Nic McMahon, CEO of United Language Group (ULG), joins SlatorPod to talk about the language service provider’s (LSP) mission in connecting language and culture to create meaningful outcomes.
Nic recounts his journey in the language industry, from junior project manager at SDL to ascending to the role of CEO of ULG. He highlights ULG’s goal to go beyond translation services and prioritizing outcomes, cultural connectivity, and bridging gaps for better accessibility and engagement.
The CEO explains that one specific area driving demand for ULG is the welfare cycle of non-English speaking communities, where the LSP aims to ensure access, equality, transparency, safety, and privacy in their language services.
When working with municipalities and private sector healthcare providers in the US, Nic emphasizes the importance of understanding cultural influences within the target audience’s network. Nic shares that HIPAA is a significant factor in the healthcare industry, particularly in relation to translation services, and is intertwined with transparency and patient safety.
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Nic discusses how ULG uses workflow automation, translation memory, and call management technology to help remove manual processes and improves the efficiency of translation and interpreting cycles. AI and machine translation have seen rapid adoption in recent years, with AI-driven neural machine translation (NMT) being used in 60% of projects at ULG.
The pod rounds off with ULG’s outlook, where they are particularly interested in leveraging AI, such as optical character recognition, language recognition, and NMT to enhance outcomes and engagement.
Transcript
Florian: You’ve been in the language industry for quite a bit of time and you’ve worked with VIA, SDL, Jonkers, Lionbridge. Tell us a bit more about your time in the language industry and how you came to lead ULG.
Nic: It’s been a very interesting journey. I started off as a junior project manager for SDL right now, obviously part of RWS, but all of it ultimately was sort of accidental. I didn’t really know what I wanted to do when I left university. I liked buying things, so I got a job buying things. I went into procurement and then the guy next to me was in project management and he seemed to have a really easy life and relaxed. It was a very different industry. And I was like, wow, that project management job looks awesome. So I looked for a project management job and I got one at SDL under Mark Lancaster and Dominic Kinnon. And then from there, it requires a very good industry. You get involved in a lot of different cultures, a lot of different languages. I’ve got to go to a number of different countries and it’s been a really, really good industry. And I’ve just managed to sort of navigate through the various stages. I was project manager, account manager, senior account manager. I was in charge of national sales for the US for SDL. And just one thing sort of led to another in terms of opportunity the industry brought to me. And I was lucky enough to be in the right time at the right place and get the opportunity to progress right through to becoming CEO of ULG.
Florian: That’s amazing. Junior PM to CEO of an $80 million company, so to all the PMs that are listening. Tell us more about ULG. Probably the extended elevator pitch, because it’s not a startup. So just tell us more about the company. Where is it based? Kind of key tech, key services, key value proposition sectors, etc.
Nic: There’s obviously a lot. It’s not like we’re a huge company. We’re relatively complicated at $80 million. So there are a number of different things we do. But effectively, it’s language and cultural solutions. And I think when you start thinking about where the industry is going and what is the evolution of the industry around AI and all of those topics that a lot of people are touching on right now, it’s the concept of language and culture. And we try to connect language and culture to create more meaningful outcomes. And so for me, language has always been a proxy to the end goal, right? We have an internal analogy that we give, it costs a million dollars to do a Super Bowl ad. So how much of that million dollars went into putting it in English? And the concept, of course, is none of it was in English. So that’s a million dollars on engagement and cultural connectivity. And as an industry, because the language has been so expensive and complicated and difficult. And as a project manager, I worked on many projects that were complicated, difficult, time-consuming. By making, getting it into English the hardest thing, or French or Japanese, my ability to really then connect and engage with my audience, I’m all focused on the outcome diminished because I had to put so much effort into getting it into language. So it’s like, I take that million-dollar Super Bowl ad and I charge you $970,000 to put it into English, and then you got $30,000 to do engagement and connectivity. And I think as a company, United Language Group, that’s what we tried to go beyond words alone, right? And we tried to go into outcomes and cultural connectivity because I think that is ultimately what people really want when they think about language. They really want to connect, they want to engage, they want to build accessibility and bridge gaps.
Florian: You mentioned accessibility and also maybe kind of segue to that language access topic in healthcare because I know that healthcare is a big focus of ULG. So tell us more about the healthcare business, but also for those of us who are not very familiar with the super complex and super large USÂ healthcare system, what parts of the healthcare system are really driving LSP demand and how is ULG kind of meeting that demand?
Nic: Effectively there’s a couple of things. One is from a ULG perspective, the way that we look at is that we do different verticals, right? We have legal, we have enterprise, but a very interesting vertical is what we would refer to as the welfare cycle of non-English speaking communities. So if you sort of think from a cultural basis, you come into a country, whether that’s in the EU or it’s within the US. You bring along with you not just a language, but you bring a culture and we refer to it as a cultural node network. And there are different elements on that network that influence your behavior. So I’m English and even today I look at the BBC and so it’s a source of data and a source of information for me, for whatever reason, I’ve chosen to trust as a point of data. But that’s part of my cultural node network as there are different things that drive the way that I interact and engage with different services in community. When you think about United Language Group, we’re welfare, it’s a welfare cycle. So, educational systems, we do a lot of education and cities and states, we have over 1000 city and state customers and social benefits and welfare, we cover all social benefits and welfare for a national level, health-care providers, insurance providers. So every single aspect of the welfare cycle as a non-English speaking immigrant or citizen of the US or the EU, we cover that group. And what you start to see is access and equality in many aspects within community is ingrained within system and process. And so, when you start to think about healthcare systems or medical device systems, transparency, do you really understand the services that are available to you? Do you know how to access them? Do you know what they entail? Do you know the risks involved? Safety? Do you really have the right level of information and support to really understand what type of risk you’re taking as an individual by pursuing some of these solutions and services and then also privacy? It’s huge. Especially when you’ve got such a complicated US healthcare system. It involves billions and billions of dollars. Your information and the passenger information can have a profound impact on your cost of insurance. As a healthy family, I pay about $1,600 a month in healthcare insurance between my contributions and the company’s contributions. It’s an enormous cost. Yeah, it’s crazy from a European perspective, when I moved to America, you just can’t believe it. So, if suddenly there’s a piece of data around me because I got involved in a clinical trial, or because I took a certain sort of medicine, or because I have a certain sort of procedure, that information going through can have a radical impact to the future cost of those services to me on an ongoing basis. And so, patient safety, transparency of information and privacy of data are three massive driving forces within healthcare, but also the broader welfare bubble that all of these things come together in.
Florian: Within that you do both translation, like written translation, but also very heavily in interpreting, right?
Nic: That’s right, so we hear what patients are concerned about when they’re dialoguing and communicating with insurance providers or healthcare plans. We see what healthcare plans translate and the support material. So let’s say I translate a bunch of diabetes content. I also have the data to tell me whether diabetes is actually a concern on the phone and in dialogues and communication with those healthcare providers. And when you think about an outcome, availability of that data and support material, as well as how it’s used, is it visible to an individual and is it the right content has a dramatic impact on the outcome of that patient.
Florian: When you work with the municipalities, the cities versus the more private sector in the US healthcare providers, what are some of the key differences? Maybe starting with, I don’t know, public sector RFPs versus private sector RFPs and then delivering the service.
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Nic: I’m bashing your question a little bit. RFP is like literally the least interest of the entire cycle. So RFPs generally, they’re always way too long for everything, right? Every now and again we’ll get one that is very focused and really talks about a need and a challenge. And it’s super useful from an RSP’s perspective because you can be a lot more specific. Most RFPs are like, tell me everything about everything. Oh, that’s a good question. Let’s add that too. Governments are even worse. So if you’ve got 60 questions on a private sector, when you go to government, you might have 600 questions and they’re really just trying to cover every single base. But I think where it gets interesting is that, ultimately, everybody looks for how do I engage and get a meaningful outcome? That’s what they want to do when they ask these questions. What they’re really trying to do is how do I build my French audience for Dyson as a Hoover manufacturer? How do I build my Japanese audience for a tractor? We work very extensively with John Deere. They do a lot more than just tractors, but that’s what they’re known for. And then also, how do I make sure this immigrant finds a home and meaningful welfare and support within a city? Engagement is the common factor. I then go out to RFP and go, I don’t really know how to engage a person, so I’m going to ask you a bunch of questions around translation, but I really want to engage that person. So, then when we sort of do it, we start off with health care outcomes and we start to say, like, okay, interesting. When you say to somebody, please take this pill, they don’t take the pill, factually. And then you say, okay, like, please have a follow-up visit. Follow-up visits for non-English speaking patients are something like 10% of the follow-through rate of English-speaking patients. And so why don’t they? You’ve translated it, you’ve asked them to schedule the visit. Why don’t you? And it turns out that it’s within this cultural node network that defines the fact that they have concerns or previous experiences or doubts about the US healthcare system. They have financial issues around the healthcare systems, educational issues. And you start to realize the cultural node network really has a dramatic impact on the way and what you translate and how you approach them. So, for instance, in healthcare, we start to say, rather than, ‘you should take this pill to get better, Florian,’ we should say ‘you are a meaningful source of income for your family. And for you to retain the value and long term earning potential of you as an individual, you need to take this medicine so you can continue to work and support your family.’ And for some cultures, that’s a vastly more effective message. Which means they take the pill.
Florian: It’s like working backwards. I wasn’t familiar with this way of looking at it in this particular context. But of course, we’re all like hyper familiar in the sense of marketing, right? You’re looking at what do you want people to do, and then, well, if it doesn’t work across cultures, you’re going to kind of reinterpret, transcreate the message to make sure people have that desire. You push people into that desired behavior. Now you’re taking it in the healthcare space. So this is a much more kind of advisory approach to the client as opposed to what did you said? Just basically doing the translation. Like you will have to be aware of all these concepts and these cultural differences and then advise the client to adjust the language accordingly.
Nic: What we found is on the enterprise side, you start to sort of think, wait a minute, everybody wants engagement. So we start off with healthcare and we’re like, oh, okay, that makes a lot of sense. And we got four times member sign-ups, a very large global healthcare provider or national healthcare provider, but they are a global organization – the largest in the world. In fact, tens of billions of dollars. They’re struggling to get a community to sign up with them: a non-English speaking community. We change the messaging. We change the way that we distribute that content through a different media outlet. We then do meet and greet with trained resources to talk them through the options of their healthcare plan and why it’s worthwhile to them from a cultural – so their first point of contact is in culture – four times the rate of member adoption. So we’re like, wait a minute, that’s a big shift, right? That’s where we started to change the dial runners just providing this translation. We were like, okay, we could change the nature of what they were trying to do to create engagement. Recently, we sort of looked at this concept. If you’re selling tellers in America, you do July 4 and Super Bowl. If you want to do that in Mumbai, Mumbai buys on feature and function. And then maybe in the old days you’d be like, oh, maybe it’s soul transcreation, but really it’s like do you have the right content and materials before you do the translation and the outreach? Do you have the right materials and the right content to do engagement and involvement of the audience, the target audience?
Florian: Then who does that within the organization? I mean, is it kind of a solution architect role that would take debris from the client and then proactively propose some of these value add solutions?
Nic: It’s very interesting. It’s project managers and program managers and solutions architects. So it’s a team. When when you look at something, basically we need a group of people to come together to like look at the different aspects of the requirement. And I would say, honestly, just to be really transparent, it’s not like this works and it’s awesome, but we’re probably about 30/70%. So 30% of our people now start to look at solutions and how to create language solutions around engagement and interaction and 70% of staff is still really yeah, it sounds awesome, but we still would like you to translate that stuff.
Florian: It’s always the case, but you got to proactively go out and tell the clients that there could be more. Right? I wanted to ask you about this. I’m not sure if you know a lot about this, but I keep hearing HIPAA compliant and HIPAA compliance in the context of translation, again not being US-based, sometimes I’m wondering what that is. Is it a thing in the language industry and in what way is it a thing and in what way does it or does it not drive demand?
Nic: It’s wrapped up and you’ll see a lot of similar proxies too in life sciences, but it’s like, it’s wrapped up with the transparency and patient safety and so basically a good example, but it’s actually a medical device. We’ll give you a sense of what’s happening in HIPAA too, but you have to give in a clinical trial, you have to give guidance of what’s actually happening, what are the actual results. And then there was a new requirement from the UA that you have to give like a lay perspective of that because otherwise it’s like drug xxyyyz54 was patient tried on 54 trial patients within this ethno demographic. And you could read like the 54 pages of regulated content feel like, what does that mean? What they really need to do is sort of say like, hey, we think this can treat liver cancer, but if you take it you might die, right? And they need to give a much more simple version of the risks and challenges involved in a process. And so when you look at HIPAA and you look at support within the healthcare industry at large, they’re trying to be more transparent and provide greater detail around what’s happening to your data. Where does that data flow? Who’s using the data? Who has access to the data? And then they’re trying to provide that level of transparency so everybody within healthcare can start to get a bit more of a conscious decision about managing their health care decisions and having access and control over their health care data so that it’s not basically effectively becomes used against them. I guess it’s interesting, somewhere here on my desk, I have like 23andMe – the DNA test – and I got it for my birthday a year ago, and I still haven’t processed it. And I would like to because I really want to know my background and I love culture and all of that stuff. So it’s fascinating to me, but I’m still like, oh, but what happens when they tell me all my health care data? Who’s going to get it? And I think it is very secure. They’re a very robust company, but it humanly, makes me nervous. And HIPAA regulations are required to make sure that informed consent documents are clear. Like you understand it, you know, what’s required. And then increasingly, and this is a good link back to your question on the public sector, too. Companies want to do the right thing, right? Healthcare companies, they really do. They want to make patients better. But the problem that healthcare companies have is that they have 80 English-speaking patients that are American and then 20 other language groups. And so the prioritization keeps getting pulled down. The government sees that there’s disparity around COVID, right? They see negative health-care actions with HIPAA, with the EU regulations around medical device labelling and things like that. You see this move from governments to say, we actually need to prioritize it and change the priority in society, because if we keep leaving it up to the entities. It’s not that the entities don’t care. They do. I honestly do believe they care, but it’s a priority number six. So the reason you’re seeing regulations, and it’s fascinating also, we’ve had like a 300% increase in grant work because governments and city entities are going to force accessibility, transparency, proper, realistic, honest data. They’re going to force that through regulations into the healthcare sector.
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Florian: What do you mean by grant work?
Nic: What happened, and everybody knows this, but what happened in COVID, right, is it turned out the people that don’t speak English were getting stiffed by the system, right, because they didn’t have access to information. The information wasn’t clear and transparent. It wasn’t detailed, it wasn’t depth. And you know, from years that I know, from the years in the translation industry, I still think it’s like 5% of content is translated. There’s some ridiculous amount. In healthcare and welfare generally that matters quite a lot, because when there was a public health emergency, all of a sudden, people were really not getting equitable service, and that led to inequality in terms of deaths and negative outcomes. I mean, it was a very serious thing. So then the government starts to look at it and say, like, we need to improve data. We need to improve transparency and data availability. And that’s the regulatory stuff you’re talking about. But they also say, well, like, wait a minute, we should look at why. Like, why is it disparity? So part of the reason for disparity is that you need someone to help you navigate health care programs, but who pays for that navigation? Who pays to inform a Latino community in Florida about what’s really available to them and how to navigate a health-care plan? When they’ve just come to a country where they didn’t have private health care, all of a sudden they’re like, oh my God, what is this? The government will fund that through grants and the government will look into how can we bridge these gaps? What available technologies or what available solutions are there to bridge these gaps? And they’ll put very real money behind bridging accessibility and equality gaps in a way that private entities, they would like to and they do fund some of it, but not with the same priority that they fund the 80% audience or the general audience.
Florian: This is in the US. It’s funded by like city level, kind of sub-state level, or maybe state, and then below.
Nic: Biden too is in language access support too. Basically, you can go in and look for state and national grants, and you can pursue those state and national grants. And in many ways, they provide a very meaningful source of bridging inequality. But you’ve got to want to solve inequality. You can’t make money on a grant, literally, you can’t. But what you can do is you can bridge equality and you can, sort of, create services and support. And what we found commercially, just to be really clear, is if we do the grind work, it provides a basis for a better and deeper understanding of the overall inequality of all, which we can then get more commercial value with private enterprises.
Florian: Let’s briefly talk about technology. I know you have your own TMS, you call it Octave TMS. Can you just take us a bit through the, for lack of a better word, to stack, like TMS CAT interface, like translation productivity interface, machine translation, and maybe if you want to also branch into the interpreting side of things, just walk us through a bit how you solve the tech problem there.
Nic: I don’t think that’s the tech problem. Workflow automation is super useful and super important. And then integration of workflow automation is super good and super important. So if you’ve got a bunch of stuff you’re translating, the amount of manual processes you can remove from that help greatly because it increases the efficiency of a translation cycle, which hopefully would then empower you to spend more time on the engagement activities. If they’re not at engagement and they’re really still like, hey, I just spent a million bucks on this advert and I’d like you to translate it, or a million bucks on this content and I’d like you to translate it. You want workflow automation because certainly in a top 20 level, like, every single person is going to have some level of workflow automation. And in interpretation you have a call management technology we use inContact – NICE inContact is a third-party software, but, basically, that allows you to connect to everybody on an automated basis. You got to connect within seconds to thousands and thousands and thousands of calls. And so you need a technology that does that. You also have workforce management systems that look at the demand for Spanish interpreters every five minutes and then can say like, hey, you’ve got to find some more interpreters and bring them onto the platform. But they’re really table stakes if you’re a small-scale company. At VIA, we were at like a $12 million company sold into ULG. Developing something like that is a big deal and a big step. Top 20, you must have workflow automation. I can’t imagine anyone could operate without workflow automation. And then also translation memory. I think AI is an interesting technology, right? It’s the thing that starts to drive a very interesting change of movement within our industry. And so I think that’s the next level on lock-in. But Octave is awesome as a workflow. You should definitely use our translation management system versus all those other guys. Their workflow stuff is rubbish and ours awesome. But it’s really table stakes. You should automate your workflow and you should integrate your workflow. And that makes a lot of sense when you’ve got regularly requiring translation or interpretation jobs. But I don’t think it’s really going to be the stuff that moves the dial for an internal language group or for someone that’s trying to engage and connect with a global audience.
Florian: What’s not yet table stakes is LLMs and chat GPT. What are your thoughts on that? For us here, it’s later, it’s been quite the ride for six months trying to figure out what it is, what can it do, what will the impact be. What are you seeing out there in practice and production?
Nic: I always think it’s the problem, you live in interesting times. I feel like I live in slightly less interesting times right now. I read a thing the other day. There’s significant people, including some of the Google developers, have signed up to say that AI is an existential risk that could lead to the extinction of the human race.
Florian: That one’s a little odd, I got to say. That’s a little crazier.
Nic: It’s crazy as an individual to some extent, and for me, you’ve already lived through a portion of this too, right? So before AI, less than 2% adoption. My head is like three years ago, less than 2% adoption of machine translation for us, right? There were niches, I think car user manuals were great test examples for statistical and rules-based, but it was less than 2% adoption within three years we’re now at 60% adoption of projects being touched by neural machine translation, AI-driven neural machine translation in some regard. And you think from a business change point of view, it’s like that’s fast. I’ve never seen a technology like be adopted in that rate and speed. In the early days, we have a dedicated AI team, it is awesome. And in the early days we definitely trained the AI. It was no problem, like, oh, super new, super bright, intelligent student, and we’re training it, it trains itself. It’s really a remarkable technology and I do think it’s going to create a sea change in terms of what’s possible. When you start to look at ChatGPT three and four and you start to see the concept of in three, it’s linear model. And in ChatGPT4, I’m going to be able to do cross-general application of artificial intelligence and start to say, like, oh, I can take something I learned about this other task and apply it to this new task. I think it’s a revolutionary technology and I think the concept of regulating it is very accurate. I think somebody should stop and think about it and sort of work out, okay, how do we put confines around it? But ultimately that’s a longer conversation, but I think it’s very worth thinking about regulations. But for us, on a much simpler basis, over the next couple of years, we’re using AI. What AI can do for us is help to identify components that can allow the neural node network to be travelled from one culture to another. So we can start to say like, okay, based on conversations that are happening, what are the major concerns of the Vietnamese audience within Miami? And it can take a whole range of data and start to sort of give us guidance and say, you know what, this is what concerns that target audience. And then that unlocks our ability from a human perspective to sort of say, okay, if they’re the major concerns, how do we best interact with those major concerns and sort of try to find a balance that AI can unlock the data and the concerns and give us guidance in terms of how you can navigate. This cultural node network has about 60 different nodes in it. If you think about education, income, education level, history with a given part of the welfare cycle, it can be very complicated. So if you’re feeding it in all sorts of data, an AI is very good at unlocking that and starting to say like, okay, like cross-culturally, this is what I think, this is an area, I think you can look at as an area of disparity. So I see tremendous potential with AI. It’s very, very exciting for us. I think it can really shift the dial away from, like, how do I get it into French, into what is that French doing for Vietnamese people, Polish people? Immigration across the world is a very interesting area for us, so we’re excited about it. But AI, at a human level, I think it’s fascinating. It’s crazy. What it’s capable of?
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Florian: I’m using it for all kinds of tasks. Not really kind of like linguistic tasks, in the sense that I don’t use it for summarization yet or translation or anything like that. It’s more kind of like more mundane, like reformatting or kind of giving me ideas tasks. So, yeah, also, I wonder how it’s going to get used at scale with these volumes that we’re dealing with in the language industry, talking about millions and millions and millions of words. I don’t know if the API can handle this just yet at a cost-competitive level, right? All right. Yeah. We could talk about this whole ChatGPT for a long time. It is revolutionary, and it is going to impact the industry in one way or the other. But so far, if you draw to the experience we had with neural machine translation, you said it. I mean, I remember back in my LSP days, it was like 1% or maybe 0.1%. That was, like, empty. It just wasn’t useful, right? And then with neural, you go to like 50, 60, 80% of your business going through it. So I think the language industry is quite good at adapting to this. So I wonder how well it’s going to adapt to this one.
Nic: The concept of translation. We’ve got to get the price point of translation down. I’m not saying, like, let’s stiff all of the suppliers. We don’t do that. We have a fairly good relationship with our supply base. But for us to get up to full transparency, full visibility, full engagement, and then the ability for me to move my focus from getting it into Japanese to be, does this really connect with and speak to a Japanese audience? I need to get the cost of translation down in a very real sense, because then it can become my million dollar ad, let’s say, and then let’s do something more realistic, which we actually work with. We don’t work with the million dollar ads. We do like learning content. So if I’m looking my learning content, what I really care about is, does this connect to a Japanese audience who’s trying to understand and engage with harassment content? Does this really speak to them and does this give them an opportunity to provide harassment feedback? Am I engaging in the right way? And if right now, if I’ve got my, let’s say, $15,000 to translate a learning course, that’s $15,000 on translation. I’d much prefer to spend $1,000 on translation and then $14,000 on perfecting that course and creating course and then using AI voice to text to create the audio overlay to create a more compelling course in Japanese. And what we found is that customers are very focused on their core market, and there’s a lot of value in their core market, and there’s still new features, new benefits, new content they can bring to their core market, but they kind of want to put the other languages for someone else to help deal with them. So if we say, like, okay, we can adjust your course and we can make it more applicable or more engaging in that target market, that’s a service they’re still willing to use LSP for because they don’t have their own internal Japanese team or guidance to give them that feedback. They do have a sales team. And when we first started, I was like, well, the sales team could do that. The sales team doesn’t do that. The sales team sells the available product. So if you want to shift that product a little bit, we found a lot of people are pretty open to the idea that we could help them from a language perspective, drive engagement. But to do that, you’ve got to reduce the cost of translation. And I think for linguists and things like that, the more they can sort of think about, okay, you’re a healthcare specialist or you’re a heavy machinery specialist, how could you bring knowledge and awareness of that heavy machinery and the target market and the way it’s sold and the way it’s communicated? How could you bring more of that value to the table rather than, yes, I put it into Polish?
Florian: I want to briefly touch on kind of MnA and funding and all of that in the language industry, half of which is kind of owned by either venture capital or private equity. ULG also has a private equity investor on board. How do you see the current environment also in terms of MnA? You did a few acquisitions in the past. Where do you think this is going? Does something like the LLM revolution make it easier or harder to talk to investors? And maybe if you can shed some light on your plans in terms of MnA going forward?
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Nic: We’re still a fragmented industry, right? And I think a lot of the investor money, is sort of, one is like the global economy, right? They’re all like pausing a little bit on the global economy to sort of work out what’s happening there and how is that going to shake out and when we’re getting past it. But there is a lot of investment money. And then the PE model specifically. I helped go public with SDL and we went to the London Stock Exchange. I’ve had a private funded, company owned company. I worked directly for Shannon Balance, who owned VIA as an individual, effectively an individual owner. And then PE. PE is a very attractive mechanism for people with asset and capital. So if you got a lot of capital, the PE model is a very interesting way to get into industries and spread bet your risk across different industries. And I think, like, the year before last, there was something like 6800% increase in private equity. So it’s like people can if you’ve got a lot of money, they can move it into equity funds. And it is a very interesting way for them to get involved in industries and in specific companies and sort of fund some level of startup or new ideas and innovation. So the available capital is very high. The global economy sort of sucks a little bit, right. And everybody, I think, is just sort of trying to work out, okay, what’s having a look? And then AI is basically sort of like, in my mind, everyone’s waiting to work out, okay, how does AI pan out? And once I know how AI pans out, I mean, I’m ready to invest, but they’re sort of looking the economy to stabilize and AI to come out with the future, and then I think they’ll be back to investing. And I think for us, as an industry, we’ve got to look at that too. We got to look at, okay, what is the right range of services and support and products that we utilize? Where do we sit in both the global economic outlook and the AI outlook? And then I think there’s no lack of money available.
Florian: One of the companies you worked for actually closed post ChatGPT announcement. I mean, Jonckers, that was in February or January when they announced it. That’s kind of post the November launch of ChatGPT. All right, so any excitement, projects, initiatives upcoming at ULG this year, next year? Anything you want to share?
Nic: Outcomes is everything for us. AI, it’s a super, super vague, but you can imagine why. But outcomes and AI are very interesting for us, and I see the two as very linked to be able to drive us forward. We got into a medical claims thing, which is a good example where we go we do medical claims. There was always a translation request. We use six different AI technologies on the medical claims. It’s no longer a translation request. It’s now a data process request. And so we still use translators to sort of oversee it, but it’s radically less. And it’s gone from $120 to process a medical claim to about $25 to process a medical claim. And so there’s opportunity in terms of how you can use AI and sort of shift the nature and focus. Because the point of the medical claim was nobody wanted a really nice, flowing translation of a medical claim. But it was like, I did need it into English or into French or whatever the target processing language was. But in reality, what I want to do was just confirm that you’d had a certain type of procedure that cost a certain amount. And I just really wanted the data out of it. So I used the translation as a proxy to get the data. AI has been able to come in and replace that. Now we use an AI platform to do a significant portion of that work. Or actually, it’s not one platform because you have to do kind of optical character recognition that has its own thing. You do language recognition like that has its own thing. Then you use a neural machine translation. So it has a bunch of different aspects in it. But I think they’re the type of interesting things for us. That’s where we’re very focused. Welfare cycle is very meaningful to us. Beyond the welfare cycle, every company needs some level of outcome and engagement, and AI can drive that for us, and that’s what we’re pursuing. It really makes very big differences. But it’s not about traveling more. It’s about engaging more.
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