How SS&C Blue Prism Helps Businesses Escape AI Pilot Purgatory

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[00:00:34] How many AI announcements have appeared in your LinkedIn feed before breakfast? If it feels as though every company has launched a new co-pilot, agent, model or platform before you've even finished your first coffee, don't worry, you are certainly in good company. But while the announcements keep coming, the reality is many businesses just remain stuck with expensive pilots.

[00:01:00] Uncertain returns, disconnected experiments and growing pressure from the board to show some real results, measurable value, improving business outcomes. Well today I'm joined by Brad Hairston. He's a director of strategy at Blue Prism. And together we're going to discuss why some companies are generating measurable value from their AI projects, while others continue spending without moving forward. And Brad has lots of experience.

[00:01:30] He brings 30 years of consulting experience and that currently works with the Customer Zero program, where the company deploys its own automation technology internally before taking it to their customers. Now according to Brad, this has helped the company grow revenue by around $1 billion without adding headcount. So if you're looking for ROI, we've got a few answers for you today.

[00:01:55] But we'll also discuss why deterministic automation still has an important role. And discuss what businesses should have in place before allowing AI agents to take any action. And how employees can build automations without creating yet another collection of disconnected workflows. So if your AI strategy currently resembles a crowded draw that's just full of promising experiments,

[00:02:23] I'm hoping that this conversation will help you decide what deserves to have your attention and move into production. And with that scene set, let me introduce you to Brad right now. So thank you for joining me on the podcast today. Can you tell everyone listening a little about who you are and what you do, Brad? Brad Hairston. Sure, absolutely. Thanks for having me, Neil. So my name is Brad Hairston. I live in Dallas, Texas.

[00:02:51] I spent about 30 years in the consulting industry prior to coming over and joining SS&C Blue Prism about seven and a half years ago. And I am part of what we call our customer zero program here at SS&C, which is focused on implementing our agentic automation platform internally across the company.

[00:03:17] And customer zero is really kind of part of the fabric of what we do. We like to deploy our own technology before we go to the market and we sell it to our customers. And over the last few years, we've actually been able to grow about a billion dollars in revenue without adding headcount as a result of what we're doing with customer zero. So it's a pretty important part of our company.

[00:03:43] And I'm getting to experience it myself now with our agentic automation platform, which has been a lot of fun. Yeah, it's incredible what you've achieved here. And one of the reasons I'm excited to get you on here is there's so many big talking points at the moment around ARI, return on investment value, improving business outcomes, etc.

[00:04:06] And you're someone that spent years speaking with automation leaders and especially on the enterprise front lines way before everyone got excited about AI. So fast forward to present day, what is it that's separating companies that are generating measurable value from AI and those spending heavily but seemingly standing still or caught in pilot purgatory, should we say? Yeah. And unfortunately, most are standing still, it seems, kind of at the starting line.

[00:04:37] Yeah, I think, Neil, I've noticed most of the companies that really are getting value are they're not just focused on the AI technology, they're focused on getting the right operating model in place. And, you know, they're, they're not just asking, where can we plug in AI? You know, what kind of AI projects can we mobilize? They're really thinking primarily about what business outcome are we trying to improve?

[00:05:04] So, you know, corporate strategy is still around, it hasn't gone away, companies are still trying to achieve a well defined strategy in order to get ahead of their competitors and, and AI has entered the scene and everyone's gotten enamored with we just we got to go get something up and running with AI. Well, the ones that I see that are making the most progress have still connected it directly to their strategy and their priorities.

[00:05:30] So, I think that's first, you know, one thing I would point out is it's not just about going with the hottest, greatest, newest model and let's get it going. It's what are the most important business outcomes that we need to achieve and then let's start there and let's see what AI can do to enable those.

[00:05:49] I've also found that a lot of companies that are getting some good initial results are building upon their, their previous investment with RPA and with other automation technology. So, this, this idea that deterministic automation has just evaporated, it's no longer, no longer useful is, is highly incorrect.

[00:06:15] There is absolutely still a purpose for deterministic automation and we're not just going to migrate everything over to the AI agents. I don't think any company on the planet will have enough budget to pay for automation completely run 100% by, by AI agents.

[00:06:35] So, there's still, there's still a purpose for deterministic automation and the companies that are taking those processes that they've partially automated with RPA, then adding, you know, seeing where they can add AI to take it further and maybe re-engineer, redesign the entire process. That's another really successful, I think, practice going on with, with the leaders.

[00:06:59] And then, and the final thing is that the companies that are really setting themselves apart are building reusable capabilities. So, that, that involves, you know, what they're doing with governance, with orchestration, with security, with how they're measuring results, all those things. So, every AI initiative that you deploy, each one should be faster and cheaper to deploy.

[00:07:27] And if they're not getting faster and cheaper each time, then you're doing something wrong. So, that's another really, really significant thing that I see in the ones that are leading the pack is they, they're not, you know, just trying to deploy as many AI projects and pilots as they can. And they're really focused on the right ones. And they're, they're trying to do something that establishes the foundation for, for scaling it, you know, beyond the pilot stage.

[00:07:57] Yeah. And there is also increasing scrutiny around getting return on investment from AI. You mentioned tokenomics there. I mean, a massive topic right now. And indeed, something we don't talk about enough is the culture. You can put, it doesn't matter how great the technology is, if the culture's not ready to accept any new tech, then you're going to run into problems too.

[00:08:19] So, I'm curious from what you're seeing here, what are you seeing from where, I don't know whether an organization is genuinely ready to get value from AI before committing even more budget to new models, co-pilots and AI agents. What are you seeing around this? You know, it's always a good idea to assess organizational readiness for any big technology change.

[00:08:45] And this is certainly an era of constant technology change. But leaders also don't need to take too long on this step because everybody's moving at lightning speed. And if you take too long, you're really going to be behind. So, I would start there by saying, yes, assess readiness, but do it quickly and do it effectively.

[00:09:07] And so, the first thing I would say to leaders is, you know, make sure that there is a, this is going to sound pretty fundamental, but make sure there's a clear understanding of the processes that the company's trying to automate. And you want to make sure you're getting the right people out of the business. This is not just a bunch of software engineers that are playing with technology and they're handed a process and they need to go and try to get it to run with AI.

[00:09:34] Because AI amplifies what you, what you already know and what you don't know. And so, if you don't really understand how the work's getting done and where the bottlenecks are and what the outcomes are that you're, you're trying to improve, it's going to be really hard to measure the AI impact. So, make sure you have the right people, the business means the people that do it, do the work day in, day out. They should be right in the center of this, this type of effort.

[00:10:02] Then I think you need to make sure the right data foundation is there. I mean, AI is, is only as valuable as the information that it can access and, and people are not going to trust the outputs if the data foundation is not sound. That's another pretty obvious thing, but worth pointing out. And then, you know, leaders need to make sure governance is in place and, you know, there, there are some fundamental questions they can ask.

[00:10:30] Do you know who owns each AI agent once it's live? Can you, can you trace the data that each of those agents touches? Can you understand what permissions they have? Is there someone accountable for an agent if it goes off the rails, which can happen? And if the answers to those questions are fuzzy, then you're probably not ready to get going. You're probably not ready to scale.

[00:10:58] And, you know, from the beginning, I think you need to have a working group. There needs to be, I'm not a big on establishing committees, but there does need to be a working group focused on governance right from the beginning. And they don't have to have everything completely ironed out, but they need to build it and they need to start putting that together as the program is advancing. And I was talking with industry analysts yesterday on our podcast that we, that our company publishes.

[00:11:26] And he said, this is the year of governance. Companies are realizing they didn't spend enough time on governance. They rushed out of the gate and they started deploying all these pilots. And so they're, they're catching up. They're getting that in place. And I agree with that. I think found, you know, governance has got to be rock solid, but you can start with a partial view on that and then, and then build it as you go.

[00:11:51] And the last thing, Neil, I would say is that, you know, we said organizational readiness. So you do need to make sure the people are ready for a change like this. And there's going to be apprehension. There's always apprehension with any type of change that happens in a company, but there needs to be a healthy dose of optimism and excitement around what this is going to create in terms of new opportunities, how it's going to make our jobs easier.

[00:12:21] It's going to make our jobs more human. And I think it's important to figure out how to communicate that the right way. And, and that's what I see some companies doing really well. And I see other companies not doing so well, but it's, it's important to have a vision for that and to communicate it and to get people really excited about what this can mean to them individually and how it can make their jobs so much better. Yeah, I completely agree.

[00:12:49] And as you said, the companies succeeding with AI are not necessarily the ones chasing the latest technology or model, but often have the correct operational and cultural foundations in place. So just to double click on that a little bit, what kind of operational and cultural foundations do many of these that are succeeding have in place that, that others are missing? Yeah.

[00:13:12] I mean, building on the last answer, I would, I mentioned having the right people involved, the business subject matter experts to go, to go a little further. I think these companies, you know, they're culturally, they're process centric. So they understand how workflows through, through the company and they, they, they can identify where AI can remove friction, where it can accelerate decision-making.

[00:13:39] It can improve customer service, things like that. They, and so they're looking to redesign processes where possible, not just tweak existing ones. I think that's an, that's an important kind of cultural component to, to companies that are doing it right. I mentioned governance and the importance of that. I mean, that, that really is a trust builder. So I would, I would throw that out again.

[00:14:06] That is culturally, I think very, very important. And, and then, you know, I would say also they foster, these companies foster a culture of augmentation versus replacement. There, there is a, a bad reputation of AI that it is all about replacing humans and driving headcount reduction. And that's the only thing it does.

[00:14:30] And many companies that are going through headcount reductions, whether or not they're using AI, they're coming to the press and they're saying, oh, this is because of AI. We're doing this because of AI. The, the so-called AI washing that is, is happening with regularity. The companies that do it right, I think, call that out ahead of time. They, they make it clear that this is about augmentation. It's about making humans more effective, making our jobs more human.

[00:14:59] And, and so that, again, I've already mentioned that a couple of times. That is really important, I think, culturally. And then the final thing, when you talk about culture of an organization, having executive sponsorship that is tied to business outcomes.

[00:15:16] Since we're now so focused on outcomes and how to achieve those and agents are focused on outcomes, having executives out there that are willing to sponsor these initiatives and really sponsor the, the actual business outcomes. They're not asking questions like, well, how many AI projects do we have? How many AI deployments have we done? They're asking questions like, how are we serving our customers better? How much more productive are our teams?

[00:15:45] How much higher, you know, how much higher quality do we have in our work product as a result of these things? So that's also something I think that is differentiated about companies that are culturally aligned to what AI can do and, and just the promise that it can deliver on. And we're recording this right in the middle of 2026, where everybody has been talking about nothing but agentic AI or, yeah, or so it seems.

[00:16:12] And of course that is raising the stakes, especially when we're moving from systems that just recommend actions to systems that can potentially execute them. The last two weeks alone, we've seen chat GPT bring up chat GPT work. Many listening will be connecting their inboxes and calendars, files and folders, et cetera. So what controls and oversight and most importantly, accountability needs to be in place before businesses can trust these agents with real work? What are you seeing here?

[00:16:43] Yeah, you know, I think it's a great question because, you know, there's a fundamental difference between AI that gives you an answer and AI that takes an action. And the moment that AI can make a payment or update a customer record or initiate a business process, the risk profile dramatically changes.

[00:17:07] And so before an organization is going to let agents perform work, they really need to establish some guardrails, some boundaries. There needs to be the right level of human oversight. There needs to be complete transparency. And then finally, there needs to be orchestration. Orchestration is a big deal with us right now with our agentic platform that we have released in the market called Work HQ.

[00:17:35] It is enabling companies to have that orchestration layer that goes across all these different technologies now that are kind of coming together. So let me let me talk about each of those just a little bit more. So boundaries. Every agent should have a well-defined role. It should have limited permissions and it should have a specific scope of responsibility.

[00:17:59] So just like you wouldn't give a new employee unrestricted access to everything, you shouldn't give an AI agent the same. I mentioned human oversight. So human in the loop is more important than ever, especially now with agents. And so not every decision requires a person in the loop, but the highest risk decisions absolutely should have that.

[00:18:24] And the good thing is human in the loop is now very possible and it's easy to involve in these processes, these agentic workflows. Transparency. I mentioned every action that an agent takes should be explainable. It should be logged and it should be auditable. And if it's not, again, I would be very wary about launching these agents into production because you really need to have that audit trail.

[00:18:54] Not that they're going to go wrong right out of the gate, but you just you need that explainability because it's going to come into play quite often. And then back to orchestration, which I mentioned, we're very, very high on at the moment and the market in general is high on. You know, agents shouldn't operate independently. They need to work with business workflows that enforce policies and approvals there. You know, there's exception handling.

[00:19:23] There are compliance requirements. So agentic workflows that weave all these things together with APIs, with digital workers. I mentioned deterministic automation still playing a role. All these things combined are contributing to what I believe are the most trusted AI agents. And that is so important to have in place before companies are really going to get behind agents and scale it to the level that it can be scaled.

[00:19:53] And looking back at your career, I think automation has traditionally required specialist skills. But of course, with no code tools, it's making it accessible for so many more employees now. And anyone can just have a little play and experiment and achieve so much. It's incredible. But on the flip side of that, how can businesses better democratize that automation without creating a new generation of disconnected workflow security risks and governance problems?

[00:20:22] Because so much changes and so much changes the same on some of these risks. Yeah. Yeah. Every organization should want this, right? Democratization of AI. I mean, the people closest to the work usually know best where the inefficiencies are. So it's in a company's best interest to democratize it. But democratization doesn't mean decentralization.

[00:20:49] And that's where many organizations, I think, get into trouble. The most successful companies use most often what's called a federated model. This is what we do at SS&C as well. So we empower the business teams to build their own automations. But we provide a shared platform with common standards and templates and best practices.

[00:21:15] And we provide some central oversight over certain things that really need to be centrally managed. This has been really key to our success in Customer Zero because we have a very large organization with very large independent business units that are in very different types of business within financial services and healthcare and other regulated industries.

[00:21:42] And for us to try to bring all that together and have one organization that is doing all the automation, all the AI work for the whole company is just not practical. And as I said, we're not close to the details. We don't know the intimate details of what they're dealing with, where the inefficiencies are, where AI can play the biggest role. So that is the role we followed.

[00:22:05] It has allowed us to scale automation rapidly and to achieve some really significant cost savings and revenue enhancement. And that's what we're now building on with our Work HQ platform. We're starting to roll that out across the company. And we're doing it through these business unit-specific centers of excellence that exist. And that model has really, really made democratization.

[00:22:34] It's brought it to life at SS&C. And the results have been very, very significant. And there is also an enormous pressure on leaders to demonstrate quick AI wins, especially after so many failures over the last couple of years. But building the foundations for sustainable adoption, it takes time. There's no rushing that.

[00:22:55] So any tips on how organizations and people listening could better balance the demand for that near-term ROI with operational work that needs to scale AI successfully? Always a balancing act. But any tips or advice around that? Yeah, I think most organizations are under extreme pressure to go after those quick wins and get them deployed as fast as possible.

[00:23:21] And building the long-term foundation is often seen as a competing priority. But the companies doing it right are doing both. They're doing both simultaneously. And you really have to do that, I think. Or else you're going to end up with all these different pilots that are struggling. And instead of getting one success or two successes out the door, you've got 50 projects that are all in limbo.

[00:23:49] So I think you've got to be intentional about where you start. You don't chase after dozens of disconnected experiments, proof of concepts. Identify the highest value business problems where AI can deliver measurable impact. I would start there and then make sure that every AI initiative is contributing to the foundation.

[00:24:15] Whether that's something a governance principle, a data practice, a security control, contributing to kind of the operating model, reusable capabilities. All those things are helping you kind of build it as you go. And you don't have to be perfect when you start. But as you contribute these things along the way, the next AI project is going to benefit from those. And then the one after that is going to benefit even more.

[00:24:45] So the companies that struggle, they optimize only for speed. They're trying to get things out the door. They're celebrating demos. But they're not focused enough on those repeatable capabilities. So we're going through the same journey that most in the industry are going through with Customer Zero. In fact, Neil, we are recording interviews with people internally that are going through this journey.

[00:25:13] And we're planning to release almost like a Netflix documentary about this journey that we've gone through. Because we believe our customers and others can benefit from our experience. And they can see how we've gone through it and the things we've struggled with and the learnings we've had along the way. So it definitely is a journey. And every day you're learning from the day before and you're building on that. And you're getting better and better and faster and faster.

[00:25:41] And so it's not something that anyone starts off with and they've got all the answers and they've got all the tricks of the trade. It does take time. But there is a process that you can build upon each and every day. And then take that and formalize it and make it part of your DNA going forward. Oh, wow. I absolutely love that. Sounds like we will need to get you back on when that comes. Any ballpark time you can tell us when that will be released?

[00:26:10] I think the goal is to start releasing certain episodes of that documentary. I don't mean to oversell it. I think it's probably going to be pushed out on social media primarily.

[00:26:22] But we do have a plan to have multiple episodes that we push out that start to talk about what we've experienced and what a GenTech automation has looked like, how we've built upon some of the automation from the past and how we've made that better and what that's done for us. And it's a story that needs to be told. And I think a lot of people are looking for success stories.

[00:26:48] They're looking for learnings that come out of a real experience. And what we're going through is very, very real. It's not just a demo or skunkworks kind of stuff. I mean, we are automating and transforming our company with our platform to get real outcomes realized. And so there's a lot that we can share from that. Yeah, I think you're bang on the money there. I think everyone is searching for those success stories.

[00:27:15] But I'm noticing a bit of almost tiredness towards just another shiny demo, another keynote here, another release there. It's those success stories that people are looking for. And I suspect we will have a business leader listening to our conversation today who spent the last few weeks scrolling down their LinkedIn news feed, feeling overwhelmed by all this talk of co-pilots, agents, automation platforms, constant AI announcements and upgrades, whether it be Claude, Gemini or OpenAI.

[00:27:45] And for those people listening, any practical steps that you'd recommend that they take to identify maybe where AI can genuinely improve how work gets done in their industry, in their workplace and what to measure to start measuring that deliverable value too? Yeah. The first thing I'd say is don't worry. You're not alone. Everybody is overwhelmed. There's significant tension and bearing down on everyone. And the technology keeps changing.

[00:28:15] Just when you figure something out, two days later, it's replaced by something, some new innovation. So it can be unsettling at times just how fast things are moving. And we all feel like we're back in college or an MBA program, relearning business and technology each and every day. So, yeah, I would say take a breath, relax. You're in good company. Then I would emphasize a few things.

[00:28:43] I would say, I've already mentioned this a little bit, but don't start with the technology. Start with the business. So prioritize by business value, not by, ooh, Claude has a new model. We got to get something on this model immediately. Look for processes that are high volume, that are high cost, time consuming, or critical and or critical to the customer experience.

[00:29:11] And I've mentioned, you know, bringing the right people forward, the business process owners, the frontline employees, the risk and compliance experts, the data leaders. All of those people have got to be brought together to make this work the right way. And, you know, much of the new technology, the agentic automation platforms like ours, Work HQ, are designed to be used by business people, not by software engineers.

[00:29:40] So everyone can learn how to use this kind of technology. And we're not just looking for software engineers that can code AI algorithms. There's plenty of those. We need business people that understand the work, understand the business processes. I would also encourage starting small.

[00:30:02] Don't take the most complex, multi-step process in the entire company and try to automate that with AI and AI agents and RPA right out of the gate. You need to start small, learn it, get some successes, build on that, and then start to move up to more complex stuff. And the final thing I would say, and this is a bit of a pet peeve, but don't just go and try to find an AI use case.

[00:30:31] Find a business problem that is worth solving and then figure out where AI and automation and human expertise can come together to solve that business problem. That is absolutely, in my opinion, the best way to approach this is because people are hung up on use cases. They're told that this is an AI program. We're going to use AI. And so they're looking for ways to use AI. Start with the business problems.

[00:31:01] AI will find a way in most cases to enable and to support the accomplishment of the objectives tied to that business problem. So, and I mentioned corporate strategy. It hasn't gone away. We still need to execute our company strategy. So it's a good place to start. If it's not connected to business strategy, then you really need to question whether it's even worth doing. Excellent advice. And we have covered so much today.

[00:31:29] So for anyone listening that wants to find out more about Blue Prism, explore what you're doing, follow announcements, and maybe keep an eye open for some of those episodes that are going to be dropping in the near future. Where would you like me to point, everyone? Well, everyone's welcome to follow me on LinkedIn. Easy to find. Also, we have, I mentioned our podcast.

[00:31:49] So we've been publishing content to a podcast called Transform Now for a little over six and a half years. It's on Apple, Spotify, YouTube, and it's focused on thought leadership in agentic automation. And so we bring on a lot of interesting people to that podcast to share their points of view.

[00:32:15] And as we all know, there are many disparate points of view on where things are going and what's going to be successful and what's not going to be successful. And so we really try to highlight those points of view. So that's a good place to find me and to find content that we're publishing. We do two episodes a week on Transform Now. But I'm also easy to find on LinkedIn, and I welcome new connections there. Excellent.

[00:32:44] Well, we did cover a lot from getting control of AI that's already running inside organizations without permission to how businesses can better democratize automation without writing a line of code and most importantly start delivering real measurable value. So for anybody listening, I'll be including links in the show notes at the blog post associated to this episode over at Tech Talks Network. But I'll include a podcast, a couple of episodes there, your LinkedIn website, everything that you need will be there.

[00:33:14] So please, I encourage you to check that out. But more than anything, thank you, Brad, for helping us get beyond the noise and talk about some of those success stories and the real journeys that are happening out there. Really appreciate it. Thank you, Neil. Yeah, it's been a pleasure. Thanks for having me. I appreciate it. I think one of the great points from Brad's advice today is worth carrying into every AI conversation. And that is before we talk about tech, AI or whatever, let's start with a business problem that deserves to be solved.

[00:33:42] And yes, I know I'm making that sound incredibly obvious, but it is easily forgotten when another model arrives, a competitor announces a new agent and somebody returns from a conference convinced that the business needs 47 new pilots by Friday. But thankfully, Brad's recommendation is refreshingly practical. Understand the process. Bring in the people who perform the work. Examine the data. Establish ownership.

[00:34:12] Set permissions. And decide where human approval is required. Then begin with a manageable problem and use each project to build capabilities that make the next one that you do faster and less expensive. Because let's be honest, there isn't a prize for collecting the largest number of AI pilots. The real value comes from improving customer service, reducing delays, increasing productivity, raising quality,

[00:34:40] or supporting another result that the business can measure. So a big thank you to Brad there for sharing what the company is learning through its own Customer Zero program. And remember, you can follow Brad on LinkedIn, hear him on the Transform Now podcast. And I'll include links in the show notes for that. So pop over to techtalksnetwork.com. You'll find that. How you can meet me on the road at a tech event near you. How you can work with me.

[00:35:07] Or just find out more information about Brad and the work that they're doing there. Or listen to their Transform Now podcast. Go on, I'll let you have a sneaky listen. But make sure you've still got room for me. But before I go, quick question. Is your company choosing AI projects because the tech is available and ready? Or because there is a business problem genuinely worth solving? A little bit of homework there for you to take away and encourage you to come back and keep listening.

[00:35:35] So I'm going to walk off now into a virtual sunset. But have no fear. I'll be back inside your virtual podcast feed tomorrow morning. Speak with you then. Bye for now. Bye for now.