What happens when an organization reports healthy infrastructure while weak data, hidden spreadsheets, and undocumented processes quietly increase its financial exposure?
In this episode of IT Infrastructure as a Conversation, I speak with James Briers, Founder and CEO of Intelligent Delivery Solutions and creator of GovScape. James works where infrastructure, data, governance, and commercial reality meet. His argument is that governance has to move beyond policies, dashboards, and periodic audits if businesses want to understand their actual operating condition.
James describes a recurring problem in professional services. Financial information passes through several systems, but inconsistent records leave finance teams invoicing too much or too little. Employees then spend additional time investigating discrepancies and manually reconciling the numbers. His proposed response begins with data integrity across those systems, followed by continuous monitoring that identifies issues before they affect billing. A person remains involved to validate the final result, but the repetitive reconciliation work becomes easier to manage.
We discuss how continuous governance differs from a six-month or annual compliance exercise. James uses the idea of constant pruning. Rather than waiting for a large audit to expose a backlog of issues, teams can see smaller changes as they happen, assign responsibility, and correct them while the operational impact remains manageable. The goal is not another dashboard. It is an accurate view of the current state and a clear action for the person who owns the response.
That information becomes far easier to discuss at board level when organizations translate governance gaps into commercial terms. James believes controls, risks, data quality, and unresolved issues should be connected with the potential cost to the business. This turns governance from a line on a transformation plan into a boardroom discussion about cash, risk exposure, and return on investment.
Cloud migration is another area where good intentions can carry old problems forward. Moving unreliable data into a modern platform can produce a better-looking system with the same contradictions underneath. James recommends treating data quality as the starting point, repairing known problems before migration, and continuing to monitor the information once it reaches the new environment.
We also discuss shadow systems and shadow AI. The spreadsheet on one employee's computer may sit outside the official service map while remaining central to a business process. AI agents can create similar dependencies at greater speed. James argues that organizations need visibility into which workflows use AI, what data those agents access, where that data moves, and whether the result creates a return or another source of cost.
Can companies turn governance into a continuous business discipline before hidden data problems reach the customer or the board? Listen to the episode and share your thoughts with me.
[00:00:01] What is poor governance actually costing your business, especially when that dashboard still remains green? Well, my guest today is James Briars. He's the founder and CEO of Intelligent Delivery Solutions, or IDS.
[00:00:19] And he's also the creator of Govscape. And James has spent over two decades working with organisations where data quality, infrastructure dependencies, commercial pressure and transformation all collide. So today we're going to discuss why six-month and annual audits often discover problems well after they've grown expensive,
[00:00:42] and how inconsistent data can distort invoices and increase manual reconciliation. And also why moving old data into a new cloud platform doesn't repair it. And he will also explain operational governance intelligence, the value of continuous monitoring, and thankfully why human judgement remains an advantage as companies introduce AI agents.
[00:01:11] So if governance usually just appears as a line on a project plan, this conversation will ask what changes when it becomes a number that actually the board will stand up and take notice of. But enough from me. Let's get James onto the podcast now. So thank you for joining me on the show today. Can you tell everyone listening a little about who you are and what you do?
[00:01:35] James Briars, founder and pot washer, everything that you want to be in as part of an entrepreneurial kind of role within a company from IDS. We've been in business for 11 and a half years. And we've been through lots of ups and downs and all kinds of good, bad, ugly along our journey. We merely focus on the data elements involved in supporting AI.
[00:02:02] So we build the foundations by making sure data is good, strong, integrity is there, the quality is there, and it's as complete as it possibly can be as it feeds and supports any kind of AI initiative. Absolute pleasure to have you join me today. And I know what you mean about having to perform so many different roles there. And looking at your email and the correspondence we've had and the research I've done, you distinguish uptime from operational health.
[00:02:32] So what hidden dependencies, shared credentials or maybe undocumented workarounds can actually make a green dashboard dangerously misleading? Yes, obviously. I mean, I think the main crux of the problem generally comes from accountability and taking action where governance is concerned. I mean, a lot of organizations have the governance kind of practices, the compliance, the dashboards, the information is all there.
[00:03:01] But quite often we find that people just rely on a six month or a 12 monthly audit to before they actually find the problems. And then they go off and, you know, in a mad panic trying to fix them. Whereas we come at it from a more proactive kind of approach. And then it's real time kind of views of where everything's at. And we look to present the actions that need to be undertook on a daily, weekly basis. So everything is always on the front foot as opposed to on the back.
[00:03:31] I'd love to hone in on a very real world problem here that might resonate with the listeners and also maybe give them a few actionable takeaways from that. And are you able to provide maybe a real life use case or an example of poor data quality or weak process governance? And then that then turns into revenue leakage and ultimately how an organization can trace that loss back to its source.
[00:03:56] Because we all know about firefighting and being reactive to situations like this. But it'd be great to hear a story and a more proactive response to it. Yeah, I mean, I'll do my best to recall without actually releasing any kind of names into the wild as well. So it obviously an obfuscated view. But yeah, we've dealt with an organization in professional services. Well, I'd say it's not just one.
[00:04:22] This is quite a common theme where the consolidation and reconciliation of the financial information becomes a problem, especially when you've got multi-systems that are feeding the financial kind of forecasting, the invoicing process and all that sort of good stuff. And, you know, the disparate kind of systems present another problem where the integrity of the data is not upheld.
[00:04:51] So you have contradicting information across many different systems. So when the finance team comes down to actually generating invoices, it's completely out of whack with reality. That then presents the problem of actually these organizations then having to go and fact find and rectify the problems, which, as you can imagine, you know, the financial hit becomes more apparent because they're either invoicing too much or too little.
[00:05:19] But then on top of that, they're having to hire more people to actually manually kind of reconcile the information. So where we've kind of provided the fix around that is by basically making sure the integrity between all of the systems is connected, data synchronized on, again, a proactive basis.
[00:05:39] And obviously that process in itself provides you with the opportunity to improve the quality of the data because the one version of the truth becomes the kind of destiny of those kind of services and solutions. But then on top of that, you know, we relate on our platform and that constantly then monitors and make sure that, you know, any kind of issues are kind of identified before they become a problem.
[00:06:06] And then, you know, from that perspective, the, you know, the reconciliation of their invoices becomes more of a button clicking exercise and then human in the loop then confirms and validates and then away they go from an invoice point of view. And so much of what you said there will resonate with people listening around the world. And another phrase I'm hearing more and more, especially around the world of AI agents, et cetera, is continuous governance.
[00:06:33] So how is this different from a periodic compliance exercise? And what does it actually reveal that maybe those traditional annual audits can't? Well, I mean, I don't think it is the fact that it's not revealing more. It's because you're constantly pruning. You know, you've got due care over your environment, your organization, your structures, your databases, your policies, your frameworks.
[00:06:59] And you've got more control because you're actually proactively looking. And again, with the ability to force accountability means that you don't really feel the problems because you're constantly fixing. Yeah. And you're ahead of everything and the information, the intelligence that you generate, again, then just feeds the continuous never ending improvement of your institution or organization. Because you're always on and identifying the problems before they become anything major.
[00:07:29] When you wait six months or 12 months, then everything feels bigger and worse because it's a much more kind of monumentous kind of challenge. And then you're then behind the wheel chasing those fixes. So you're then again, you know, you're putting those fixes from 12 months ago into place. Then another 12 months goes and then you find another issue.
[00:07:50] Whereas if you're constantly ahead of the game and you're constantly tweaking and fixing and kind of honing the solution as you go, then, you know, you're always going to be producing a quality environment to work within. I love that phrase you used earlier, you answered that, constantly pruning. It's so true.
[00:08:10] And when I was doing a little research on you, I was also reading that you argue that human intelligence remains a competitive advantage in an AI-led company. And we've all seen so much noise on our LinkedIn news feeds. And this is such a refreshing counter narrative to some of the doom and gloom we see out there. But for people listening, what judgment should people retain? And where can AI strengthen rather than dilute them? What are you seeing here? It's an efficiency gain, AI.
[00:08:40] I think there's a lot of stories out there. And, you know, as you said, the doom and gloom and the fear mongers were, you know, we're expecting armies of robots to be walking down roads and whatnot. To monitor and manage our day-to-day activities as well as take our jobs and, you know, and all that sort of good stuff. And the reality is just, it couldn't be any further from that. AI is there to be leveraged. It's there to enhance, you know, our careers, enhance our jobs, enhance our services.
[00:09:11] Because, you know, it can take away the mundane. And that's when all said and done, that's where the problems lie, right? You know, it's the mundane activities of our roles that you take your eye off. And, you know, more often than not, you make mistakes. You know, so it's getting, AI is good to remove those kind of mundane tasks.
[00:09:30] And then the more kind of intuitive, intelligence-driven kind of tasks are then what, you know, the professional, the employee, whatever you want to describe them as. They then become more empowered because they can do the more interesting parts of the job better because they've got rid of the distractions. Another area that's getting a big focus at the moment is operational governance intelligence.
[00:09:56] Sounds cool, sounds valuable, but of course, boards need real evidence. So what kind of measures translate infrastructure, health and governance into a real commercial language, that language that everyone can understand and keep the CFO happy too? Well, I mean, again, it's about, you know, taking the truth of any given situation, any current state and, you know, driving it into a meaningful kind of metric.
[00:10:22] And, you know, there's nothing more meaningful than actual cash, you know, and that then focuses the mind when you convert, you know, any kind of governance score, your controls, your gaps, your risk, your issues against all of the, you know, the truth of the matter. So, you know, you're not just looking at policies and frameworks, you're looking at the actual data that the business operates on. So that's structured data and non-structured data.
[00:10:45] And you combine it all together, you know, within your system and you use, you know, methodologies and whatnot to calculate the actual risk, the cost of risk associated to your business at any one given time. That's where it becomes more meaningful.
[00:11:28] Your agents are provided with real-time company data and guardrails for company protection. Create the business you always dreamed of with Denodo. And you can do that by simply visiting denodo.com to learn more. But now, back to my guest. And if we do have a CIO listening to our conversation today, maybe they suspect that cloud migration has multiplied old risks rather than remove them.
[00:11:54] Are there any signals that the team should instrument first? Anything that you would advise there? I mean, I'm quite biased from this perspective. I think, you know, everything should start with your data. So data first, no matter what you do. I think a lot of transformational issues that we've seen has been inherited because they've prioritized other barriers of delivery first and kind of underestimated the impact of data.
[00:12:22] You know, data becomes just something you lift and shift and move from one system to another. You know, and you've got the shiny new platform, but you've still got the problematic old data that's been migrated and, you know, really shoved into the new system as opposed to taking a bit more of a strategic view and fixing the problems that you have within the data. And then you transform it. And then it goes into the newer system in much better health and shape.
[00:12:48] And then you, again, using the word that I mentioned before, once it's in there, you then start to see the issues and the nuances within the new system. And then you're in a pruning kind of exercise again. And it just, for me, I think starting with the data is exactly where you need to be focusing because that's where your quality problems come from. So that's where you identify the problems of workflow as well.
[00:13:11] And I suspect we will have many people listening that work inside organizations that know full well that they are undocumented workarounds in the workplace there. But unfortunately, they do depend on them to keep the work moving now and again. So how can a leader expose and replace some of those practices rather than or do it without destabilizing the entire operation? Is this something you come across a lot as well? I suspect you've got a few war stories there.
[00:13:41] Yeah, there's plenty, probably too many to drill into. But I mean, yeah, I mean, it comes down to transparency. You know, there's always Joe from, you know, accounts have got a separate spreadsheet that sits outside of any kind of known system that works for them in the day-to-day job. And it's, you know, doing lots of discovery and trying to find those kind of needle in haste, that shadow systems or shadow processes that aren't well documented or documented at all.
[00:14:12] Getting into the nitty-gritty around your process mapping and understanding exactly what the workflows are, where the dependencies are upstream, downstream. I mean, that's all where, you know, where we find the problems is just being nosy and asking lots of questions and, you know, but tell me why and why. You know, and you basically push and push and then you find these, again, it comes down to data,
[00:14:36] these disparate kind of data sources that sits on, you know, somebody's machine that are pivotal. But it's good to find those because if that is part of a process, then it should be, you know, involved and included and enhanced with any kind of transformation. Because obviously it's clearly making somebody else's job better and easier, you know, so you should include them.
[00:15:01] And I think it's just a case of transparency, really, you know, being able to ask those deep and searching questions. Now, thankfully, you know, I'm not the person who asks those questions. We've got a bunch of very talented consultants that work for us. So, you know, thankfully, you know, it's not just there. It's not reliant on my kind of wisdom or experience.
[00:15:24] And of course, this year, all anyone has been talking around is AI agents, businesses around the world trying to get to grips with it. And individuals, teams, the entire organization could have hundreds, if not thousands of agents out there. And they can introduce new dependencies that conventional service maps won't capture. So what do you think must change now in operational governance when software begins making and carrying out decisions on its own? I think it needs to move quicker. Yeah.
[00:15:54] I think there's a constant change cycle that happens. You introduce agentic AI. It does identify problems. It then, you know, maybe makes suggestions on how to fix those. And, you know, then organizations and employees make knee-jerk decisions and say, well, okay, that's what we'll do. But then what you're introducing then is shadow AI. And again, that becomes out of control.
[00:16:20] So, I mean, it's key to capture those shadow AI kind of initiatives. And again, it's about that constant kind of awareness and having, you know, an ability to cast the net over as much as you possibly can and constantly be looking and checking and asking. And analyzing what's in play, what the current state is, what the actual current state is. And identifying those scenarios where shadow AI is becoming introduced with an organization.
[00:16:49] And is it producing return on investment or is it becoming a drain on investment? You know, and it's identifying that kind of a scenario. And again, it's about having the right practices that, you know, a platform, for example. Shameless plug from my side there. But it is, it's having something like Gulfscape, which, you know, you identify what those workflows are, what the AI workflows are, what data is driving them, where the dependencies are.
[00:17:17] And then you're constantly checking and monitoring in a real-time kind of scenario, where the data is going, what's using the data, how it's been used. If something's being, you know, sent by an unknown kind of system, then you need to know about that. And it's about finding those issues. And again, the devil's always in the detail with all of this. And again, I keep saying it, it all comes back to the data. You know, and that's where you've got to have your control.
[00:17:45] And that's where your lens has got to be focused on. And when working with AI, data is certainty. One of the big phrases I hear a lot at tech conferences is no data, no AI. And only when you have that certainty can you make decisions that you can scale and have AI that you can trust and ultimately believe in. And I know this is a topic you're very passionate about.
[00:18:10] And I'm curious, if you look at all the conversations you've had, whether it be at an event on a show floor or whether it be with customers that are approaching intelligent delivery solutions, is there any trends that people are coming and asking for help with at the moment? Is it essentially around agents or is it something completely different? What are people coming to you for at the moment? To be fair, I mean, a lot of the conversations we get involved with are around the uncertainty that AI brings. You know, there's an element of fear.
[00:18:39] You know, people are a little bit kind of paralyzed in a way. You know, they know that they want to start introducing AI. But again, the fear of introducing it is causing, you know, paralysis, if you want. And again, it's because they don't know where to start. And, you know, they recognize that they could quite easily get themselves into trouble if they've not got the foundations in place. So, I mean, it's the trend is quite clear now.
[00:19:05] And a lot of organizations are recognizing that they can't just, you know, download some bots or agents from a service provider and install it. And away they go. I think a lot of those kind of pilots are still, as I describe as pilots, you know, months and months and months after they've started because they just can't figure out what it is that's causing the problem. And again, it comes back to the foundations, you know, and data should be the foundations.
[00:19:34] You know, you don't get a house builder starting building a house by creating the roof. You know, it's the last thing, you know, they put the footings in and get them solid and right. And they build up from there. And that's exactly the same with any kind of AI transformation. It's got to be treated in a very kind of project program driven way. You know, just because you're using AI, it doesn't mean that, you know, you've got to take away any kind of engineering principles.
[00:20:00] And, you know, design first kind of mindset and, you know, making sure that quality is at the very, very core of everything you do. You know, they'll just take an agentic solutions word for it. Again, human in the loop, check it, make sure it makes sense, make sure it meets standards. You know, make sure, again, ultimately that the return on investment is clear and it's there. And again, those are the kind of things that we tend to be involved with from a conversational perspective.
[00:20:29] I think your words will not only resonate with businesses listening around the world, but also offer a little reassurance that everyone is going through this certain amount of fear and uncertainty when it comes to all things AI. It's nothing to be ashamed of. I think it's easy to look down your newsfeed and think everyone else has got it right and you're lagging behind. And I don't think that is the case very often.
[00:20:50] And anyone that is feeling this way and maybe want to carry on a conversation with you about anything we talked about today when it comes to all things IDS and indeed yourself, where should people go? Well, we can take a look at the website. Although, disclaimer, it's about to be updated. So don't judge us on our IDS website. In other words, we can look at that. So it's intelligent-ds.com or we can go to a new platform, which is gulfscape.app.
[00:21:19] So gulfscape is G-O-V-S-C-A-P-E.app. And just take a look at the new platform website that we've recently soft launched. Awesome. Well, I'll include links to everything that you mentioned there. I'll also pop in your LinkedIn profile as well so people can get a hold of you. There's so many big talking points at the end of this conversation. I encourage people listening to reach out and let's keep it going and learn from each other. But more than anything, thank you for starting it today. Appreciate it. Appreciate it, Neil.
[00:21:49] Thanks for having me on. I love how James left us today with that simple idea that many transformation programs learn the expensive way. Because a new platform cannot compensate for an unreliable data, hidden spreadsheets that people are using on the download, undocumented dependencies or nobody taking responsibility for an issue. And continuous governance gives teams the chance, though, to find and correct smaller problems
[00:22:17] before they become a six-month cleanup exercise. And it also gives boards a better way to discuss governance by connecting controls, gaps and risk with money rather than another collection of status colours on a dashboard. So for AI projects, the same discipline applies. Start with sound data. Keep people involved in judgment. Test the result against agreed standards and be clear about the return. So a huge thank you to James for joining us today.
[00:22:47] I will add links to everything he mentioned on the blog post associated with this over at techtalksnetwork.com. Remember, over there, you can learn how to work with me, meet me in person at a tech event, send me an audio message. We'll just browse through 4,000 interviews. Whatever it is, I've got something for you. But before I go, one question to ponder. What would your green dashboard reveal if governance was measured continuously? Something to think about. But that's it for now. Speak to you tomorrow.
[00:23:17] Bye for now. Bye. Bye. Bye. Bye. Bye.