Certinia And Spaulding Ridge On AI, ROI, And Services Teams

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[00:01:24] What does AI actually look like inside a professional services firm, once you manage to move past the hype and start looking for very real results? Well, in today's episode, I'm joined by not one, but two guests. One from Certinia and one from a company called Spalding Ridge for a conversation that I'm hoping gets straight to the point.

[00:01:46] Because today, we're not talking about futuristic promises, shiny demos, the next big thing. None of that. Instead, we're going to talk about how AI is already being used right now to improve resource planning, project delivery, workforce optimization, and the day-to-day realities of just getting work done. What I'm particularly looking forward to today is going beyond the usual headlines and digging into the practical side of adoption.

[00:02:15] Because we talk about what a hybrid workforce of humans and AI agents actually looks like today. Because today, we're going to talk about what a hybrid workforce of humans and AI agents really looks like, where firms are seeing real traction, and why the biggest barrier often has less to do with the tech and more about incentives, culture, and processes.

[00:02:37] So if you're doom-scrolling down your smartphone, trying to work out what is real, what is useful, and what leaders need to change now, and what is just hype, hopefully today's conversation will give you a few pointers in the right direction. But enough from me. It's time for me to introduce you to both of my guests. So a massive thank you for joining me on the podcast today. Can you tell everyone listening a little about who you are and what you do?

[00:03:06] Sure. Thanks for having me, Neil. My name is DJ Paoni. I'm the CEO of Certinia. And at Certinia, we provide an AI-driven platform that helps services organizations just manage and sell and deliver their projects with a focus on resource optimization and customer success. So look forward to the conversation. I'm really looking forward to chatting with you today. But we have not one but two guests joining us today. Jay, would you mind telling everyone listening a little about you two?

[00:03:35] Yes, thanks for having me, Neil. My name is Jay Lobs. I'm the CEO of Spalding Ridge. Spalding Ridge broadly serves the office of the CFO Delivering AI and Data Solutions. So thank you to both of you for joining me today. And DJ, to begin with, when we talk about AI inside professional service firms today, are there any real examples of how teams are using it right now to improve resource planning, project delivery, or workforce optimization?

[00:04:05] And the reason I ask that question is we've all seen the hype in our news feeds from generative AI to agentic AI. But now we're talking about ROI, making a measurable difference. So what are you seeing here? Yeah, Neil, it's a great question because we're moving past the hype phase that you just mentioned of AI into what I'd call the era of compressed outcomes. And what I mean by that is historically,

[00:04:30] professional services were defined by the billable hour, right? A model that almost penalized efficiency. And today, our most forward-thinking customers are using AI to kind of flip that script. And so we're seeing a couple of shifts. First shift is the agentic project. So real-life example of AI at work, you know, we recently saw an AI services firm collapse the timeline to build a commercial website for one of their clients from eight months to eight weeks. And they didn't do it

[00:05:00] by just like working harder. They used coding agents to handle the architectural, you know, heavy lifting, which really allowed their senior consultants to focus entirely on, you know, the high level, more strategic items rather than the syntax of the build. The second shift is the implementation factory. So think of implementing an ERP system. You know, traditionally, that's known to be, you know,

[00:05:25] long, grueling implementation cycles. And one services provider, just to give you another real world example, has built an implementation factory. So they're using AI to automate the jump from requirements gathering to, you know, system configuration, data migration. And they've shrunk mid-market deployments from six months down to six or eight weeks. Very impactful use of AI.

[00:05:50] Now, when you tell a CEO or a partner of a services firm that you can turn an eight-month project in the eight-week one, I think the first instinct is going to be a little bit of fear. Like if I bill by the hour, I just lost 75% of my revenue on that project. And so the goal isn't to bill fewer hours. The goal is to deliver more outcomes per human. And AI can certainly deliver that. So if you put some math behind that, in the old world, let's say, you know, because of human availability bottleneck, a firm

[00:06:20] can handle, I don't know, call it like 10 enterprise migrations a year. In the implementation factory model, that same team can now handle 40 or 50. So the goal isn't to bill less. The goal is to increase your throughput so you can capture, you know, a massive amount of market share that your competitors who might still be stuck in that eight-month cycle just simply can't touch. So I'd say AI is absolutely changing the game when it comes to project delivery for sure.

[00:06:47] I love that. And Jay, just to bring you in on this, from what you're seeing with clients at Spalding Ridge, where are companies actually gaining traction with AI today? And where are they still struggling to turn ambition into real outcomes or stuck in pilot purgatory? What are you seeing here? Well, Neil, as you said earlier, there is certainly a lot of hype out there. And I would also add,

[00:07:10] there's a lot less patience from the C-suite and board of directors to show actual ROI. And often there is a gap between, you know, what these expectations are, but also still what's possible. So where we are seeing actual success is when our clients identify specific problems,

[00:07:33] have an executable plan, one that is possible and clearly generates ROI while not trying to solve everything at once. So to use a baseball reference, you're thinking more in terms of singles and doubles, not trying to hit home runs. But I would also add strikeouts are not any more acceptable. Experimentation is kind of gone by the wayside. If a company is trying to build momentum for their

[00:08:03] strategy, they have to have this building of singles and doubles to execute it over time. And there's also a lot of fear, uncertainty and doubt, especially around employment and fear of AI replacing jobs, et cetera. And DJ, I know you've spoken about the idea of more of a hybrid workforce made up of humans and AI agents. So we hear a lot of stories like this and examples,

[00:08:28] but what does it actually look like in practice inside a services organization on a day-to-day basis? Are you seeing any real world examples of that at the moment? Yeah, I'd say to first understand the hybrid workforce, like people and agents, I feel like we have to stop thinking of AI as a search bar and start thinking of it as a specialized colleague. So in a services factory model, we're decomposing a project in the digital

[00:08:57] assembly line where humans and agents pass the baton back and forth. So what that might look like on a typical Tuesday in a services firm, instead of a consultant starting with a blank slide deck, a requirements agent analyzes thousands of past successful projects to suggest an 80% complete

[00:09:19] blueprint. So now the human's job shifts from drafting that to curating it. So in this example, the baton comes back to a human who would then apply the specific nuance of that client, like their culture, maybe some politics, et cetera, et cetera, that a machine or an AI agent just can't see. And then they could deploy configuration agents that can instantly translate those requirements

[00:09:47] into system settings. And all while that's happening, coding agents are writing and auto-testing that logic. And so this doesn't replace an architect. It gives an architect a 10X power tool. And then I think this is critical. The human acts as the ultimate quality controller. So they're not bogged down in the syntax of everything. They're ensuring that the solution actually solves the

[00:10:11] business problem. So the real magic isn't AI instead of humans. It's the orchestration of the two. And so we're seeing firms move toward a model where digital agents handle the repeatable, you know, high volume tasks while humans provide that empathy, the nuance and some real critical thinking and problem solving. I would add to that, having just done annual performance reviews,

[00:10:37] that our top performers are the ones that are using more AI. We actually did an analysis. The correlation is high because they look at this as an opportunity to do higher value added activities. And as DJ just said, you know, the lower, more kind of repeatable things, they don't, the top people don't want to do that kind of work anyway. So we're not seeing it as a threat. Our teams are viewing this as an opportunity.

[00:11:04] I love that. And I think over the last couple of years, we've seen so many examples of people that got excited by the distraction or the shiny new technology, the new solutions, and ended up with a lack of ROI. They were stuck in pilot phase because they didn't go problem first, they went tech first. So Jay, when organizations hit Roblox with AI adoption, is the biggest challenge the technology

[00:11:28] itself? Or does it come down to processes or culture? And how can people adapt to new ways of working? Because very often I've seen throughout my career that it's not the tech that's the problem, it's very often the culture and the mindset shift and the investment that is required there. But what are you seeing? Neil, I agree with you. The issue is not the tech. As you said, the shiny object around the tech, that is

[00:11:54] often the issue. You know, we see organizations struggling to select which tech because of the absolute flood of new things out there, while also, you know, credible innovation coming from some more of the legacy players. But my experience certainly is this is a people process culture thing. The mindset that things

[00:12:18] need to change to appropriate leverage all the tech innovation, the best teams get this. They have an open mind that they're going to be doing things differently, processes, they're going to change. But once again, the good people, they get this. They understand it's going to make them better, more efficient, efficient, and they're going to generate more and better insights for their companies.

[00:12:44] And I would add to that, what I've seen is when organizations hit a wall, it usually becomes a human friction point. So, you know, one might be incentive management. So if you tell a project manager to use AI to be 50% more efficient, but then you still bonus them on, you know, total billable hours or headcount under management, you've created this logical paradox. So they'll, you know,

[00:13:10] subconsciously resist the tool because it threatens their value. So you have to kind of rewire the scoreboard before you hand out the equipment, so to speak. And I think you both have quite unique vantage points to exactly what's going on on the ground here. And a question I'd love to ask both of you, and DJ, I'll send this to you first. How are you seeing AI changing the way that decisions are made in professional services, especially when it comes to

[00:13:36] staffing projects, forecasting demand, managing client expectations, all these things that we see inside corporate America and indeed all around the world? Yeah, Neil, I'd say AI is moving us from a culture of, you know, reactive management to one of predictive orchestration. And so I see AI changing the decision-making framework in a few fundamental ways. First, historically, you know, staffing a project was this manual game of like who's available.

[00:14:05] Now, AI will look at the skills, will look at past performance, and even the learning trajectories of these consultants to find, you know, what's the perfect fit. So we're not just filling a seat, you know, we're optimizing for the absolute highest probability of project success for that project. And second, I think it's pretty exciting part as AI makes the commodity parts of a project faster, faster, and less expensive. Demand doesn't shrink, I think it expands, you know, when a client can launch

[00:14:35] a site in eight weeks instead of eight months, they don't stop there. So they use the save budget to launch three more, you know, launch four more, launch five more. So we're entering a cycle where lower friction leads to higher volume there. And then, you know, because of the basics, you know, the coding, the data mapping, the configuration, that's now automated, the human decision making shifts. So we can spend our time on things like change management, and business process reengineering, and culture,

[00:15:04] and things like that. And managing client expectations, I think is where the rubber meets the road for a services CEO. You know, in an AI augmented world, we're moving away from this big reveal at the end of the project toward just continuous value validation throughout. So clients are expecting just real-time visibility throughout the entire implementation. You know, that's what they're looking for. And I think AI certainly is changing the game there.

[00:15:34] And Jay, how are you seeing AI impacting exactly how decisions are being made in professional services? So from our side, we kind of look at this as being on a continuum of readiness. And what I mean by that is some of our clients are all in and maybe are even too aggressive on what they're trying to do. And some

[00:15:58] on the other are very cautious and, you know, not completely comfortable where all of this is going. And so really trying to meet our clients where they're at and evolve with them. And also just managing our teams and trying to, you know, forecast our numbers. There's some challenges that go along with this as we have to be patient and focus on delivering that, you know, ROI and outcome,

[00:16:27] as we've said numerous times here, but it's not always a straight line. And as DJ mentioned, the incentives have to be set up appropriately, you know, manage, or I should say bonusing consultants on billable hours, those sorts of things, you know, that has gone along the wayside. And, but once again, for our best consultants, they like it when their performance is tied to

[00:16:55] the real outcomes for their client. They love to see that success as opposed to just, you know, who can put in the most hours. And for those cautious leaders that might be listening to our conversation today, who might be looking to learn from others that have maybe rushed in with wild abandon and they want to learn from them so they don't make the same mistakes. Well, what are some of the early mistakes or even false starts that you've seen companies make when they introduce AI into their

[00:17:24] service teams? And how can leaders avoid repeating them again? I'll ask this to both of you, but DJ first. Yeah, I think sometimes we see like a pioneer's tax being paid by firms that really rush into AI without a blueprint. So let me explain that a little more. If I had to distill, you know, the early mistakes into a few categories, it'd be first, you know, many leaders are treating AI as a

[00:17:50] plugin, you know, this like digital shiny object that they slap onto an archaic broken process. So if you automate a chaotic workflow, you just kind of get automated chaos. And so the winners are those who are reimagining the services delivery from the ground up, rather than just trying to make a 20 year old methodology, you know, 10% faster. And second, I think a huge false start is deploying AI without explainability. So if a resource manager is

[00:18:19] told by an AI agent to staff consultant A instead of consultant B, but they don't understand why, you know, they might just revert back to their gut instinct every time, right? So you have to build transparency into the tools so that your team really trusts the AI model. And then third, I think this is the most dangerous one, where companies are focused entirely on productivity, you know, doing

[00:18:44] the same with fewer people instead of the potency, doing higher value work that you just couldn't do before. So if your only KPI for AI is hours saved, I think you're missing a chance to capture some new markets. And so how do you avoid repeating them? You know, don't start with the math, right? Start with the data. Your AI will never be smarter than your data. So it doesn't matter how fast the engine is if the fuel is

[00:19:13] contaminated. So if your project data is siloed in spreadsheets and slack pings, you know, your AI will be hallucinating from day one. So you need that, like single source of truth. You need the unified platform where, you know, project delivery, finance and resources kind of all live together. And I think at that point, AI, AI will have the IQ to actually really be useful. Such a great point there, especially around that obsession with just productivity and nothing else.

[00:19:42] And Jay, I suspect you've seen a few early adopters making mistakes or false starts. Anything that you've taken away from that? Well, where I would add on to DJ's comment on the blueprint, I think you also have to add to that the cost of ownership over time, because we have seen a spaghetti web of AI agents popping up all over

[00:20:11] the place in organizations. And as DJ alluded to, you know, without some central, at least data model to guide and to be able to build around going forward, we're seeing some really cool point solution innovation that actually doesn't fit the overall enterprise architecture strategy of the firm that

[00:20:37] they have to maintain over time. And so just because for one single thing, you might be able to develop something very cool and clawed, not that clawed's an amazing tool, but does it fit? And is it going to work for you over the course of the next, you know, six months to a year to have like that kind of range? We need to think a little bit beyond just today and now.

[00:21:04] 100% with you there. And as you said, we've been on this journey from Gen AI to Agentic AI, and I've got this spaghetti web of AI agents that are popping up. There's the misalignment with infrastructure and the next shiny new tool that's coming out. So as we look ahead for the rest of this year, maybe into early next year as well, based on what you're both seeing in the field today, what practical adjustments should businesses be thinking about now? What should they be,

[00:21:32] decisions should they be making to ensure AI actually delivers value, delivers ROI and a measurable difference rather than just becoming another layer of complexity and add to that technical debt that they've been building up for decades. And DJ, I'll go to you first on that. Yeah, I'd say the most successful organizations that we work with have realized, you know, kind of one thing like AI is not an IT project. It's a top down mandate. And so to ensure it delivers value rather

[00:22:02] than just kind of more noise in the system, I think leaders have to make, you know, some practical adjustments right now. I think first, you can't delegate your AI strategy to just your IT department. I think the CEO, even the board must define what does success look like? You know, is it a 30% increase in margin? Is it a 50% reduction in project delivery time, like define that success? And that

[00:22:27] needs to come from the CEO. And without, you know, a top down mandate, I think the organization just naturally going to resist the change because AI, you know, by definition disrupts the comfort zone of how people have been working for decades and decades doing the same things over and over again. And then second, AI is a reflection of your data. And as I mentioned this before, if your project data is trapped in shadow IT or disconnected, you know, spreadsheets, your AI will be functionally

[00:22:55] illiterate. And so leaders have to mandate this single source of truth. And so at Certinia, you know, we see that the firms winning right now are the ones who've consolidated, you know, their professional services automation, their resource management, their financial data, all on one governed platform. And then third, and we've talked about this, but you've stopped measuring your teams solely on utilization or hours. Because if an AI agent does 60 hours of work in six minutes, you know,

[00:23:24] your utilization metric just blew up. And so you have to adjust your KPIs to measure things like outcomes, to measure velocity, measure customer impact, measure value. And if you don't change the way you measure people, as Jay had mentioned before, they're never going to adopt the technology. So I'd say, don't ask your teams if they should use AI. Tell them where the business needs the most

[00:23:49] leverage, and then provide that governed platform to let them build it. And I think complexity happens when you have a thousand random acts of AI, and value happens when you have one unified vision. So much gold there. And Jay, anything you'd like to add that any leader that's listening to our conversation today can take away? And ultimately, I'm sure that AI actually delivers value to their business too.

[00:24:15] I'll answer from managing a consulting firm. What we are talking to our teams about is, once again, this continuum concept on meeting our clients where they're at. So if you can take client A from, you know, two to three, but client B is ready to go from, you know, six to seven, whatever we can be

[00:24:38] doing to helping them on their journey. Because although there is so much hype out there, not everything is happening at once. And so, you know, being agile and focused on delivering, you know, once again, that outcome for wherever their particular client is at on that continuum. And I think that is a powerful moment to end on. But before I do let you both go, Jay, first of all,

[00:25:04] for anyone listening wanting to find out more information about Spalding Ridge, the things that we've talked about today, connect with you or your team, where would you like me to point everyone listening? Yeah, please point everyone to our website or our LinkedIn channel. Awesome. I'll add a link to both of those. And DJ Paoni, anybody interesting finding out more information there, connecting with you or your team, where would you like me to point there?

[00:25:31] Yeah, Neil, thanks for having me. I would say same thing, go to Certinia.com and you can also follow us on LinkedIn and Instagram. Awesome. So for everything that you've both mentioned, I will be adding links to it all. So I encourage people listening to check out the show notes, go visit and start that conversation because we've covered so much today and we can only cover so much in a 30 minute podcast, but about how AI is being used right now inside firms, not just the headlines that we see on our news feeds,

[00:26:01] how real people are using AI to solve real world problems. And also talk about some of the challenges and the adoption issues that we've seen in recent years, but ultimately giving people tangible takeaways into what's working and what isn't. And I invite everybody listening to pop over and see me, Tech Talks Daily. Let me know your insights. I'd love to get you on here too. But more than anything, DJ and Jay, thank you so much for sharing your insights. Thank you, Neil. Thank you, Neil.

[00:26:30] So many big takeaways in this conversation, but I think one of the big ones is that this conversation around AI in professional services is finally getting so much more honest. And DJ and Jay made it clear that firms seeing value right now are not the ones chasing every new tool posting it all over their LinkedIn. They're actually the ones starting with very real business problems,

[00:26:56] getting clear on outcomes, what they want to achieve and rethinking how their teams work, how they measure success. And possibly most importantly, where human judgment matters most. All these things are so much more interesting than simply asking whether AI saves time. And I also thought the point about hybrid workforce landed particularly well too,

[00:27:21] because the future here is not humans versus AI. It is figuring out how people and intelligent systems can work together, but do so in a way that improves quality, increases capacity, and gives talented teams more time to focus on what actually matters. So if this is a topic that you're wrestling with inside your own business, I think there's plenty in this conversation worth thinking about. But you've heard from me, you've heard from two guests from two different organisations,

[00:27:50] and I want to hear your view. This is a dialogue, not a monologue. So please tell me, are you or your company still experimenting? Are you finally seeing AI deliver real value where it counts? What would you do differently if you could do it all again? All these things. Pop over to techtalksnetwork.com, send me a message. If you want to come on here and have a chat with me. Ultimately, you are all part of this community, and I want to hear from you. But that's it for today. So thank you for listening as always,

[00:28:19] and I'll speak with you all again very soon. Bye for now.