Part 5: Deb Ashton – The Billable Hour Is Running Out of Road

Unlocking Value: Navigating Tech-Driven Change with Deb Ashton Here's something I think about a lot. Most of us who run professional services firms only ever see one firm close up, and that's our own. The people in this miniseries see hundreds. I'm John Howard, the host of Unlocking Value, and this is a six part miniseries about how to navigate the huge technology driven change that's going on in our sector. Most of the guests in this series are founders and leaders of companies that build the professional services automation software our industry runs on. That means we're getting first hand insight from people whose systems support thousands of professional services businesses and 10s of thousands of users across the world every single day. These are conversations about a time of huge opportunity and some significant threats emerging in our market. As you'd expect, we talk a lot about AI and technology, but they're mostly conversations about what's driving growth, how client expectations are changing, and what the most valuable firms of the future will look like. The series starts somewhere you might not expect, with someone who is at the leading edge when technology turned a very different industry upside down long before it reached ours. Wherever you are with your own firm, I think you'll take a lot from these conversations. Thanks for tuning in. I'm joined on this episode by Deb Ashton. Deb Co founded Certinia, a professional services software business, back in 2009, and today she runs its customer experience team. So she spends her time hearing directly from services firms of every kind, from teams sitting inside big technology companies and large independent consultancies, plus also from the Advisory Board she runs across the world. Early on, Deb made a point. I keep coming back to that. Services and technology firms have spent years helping everyone else through change, and they're now on the receiving end of it themselves. So this is really a conversation about how professional services firms are having to change as businesses, not just the technology they use. And Deb hears that from a wider range of firms than almost anyone. One thing we get into is how firms charge. Speaker 2 For what they do. Speaker 1 The slow, hard move away from billing for time and towards pricing for the value you deliver. Deb tells the story of one firm that took the best part of 15 years to make that change, which tells you both how worthwhile it is and how hard we get into why she thinks the firm's pulling ahead aren't the ones with the cleverest tools, but the ones with the cleanest data and the most disciplined ways of delivering their work. It's a conversation about a sector that spent its life helping everyone else through change and now has to do the same for itself. From Salesforce ISV to Global PSA Leader Without further ado, here's Deb Ashton. Speaker 2 Deb, welcome to the show. It's great to have you. Speaker 3 Hi there, John. Nice to be here. Excited to talk to you today. Speaker 1 Oh good, one of the reasons I've been. Speaker 2 So keen to have you on the show is that you bring a really kind of unique perspective, I think on professional services market. As one of the Co founders of Certania, you've spent a lot of time working with services businesses of all different shapes and sizes. And in your current role, you're particularly close to the voice to the customer. So, yeah, I'd love to as we go through conversation, explore what you're hearing from conversations from senior leaders right now where you see services firms feeling pressure, what they're excited about, what's changing and how they operate and deliver and linked to all that kind of what that might tell us about the future of professional services and as as a sector, but. Speaker 1 I guess to get us going, could you just kick off by giving listeners a bit of a background on you and the work you and the Sartinia? Speaker 2 Team are doing now. Speaker 3 Yeah, absolutely. So, yes, thank you for the warm introduction. I am responsible for customer experience here at Sartinia. I am one of the original founders like you mentioned. So I've been with the company now since 2009 and started it with our founding CEO. When we spun out of a large international European headquartered business called Unit 4 back in 2009, where we'd started building the very first. We were one of the first Isvs to build natively on Salesforce as Salesforce were launching their force.com platform. So that's kind of how we started from financial management. We then grew into the professional service automation space. And from that beginning point, I was responsible for management engineering customer support globally. And I also started the customer success discipline for what was financial force, which is now Satinia. And I based our customer success model very much on how sales Force run their customer success business as we were taking a lot of inspiration from sales force at the time as we'd started the business on theforce.com platform. So over the years as the business has grown, my role's changed. And then I did a a sort of a career pivot in about 20/17/2018 where I moved from running product management and engineering as I'd grown that quite significantly at financial Force. And I moved to customer experience and I actually started the customer experience discipline here at Cetinia back then. And I've been running customer experience ever since it and it's had different guises with different leaderships. But right now, my job is to understand the journey that our customers are on, the experience throughout that journey, look for where there are opportunities for us to improve the experience and then drive change here internally within Cetinia to ultimately improve and deliver the best possible experience to our customers. So I act as a change agent internally driving different programs and initiatives that impact the customer. And then I run different programs with our customers globally to make sure that we get that voice of the customer understood at a granular level and then sort of do a ton of analysis to understand what are the themes of the feedback that our customers are sharing. And then you know what do we need to do to that could be product related or service related here in internally at Satinia. Speaker 1 No, that's great. Thank you. And just to give. Speaker 2 Give listeners a kind of sense of scale and who your customers are. Like what size of PS businesses are you working with? Kind of how many seats if you will across like because it's it's not a small enterprise you're running. How AI is Fundamentally Shifting Professional Services So we help many different kinds of services organizations run their business. And so if you think about the spectrum of services type businesses, you have organisations with embedded services inside technology companies like Cisco, AWS, Salesforce, Siemens, they're all our customers. And then you have like Pureplay independent services organisations like consultancies or tax and advisory services. So like KPMGPWC, IBMACA, Cooper, Parry, they are all pureplay services organisations. They're all running their professional services on our solution as well. So, yeah, I think that that's some examples, but we really support, you know, all the way from small businesses, which we classify as, you know, 50 employees to 200 employees and then to the mid market up to 1000 employees and then, you know, beyond. So some of our largest clients are running, you know, 100,000 consultants on our professional services cloud platform. Speaker 2 No, thank you. I think this kind of gives context of, you know, that when you say voice to a customer, there's actually a huge diversity in the kinds of voices you're hearing, but all kind of centred in the professional services space, which which I think is really good. So when you're talking to your customers advisory boards, I know you work with and have a have a specific role around advisory as we were senior people across the industry, I suspect you're hearing lots of patterns. When we spoke previously, one of the things that stuck with me was you said you kind of feel like services and technologies businesses have spent years helping other organizations manage change, but now are kind of on the receiving end of some of that themselves. So that kind of feels like a good place for us to start because this is bigger than a technology conversation. It's really about how PS firms are are changing as businesses so. Speaker 1 From the customer. Speaker 2 Conversations you're having, what are you hearing that makes you think services businesses are now kind of having to transform themselves? Speaker 3 Yeah, very pertinent question to I think where we are right now it you're right. We, we run executive advisory boards globally with our top most strategic customers in region. So we have probably about 50 different organizations represented globally. So when I say globally, we run an Advisory Board in North America, one in Europe and then one in APAC out of Australia. And those organizations we meet together in person twice a year. It's a private meeting and we discuss what are some of the top topics that challenges business evolutions, industry trends, market dynamics. You know, it could be any, any of those topics that we're exploring during those meetings and we're really showing up sharing, you know, thought leadership experience and things like that. And what's been really interesting over this last 12 months is the just the huge shift in conversation primarily driven with the evolution of AI and how AI is becoming is gone from an exploration of what can it do to now, you know, 12 months later, organizations are leveraging AI, they're deploying it into production environments. They're seeing changes to their organization and how they operate as a result of that. And, and as organisations are, are sort of exploring that, it's kind of like fundamentally shifting the way that we all think about work. And I think especially in the services world, the AI tools and platforms are are just creating this shift in how they operate, how they deliver work to their customers. And then the demand from customers is changing as well in how services organisations show up and deliver work and the revenue models behind that. So, yeah, so I think that's what we're seeing in in terms of some of the the sort of the transformations in services organisations today. Quantifying Value: A 15-Year Journey to Outcome-Based Pricing So when they talk about the kinds of work and the way they're doing, things like are there. Speaker 1 Emerging kind of patterns of what people are. Speaker 2 Doing that they're adopting, are there common threads of things they're struggling with as they look to change, I guess how, how are they finding that as a, as a set of leaders that you're talking to? How are they finding that transition? What, what's working? What are they struggling with? Speaker 3 So I think what we're hearing about what's working from the customers that we've been engaging with, those businesses that I mentioned earlier is everyone is able to find strong use cases to leverage AI platforms integrated with business systems to drive efficiency of the human workload. So leveraging this sort of human AI working environment and can to successfully leverage AI to significantly reduce administrative overhead of work. And there's, if you think about a project manager creating project plans, statements of work, that kind of thing, a lot of that can be automated leveraging AI tools. And so that I think that's, that's something that's that we're seeing organisations make progress in that direction. Certainly as a vendor in this space with Satinia has recently released a new AI platform called Veda that powers professional services automation and helps us unify that flow from sales through delivery, finance, customer success. And that ability to sort of automatically generate some of that documentation is something that we'll be able to help organisations do. We are helping organisations do as well. So I think that's one of the areas that seems to be progressing well. One of the challenges that we're seeing, you know, where we're working with our customers and what our customers are doing is this shift from traditional time and materials pipe work, fixed price work to outcome based models or value based models. And I think this is a challenging area for us all right now because our customers and our customers customers are pushing for more outcome based pricing of services or value based pricing of services. And it's very difficult to quantify and where timing materials is easy, you know a huge shows up, they deliver some work, they fill in a time card and you bill for that time at an agreed rate. And leveraging AI just changes that whole paradigm because something that might have taken a human five days work and AI agent could do that in minutes. How do you build for that? So it's no longer really about the billable time. It's about the value that you're delivering through the deployment of that technology solution or services capability. And so that fundamentally shifts the conversation. And but the like the measurement of the outcome is the challenge that we're all facing right now and a lot of the organizations we're talking to is facing right now. Speaker 1 Well, if we stay. Speaker 2 On that for a second, I guess lots of people we talked to facing that same thing. I think it's, it's how do you and there's lots in that. It's like what's the right and fair price? What are the costs? Because AI stuff isn't free. So kind of how do you bundle it in? How do you kind of price for value? I guess I kind of feel like everybody likes the idea of value based pricing, but doesn't necessarily when you because we've all relied on time as a proxy for price, at least for so long. It can be a bit uncomfortable when you can't see things backed up in that. Are you seeing examples of people who've kind of cracked how to do that, or at least cracked kind of frameworks for how to think about pricing and value in a different way I. Speaker 3 Have seen a couple of examples where customers have started to build solutions around that I don't want to. I'm not going to mention the client's name. Speaker 2 No, of course, yeah. Speaker 3 But one of our Advisory Board members actually presented the last board meeting a how they have implemented and rolled out outcome based pricing and they and the methodology that they had built around that, they shared that with the other board members. So this was actually a transition that this organization went through over about 15 years whilst they collected the data from their customers from running implementations And what they realised from different customer conversations where they were creating negative sentiment with customers because they were always getting into this negotiation around the price for time and materials based invoicing. And in one particular negotiation, which is what drove this transformation. In one particular negotiation, they actually changed the conversation from discussing the price and the hourly rate to discussing the value that they were going to deliver. And they pivoted the conversation from a negative conversation to a positive conversation as they talked about value and value that they would deliver and that they would commit to deliver. They agreed a series of milestone payments and pricing based on that value and based on the ROI the customer was going to see. And it transformed the conversation. It transformed the sentiment of this particular customer that they were interacting with. And then based on that, they went into how could they build a methodology around that? How could they make that conversation a repeatable and scalable conversation over a period of time? And they said they created a series of tools and templates internally to allow the organization, the services organization to have these conversations with customers, to have this sort of value driven conversation. They built a scope, like a prescriptive scope that they would use with their customers and then a deployment road map for how that you could implement that kind of solution and talk about it. They branded the methodology so that they could track post sort of deal signing for the services work. This methodology would allow the team to then go execute the project in alignment with the original scope for success. And yeah, so that's how this particular organization went through that process and they managed that outcome value throughout the entirety of the delivery of that project. And what they found is those customers where they've delivered successful value outcome have been more likely to expand and grow within that organization, which is just a great result. Speaker 2 Yeah, Obviously you'll be able to do that. You've got to be able to articulate what the value that you bring is and what the impact and like, you know what, not what are they paying for in terms of time, but what are they paying terms and for in terms of impact the business. So I think it to be able to do that, you've got to get your head around what is the value you're actually bringing to customers. I think that's part and the customers have to think about what's, you know, beyond the reporter, beyond the system, what is the value I want from this engagement. So it forces a degree of thinking that I think is just different from the way we've I think we've all we've not been focused on value. We haven't had to be able to be as clear about it. And it's, it's a very different concept, which can explain why it takes more than a few years to get to something that works really well in a standardized way. Frontline AI Agents and Their Analytical Advantage One of the things you've, you know, I've spoken about before is what you've seen is the distinction between professionals, call it professional services management and professional services delivery. So it may be kind of the way you run projects as opposed to the kind of client facing delivery. And that's, you know, another kind of way. I think things are changing. Historically, a lot of technology in the services space has been designed to help firms manage how they work, the finances, the utilization, the forecasting, resourcing, all that kind of stuff. But it feels like technology is increasingly moving to being closer to the client work itself. So. Speaker 1 Just. Speaker 2 If I've, if I've kind of understood you right, like how do you see the difference between services management, service delivery and I kind of go from there maybe into some questions about how you see PSA changing. Well, let's start there. Speaker 3 I think we are seeing a bit of a blurring of the lines there between services management and services delivery. And if we sort of think about some of our most mature customers, they've built these integrated systems where they're using to run their professional services businesses. They're integrating with their sales and CRM. And then their financials as well for billing and and sort of GL on the back end. And that's allowing them to leverage AI tools to realise a ton of value from that integrated connected system. And I think that's that first of all, that's really important to have that backbone of like that reliable data backbone to actually build and leverage AI on top of. And then these the AI tools that you can then use as a professional, just focusing on professional services that you can then use. It's the agents are actually on the front line in these hybrid. We talked a little bit already about creating these hybrid teams with humans and AI agents to actually deliver the work. So today the direction we're seeing service organisations go down is that they're using these agents to deliver work on the front line, which is work that you know, you've got a clear process defined for it. It's it's repetitive work, probably normally given to a more junior consultant and the agent can go and autonomously deliver the work and get stuff done on the front end. And that's delivery, that is services delivery work that's being driven from your professional service automation tool. Speaker 1 You think I made an example of that to bring that to life about? Speaker 3 Yeah. So for example, if I just think about this just systematically and going back to services management, the professional service automation tool historically is there, You know, the humans are doing the work, they're inputting things into it like project updates, risks and issues, things like that. They're creating their project plans. And that's all sort of happening between the human and the system of record. And as we evolve, we have evolved our professional service automation tool from a system of record to a system of action. And so those agents now that are embedded within the Vader AI product, within our technology stack, they're actually executing work and leveraging all of this unstructured data to actually get things done. And so for the example, there's a few different examples, but I'll, I'll, I'll rattle off a couple. One for example, is our agent can sit within say Slack or Teams, which is like a communication tool. And it will be our project agent could be sat within a Slack channel that's been built for the project to everybody to stay In Sync with the project. And it will be able to understand conversations that are happening and identify risks and issues that have been described in the Slack channel following a meeting, You know, transcriptions of a meeting, identify the risks and issues and then automatically update the update the RAID log within the system based on the risks and issues that it's seen in the unstructured data, the meeting transcripts, the Slack channel conversations. So that's one example. Another example could be identifying there's a resource management agent that we've built. And the resource management agent can help provide staffing recommendations for a project. They can also identify resource conflicts across multiple different projects. It could advise so, So if someone's had to take a leave of absence, it can recommend alternative resources to replace that individual, but not just recommend them. It will actually make the system changes to replace the resources on a particular project due to some conflict or a leave of absence or whatever, which just removes just a whole load of like manual effort. So I guess that's kind of more resource management example there. The RAID logs more of a frontline example. They probably provided a couple of examples there for you. Speaker 1 You know, I think that's. Speaker 2 Helpful because I think on something like the raid log a there's a kind of just time saving that. But I, I think also, you know, if the, if the agent is showing up in all the different places, it probably sees connectivity thing across things that if you're not, if you're not in all those conversations or if your heads while you're in the conversation focused on one bit or you get distracted and you look out the window and you miss it. Like it's, it's got a ability to probably see threads and connectivity that the human probably would spot, but might not, or just might not be able to process the volume and then really get into. So that I think is a great example of like you can see how that directly, not just the uploading of of the stuff into the RAID log, but actually being able to help people think whether that's your team, that's the delivery team or the clients team that's receiving really think differently because they're seeing a big picture in a way that you just couldn't have because the Volume 4, particularly on something really big and complicated, I would think. Speaker 3 Yeah, yeah. I mean 100% right John? The amount of data that can be analysed and consumed in a consistent manner using agents is and the speed at which it can be done is phenomenal compared to sort of how quickly humans could get through the same analysis of data. And you know, humans are more likely to get hit with fatigue or distracted from, you know, something else on their desk hang up. Whereas the agent will just go and execute the task that it's been given to do and give you the results in an in an exceptionally fast time. So it just can scale at a rate that humans just can't. Intelligence Work for AI, Judgment for Humans So if the agents are doing some of that kind of admin work and more than kind of the the PMO type stuff traditionally like where, where do then the managers and consultants like spend their time? Speaker 3 Yeah, good question, good question. And and this might dig into one of the, you know, we talked about challenges earlier that organizations are facing, but, and I'll come to that in a second. So I think that, you know, there's, there's a way to sort of split the the description of work down. So the agents focus more on the intelligence work where they're analysing these huge volumes of data and pulling out information and, and able to provide recommendations or even execute tasks. And then the humans handle more of the judgement work where they are providing the guard rails and the governance to make sure that the tasks that agents are doing, they're doing it in a consistent and repeatable way. The humans manage the, they have empathy and they manage the relationships, something that the agents can't do. They can provide the context in order for the agents to do the work. So I think that that's how we're seeing the difference kind of shape up. So the, the, you know, large volumes of data analysis, documentation, writing, all of that can be can be generated by the agents through sort of by this intelligence work in inverted commas, the intelligence work versus the judgment work. Speaker 2 Well, it's interesting as so you use the phrase intelligence we and can talk about kind of judgment versus process. But that kind of contrast like like in lots of conversations I've had with people in recent months, that dynamic between we had a guest from AP value kind of value creation team a couple of episodes ago who again was focused on he, he was talking about about kind of judgment versus process, judgment versus intelligence. That contrast really does seem to be what and we've certainly find our own work and our customers find like very little sits purely in one quadrant or the other. Lots of it spans. It's a combination of judgment enabled by process or process enabled by a bit of a judgment, but that it is a helpful, helpful distinction and way to help people separate out the tasking a bit. I think so. Speaker 1 To make all this. Speaker 2 Work, presumably the kind of the operating model of the firm has to change. What do you think has to be true inside a professional services business before that agentic type work can start to happen and be genuinely useful? Speaker 3 So I think that I'm going to go and I just sort of teased it a moment ago when we were just talking. I think one of the big things that organisations have to do is have a robust data model that underpins the AI platforms. And I was talking to a Chief customer officer probably like a month, six weeks ago and we were talking about the PS cloud and CS cloud deployments. But they they were talking in context of their business problem. It was, we've been waiting a long time to have the funding inside our organization so that we can actually deploy a system to run our professional services organization. But our time is now because we're under so much pressure from our investors and our board to leverage AI tools and platforms to improve efficiency of how we operate. And I can't do that unless I have a data backbone, a system that is capturing all of the information about my professional services business, all of the information about my customer success business and where we're talking obviously in context of Steinia. And your solution can help me do that. If, if I, if I deploy your solution, then I can build the AI on top of it and I can deliver what our investors are looking for. But it was like this, this like fundamental thing that I can't deploy AI, I'm under pressure to deploy AI can't do it unless I've got the data backbone. And so I need the system of record first to allow me to build AI on top of it. And I think that is what professional services businesses have to consider. And it's not just though one system of record, it's an integrated set of systems that are connected so that you can access data from across the end to end life cycle from CRM sales through delivery and the success and the renewals through to financials like that end to end building that integrated connected system is, is I think absolutely crucial. Speaker 2 And underpinned, I would argue by let's see what you think it kind of a degree of kind of rigor around knowledge and IP and how the firm delivers its work and the kind of intellectual basis on which that is done and the frameworks which that is done, that is equally structured so that those things can be tapped into as well. In a way, the codification of that just has not been as necessary. In the past it would have been great if we'd had it, but now it's almost like we have to have it if you're going to leverage the AI on top of it. Speaker 3 Yeah, yeah, you're right. The context needs to be there inside the the solution. So, you know, having that, that context, that institutional knowledge about how services businesses actually run baked into the model, baked into the data structure is, is, is really important as well, just like you say. Maximizing AI Efficiency for Revenue and Margin So like your customer who is saying and, and just for listeners context, CS Cloud and PS Cloud are two of their kind of components of, of the Certainia offer from from just from what you mentioned earlier. But when you're hearing customers talk about, OK, the moment is now for this kind of capability, are you seeing firms make the investment because technology investment if you're a bank or you're an airline or something like that has been something you've had to do for a long time. I think technology investment, if you're a professional services business, has been in, at least in my experience, less of a priority. So are you seeing that change at all? And if so, kind of how are leaders wrapping their head around that? Speaker 3 What we're seeing right now is there is in our professional services businesses that we're working with existing customers, new customers that we're talking to. There is technology investment there to deploy AI solutions and AI platforms. And everyone say that's probably a bit too much commitment. But like 80% of the organizations that I've spoken to will be able to get investment, even if it wasn't budgeted for, they will be able to get investment to deploy AI tools because that's something right now, it just feels like everybody's in this foot race to leverage AI in different innovative ways to improve the efficiency of the business. And I did reference like when we were just talking a moment ago, one of the challenges, one of the other challenges that I've seen is so you use AI tools and you save a couple of hours from this person today because they've got some, you know, open AI or Gemini, and they're able to do that work more efficiently. So you're saving a couple of hours per person across the whole organization. How do you scoop up that two hours per person? And how does that actually impact an improved revenue or improved margin? And like that's one of the challenges of the moment that some of the organisations are struggling with. That's awesome that we've improved the efficiency of that individual, but how do you harness that and see results somewhere, financial results somewhere as a result of that? You know, and I know some organisations are just stripping headcount out, they're reducing headcount, so they're reducing costs, so improves the margins due to the efficiency gains. But that's not the only way. Some organizations thinking different, we're not going to get rid of any one of the organizations that was sat on an Advisory Board that I was on said, well, we've made a commitment to all of our employees. We're not going to reduce headcount at all because we've deployed AI and I'm like, you know, that's a really strong commitment to make to the employee base. But that's a different kind of message that they're finding ways to improve their internal operations to sort of harness the efficiency and leverage that. And I don't know all of the details how they're doing it, but that was their approach. So different organizations are approaching the problem in a different way. But yeah, to answer your question, I do think there is investment right now in professional services from a technology point of view, especially as it relates to AI platforms and and not just AI platforms, but also AI solutions within vendor product lines like ours, for example, and you know, the other organizations that sit in the ecosystem with us. Custom AI vs. Vendor Solutions: Costs and Strategic Focus And I think that that will as as we get from kind of everybody's got their LLM of choice into actually we want, we want company wide skills, we want company wide processes. We're building the broader then we have to truly get the most of it. So that might do some individual efficiency, but that's not going to transform the way the business works. We've also talked about the kind of a debate you're starting to see in the sector about whether people should build their own AI platforms, agents, can they just vibe code their way to success or whether they should, you know, will want to rely on the Jinta capability or kind of broader capability that vendors might be embedding in their own product. And that kind of feels like the same buy versus build technology debate that we've been having from a long time. So I guess from what you're seeing how our customers that you were working with thinking about that build versus buy platform when it comes to I guess AI, but also when it comes to broader technology, if people think they can just, I don't know, write their own PSA or write their own fill in the blank. Speaker 3 Great. Yeah. Yeah, absolutely. I mean, it's just like the the topic du jour, isn't it at the moment, this build versus buying in your rights, like back to the debate of 20 years ago. And so this is a yeah, this is a conversation we've had with many of our customers, many organisations out there. And what we're seeing is a lot, especially technology companies are building leveraging AI platforms. And you're right, there's a little different LMS. There's seems to be either, you know, Anthropic and Claude, Open AI, ChatGPT and Gemini, like the three that we're seeing most frequently. Some organisations are landing on one or two and some organisations have got all three and they're using them for different use cases. And certainly I've seen our customers build our agentic solutions on top of our platform, on top of sales forces platform, leveraging their own capabilities, their own IT and development resources. And I think a lot of it's been, as we're all learning how to use AI and what it can actually do, sometimes you just got to build some stuff to figure it out. And, and sorry. And I, you know, also to mention as well, some of our customers and partners are also leveraging sales forces, agent force as well. And our solutions that I, I mentioned will run on agent force from sales force. And also you can bring your own model. So if you want to show up with an anthropic model and, and leverage that over Cetinia data, that's, that's absolutely fine. You can do that. And so we're seeing our customers building these integrations for themselves. But what we're also hearing as well is, you know, yes, we we can do this and we've actually built some agents over the Cetinia platform, but we'd really love it if Cetinia had the agents that we needed. We'd rather be running those agents from packaged application managed and maintained by a vendor long term so that actually we can redeploy our resources onto other critical projects that haven't got, we know where we don't have a system of record and system of action to run it. So whilst we're seeing a lot of organisations have spun up their own sort of AI solutions on top of Salesforce Satinia, we're also seeing a big demand for the packaged agents as well. And that's kind of overtaking the build at the moment, you know, so we're, we're engaged in a lot of conversations sort of showcasing how we can, you know, what we've built, how the agents work and sort of understanding the dynamics with the customers that we're talking to as to how that can help them expedite what they're doing and reduce the need for them to build things themselves. Speaker 1 And I guess if you were. Speaker 2 Talking advising leaders on kind of how they think about the trade off between buy and build and I guess control versus speed, risk versus cost. Like kind of where might they land and what it might make sense to develop themselves and where they really like lessons from the past would suggest they probably shouldn't? Or how how would you help them think about that? Speaker 3 I think if you're building a custom AI solution yourself, whilst they're very quick to build and get going with, especially using Claude can just generate code at such high rate. The there's the hidden cost, like the long term cost of the fact that you've got to maintain that custom AI solution. You've got to reconfigure it or redesign it when new models come out, when new solutions come out that it's built upon, it's integrating across multiple different pieces of data and solutions. And that's going to consume additional time, you know, and thought process to actually make sure it adapts to the new integrations. And that time and money can be sort of invested in client facing related work versus sort of a custom built AI. And then I think that if you're leveraging A vendor solution, that vendor brings with it decades of knowledge of like institutional knowledge on and just in context of us in how professional services organisations work. But you know, whichever vendor it is that you're working with, they have that institutional knowledge that they've built over time of working with many different businesses across their lifetime. And they bring that to their solutions that they're delivering. And that's baked into the models right from the start in, again, just in our context, because that's what I understand, we understand how to create a project, profit and loss, how to manage revenue recognitions. And that's built into our logic and that's built into our system. And so our agents understand that just out of the gate. And if you're building it custom, you've got to train the agents on all of that information and give it all of that institutional knowledge. But we've captured that, like we said right at the very beginning when we were just doing introductions, we're working with organizations from 50 employees to hundreds of thousands of employees. And all of that IP has been invested into these ages that we've built. And that context is there and it's known. So I think that's how you weigh up the difference and sort of help make decisions on whether to build versus buy. Speaker 2 And it might be that kind of like where it's core just business process stuff and kind of the way projects work and the way sales work, we find it's like that, that kind of core backbone and spine of how a business should work. Like why would you reinvent that? Because there's so many moving parts in that. But like then if you're going to invest your time well, then what do you need to be able to do for your clients on top of that? Like what is unique about your business and kind of how you operate in the IP and kind of investing the time in that side of the the capability bill that nobody else can do because that's what you do for your clients and not trying to reinvent how PS works as a sector. Data, Discipline, and Hybrid Work for AI Success Yeah, that's exactly right. That's the, that's the exactly the right point because you've got your own secret source that you know is the reason why you're working with your clients, and that's the bit that you can bring to the table and invest your development resources in. Speaker 1 Yeah. I mean, I guess if we kind of start to pull. Speaker 2 All the threads together, there's a question of kind of what does all this mean for leaders and investors? Because I think the firms that create value over the next few years probably aren't going to be the ones that are doing the most talking about AI and playing around with tech. They're, they're one of these ones who get the kind of cleanest data, clearest operating model, really good delivery discipline, kind of ability to collect, connect, as you've said, the technology to the client work. If you were sitting with a leadership team of a kind of mid market professional services firm or even a bigger firm, what do you think they ought to be paying attention to over the next 1224 months? Speaker 3 I think that that is a good question, John. That is a good question. Over the next 1224 months, I think that leaders and investors are going to be watching their organisations that, you know, their portfolio companies going through a significant amount of change. The whole industry is and I think that it's really important to I'm just going to go back to the to the data. You've got to make sure that you're the investments you're making in those organisations are grounded in clean governed data with robust data models. And the, the architecture that you're deploying is sound and brings these connected systems, ensures that the systems are connected. And the investors who are asking the question, what does AI change? And how am I now getting work done? Those leaders are going to be ahead of the leaders that are saying, how do we use AI? Do we have an AI story? And, and everything is moving. I cannot stress how quickly everything is moving. So you know, from, you know, every 90 days there's a like a huge evolution has happened, you know, in 90 day intervals. So just the pace at which everybody's operating at is greater than anything I've ever seen in my career before. And we've been through some massive shifts during my career. I remember the Internet or mobile phones introduction of SAS platforms, but I've never seen anything move at the at the speed we're moving at now. Speaker 2 I guess if you if you, because I suspect you work with a lot of customers, you'll probably see some that you think are really progressing well, some that may be struggling a bit more. Are there things you observe about kind of the, I guess the DNA of the firms that seem to be kind of really grasping opportunity and moving it forward versus those that are perhaps struggling a bit more? Speaker 3 Yeah. I think that the organizations that are able to move fastest are those that have got, they've got data integrity. So they have got standardized workflows which are going to be easy for AI to automate the repeatable processes that are already well known within the organization. And you know they need to be consistently adhered to across the organization. And a process can start manual and then you can automate it and then you can leverage AI on top of it. So I think standardised workflows, data integrity we talked about and designing for a hybrid workforce, designing processes and operations for a hybrid workforce and how humans and agents are work together, managed together, trained together like that, that they aren't separate things anymore. That is they are hybrid teams. Those hybrid teams are in operation today in our organisations and designing for that I think is is really important. Speaker 2 That's a piece. I have it talked a lot to people about, but I think that it's interesting, we used to talk about hybrid as kind of did you work from home or in an office? And now we talk about, do you work from hybrid? Speaker 1 Starts to have a different. Speaker 2 Meaning, what do you think? I guess, are you, are you seeing people who kind of got to grips with that, I guess the human side of that agent person collaboration and how they're getting people trained or supported to do that? Or is it still too early? Speaker 3 No, I, I, I mean, we're seeing organisations who have already operationalized that there's a large services organization we work with. And one of the leaders from that organization made this statement and he said we are moving from a position where 70% of the work is done by humans and over the course of the next three to six months, we're going to see that shift to 70% of the work is done by agents. And that shift is happening like he's, he made that statement six months ago and he's operationalizing that. And many organisations are operationalizing that as another big technology company that we work with, with embedded services team. And they have been training and operationalizing their agents to work alongside their consulting delivery teams. And they put their agents through the same training courses that they're humans go through to create the, the, the the guard rails for those agents to actually work, but to drive consistency in how they work, to train them on empathy so that they show up in a client facing environment with an understanding of human behaviour. So and that's operationalized today, you know, so it's just really fascinating to be part of this, of this whole different. Speaker 2 Challenge for the for the learning and development team within a consultancy when the part of the people they've got to develop or not humans they can sit and have a coffee with. Career Reflections, Key Advice, and Episode Wrap-up So yeah, well, I mean, there we can go on for a long time. We really appreciate all the insight, particularly given just how many people you're talking to all the time and the breadth of customers you've guys have. So really grateful for the conversation. I close all of these with the same couple of questions, which I'll put you away if that's all right. The first is about kind of looking backwards a bit. If you go back and do one thing differently in business in your career, what would that be? Speaker 3 Yeah, that's a good question. So just philosophically, I, I don't live with many regrets and I'm really, I look back at the business that we've created and the career I've personally had and I'm extremely grateful. I'm grateful for the opportunities that I've had throughout my career and where I am today. And I'm really proud of what we've achieved with Financial Force and Satinia. And I was thinking about this question, like, what would you change then if you don't live with any regrets? Oh my God. And I think. Speaker 2 Thing is an OK answer. Speaker 3 Well, I think that with Financial Forces 10 year, we've been incredibly successful. We've got an amazing customer base, we've got a fantastic leadership team and investor portfolio. Today. One of the things that I wish is that because over the years Financial Forces were sort of growing very slowly, then we went through exponential growth period, then we went sort of flat for a bit and then, you know, great growth period over the last three years. I wish that could have been more linear. Like I wish we could have grown in a more consistent fashion over a lot like for that period of time that we've been in existence since 2009. And I think we'd be like, and I want to, I do want prefaces, right? I'm very grateful and proud of where we are today. But I think we could have been perhaps bigger, we could have had a greater presence globally if we'd seen that more consistent growth over over those years and not, not some of those that sort of like flatter periods. And that that's probably one of the things that I, I wish, you know, if I could have done differently, looked at how we were growing and the messaging at the time and you know, some of the reorgs we did maybe do it harder than that differently. Speaker 2 Yeah. I think that's interesting because I think lots of firms experience kind of it goes really well, then it flattens or it might go back a bit then it goes really well again. I think it kind of, we all wish we could smooth those things out, but it might have been harder to work probably in with hindsight, you could probably say, well, if I'd just done that thing at that point, it would have been yes. Speaker 3 Well, you asked me that question, so I'm sitting here with hindsight and I'm thinking, well, that's the thing, I know what I should have done now 10 years ago. Speaker 2 That's we'll do that on a different episode that kind of what how could how could certainly have been even bigger. The last one is just really kind of what piece of advice. If you were talking to somebody who's looking to build long term value in a professional services firm, what bit of advice would you give them? Speaker 3 Yeah. I think, I mean, we talked about this quite a bit, but I can't get away from the fact that the number one priority here to be successful as a professional services organization leveraging AI to improve the efficiency Dr. the business, the first thing you've got to invest in is your data foundation. You've got like that seat Chief customer officer said to me a few few weeks ago. You must have the data backbone deployed that brings together your sort of customer engagement from CRM through to financials with delivery and success, you know, all tied together. That data foundation will allow you to be so much more successful with your AI platforms, tools, agents that you want to deliver. Speaker 2 I completely agree. And I quite frankly, if AI didn't exist, I'd argue that data platform that has long been really, really important and a bit under under appreciated or or appreciated when it kind of you were going to an investment or something like that, you appreciate it when you had to and you wished in high time you'd done more about it, but kind of all the more important now. So yeah. Speaker 3 Yeah. No, you're right that. Yeah. So you can't sit on your laurels any further. And the organizations who are seeing the best returns are the ones not with the most sophisticated tools, but the ones with the cleanest operational data and the most disciplined delivery models. Speaker 2 Now that's great. Look, really appreciate, appreciate the time. If anybody like to learn more about you, certainly what's the best way to get in touch? Speaker 3 Probably access my contact records directly through LinkedIn is probably one way to get in touch with me directly and you can come in through our website as well and connect with an agent on the website and get in touch with us that way as well. Speaker 2 Fantastic. All right, we'll look, we'll put your LinkedIn stuff in the in the show notes. Really appreciate your time. Hope you have a great rest of your day and look forward to speaking to you soon. Speaker 3 Thanks very much, John. Great to chat with you this morning and I'll speak to you soon. Speaker 1 Great. Thanks. Thanks for listening. This was one of six conversations in our miniseries about how to navigate the huge technology driven change that's going on in our sector. If you've enjoyed it, we'd love for you to follow the show, share it with others and come back for the rest of the series. And do connect with me on LinkedIn the links in the show notes. I'd love to hear what you make of the series or who you think I should be talking to next. Thanks again and see you next time.