profitable. And our job is to work out, well, how is how is your business making money today? So what's your operating costs, what's your um what's your uh sales like, and we can look at that and look at the data off the off the back of that and try and help you go, well, where can we use technology to improve one of those, either save costs or or make money? And we do that in kind of four ways. So we look at your uh data governance, so the compliance with the GDPR, that's the kind of um the ugly child I like to call it of this. But it's such an important baseline, as you say, to any business. And if you don't have that foundation right, you know, it could cost you a lot of money if if the information commissioners officer office, the ICO come in and you know, do they find an issue. So that's the first thing. We then look at data, we can build data platforms, we can build business intelligence, BI systems, uh build dashboard reporting and tools like Power BI or Tableau or things like that, or even Excel. Um, and then we also look at automation, so we do process automation. So if you're doing a lot of manual uh data entry or minute m moving data in between systems, system integration stuff, we can help with that. And then finally, uh machine learning and AI, so we can help you with looking at a lot of the LLM models, large language models that everyone's using, like ChatGPT and Copilot and Claude and ours, or we can look at traditional machine learning, optimization, you know, analysis of time series data, predictive maintenance, that type of thing.
SPEAKER_00:Brilliant. Right. So um I was in the tech sector for a long, long time, 30 years. Um so I know a little bit about technology and a little bit about cloud and big data and and and and that sort of thing. Um I know very little about AI. I certainly don't practice um a lot of the kind of principles or or or you know uh that that we hear a lot of people doing um today. So a lot of people have started to use Chat GBT, they think that's AI and that's the the uh the start and the end of of what AI is. But it's a lot richer than that, right? So before we get into what AI is, if we kind of take a step back and um you know effectively talk to me as if you're giving an AI for dummies course, taking it right back to basics, what would you what would you suggest organizations, small or large or mid-sized, start to think about at the very early stages in their journey?
SPEAKER_01:So I think the the the starting point for me is always that any piece of technology is just a tool. And unless your business is a technology business, so that that software as a service company that I worked for before, um their business was software, so technology was a core part of their business. But for most organizations, technology is an enabler or it's a you know, and and and so it becomes it can be looked at as an overhead, right? So I don't want to come too negative at the beginning, but the but the reality is you have to really understand what problem is it that you have that you think technology can solve. You know, that's the starting point for any conversation. AI is just a tool in your toolbox, and it's kinda like the way I always describe it is it's like um hiring a handyman or a handywoman who turns up with a hammer and that's all they've got, you know, and other tools are available in your toolbox, you know. And so if you only have a hammer, everything looks like a nail, as the saying goes. And and certainly the case for AI where people are using it to, you know, do things that it perhaps uh isn't isn't right. So to get started, I would always be saying to people, well, what real problem are you trying to solve in your business? Is it you know we we get asked all the time, can we do this with Chat GPT? And then usually my first question is, well, what what is it that you see as the issue you're trying to overcome? Is it and why? And why? And and and do you want to cut costs? Do you want to increase your sales? Those are two different problems, you know. And and the way you would approach them using technology and AI is is totally different.
SPEAKER_00:Right. Okay. So um I I've heard you do a presentation uh historically around breaking it down to the different components of of technology, right? AI is not new, right? It's been around a long, long time. Um it's a means to an end for a lot of um you know, a lot of uh business process re-engineering and what have you as you suggested there. So if if you look at the other components um of a data strategy, if you like, what what are some of those things that you would uh you would you would consider it either before or along line AI uh alongside AI should I say?
SPEAKER_01:Yeah, so AI to some extent is kinda it can be the final step in a journey of technology. And generally the foundations are around data itself. And oftentimes when when businesses go to adopt technology like data or or AI or any kind of technology like that, they'll find out that their foundations of data are actually not sound. So maybe their data quality isn't very good, it's inconsistent, there's duplicate data, there's data missing, and oftentimes that's the barrier to adoption where people say, Well, if we could do this, if we had this data, we'd be able to do this with AI, sure, but then you need to solve the data problem first. And sometimes people don't want to put the work in. What what AI is great at is historically technology was complicated because you had to do all the thinking up front. So if you wanted to implement a piece of technology, I know you worked with some big vendors before, you had to do all the thinking of how that would work at the beginning. So you had to really plan things out. What AI allows people to do is have that problem solved later in the process. So you can let AI do some of the upfront work. Now that can be good and it can be bad because it can make you lazy and it can make people say, Okay, I'm not going to deal with my data quality because I'm just going to brute force it using AI. And you know, that might get you somewhere, and in some cases that might be okay. But if you don't address the foundations, then you know it's the the usual thing where if you put rubbish in, you get rubbish out.
SPEAKER_00:So what would some of the things you you would do to uh to address that foundation? What other types of tools you talk about tools in the toolbox, what other sort of tools would you think about and use to do that before you got to a stage of using AI in that kind of example?
SPEAKER_01:So for me, the the starting point is businesses should really have a centralized data platform. You know, I mean let's let's take an even further step back and get more uh philosophical, right? So what what is the point of data? Let's start with there. And for me, the the data's only got one use and one use only, and that is to make better decisions. That's all it's for. Right? The reason people gather it, collect it, store it, process it, and use it is to make better decisions. Because if you don't use data to make decisions, you are guessing. The dictionary definition of guessing is making a decision without any information, right? And data is the foundation of that. And obviously, lots of people run their businesses on you know, they just what they feel in their in their experience, and and of course, there are lots of people that get that right for a long time and and and make a successful business. But if you don't have good quality data there, that's going to help you make those decisions. So having a centralized data platform, so that means collating data from multiple sources, so you maybe your CRM, your ERP, your HR system into one place.
SPEAKER_00:Could you define that as a system of record for want of a better word?
SPEAKER_01:Yeah, well we we we would probably call it a business intelligence system, right? So that's probably the foundation. Now you don't need that, that's not mandatory, but that's oftentimes when people come to us about AI, that's really what they need. And actually, AI is a very small part of the overall problem they're trying to solve. And it will be things like I want to be able to get this data to look at historical performance or something along those lines. Can I do that with Copilot or with ChatGPT? And we think, well, you know, actually you could do that in a business intelligence system because what AI is really good at is non-deterministic things, right? So determinism is just um for a given input, you get a given output, right? If it's messy, as in so if it's a well-defined process with that's repeatable, AI is often the wrong tool for the job. So when you talk there about tools, that would be something that you would just use, you could use traditional automation to automate. Because if you know that when I say when A goes in, B comes out, and you want that to happen, that's not really an AI problem, right? That's uh it's just a process that you want to automate.
SPEAKER_00:Where AI becomes And that might be a simple integration between one tool and the other tool.
SPEAKER_01:100%. So there's cloud tools you can write. I mean, we have we have software developers in in Head for Data who write code and you can do that with code, and that's deterministic then. So you know that if with input A you get output B. AI is not like that. If you ask two AI models the same question, you're going to get two different answers. In fact, even if you ask the same AI model the same question twice, you'll get different answers, right? Because there's a certain randomness to them. And where AI is good is where then the intent is unknown. So for things like chatbots where there's not really a defined process flow. So as in a customer may land on your website, they may have a question, they may be. Exactly. But even then you can still process that data to make it deterministic, right? So determinism and structured is is kind of two separate things. Structured would be a table is structured, um, as you say, voice or or maybe log, some systems can sometimes be unstructured data, but that might be repetitive unstructured data, which makes it deterministic, if that makes sense. But if it's if it's n if it's non-repetitive, as in you know, so a good example might be voice has you know multiple languages or multiple dialects or different things, then it becomes messy. That's where the eye does become useful. Um and using it in that way is is more complicated, right, than just normal process automation. Or where where intent is unknown, you know. So you if if if someone lands on your website and you have a chat bot, for example, you don't know what they're going to ask up front, right? Whereas if you go on a website to book a room in in one of your buildings, you've got a process for that. You need to know their name, their you know, their address, their payment details. There are deterministic things that you need to know every time.
SPEAKER_00:Right. Okay. So examples of some other tools. You talked about um automations being a a tool set in itself, if you like, or a bunch of tools. Um Web tools, I guess that they wouldn't be regarded as AI as such, that but it's a different different category. What other types of tools within that portfolio should um organisations be looking at as part of that data intelligence strategy?
SPEAKER_01:Yeah, well, I think it it comes down to you know what are your processes in your business, right? And many businesses have really well-defined processes, particularly if they've if they've got um accreditations like ISO 9001. So we have ISO, uh we have 9001, which is the quality processes, and then we have 27,001, which is cybersecurity. And obviously for 9001 for quality processes, you need to have well-defined processes. Now, if you have well-defined processes, you can then decide which processes need a person and which don't. So, examples of that might be I take data from an email inbox and I manually enter it into a financial tool like a Xero or a SEG or whatever, right? That process could probably be automated without a human. So you want to remove the human in the loop part of that. But then that same process might have an invoice creation part of this, and you might want a person to check the invoices before they go to a client. So the process of entering the data can be fully automated, but the process of creating invoices would be augmented. So the pro invoice would get created manually, but sorry, the the invoices would get created automatically, but we wouldn't be sent automatically because you'd want a human to check it first. So this is where you really need to take your processes, dissect them. And at the beginning, I said we're business reverse engineers. This is where this theory and and and this logic comes in. You know, clearly we're not experts in every single business, but what we are really good at is understanding the processes and how a business works because and how to apply tech to those processes in the right way in the world. Absolutely with the right tools. 100%. So, you know, fundamentally the way I always approach it is all of every business, so if you're listening to this and you're a business owner, your business is a group of people working towards a shared goal. That's it. That's what business is. The next level down is those people follow a bunch of processes to work towards that shared goal, right? So that's level two. That's the level that you can optimize as a business owner. And you know, I talked there about some processes can be fully automated, some can be augmented, but there's another third part which is some processes can be completely reworked with technology. So a process that needs, you know, five steps because you're limited in in the application of technology within your business, that could be completely reworked to be far more efficient or or use a lot of shortcuts, and and that's going to save you a lot of time and effort, you know. And I think the the real the real benefit of technology is it's a force multiplier that enables smaller teams to do the work of bigger teams.
SPEAKER_00:Okay. So um so help me dispel the myth of chat GBT is AI. It is a form of AI, it's part of the AI world, if you like. So what would you say when somebody asks you is Chat GBT AI? How would you answer that?
SPEAKER_01:Well, so what I would say is um uh there's there's no sadly there's no AI council for the for the taxonomy here, um, so that there's a certain amount of interpretation in terms of um what words mean in this in this space. However, personally, artificial intelligence is the big umbrella, right? So for me that's everything. Anything that's using um code to do something clever is AI to me, right? And ChatGPT is a form of generative AI. So the reason it's called generative AI is because it can generate text, right? Or generate video or voice, depending on the model, right? ChatGPT uh mostly does text. And those are part of that family of generative AI. But people have been using AI, as you mentioned earlier, for a long time. So if you've used Siri or Alexa and it's decoding your voice, that uses AI, that's machine learning. Um maps routing, if you've ever used a satnav or your Google Maps or or Apple Maps, those use AI as well to do the routing. Then you've got um face recognition, you've got recognition. Recommendation engines on those platforms. So those are all parts of AI. So it is correct to say that ChatGPT is AI, but ChatGPT is part of a family of tools that live within the AI ecosystem.
SPEAKER_00:Okay, so a family of so a family of tools within the ecosystem. So there are other types of tools or other subfamilies, if you like, within that within that portfolio. So um again, as part of the presentation that you uh that you went through, which which I was part of several weeks ago, um you built it up as a you know you talked about chat GPT, part of the family, and then you talked about other other flavours of AI. So what types of um what types of AI involvement or engagement do you typically have with your clients?
SPEAKER_01:So one one example that we've we've done recently is um a property factoring company uh who who are the third biggest factor in Scotland, they um factor around 40,000 properties across Scotland. And to do that they have about 45, 50 property factors. And so they came to us um and said, Look, we feel our workload across our property factors is quite unbalanced and unfair, and we'd like to do a better job of managing our people, right? And and and let's take a step back and say we're business reverse engineers, right? That business is a service business, right? So their primary cost is staff. You know, the factors, the the people that live in the factored buildings pay a fee to have their building factored, and the primary cost for the for the factoring company is the staff that need to do the work to enable that to happen, right? So again, that's the reverse engineer. So they wanted to optimize the workload to try and reduce cost and to make it fairer and increase staff retention and things like that. So what we did was we looked at their business and we said, well, the first part of this is how do you define work? What is a unit of work when it comes to property factoring? And so we had to take them on a bit of a journey. And that included looking at their data, cleaning it, you know, a lot of a lot of data cleaning to get that data to be good enough.
SPEAKER_00:Found like you're more business consultants.
SPEAKER_01:Well, uh funnily enough, I was doing my insurance renewal recently and and an insurance broker did say that that I had always thought of as IT consultants, but he said, Well, actually, you're more like a management consultant, really. Right? And and so you can certainly take that view. But this is really just to build the foundation to get the technology. So I want to be clear that we are software developers, we build custom tools for people, but we have such a broad range of understanding that that's where we go back to to start that process, right? So that's the sort of value that we bring. So we had to look at that, help them clean their data up, help them understand their data. And then we were able to build a unit of work. What is a unit of work for a for a given development or property? Now, once we had that metric calculated, we were able to then build an optimization algorithm. So this is using AI, so this is a subset of AI about optimization, where um we looked at the amount of work each development was and also where it was spread geographically. So, for example, you don't want somebody out there four offices, you don't want someone out in Verness office doing all the properties in WIC and Olapool, and then someone else doing all the ones around Inverness, right? That would be unfair. So the Aldum had to take account of that. Travel time was one of the metrics. So we looked up the travel time from their offices to every development and worked out and took that into account when we when we built this algorithm. So that optimization algorithm was the AI part, which we were able to say, look, now we think this is who should get which property, and there was there was rules based on seniority and experience and things, so we had categories of of end users, if you like, of the of the of the algorithm and the developments. And they and they did that with us and they loved it. And and when you go for an interview there now, they actually show you that to show you, look, we take care of our staff. They are a very caring business in terms that they want to keep their staff present and engaged, they've got a lot of good people that work there, and you know it was a real a real success. But then going a step further than that, what we asked them to do was say, well, give me your let your fee information now. So now we know if you again if you look at the business, the cost is the work, right? That's that's the amount of effort you put into your business, but the income is then what you charge. So now let's take the income and plot it against the work and work out what developments are profitable and what ones are not profitable. And that exercise resulted in them, you know, reviewing the status of the.
SPEAKER_00:They reviewed their cost base to begin with, and then you flip that over. Now let's look at your revenue or your income base, and then marry the two things together to see better.
SPEAKER_01:And that was something we brought to them. So that wasn't something they thought uh I and that's not to s to slight them as such, but just to show you that we we always describe ourselves as a technology partner. You know, we're not consultants. I mean, technically we are by the definition of the word, but we're not here to kind of tell you things we already know, right? We're here to actually look at right, how can we help your business grow? So off the back of that analysis, when they looked at the profitability of clients, they decided to not renew the contracts of some clients because they just weren't profitable, right? So the amount of work they were doing compared to what that client was willing to pay for that work just didn't make economic sense. So I mean you know, that sounds like a bad thing, they lost clients, but they actually were able to confidently take clients out that weren't profitable. And what I explained to them is this is really going to help you when the other the other aspect to this is if you have a client now that wants to leave, you know, because churn is just the nature of any business, if you have a client that wants to leave, if they're extremely profitable, you can offer them a discount, you know, because now you know who's profitable and who's not. So you can potentially retain a client and say, Well, look, can we work something out rather than you you churning? Which again goes back to the point I made earlier. What's the point of data? It's to make better decisions. So to get that level of understanding, like this is a step, no one in the facting business has touched this. You know, they are light years ahead of their competition. And the reality is technology is an enabler that means they'll be able to eat everyone else's lunch, right? Because everyone else is trying to work this out manually or not even thinking about it at all, and they're already in the space age, you know, and and that really is the value of what we do.
SPEAKER_00:And how um are they able to measure any of that impact yet, or is that um further down the line?
SPEAKER_01:Yeah, so I think that's definitely a uh a longer piece that we we need to do, and we're taking them on a journey of um, you know, implementing technology, building a data platform, looking at their business more broadly, hopefully. Um and so you know they've got a journey to go on, they're not you know at the end result yet, but they are um able to be far more intelligent with their decisions and where they deploy their people and how they make decisions internally and which clients to take on. And it's just a fascinating case study in a in an industry that's probably not particularly data savvy or technology savvy, very kind of um slow to adopt these things, and um it's just revolutionized what they do.
SPEAKER_00:Yeah, I mean on the way up here we were talking about um case studies in general in terms of the impact analysis or the impact that certain technologies can have on on the you know on the business process that you're looking to improve upon or whatever. Um do you um well I guess I've got two parts to this question. Firstly, is um does it matter which sector uh a business is in when they come to approach you for help, or are you stronger in certain sectors? That's the first question.
SPEAKER_01:Yeah, so we we are we are s essentially um sector agnostic. So we've spoken to book publishers, social um value providers, our our main kind of businesses really is construction property and energy, right?
SPEAKER_00:They're the ones that discrete manufacturing type organization.
SPEAKER_01:I mean we we that that's purely just because we have a decent amount of domain experience in those areas. So those are areas we're stronger in, particularly for me in property, because obviously in energy, because those were fields I worked in uh previously. But the team has experience in different things, recruitment, um, you know, local authorities, etc. So there's a broad range of experience, but those are probably the areas but fundamentally the problems are the same in most businesses. Do you have good quality data? Is someone in charge of it? You know, what's your holistic view of your business? What's your cost? Right, exactly. So the problems are are very similar. However, obviously there are some areas that we can hit the ground running in.
SPEAKER_00:And I guess the second question which um uh which I had around that is uh do you build up case studies? Do you have case studies and of use cases, uh whether they're anonymised or um publicly public domain kind of organisations, do you keep that type of type of information and use it as a sales tool?
SPEAKER_01:Yeah, we do. So we're very you know, going back to what I said a minute ago, we describe ourselves as a technology partner and we have great relationships with the clients that we've either we either continue to work with or clients we have done one-off pieces of work with in the past. And if you go on our website, which you'll you'll get at headfordata.com, um you can see case studies. So I think we've got about 12 now or something, and there's tet there's about seven or eight testimonials of um, you know, client feedback, and you can see the companies and things like that. And you know, the testimonials are probably the thing that makes me the proudest in my business. You know, if you if you look at your you know, I'm very much a a person who likes impact of what we do. I get it gets me excited, and there's nothing more exciting than building a tool for someone and then them using it, you know. And you know that story 100%. And the in the story of the factoring company taking clients out and making their business more profitable, that's a great success story for us. And I could give you, you know, we don't have time to go through them all, give you ten more of those if you want. And and and because we have such good relationships with our clients, that we have fantastic testimonials.
SPEAKER_00:So do you do you have a an entry-level type of project size, or does it not matter you'll go to the ground level and build it up?
SPEAKER_01:What we offer, just f for for anyone listening who's interested, is we do a free half-day workshop, which we call intelligent futures, right? And and that's my sort of tagline for business going forward is right, the future has to be intelligent. If you're not doing that, as I said earlier, someone's going to eat your lunch. Because what technology allows people to do is with a smaller team, do more work, right? And so with a lower cost base, do more work. And then the next level is the lower prices for your clients, right? And it's not it's not about a race to the bottom, but it's ultimately about you know business is the is unit economics at the end of the day, and if someone else can do something for half the price you can, that's where people are going to gravitate to. Clearly, there's a quality element, you know, and and quality matters too, but fundamentally that's that's something that that happens. So what we do is, and and and you're more than welcome to reach out to me and and get more information, is we do a half-day workshop where we come in and we do a sort of mini gap analysis in your business of where you are today, what's your aspirations, because not all of these products we've talked about are right for everyone. We've had a conversation with a company recently who I felt could have benefited from a data platform, but they didn't really see the value in it. So we don't push it because you know I'm not here to build things for people they don't want. They wanted a very specific tool to solve a problem, which they felt was AI and I think can be done in traditional process automation.
SPEAKER_00:Right, okay. Okay, so that's good. So um, and then from that you might identify a particular process or part of their business where you can help and and maybe do a POC or a proof of concept or a minimal viable product or pilot or any of those things.
SPEAKER_01:Yeah, exactly. So that's that's generally what we're trying to do is look at do the gap analysis, try and find areas we can potentially help you. And of course, if there's none, that's fine. But if there's areas we can help, then we we would obviously then hopefully build a relationship with that client and do more work with them. You know?
SPEAKER_00:Great. Okay. So um best places to reach out to you, Colin? You talked about your website, Head for Data. Do you know the social social channels, social media?
SPEAKER_01:Yeah, so so you'll get us on uh headfordata.com. I'm also um active on LinkedIn under my my name Colin Parry. Uh Head for Data has uh an active LinkedIn page as well, which you can search for us. So it's Head for Data, not a for. Um although I do own all those domains, so if you type that in, you will get redirected to the right place. Um so you know, that that that's the best places to find us.
SPEAKER_00:That's great. That's been really interesting, Colin. And uh hopefully for the listeners, they've now got a more of a grounding and a kind of baseline understanding of the mythical AI that everybody talks about. Um it it's here, it's here to stay. Uh it's gonna completely change our universe over time. I think everybody kind of recognises that in some way or form. But just getting that kind of baseline understanding right from kind of ground zero, I think is a you know it is a place everybody needs to start. Um and unf you know uh the industry doesn't do a good job at that, right? It doesn't do a good job of educating people, um, you know, like you've done with us today. So thank you very much.
SPEAKER_01:No problem, thank you for having me.
SPEAKER_00:And uh yeah, don't be a stranger, come back and uh visit us again.
SPEAKER_01:Yeah.
SPEAKER_00:So thank you, Colin, for uh coming in today. Uh Colin from Head for Data and uh talking about all things AI, uh really kind of simplifying what it is and why we should all be uh interested in uh kind of looking at it. So uh join us again on the Sterling Business Podcast from Studio King Street very soon.